Mission-Driven sensor management analysis, design, implementation and simulation

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Mission

Mission----Driven

Driven

Sensor Management

Analysis, design, implementation and simulation

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Mission

Mission----Driven

Driven

Sensor Management

Analysis, design, Analysis, design, Analysis, design, Analysis, design, implementation and simulation implementation and simulation implementation and simulation implementation and simulation

Proefschrift

ter verkrijging van de graad van doctor aan de Technische Universiteit Delft,

op gezag van de Rector Magnificus prof. dr. ir. J.T. Fokkema voorzitter van het College voor Promoties,

in het openbaar te verdedigen op maandag 12 november 2007 om 15.00 uur

door

Fok BOLDERHEIJ

Master of Science (Cranfield University, UK)

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Prof. ir. dr.h.c. P. van Genderen Prof. dr. ir. L.P. Ligthart

Samenstelling promotiecommissie:

Rector Magnificus, voorzitter

Prof. ir. dr.h.c. P. van Genderen, Technische Universiteit Delft, promotor Prof. dr. ir. L.P. Ligthart, Technische Universiteit Delft, promotor

Prof. dr. ir. F.G.J. Absil, Nederlandse Defensie Academie

Prof. dr. ir. F.C.A. Groen Universiteit van Amsterdam Prof. dr. ir. J.M. van Noortwijk Technische Universiteit Delft

Prof. drs. M. Boasson Universiteit van Amsterdam

Prof. dr. A. van Deursen Technische Universiteit Delft

Prof. dr. ir. F.G.J. Absil van het Koninklijk Instituut voor de Marine heeft als begeleider in belangrijke mate aan de totstandkoming van het proefschrift bijgedragen.

ISBN 978-90-76928-13-5

Keywords: sensor management, situational awareness, threat assessment, risk estimation, (Dynamic) Bayesian Networks.

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V

Acknowledgements

Some people state that the execution of PhD research can be managed as a project. When putting the finishing touch on this thesis, I draw the conclusion that this statement is incorrect. From the experience I have gained with project management during my career, I recollect that the art of project management involves the careful balancing of the elements time, finances and quality and requires meticulous planning of the progress of a project. One of the most important aspects of PhD research, innovation, cannot be planned however, as it requires creativity and this human quality cannot be forced to manifest itself. It is possible to stimulate creativity by organising brainstorm sessions or by exchanging ideas and opinions with experts but when at the end of the day the starting piece of the jigsaw puzzle is not found, all that remains is a heap of seemingly related but unconnected pieces. This is what happened to me: after more than two years in which I spent half my time executing this research I had collected a lot of ideas and topics that were somehow related to the problem area but still no clear structure was visible. During a desperate search in a remote database I discovered the paper a game theoretic approach to search by Harland Romberg that provided the required corner piece of the puzzle. The ideas triggered by this paper enabled the construction of the

framework in which most of the already collected pieces fitted. Because creativity cannot be planned and this is a fundamental concept in research, only the writing of the thesis can be managed as a project but only after most of the research is finished.

I especially want to thank my first supervisor prof. ir. dr.h.c. Piet van Genderen and prof. dr. ir. Frans Absil for supporting me during the first years of the research when I thought it was adrift, for assisting me in keeping the research on the right course after I discovered the navigational track and for helping me to detect and avoid submerged obstacles. Piet van Genderen often approached the subject matter from unexpected angles, which invariably resulted in very interesting discussions and topics that required further examination and therefore these discussions were a valuable contribution to the final results. Frans Absil always reviewed the results thoroughly and provided detailed comments that helped me to improve the quality of the work. I want to thank my second supervisor prof. dr. ir. Leo Ligthart for showing me how to shift the emphasis of the thesis from the problem that had to be solved towards the innovative aspects of the research. I also want to express my gratitude to prof. dr. ir. Jan van Noortwijk for showing me the shortcomings in the notation of the Bayesian calculus and prof. drs. Maarten Boasson for discussing some design and architectural issues.

This work started as a part of the STATOR (Sensor Timing And Tuning on Object Request) research program on sensor management supported by Thales Naval Nederland, the Royal Netherlands Naval College, and the International Research Center for Telecommunications and Radar of the Delft University of Technology. I greatly appreciate the exchange of ideas and discussions with the other project team members of whom I especially want to name dr. ir. Hans Driessen, dr. ir. Yvo Boers, dr. ir. Maurice Houtsma, ir. Perry Verveld, ir. Jitze Zwaga from Thales Naval Nederland and ir. Rene Thaens from the NATO C3 Agency.

In order to support this research, many interviews were made with staff of the Royal Netherlands Navy. Their willingness to share their views on the Command and Control process functions and operator roles is greatly appreciated

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VI

STATOR project provided an excellent opportunity to satisfy these requirements. It turned out however, that the combination of lecturing and research was not as straightforward as I had anticipated. Because these tasks are only loosely correlated, much time is lost by switching between them. I would therefore like to thank the (ex) staff of the Royal Netherlands Naval College for their advice and support in executing these tasks and in particular dr. Theo Hupkens and ir. Ben Bruggeman for helping me to combine the lecturing and the research tasks, exchanging ideas and discussing the suitability of candidate

methodologies, dr.ir. Arthur Vermeulen and ir. Raymundo Hordijk for helping me with mastering Matlab and Deborah Trimpe Burger (MA) for proof reading the manuscript.

About a year after the STATOR project was finished, I was posted to CAMS-Force Vision to develop the sensor management concept further into an operational system. I want to thank the management of CAMS-Force Vision for providing this opportunity and granting me time for finishing the thesis. I also want to thank my colleagues and especially LTZE 2 OC ir. Wilbert van Norden for his work on the scheduler and discussing new concepts.

And last but certainly not least, I would like to thank my wife Ans and my daughter Sanneke for their patience and forbearance when I consumed valuable family time building the prototype and writing this thesis. They only imposed the restriction that I was not allowed to take the work with me on holidays (our notebook computer was only to be used for storing photographs and searching the internet for the opening hours of family attractions).

Nevertheless, more than one planned weekend walk on the beach was lost by an incorrect estimation of the time required to finish a subroutine, a section or a chapter. However, they never really complained about this and were always there to support me when I needed them.

