Technische Uni
vers
ite
it De
lft
Mission-driven Resource Management
for Reconfigurable Sensing Systems
T.H. de Groot
Almost any process that undertakes actions relies on situational
awareness. Such awareness can be supported by using sensors. In
dynamic situations the observation task can get complicated and
sensors may need to be reconfigured in real time. Choosing the right
configuration and allocating the available resources, such as time, of
the sensors is very challenging. This thesis proposes the concept of
mission-driven resource management to automatically and optimally
decide on such choices during the various sensor life cycle phases.
M
ISSION
-
DRIVEN
R
ESOURCE
M
ANAGEMENT
M
ISSION
-
DRIVEN
R
ESOURCE
M
ANAGEMENT
FOR
R
ECONFIGURABLE
S
ENSING
S
YSTEMS
Proefschrift
ter verkrijging van de graad van doctor aan de Technische Universiteit Delft,
op gezag van de Rector Magnificus prof. ir. K. C. A. M. Luyben, voorzitter van het College voor Promoties,
in het openbaar te verdedigen op donderdag 22 januari 2015 om 10:00 uur
door
Teunis Harry DE GROOT
elektrotechnisch ingenieur geboren te Voorburg, Nederland.Prof. DSc. A. G. Yarovoy Copromotor: Dr. O. A. Krasnov Samenstelling promotiecommissie:
Rector Magnificus, voorzitter
Prof. DSc. A. G. Yarovoy, Technische Universiteit Delft, promotor Dr. O. A. Krasnov, Technische Universiteit Delft, copromotor Prof. ing. F. Le Chevalier, Technische Universiteit Delft & Thales Prof. dr. ir. A. Verbraeck, Technische Universiteit Delft
Prof. dr. M. J. de Vries, Technische Universiteit Delft Prof. DSc. H. Griffiths, University College London Dr. F. Bolderheij, Nederlandse Defensie Academie
Keywords: resource allocation, sensor management, objective functions, reconfigurable systems, multi-functional sensors
Printed by: Ipskamp Drukkers, the Netherlands
Front: The cover depicts one of the most precious resources.
Copyright © 2015 by T.H. de Groot ISBN 978-94-6186-391-1
Effective management always means asking the right question.
P
REFACE
On the one hand, the term ‘resource management’ is inappropriate for this research, be-cause it may shift the reader’s mind towards the available ‘resources’. The actual focus of this research is the objectives, which is independent of the available resources. On the other hand, the term ‘resource management’ is quite appropriate, because it is descrip-tive for many related research that has focussed on the resources.As a result, the subject of research in this thesis is unusual for the domain of ‘resource management’, or at least, within ‘sensor management’. Many fundamental aspects and results presented in this thesis have, according to my knowledge, never been discussed before in such a manner for ‘sensor management’. Therefore, this thesis provides novel scientific solutions for sensor design and resource management.
Teun de Groot Delft, July 2014
C
ONTENTS
1 Introduction 1
1.1 Unpredictability in the Security Domain . . . 2
1.2 Modern Sensor Technology. . . 3
1.3 The Concept of Sensor Reconfigurability . . . 4
1.4 Problem of Optimal Resource Allocation . . . 5
1.5 Sensor Management in the Open Literature. . . 8
1.6 Selected Challenge in Resource Management. . . 11
1.7 Research Approach . . . 12
1.7.1 Cross-disciplinary . . . 12
1.7.2 End-user oriented . . . 13
1.7.3 Falsify and correct . . . 14
References. . . 15
2 Definition of Optimality from the Mission Perspective 21 2.1 Interaction with End-users . . . 22
2.2 Discussion on Terminology. . . 22
2.3 Mission-driven Objective Function. . . 23
2.3.1 Utility . . . 23
2.3.2 Expected-utility . . . 25
2.3.3 Variants of expected-utility . . . 26
2.4 Generic System Architecture . . . 27
2.5 Conclusion . . . 29
References. . . 29
3 Concept of Operational Tasks for Reconfigurable Sensors 31 3.1 Hierarchical Resource Management . . . 32
3.2 Abstraction towards Operational Tasks . . . 32
3.3 A Model of Reconfigurable Sensors for Defense Operational Tasks . . . 33
3.3.1 Allocation of resources. . . 33
3.3.2 Operational tasks for asset defense. . . 34
3.3.3 Decomposing into sensing tasks. . . 34
3.3.4 Scheduling of sensing tasks . . . 36
3.3.5 Probability of successful task completion . . . 37
3.4 Mission-driven Optimization in a Defense Mission. . . 39
3.4.1 Allocating resources to operational tasks. . . 39
3.4.2 Improvement over task-driven method . . . 43
3.4.3 Re-deploying multi-sensor station. . . 43
3.5 Conclusion . . . 43
References. . . 45
4 Quality Metric for Heterogeneous Operational Tasks 47
4.1 Probabilistic-based Metric . . . 48