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VII

Abstract

The management of sensors onboard of the vessels operated by the Royal Netherlands Navy is becoming increasingly knowledge intensive due to the fact that these vessels are equipped with state of the art sensor systems that provide more functionality and more accurate information at the cost of more complex control mechanisms and due to the shift of

operational areas from the fairly stable environment of the Atlantic Ocean into littoral waters with often dense civil traffic and rapidly changing geographical and meteorological

conditions. The shrinking defence budgets on the other hand drive a demand for crew reduction, shorter education times and less training opportunities thus reducing the synergy created within teams of operators and the knowledge and experience of individual operators. This perception leads to the following problem definition: management of a set of complex sensor systems under often rapidly changing environmental, operational conditions and temporal constraints requires more skills and knowledge than is currently available. This problem definition justifies the execution of research into the design of a sensor management system that is capable of optimally controlling a set of complex sensor systems under rapidly changing environmental conditions with respect to the mission objectives. The sensor

manager should also provide assistance in determining the optimal use of the sensors in the planning stage of the mission.

From the problem definition, the functional requirements were gathered in the course of which attention was also paid to the identification of the relevant non-functional (quality) requirements to establish the starting point of the research.

The first step in the research concerned the analysis of the current sensor management process and its position within the command and control process. It turned out that it was completely operator-driven and that no clear guidelines existed. A literature survey yielded a model of the command and control process and from it a high-level sensor control cycle based on the Observe-Orient-Decide-Act loop could be constructed. This cycle uses information gathered by sensors. This information is correlated, interpreted and used as the basis for consequent (sensor management related) actions. From the survey, the command and control processes that deliver the required sensor management information could be identified. The survey however did not disclose generic sensor management principles and therefore in a second step a series of interviews with operational experts was conducted. From these interviews the conclusion was drawn that sensor management should support both the complete and accurate (timely) compilation of a mission relevant picture of the man-made and natural environment and the direction of appropriate actions (in a military context: the weapon assignment and weapon direction processes). A novel approach introduced in this research was the modelling of this environment as a set of objects that could potentially endanger the mission objectives. This approach enabled the description of the search for completeness as the pursuit of a one-to-one relationship between each relevant (with respect to the mission) element of the environment and its object representation within the picture of the environment that is compiled by the Command and Control process. The accuracy of this picture is modelled by means of the uncertainty related to the relevant properties (or attributes as they are called in the object-oriented terminology) of each object.

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VIII

The perception that the picture of the environment is composed of objects instigated an object-oriented design approach resulting in the development of a three-stage sensor manager concept.

The first stage of the sensor manager reviews the uncertainty connected with each attribute of an object in the compiled picture and determines what task(s) should be executed to reduce this uncertainty.

The second stage of the sensor manager selects the most appropriate sensor for this task from the set of available sensors based on the quality of information provided by each sensor. The third stage of the sensor manager determines the sensor settings for the particular task output by the second stage and hands tasks back to the second stage if the sensor has

insufficient resources to execute all tasks. The second stage then assigns these tasks to a less appropriate sensor.

It is clear that the process of returning tasks to the second stage of the sensor manager should start with the least important task and therefore a prioritising mechanism was required. When existing prioritising mechanisms were reviewed, it appeared that they could only be used in relation to objects that were already detected and that these mechanisms could not be utilised to allocate surveillance functions to search for the expected objects in order to ensure the completeness of the compiled picture. Hence a new type of prioritising mechanism was developed that is capable of estimating the risk of both the expected but undetected objects and the already detected objects. This mechanism determines the costs of the damaging event caused by the object, being its lethality and the probability of occurrence of this event. To estimate this probability of occurrence, an event tree was constructed that was based on the state diagram of a missile system and those events that lead to a state transition. This event tree was consequently converted into a Dynamic Bayesian Network that estimates the probability of being hit by this missile from the underlying events.

Using this novel prioritising concept, the high-level design of the sensor manager embedded in a basic object-oriented combat management system featuring those processes that deliver the required sensor management related information could be completed. From this design a prototype was constructed that was tested in a simulated environment using a scenario that was drafted with the aid of operational experts. In this simulation the management of a Multi-Function Radar was tested as this type of sensor is renowned for its difficult deployment. It was assessed that a sensor manager capable of handling this type of sensors can be expanded without too many problems into a multi sensor management system by making more

resources and functions available.

The execution of this scenario showed that it was possible to deploy the multi-function radar completely autonomously by utilising prior information and from this result it can be deduced that mission-driven, autonomous sensor management is feasible. The prototype also provides the possibility to integrate radar performance prediction tools that predicts the sensor

performance for the prevailing meteorological and environmental conditions thus effectively addressing the identified problem.

The generic sensor management principles that were postulated do not restrict the sensor suite to being located at a single platform and therefore a sensor manager that is developed along these principles is theoretically capable of handling a suite of distributed sensors. The

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XI

Samenvatting

De inzet van de sensoren aan boord van de schepen van de Koninklijke Marine vergt steeds meer kennis als gevolg van het feit dat deze schepen zijn uitgerust met zeer moderne sensoren die zeer nauwkeurige informatie kunnen leveren en vaak meerdere functies kunnen uitvoeren hetgeen echter gepaard gaat met complexe wijze van bediening. Een andere reden voor de toename van de vraag naar kennis wordt veroorzaakt door de veranderende wereldsituatie: de operatiegebieden van deze schepen zijn verschoven van de redelijk stabiele omgeving van de Atlantische Oceaan naar kustwateren, waar er vaak sprake is van druk scheepvaartverkeer en snel wijzigende geografische en meteorologische omstandigheden. Daarnaast heeft het

slinkende defensiebudget geleid tot bemanningsreducties en een verminderde beschikbaarheid van opleidings- en trainingstijd, waardoor zowel de beschikbare kennis van een individuele gebruiker als de onderlinge synergie binnen teams van operationele gebruikers is afgenomen. Deze waarnemingen leiden tot de navolgende probleemstelling: het gebruik en de inzet van een verzameling complexe sensoren onder tijdsdruk en vaak snel wijzigende operationele en omgevingsomstandigheden vergt meer vaardigheid en kennis dan momenteel beschikbaar is. Deze probleemstelling rechtvaardigt een onderzoek naar het ontwerp van een

sensormanagentsysteem dat in staat is deze sensoren onder voornoemde omstandigheden optimaal in te zetten. Daarnaast dient deze sensormanager assistentie te bieden tijdens de planningsfase van een missie.

Als uitgangspunt van het onderzoek is aan er de hand van deze probleemstelling een functionele behoeftestelling opgesteld waarbij er tevens aandacht is besteed aan de bijbehorende kwaliteitseisen.

Het onderzoek richtte zich initieel op de analyse van het huidige sensormanagementproces en de positie van dit proces binnen het overkoepelende command and control proces. Het bleek dat het sensormanagementproces momenteel volledig wordt uitgevoerd door de gebruiker, waarbij deze gebruiker geen beschikking heeft over een handleiding met betrekking tot de inzet van de sensoren onder specifieke operationele en omgevingsomstandigheden. Een literatuuronderzoek leverde een command and control model op, waaruit een op de Observe-Orient-Decide-Act loop gebaseerde globale sensor control cyclus kon worden ontwikkeld. Deze cyclus gebruikt sensorinformatie als invoer, verwerkt en interpreteert deze informatie en gebruikt deze informatie vervolgens als basis voor (sensor gerelateerde) acties. Vanuit het literatuuronderzoek konden tevens de command and control processen geïdentificeerd worden die de benodigde informatie voor het sensormanagementproces aanleveren. Het onderzoek leverde echter geen algemeen toepasbare sensormanagementprincipes op en daarom is er vervolgens een reeks interviews met operationele experts (commandanten, (assistent-)luchtverdedigingsofficieren en (assistent-)commando-centraleofficieren)

gehouden. Uit deze interviews bleek dat het sensormanagementproces het samenstellen van een compleet en accuraat (tijdig) missiegerelateerd omgevingsbeeld en het uitvoeren van daarop gebaseerde acties (in de militaire context: wapeninzet) diende te ondersteunen. Afhankelijk van de missie kan dit omgevingsbeeld zowel natuurlijke als niet-natuurlijke elementen bevatten. Een nieuwe, hier geïntroduceerde benadering ligt in het modelleren van dit omgevingsbeeld als een set van objecten die een potentieel gevaar vormen voor het bereiken van de missiedoelstellingen. Deze benadering maakte het mogelijk om het streven naar compleetheid te modelleren als een één-op-één relatie tussen de elk relevant

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Teneinde de sensor control cyclus te initiëren, zijn er virtuele ofwel verwachtte objecten geïntroduceerd. Dit nieuwe type object maakte het mogelijk om informatie van

inlichtingendiensten of andere voorkennis te modelleren. De set van virtuele objecten wordt vervolgens aangevuld met bestaande (reële) objecten die opgebouwd zijn uit

sensorwaarnemingen. Het idee om het omgevingsbeeld te beschouwen als een verzameling objecten was vervolgens de aanleiding voor een objectgeoriënteerde benadering van het ontwerpproces dat voorts resulteerde in de ontwikkeling van een

drietrapssensormanagementconcept.

De eerste trap van de sensormanager analyseert de onzekerheid in de attributen van elk object uit het omgevingsbeeld en bepaalt welke sensor taak (taken) uitgevoerd moet worden om deze onzekerheid te verminderen.

De tweede trap van de sensormanager kiest de meest geschikte sensor uit de verzameling van beschikbare sensoren om de in de eerste trap samengestelde taak uit te voeren op basis van een inschatting van de kwaliteit van de door deze sensoren geleverde informatie.

In de derde trap van de sensor manager worden de sensorinstellingen benodigd voor het uitvoeren van de sensortaak bepaald. Indien de sensor onvoldoende middelen (bijvoorbeeld tijd of energie) ter beschikking heeft voor het uitvoeren van de taak, dan wordt de taak teruggegeven aan de tweedetrap en wordt de taak opgedragen aan de volgende (minder geschikte) sensor.

Het moge duidelijk zijn, dat de taken die door de derde trap teruggegeven moeten worden aan de tweede trap, de minst belangrijke taken dienen te zijn, zodat de meest belangrijke taken zoveel mogelijk door de meest geschikte sensoren worden uitgevoerd. Hierdoor ontstond er een behoefte aan een prioriteringsmethode. Uit een nadere beschouwing van bestaande methodes bleek echter dat deze alleen gebruikt konden worden in relatie met bestaande (waargenomen) objecten, maar dat ze niet gebruikt konden worden om zoekopdrachten naar verwachtte objecten aan te sturen. Daarom moest er een nieuwe manier van prioriteitstellen ontwikkeld worden. Deze nieuwe methode is gebaseerd op het risico dat wordt veroorzaakt door zowel de aanwezige als de verwachtte objecten. Deze methode berekent de schade veroorzaakt door een object en de kans van optreden van deze gebeurtenis. Deze kans van optreden kan worden geschat met behulp van een event tree die geconstrueerd is door het toestandsdiagram van een geleidewapen aan te vullen met de gebeurtenissen die leiden tot een toestandsovergang. Vervolgens is deze event tree omgezet in een Dynamic Bayesian Network dat de kans om getroffen te worden door dit geleide wapen inschat aan de hand van de kans van optreden van de onderliggende gebeurtenissen.

Gebruikmakend van dit nieuwe concept kon het globale ontwerp van het combat management system met daarin geïntegreerd de drietrapssensormanager worden gecompleteerd. Dit combat management system is opgebouwd uit de command and control processen die de

noodzakelijke informatie aan het sensormanagementproces leveren. Uitgaande van dit ontwerp is er een prototype geconstrueerd dat getest is aan de hand van een gesimuleerd scenario dat was opgesteld aan de hand van adviezen van operationele experts. In dit scenario is de inzet van een multi-function radar getest, omdat met name dit type radar ongunstig bekend staat vanwege zijn bedieningscomplexiteit. Hierbij is de aanname gedaan dat deze sensormanager zonder al te veel problemen uitbreidbaar zou moeten zijn naar een multi-sensormanager middels het beschikbaarstellen van meer sensorfuncties en meer middelen, indien de sensor manager instaat zou zijn om dit type sensor goed in te zetten.

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mogelijk in te schatten, gegeven de heersende geografische en meteorologische

omstandigheden, waarmee het hierboven beschreven probleemstelling uiteindelijk volledig is aangepakt.