4.2 A Case Study in the Security Domain . . . 50
4.3 Probability of Successful Air Defense . . . 52
4.4 Probability of Successful Weather Alarm . . . 55
4.5 Probability of Successful Crowd Control . . . 56
4.6 Probability of Successful Drone Flight. . . 59
4.7 Conclusion . . . 59
References. . . 60
5 Automatic Resource Management during Operational Phase 61 5.1 Improvement over Task-driven Method. . . 62
5.2 A Reconfigurable Resource Model. . . 64
5.2.1 Radar surveillance. . . 64
5.2.2 Radio communication. . . 66
5.2.3 Reconfigurable RF front-end. . . 67
5.3 Automatic Mission-driven Management . . . 68
5.3.1 Operational story . . . 68
5.3.2 Algorithm strategy. . . 69
5.3.3 Management evaluation. . . 70
5.4 Conclusion . . . 72
References. . . 73
6 Revision of Optimality Definition with Prospect Theory 75 6.1 Rationality of Expected-Utility . . . 76
6.2 Introduction to Prospect Theory . . . 76
6.2.1 Probabilities, certainties and weights . . . 77
6.2.2 Gains and losses. . . 77
6.2.3 Criticism. . . 78
6.3 Two Example Missions with Critical Trade-offs . . . 79
6.3.1 Protect two high-value assets . . . 80
6.3.2 Protect an asset and capture an asset . . . 81
6.4 Evaluation of Decision Making . . . 81
6.5 Possibility for End-user Interface . . . 84
6.6 Conclusion . . . 85
References. . . 86
7 Inclusion of Operational Uncertainty with Subjective Logic 87 7.1 Definition of Second-order Uncertainty. . . 88
7.2 Introduction to Subjective Logic . . . 89
7.3 Uncertainty during Future Assessment . . . 91
7.3.1 Averaging fusion. . . 92
7.3.2 Cumulative fusion . . . 93
7.4 Uncertainty during Model Development . . . 93
CONTENTS xi
7.5 Incorporating Extra Uncertainty . . . 95
7.6 Possibility for End-user Interface . . . 97
7.7 Conclusion . . . 99
References. . . 100
8 Conclusion 101 8.1 Accomplished Results. . . 102
8.1.1 Mission-driven objective function. . . 102
8.1.2 Direct allocation of operational tasks . . . 102
8.1.3 Probabilistic-based quality metric. . . 103
8.1.4 Expectation-based management. . . 103
8.1.5 Generic and flexible framework . . . 103
8.1.6 Improvement and positive end-user feedback. . . 103
8.1.7 Revealed fundamental limitations. . . 104
8.1.8 Revision of objective function . . . 104
8.1.9 Inclusion of operational uncertainty. . . 104
8.2 Recommendations . . . 105
8.2.1 Continue discussions with end-users . . . 105
8.2.2 Investigate low-level objective functions. . . 105
8.3 Final Thoughts . . . 106
A Other Perspectives for Optimality Definition 107 A.1 Encountered Issues. . . 108
A.2 Moral Philosophy. . . 108
A.3 Categorical Imperative . . . 109
A.4 Teleology . . . 110
A.5 Implications for Automatic Management. . . 111
A.5.1 Categorical reasoning . . . 112
A.5.2 Teleological reasoning . . . 113
A.6 Categorically Corrected Objective Function. . . 114
A.7 Conclusion . . . 115
References. . . 116
B Gradient-based Optimization for Network of Reconfigurable Sensors 117 B.1 Introduction . . . 118
B.2 Multi-Resource Allocation Problem. . . 119
B.3 Case Study: Network of Reconfigurable Radars . . . 120
B.4 Optimization Algorithms . . . 122
B.4.1 Centralized Multi-start (CM) algorithm . . . 125
B.4.2 Centralized Single-start (CS) algorithm . . . 128
B.4.3 Centralized Transfer-start (CT) algorithm . . . 128
B.4.4 Centralized Hybrid-start (CH) algorithm. . . 129
B.4.5 Distributed Multi-start (DM) algorithm . . . 130
B.4.6 Distributed Single-start (DS) algorithm . . . 131
B.4.7 Distributed Transfer-start (DT) algorithm . . . 131
B.4.8 Distributed Hybrid-start (DH) algorithm. . . 131
B.4.10Synchronous algorithms. . . 132
B.5 Comparison of Algorithms Performance . . . 132
B.5.1 Performance in the two example cases. . . 132
B.5.2 Performance as function of problem size . . . 134
B.6 Implementation Discussion. . . 138
B.6.1 Physical algorithm location . . . 138
B.6.2 Single point of failure . . . 138
B.7 Conclusion . . . 139
References. . . 139
C Sequential-Hierarchical Deployment of Heterogeneous Sensors 143 C.1 Introduction . . . 144
C.2 Problem Definition . . . 145
C.3 Efficient Global Optimization. . . 147
C.3.1 Removal of identical allocations . . . 147
C.3.2 Removal of non-beneficial allocations. . . 149
C.4 Sequential Optimization . . . 151 C.5 Hierarchical Optimization . . . 151 C.6 Sequential-Hierarchical Optimization . . . 152 C.7 Comparison of Algorithms . . . 153 C.8 Conclusion . . . 155 References. . . 155 Summary 157 Samenvatting 159 Curriculum Vitæ 161 List of Publications 163 Acknowledgment 165
1
I
NTRODUCTION
Start with the end in mind.