De omschreven, algemeen toepasbare sensormanagement principes beperken zich niet tot de sensoren van een enkel platform en daarom is een sensormanager die volgens deze principes is ontwikkeld, theoretisch ook in staat moet worden geacht om een verzameling sensoren aan te sturen die opgesteld zijn op verschillende geografische locaties. Het onderzoek toonde tevens aan dat de geïdentificeerde management principes uitbreidbaar zijn naar een meer algemeen inzetbare resource manager.

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Directions for reading

In order to facilitate the reading of this thesis, some special style elements were applied. To be able to differentiate in the text between object classes, relations between objects, object attributes and object methods, alternative fonts were used:

• Object class. • Relation. • Object attribute_.

• Object methods.

The results of interviews with experts are placed in a text box.

Long citations (more than two lines) from literature are not quoted but are indented: <citation begin> ………..

……… ……… ……… ……….<citation end>

References are formatted in accordance with the documentation style laid down by the Council of Science Editors (CSE), summarised by Aaron1.

1. Aaron JE. The little brown compact handbook, 5th edition. New York: Pearson Education Inc.; 2004. p 438-46.

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This chapter explains the role of sensor systems in a maritime environment and describes the problem of the increasing difficulties in managing modern sensors in greatly varying

operational and environmental circumstances. The possible solutions to this problem are discussed and the most promising approach is selected. Finally the structure of the thesis is described.

1.1 The importance of sensor systems

“If you know the enemy and know yourself, you need not fear the result of a hundred battles. If you know yourself but not the enemy, for every victory gained you will also suffer a defeat.” This statement was made by Sun Tzu around 400 BC1. Sun Tzu understood that a clear picture of an opponent could mean the difference between losing and winning battles and, eventually between winning or losing wars thus describing the importance of situational awareness. Warfare has evolved significantly since the era of Sun Tzu due to the development of new weaponry. Grossman2 states: “the physical limitations of humans are a key factor in their search for weapons. The needs for force, mobility, distance and protection have been the key requirements in this realm”. According to Grossman, the need for force resulted in the evolution of weapons from rocks to sticks, spears and bows for storing and delivering mechanical energy and to the development of explosives for storing and delivering chemical energy and the generation of vast amounts of kinetic energy. The need for distance caused weapons to evolve from a thrown stone, the spear, the Greek phalanx, the throwing spear of the Roman legionary, the bow, the crossbow, the English longbow, firearms and artillery into missiles and aircraft. The need for mobility resulted in the development of vehicles like tanks, long-range aircraft, aircraft carriers and transport ships. Finally, the need for protection led to the evolution of different types of armour and defensive systems.

The evolution of weapon systems has only increased the need for information about the opponent, his whereabouts and his capabilities, as he is able to inflict more damage from a greater distance. This observation shows that the statement of Sun Tzu is still valid: a accurate and complete assessment of the situation is considered to be of vital tactical and strategic importance, not only from a military point of view, but also in politics, sports, economics and industry; in short in any complex system.

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Sensor systems therefore play an essential role in obtaining situational awareness and this process in its turn is vital for tactical or strategic success. This role is not limited to the military domain but also extends to those areas where early detection and tracking of potentially dangerous objects can prevent intentional or unintentional casualties or damage. Examples of these types of sensors are air-traffic control radars and burglar alarms.

This thesis will focus on the management of sensor systems in the military domain and more specifically on those sensors fitted onboard naval platforms but when relevant, the developed principles will be examined in a more general context.

1.2 Sensor management

Generally speaking, a complete and accurate overview of the situation is a critical success factor to any mission. As shown in Section 1.1, early detection and recognition of those elements that pose a threat to a mission provides an opportunity to deal with these elements thus ensuring an increased probability of achieving the mission goals. This statement is not limited to the military domain: the availability of recent navigational charts in combination with navigation systems (sensors) enables cargo vessels or ferries to plan safe navigation tracks that circumvent shallow waters, reefs and other hazards, thus supporting mission success: the delivery of goods and passengers.

The missions of naval vessels are usually more diverse or complex: these missions range from the protection of convoys, High Value Units (HVUs)i or Mission Essential Units (MEUs) to Maritime Interdiction Operations (MIOPs), Counter Drugs Operations (CDOPs) or Search and Rescue (SAR) operations. In addition to the navigational threats already mentioned, these vessels have to deal with either specially designed or improvised weapon systems that are intended to incapacitate them. The sensor systems onboard of these naval vessels must therefore be optimised for early detection, tracking, classification, identification, and

eventually neutralisation of threatening objects (the terminology used will be fully explained in Section 2). Incorrect sensor settings are likely to result in late detection or no detection at all, inaccurate tracking, wrongful recognition, false identification and failing neutralisation of those weapons, probably causing own platform damage and casualties resulting in mission failure.

From the above, the conclusion can be justified that sensor management onboard of naval vessels requires knowledge about the mission of the vessel, the role of the sensor systems within this mission and about the technical details of the sensor systems in order to translate the operational requirements into technical sensor settings. Availability of this knowledge directly contributes to mission success.

1.3 Problem definition

With respect to the required knowledge about sensor management, a number of issues arise. Since the last decade, armed forces are no longer solely used as an instrument of deterrence in order to prevent a global conflict, but are actively deployed in peacekeeping, peace enforcing and maritime patrol operations. The missions of Royal Netherlands Navy (RNLN) frigates evolved from protection of the Sea Lines of Communication (SLOCs) on the Atlantic Ocean, to CDOPs and MIOPS in a littoral environment3. Complete and accurate assessment of the situation in this type of environment is often very complicated because of the detection limitations imposed by the geographical situation, faster changing meteorological conditions and the presence of civil traffic that needs to be classified. Because of the often-short distance

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to the shore, the threat ranges from small (fast) boats manned by terrorists to land-based missile sites and long-range fighter or bomber attacks. The threat in this type of environment is more diverse than the threat in the middle of the ocean and reaction times are shorter. In order to provide timely detection, classification and weapon assignment against objects that pose a threat, more information needs to be gathered, processed and compiled in shorter times. The recent shift of operational areas from open seas into littoral waters therefore leads to more complex naval missions and a higher workload from a sensor deployment point of view.

Furthermore, modern sensor systems like the Multi-Function Radar (MFR), provide more functionality than older types, but require more technical knowledge. In this type of radar system, search, track and weapon guidance functions are combined. More functionality and high performance comes however at a cost: managing these sensors has become more

complex and time consuming in comparison with the control of conventional sensors because more technical parameters need to be set and functions or modes have to be selected. In addition there is a tendency to fuse the information derived from the separate sensor systems, driven by the need for track stability and continuity and eventually more accurate and detailed information. Therefore the sensor suite must be configured as a whole, instead of setting each sensor separately. A new trend is to centralise the management of the available sensor and weapon systems located at different platforms4 (Network Centric Warfare, NCW and Network Enabled Capabilities, NEC) in order to enhance the quality of the compiled picture and to deploy the weapons more effectively.

Apart from the restrictions posed by geographical and meteorological conditions, mission objectives can also impose limitations on the use of sensors for political or tactical reasons. These restrictions have to be applied to the sensor settings, thus complicating the sensor management task even further.

Finally, due to the lowering of defence budgets in the post Cold-War era, navies are striving for shorter operator training times and crew reduction by system automation and integration, resulting in fewer, and often less experienced operators. The resulting decrease in availability of knowledge and skills is further enhanced by the reduction of synergy created by functional and personal information exchange between operators of sensor and weapon systems.

These observations result in the following problem definition: management of a set of technically complex sensor systems under often rapidly changing environmental,

operational conditions and temporal constraints requires more skills and knowledge than is currently available.

In Section 1.2 it was shown that inappropriate use of the available sensor systems might contribute to mission failure. Therefore the observed discrepancy between the demand and supply of sensor management related skills and knowledge directly affects the operational capacity of a naval platform.

An additional problem is of a more academic nature: an initial literature survey yielded no generic applicable sensor management principles that take operational, technical and environmental aspects into account and therefore these principles needed to be formulated first before the described problem could be addressed.

These conclusions justify research into the formulation of generic sensor management principles thus supporting the design of a sensor manager that is capable of optimally

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1.4 Possible solutions

There are a number of options available to address the sensor management problem. The most evident options are:

1. Increase the quality of the available knowledge and skills by extending the education and providing more training opportunities.

2. Increase the quantity of the required knowledge and create more synergy by adding more operators to the crew.

3. Operate the sensor systems remotely.

4. Reduce the complexity of the sensor controls.

5. Implement the required knowledge in some kind of sensor management system.

Up until now no significantly different options to solve this problem were identified, all proposals suggested by experts could be related to one or more of the already mentioned solutions. These possible solutions were first analysed with respect to their feasibility.

A major problem concerning Option 1 can immediately be identified: Section 1.3 states that navies are striving to reduce training times; if the quality of the sensor management-related knowledge has to be enhanced, this should happen within the currently available training time and therefore the training methods should be altered. During discussions, the feasibility of this option was questioned by instructors and is likely to offer only a temporary solution, because if other even more complex sensors become available, the required knowledge and skill levels will rise again. Especially the required amount of scientific background knowledge poses a problem: future operators should possess extensive background knowledge in physics and mathematics in order to understand the capabilities of modern sensor systems.

Option 2 also provides a limited solution: because there is a tendency towards crew reduction, an increase in the number of sensor operators has to be accompanied by a further decrease in other crew members and therefore the complete crew composition has to be readdressed. According to a quick investigation this was very likely to cause problems in other

departmentsii resulting in a decrease of the operational readiness and thus to the related probability of mission success. Furthermore, the operator is still required to be educated in physics and mathematics.

Option 3 looks promising: it offers an opportunity for further crew reduction due to the availability of a highly skilled group of sensor operators somewhere ashore that eventually controls the sensor systems onboard of several ships. The bottleneck here is the word

available: continuous availability of the required knowledge cannot be guaranteed because of operations under radio silence, poor communication conditions or malfunctioning equipment. Especially missions in littoral waters go hand in hand with a rapidly changing environment requiring constant communication between the ship’s crew and the shore-based operators. From discussions it was learned that the operational community quickly dismissed this option. Some remarks can be made concerning Option 4: reducing the complexity of sensor control e.g., by implementing a standardised and easy-to-use set of controls that are directly related to the sensor's operational functions might result in loss of performance or capabilities and therefore caution should be practised. Furthermore, this option could lead to the modification of more than one sensor and would most probably lead to change proposals concerning more

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than one manufacturer resulting in substantial costs. Because the problem is partly caused by financial considerations, this is not a viable option. However, if the sensors are not controlled by hardware controls, but by User Interfaces (UIs) displayed by the Combat Management System (CMS), the design of a more standardised set of controls remains feasible. The costs of the design, development and implementation of a standardised control set then depends on the manufacturer of the CMS, but now only one manufacturer is involved and only one set of controls has to be developed. The already mentioned loss in performance and functionality should however be kept in mind.

The last option provides a real challenge: successful implementation of the required knowledge in an expert system demands a thorough analysis, design, implementation and validation process because of the already mentioned dangers of incorrect sensor settings. This option also requires research into the composition of generic applicable sensor management principles, as these do no not yet exist. The development of such an expert system should lead to correct and consistent sensor management and could in time result in the elimination of operator functions thus contributing to the process of crew reduction. There is however some reservation within the operational community about a completely autonomous systems. Acceptation is likely to be enhanced by providing transparent reasoning and some form of control (see also Section 4.9). This notion offers an opportunity to combine Option 4 and 5 resulting in the removal of most of the disadvantages of both options. Like option 3 and 4 this option requires modification of existing systems.

The options can be summarised as follows: option 1 could provide a fast but temporary solution to the problem and is worth investigating. The investigation of this option requires a more ‘human resources’ type of approach, which is outside the scope of this thesis. Option 2 requires the revision of the crew composition and is therefore likely to cause a reduction in the operational readiness and is therefore not feasible. Option 3 was discarded because the availability of the required knowledge cannot be guaranteed. The implementation of option 4 is likely to produce sub-optimal sensor settings, resulting in reduced chances of mission success.

This thesis will focus on option 5 (compilation of sensor management principles and the design and implementation of a sensor management system in accordance with these principles) because this option should lead to the optimal deployment of the sensor systems. The system to be developed has to provide transparency in the decisions it takes and has to involve the operator as brought forward in option 4 to guarantee the acceptance of the system.

This involvement has to take the form of operational, mission related information thus ensuring that no technical knowledge is required to operate the system this information will consequently be converted into technical sensor controls. Sensor observations are fed back into the system and are related to the mission related information resulting in new sensor controls. The context diagram5 of the system to be designed is shown in Figure 1.1.

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Sensor Management System Sensor Combat Management System Operator Sensor Settings Sensor Observations Mission Related Information Common Operational Picture

Figure 1.1. Sensor management context diagram.

This COP is used by the operators to revise the planning or to take other actions like the deployment of weapon systems.

1.5 Scope of the research

This thesis describes the state of the art with respect to sensor management both in practice and in literature. Next, generally applicable sensor management principles are developed and a Command and Control (C2) framework is implemented in a basic CMS that enables the management of a suite of sensor systems situated onboard of a single naval platform in accordance with these principles. The innovative aspects of this thesis are:

• The composition of generally applicable sensor management principles.

• A proposal regarding the reorganisation of the C2 processes in an object-oriented framework. The central element in this framework is formed by the COP, which is modelled as a set of (threat) objects representing the operational environment of the naval platform.

• The design and development of the proposed C2 framework supporting the implementation of a sensor manager.

• The introduction of a risk-driven prioritising mechanism that is based upon the Fault Tree Analysis Methodiii and is implemented by means of a Dynamic Bayesian Network (DBN)iv.

• The design and development of a prototype, three-layered sensor manager that uses the COP as a basis to assign sensor functions, select sensor systems and determine sensor settings. The scheduling process is driven by operational knowledge only. To test and validate the sensor management concept and to verify the results of the prototype, also a simulation environment had to be developed. While the sensor management concept is

iii

The Fault Tree Analysis Method is described in Section 4.7.2.3.

iv

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not restricted with respect to the type of object (sub-surface, surface or airborne objects) and type of sensor, the simulation development focussed on airborne objects and a Multi-Function Radar (MFR) due to limited available development time. In this thesis, an MFR can be

regarded as a set of single function sensors that provide different sensor functions and consume their time/energy from a common source with a limited budget as discussed in Section 4.8.2.

1.6 Structure of the thesis

The sensor manager was designed and developed in accordance with the Rapid Application Development principles, resulting in a cyclic pass through the stages of the Systems

Development Life Cycle6 (SDLC, often referred to as the Waterfall Model): • Feasibility Study.

• Systems Investigation. • Systems Analysis. • Systems Design.

• Systems Implementation (including testing and validation). • Review and Maintenance.

Documenting the research in accordance to this approach, would take the reader several times through each of these life cycle stages, thus affecting the readability of the thesis. Therefore the results were ordered with respect to the specific development stages, resulting in a thesis structure that closely follows the steps of the traditional SDLC.

Chapter 2 of the thesis uses the results of a literature survey and operational expert opinion derived from interviews to analyse the current situation with respect to the role of the available sensor systems during mission execution and the related problem. The analysis results were used to formulate previously non-existent generic sensor management principles and to construct a global design that encompasses the relevant element for solving the

problem that was identified in Section 1.3.

In Chapter 3, operational, technical and software development requirements are derived from the global design that was described in Chapter 2.

Chapter 4 rationalises an incremental, modular design approach and describes the design of an object-oriented organisation of the C2 framework that delivers the required sensor

management related information. Furthermore, a justification is given for selecting a newly developed risk estimation process for threat ranking and prioritising sensor tasks allocation purposes. This risk estimation process is modelled by using the Fault Tree Analysis method and is implemented by means of a DBN. Finally, a new approach to sensor management is introduced that takes the shape of an object-oriented, three-layered sensor that uses upgraded sensor observations in order to construct sensor settings and controls thus effectively closing a sensor control loop. This Section also discusses the selection of methods and algorithms that form the basis of these new components.

In Chapter 5, a prototyping environment is chosen and the implementation of the C2

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2 Investigation of sensor management issues

In this chapter a suitable definition of knowledge is selected and based upon this definition, the sensor management related knowledge is identified by means of a literature survey and experiences from operational experts derived from interviews. The results from the interviews and the literature survey were then used to determine the position of the sensor management processes within the CMS processes, to formulate previously non-existent generic sensor management principles and to develop the novel concept of a sensor control cycle.

2.1 Introduction

Naval vessels like corvettes, destroyers, frigates and cruisers are equipped with a payload consisting of a mission-specific set of sensors, CMS and weapons; also referred to as the combat systems. Data from the sensor systems combined with information from external sources like data link systems, if available, informs the command team by means of the C2 process about the present situation in order to support the deployment of the vessel and the combat systems. To obtain the best possible sensor observations, the sensor settings may have to be adjusted with respect to the mission objectives, the expected targets, the geographical or oceanographical and the meteorological conditions. The command team is assisted in their decision-making and controls the sensors and weapons by means of the C2 process. Some sub-processes of this C2 process are implemented in the CMS but the more knowledge-intensive sub-processes are often executed by operators, where possible supported by the CMS.

Depending on the available combat system(s), these vessels can be tasked for various types of missions:

• Combat missions within one or more of the following warfare domains:

§ Anti-Submarine Warfare (ASW): detect, track and eventually neutralise (destroy) submarines.

§ Anti-Surface Warfare (ASuW): detect, track and eventually neutralise surface targets, provide Naval Gunfire Support (NGS), provide HVU/MEU protection: operations to protect assets that are indispensable with respect to the success of a mission.

§ Anti-Air Warfare (AAW) detect, track and eventually neutralise air targets;

§ Electronic Warfare (EW) detect and analyse electronic transmissions from sensors and communication systems.

§ Nuclear, Biological and Chemical warfare (NBC): protect the ship’s crew from nuclear, biological and chemical contamination.

§ Amphibious Warfare: support and protect landing operations;

• CDOPs: operations that seek to destroy or at least disrupt the supply lines of narcotics. • MIOPs: operations that enforce embargos by blocking SLOCs.

• Coastguard tasks: e.g., fishery patrols, environmental pollution control or frontier-running patrols.

• SAR tasks.

From the above it is clear that sensor management contributes to the deployment of the vessel by optimising the sensor systems with respect to the mission requirements. Due to the

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extent. This results in a sub-optimal picture of the situation and consequently a lower probability of mission success.

2.2 Knowledge engineering

The initial investigation into the possible options for solving the sensor management problem suggested an approach that focuses on the design of an expert system containing the

knowledge required for managing complex sensor systems. In order to be able to implement this knowledge in an expert system, it has to be identified, captured and modelled.

To do so, a suitable definition of the concept of knowledge is required. Several definitions of knowledge can be found in literature. Giarratano and Riley7 dedicate a complete chapter to the representation of knowledge and although they do not give a clear definition of knowledge, they describe it as very specialized information obtained from inference or reasoning, forming the basis for specific actions. This description closely fits the definition given by the

CommonKADS knowledge engineering methodology8: knowledge is the whole body of data and information that people bring to bear to practical use in action, in order to carry out tasks and to create new information. From Giarratano’s description and the definition given by the CommonKADS methodology, two distinct components of knowledge can be

identified:

1. Relevant data and information.

2. Processes that act upon this information.

Using these two components, sensor management related-knowledge can be described as the process that transforms mission relevant data/information into optimal sensor settings. From this description, three main questions regarding sensor management can be formulated:

1. What data/information is required for sensor management purposes? 2. Which processes are delivering this data/information?

3. Which processes have to be executed to deploy a set of sensors in an optimal way with respect to operational objectives?

To answer these questions, the present situation with respect to the sensor management process has to be analysed. From this analysis and the described problem, a global design will be constructed that has the potential to solve the problem. This global design will then be refined into an expert system that executes the sensor management process in terms of required information, executed transformation process and resulting sensor controls.

2.3 Analysis of the present situation

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2.3.1 Analysis of the C2 process

Like many (industrial) systems, a naval vessel can be seen as a system that transforms raw materials into a product using available resources. Depending on the type of ship, the following resources can be identified as:

• The platform systems (consisting of the propulsion system, the power plant and the manoeuvring systems).

• The combat system(s). • The crew.

To operate, these resources require raw materials like: • Fuel.

• Ammunition (including missile systems). • Food.

The product or output of a naval vessel can be described as the capacity to execute the mission types that were mentioned in Section 2.1 or, as it is sometimes called in operational terminology: provide sea power and project sea power ashore. Because the concept mission is a keyword here, this concept and all related aspects require further investigation. In the operational literature9 three stages of an operation or mission are described.

1. The planning stage. At this stage, the mission is prepared: the mission goals as set by authorities in charge are analysed, the deployment of assets and resources is

considered and related to the most likely Course of Action (CoA) of an opponent, while taking into account (political) restrictions like the Rules of Engagement (ROEs) that are in force for this particular mission. From these considerations a plan is drawn up that schedules the deployment of the available resources. If an opponent is likely to have radar-warning receivers at his disposal, the use of active sensors like radar systems may reveal the presence of our platforms and the enforcement of tactical restrictions like emission control plans (EMCON plan) may have to be considered. All relevant information from this analysis is laid down in a sequence of operational documents10 ranging from an OPORD (operation order): a booklet that describes the operation, the participating units (army, air force, navy), the mission goals, the chain of command, the procedures to be followed, the opposing forces and their available resources, via the OPGEN (operational general matters), an operational message on policy, instructions and aspects common to all warfare areas, to the OPTASK (operational tasking) an operational message that gives a detailed description of a specific operational task. With respect to sensor management, the OPTASK could prescribe the deployment of the available sensors and the related operational restrictions.

2. The execution stage. At this stage the mission planning that was laid down in the mission documents is executed. During this stage it is vital to have a good overview of the operational situation (nothing has really changed since Sun Tzu) in order to

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3. The evaluation stage. At this stage the results of the mission are analysed and the ‘lessons learned’ are determined. This knowledge can be used in the planning and execution stages of new missions. In this stage of the mission, the deployment of the sensor systems can be analysed and new procedures may be constructed or existing procedures may be adapted thus optimising the deployment of the sensors in future missions.

Figure 2.1 shows the stages of a mission and the transitions between these stages as derived from the operational literature in a flow diagram.

Execute Execute Mission Mission Mission Mission Objectives Objectives At risk At risk Begin Begin Plan Plan Compare Compare Plan with Plan with situation situation No No Adapt Plans Yes End of End of Mission Mission End End Yes Yes Execution Stage Execution Stage Planning Stage Planning Stage Evaluation Stage Evaluation Stage No Learn Learn Lessons Lessons Learned Learned

Figure 2.1. Mission execution flow diagram.

The Allied Joint Doctrine11 defines the C2 process as the process that plans, directs,

coordinates, controls and supports an operation. The C2 process therefore effectively controls stage 1 and stage 2 of any mission and one may argue, as some operational experts do, that the third stage of the mission should also be covered by the C2 process because the ‘lessons learned’ should be documented and used in the planning process of new missions.

This definition of the C2 process in itself explains why the RNLN considers the C2 process of vital importance and why a substantial amount of research is funded to analyse the nature and layout of this process in order to increase its efficiency and to support the automation of the process, thus enabling further crew reduction. The literature review yielded a C2 process model12 that forms the basis for subsequent research into the C2 process founded by the RNLN. This C2 process model distinguishes four main sub-processes:

• The provision of Situational Awareness (SA): this process gathers data about events in the environment of the platform and compiles a picture of the environment.

• The execution of Threat Assessment (TA): this process enhances the information compiled in the picture of the environment by reasoning about the imposed threat. • The support of Decision Making (DM): this process makes decisions about the

deployment of the available systems based upon the threat in the environment.

• The execution of Direction an Control (DC): this process executes the decisions with respect to the deployment of the ship's systems or resources, thus striving for mission completion.

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Situational Awareness Threat Assessment Decision Making Direction and Control knowledge about knowledge about knowledge about knowledge about the environment the environment the environment the environment knowledge about knowledge aboutknowledge about knowledge about the system the system the system the system Mission Deployment Data sources Information transfer Primary Secondary

Figure 2.2. The C2 process model12.

Van Delft12 states:

at the primary level of this model, a direct connection is made between the data sources and the actuators based on feedback rules. The objective is to deploy the ship's systems or resources to reduce the threat in the environment based on set points determined within the secondary level. In the secondary level of the model, the DM process translates high order objectives, determined by the type of operation or mission, into these set points. Essential to this is the input of knowledge about the environment and the available systems.

This model closely fits the definition of knowledge given in Section 2.2, because data

gathered by the SA-process and is compiled into information, that is refined and expanded by the TA-process and ultimately leads to knowledge utilised by the DC-process to perform actions. Because the C2 process model was developed to provide a high-level description of the RNLN’s operational procedures, the model should be sufficiently generic to be applied within all warfare domains mentioned in Section 2.1.

A review of operational directives showed that all listed operations within these warfare domains could be described in terms of the SA, TA and high-level DM processes. These procedures are independent of the type of ship and the combat systems the ship is equipped with. At a lower level of the DM and the DC process, the execution of this process e.g., the decision about what equipment is going to be deployed and how it is deployed, depends on the system capabilities and is therefore platform-specific.

To complement the description of the C2 processes that was derived from the research reports, experts were interviewed to obtain their view regarding these processes and

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2.3.1.1 Situational Awareness

According to the operational experts, the Situational Awareness process seeks to compile a factual picture of the situation at hand. This description corresponds with the definitions found in literature: Endsley, Kaber and Onall13 define SA as the perception of elements in the environment within a volume of time and space, the comprehension of their meaning and the projection of their status in the near future. Qureshi and Urlings14 describe SA as the

understanding of the state of the environment including the relevant parameters of the system. Johnson and Dall15 state: “in order to reason about situations and threats, it is necessary to understand the kinematic relationships between the domain objects”. Ballard and Rippy16 argue: “a SA system must monitor the external environment for entities of interest, recognise those entities and then infer high-level attributes about those entities”. All definitions and expert opinions have in common that they deal with an environment, the presence of objects within this environment and the compilation of a (mental) picture of this environment and the object therein. From these definitions it can be inferred that the SA process uses sensor observations to detect, track and classify (recognise) objects (also called tracks or entities) in the operational environment. The delivered type of data is sensor specific: radar systems may provide range, bearing, elevation, velocity and sometimes classification data; optical sensors can be used for detection and may provide image data of the object for recognition purposes, and radar warning receiver data (Electronic Support Measures17 (ESM) equipment) may be used to detect the presence of objects by their electromagnetic emissions, to provide bearing data and to classify those objects by their emissions. Often, some kind of sensor data fusion functionality is implemented in the CMS, that compares and associates the information received from the different sensors and the information received from external sources received by data link systems, or, as Petterson et al.18 state:

the goals of the Multi-Source Integration are to associate and fuse tracks from the Sensor System, Ground Control Station and Data Link to get an unambiguous identification and classification of aerial objects.

This definition suffices with respect to the context of this thesis as it restricts itself to the AAW domain, but may also be extended to other warfare domains when other sensor systems are used. The identification process as mentioned by Petterson does however not belong to the SA processes, as we shall see in the next section.

2.3.1.2 Threat Assessment

While a lot of definitions of SA could be found in literature, most papers19, 20,21 dealing with the subject of threat assessment, do not give a clear definition of threat, threat assessment or threat evaluation. Ball22 defines the threat to an aircraft as:

those elements of a man-made environment designed to reduce the ability of an aircraft to perform mission-related functions by inflicting effects, forcing undesirable

manoeuvres or degrading system effectiveness.

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some doubt about its intentions and as Unknown if there is no certainty at all about the intentions of the object. Classification and identification were often mentioned in

combination, but while classification seeks to determine the object type, the identification process tries to determine the intentions of the operator of the object: a potentially dangerous object like a weapon system may not pose a threat because it is operated by an ally. The classification process was therefore categorised among the SA sub-processes, while the identification process is ranged among the TA sub-processes.

Because the resources of a platform, as mentioned in Section 2.3.1 are of limited availability, the deployment of those resources (e.g., the assignment of weapon systems) has to be

prioritised. It is clear that this prioritising process is driven by the TA process, or rather by a threat ranking sub-process within the TA process. This observation leads to the conclusion that something like a threat level exists, which is determined by the threat ranking process. A traditional implementation of threat level assignment that was found, is the determination of the Time on Top (ToT) or Time of Arrival (ToA) used for instance in Single Target Tracker (STT) scheduling: these schedulers utilise the time of arrival of incoming missile systems to determine which of these missiles has to be neutralised first. Once objects are classified and identified they can be ranked in accordance with the threat they pose. If this threat ranking process takes the form of a ToT/ToA calculation, this process can be executed automatically. The identification process however still relies heavily on operator knowledge.

2.3.1.3 Decision Making

The result of the SA and TA process is what operational experts refer to as the COP (Section 1.4) and these processes can therefore be regarded as the Picture Compilation process. The DM process uses the COP to determine whether the execution of the mission proceeds according to the plans that were made during the planning stage of the mission by utilising the analysis of the behaviour of the objects in the environment represented in the COP. Once an object’s position, velocity and type are established with some degree of certainty and the threat it poses to the mission has been evaluated as too high, actions like weapon deployment (deployment in Figure 2.2) may have to be executed in order to secure mission success. The deployment of these systems is usually safeguarded by ‘fire inhibit’ switches: once the decision has been made to use force, firing is enabled and the DM process autonomously decides about the choice of weapon and the best moment for deployment while accounting for the limited availability of the resources. Before releasing the 'fire inhibit', the operator (usually the Commanding Officer) has to take restrictions like ROEs into

consideration. The deployment of resources either contributes directly to mission success e.g., if the sensor and weapon systems are used to intercept frontier runners or indirectly if those systems are used for self-defence purposes.

If the analysis of the COP reveals that the mission objectives cannot be achieved, the mission plans have to be changed; this accounts for the feedback loop in Figure 2.1.

The information about the nature of the DM process that was obtained from interviews has a close resemblance to the DM process executed in Airborne Early Warning (AEW) aircraft as described by Huang25:

Mission-Driven sensor management analysis, design, implementation and simulation

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Acknowledgements

Abstract

Samenvatting

Table of Contents

Directions for reading

1.1 The importance of sensor systems

1.2 Sensor management

1.3 Problem definition

1.4 Possible solutions

1.5 Scope of the research

1.6 Structure of the thesis

2 Investigation of sensor management issues

2.1 Introduction

2.2 Knowledge engineering

2.3 Analysis of the present situation