Stephen Covey
Almost any process that undertakes actions relies on information. Information can be ob-tained by using sensors. Sensors can become very complicated, depending on the parame-ters to be observed and the situation. When the situation becomes dynamic, the sensors are preferably reconfigurable. Reconfigurability implies adaptable functions and settings, al-lowing sensors to adapt to new missions and circumstances. Given many possible config-urations, then the problem arises of selecting the optimal one. In order to understand this challenge, this chapter first introduces the operational context, some trends in the industry of sensors, and the related research field. Section1.1starts off with describing a real case that illustrates the challenge of unpredictability in the security domain; the current sensor technology is briefly discussed in Section1.2. The concept of sensor reconfigurability that is seen as a solution to better cope with security challenges is introduced in Section1.3. In contrast, Section1.4explains the difficulties of reconfigurability for optimal resource allo-cation in general (i.e. resource management). State-of-the-art sensor management, which are resource allocation methods specifically for sensors (e.g. radar resource management), is discussed in Section1.5. Given the current research, the remaining challenges within the scope of this thesis are identified in Section1.6. Finally, Section1.7outlines the research approach that resulted in novel scientific results.
1
1.1.
U
NPREDICTABILITY IN THE
S
ECURITY
D
OMAIN
T
HEspectrum of threats imposed to modern society has drastically widened in the last decades. An incident at the House of Parliament in The Hague in 2010 can be given as an example. A person wanted to promote his book in the Netherlands with an unusual practical joke. He sent a small radio-controlled airplane over the Binnenhof, which is the meeting place of the Dutch parliament. The airplane eventually crashed in the building and no real harm was done, but the security authorities reacted rather late. Afterwards it became clear that a small low-flying air-object as a threat within the area of the parlia-ment had not been considered yet by the authorities and the available counter-measures at that time were too limited. This event simply proves that unexpected scenarios, which have not occurred in the past or have not been considered within risk analysis, can still happen today and in the future. It is therefore vital that once a threat becomes expected, the security authorities receive means to deal with them.Subsequently, it became clear that the threats and environments are changing too rapidly relative to the speed that security measures can be adapted. This triggers the need for systems that can cope with such fast changes. The realization of such systems is a challenge that is not to be underestimated. Coping with variable circumstances means that the system development has to consider more than the well-known scenarios and satisfy some given requirements. In order to understand the new development chal-lenge, let us revisit the previously discussed scenario. Based on this practical joke, many other scenarios can be imagined, as illustrated in Figure1.1. A similar air-object, but with an explosive payload, may fly over the Rotterdam harbor. Such an adaptation is easily realized by an opponent, but compliant adaptation of security measures, such as sensors to detect the threat, is currently much harder. It is additionally unclear if this new scenario will even occur in the future or if other dangerous scenarios are more probable and/or harmful. To summarize, the extreme shifts in potential scenarios imply a big challenge for system development.
new threat?
environment?
ma 31 mei 2010, 11:23 |
DEN HAAG - Emile Ratelband heeft maandagmorgen een radiografisch bestuurd vliegtuigje laten crashen tegen één van de torentjes van de Ridderzaal op het Binnenhof. Een deel van het vliegtuigje hangt nog in het torentje, de rest ligt in stukken op de grond. De positiviteitsgoeroe is inmddels gearresteerd.
De goeroe wilde met de actie zijn nieuwe boek ‘Nederland is in de war’ promoten. Het vliegtuigje, met een spanwijdte van circa 1 meter 50, trok een sleep mee met de titel van het boek. Nadat het vliegtuigje, met het registratienummer PH RTB, een aantal malen boven het Binnenhof had gecirkeld, boorde het zich in een van de torentjes van de Ridderzaal. Het vliegtuigje, dat 0,4 liter metanol aan boord had voor de aandrijving van een 5 cc Magnum tweetakt motor, was vanochtend om half tien 'opgestegen' vanaf de bovenste verdieping van het Novotel Den Haag, dat tegenover de ingang van het Binnenhof is gelegen. Ratelband had daar een zaal op de bovenste verdieping afgehuurd, zogenaamd om een seminar te geven. In werkelijkheid steeg daar vanochtend het vliegtuigje –een zogenaamde Robbe Charter- op. Boven grote delen van Den Haag geldt een vliegverbod. Onder meer is het verboden te vliegen in het luchtruim boven het parlementaire centrum en het paleis van Koningin Beatrix, Huis ten Bosch. Piloten die de ‘no fly zone’ negeren kunnen een forse straf en een hoge boete verwachten. Emile Ratelband heeft zich na zijn actie vanochtend zelf aangegeven bij de politie. Volgens Ratelband was zijn ‘actie’ bedoeld om de verkiezingsuitslag te beïnvloeden. De illegale vliegactie is de afgelopen weken in het diepste geheim voorbereid.
Ratelband laat vliegtuigje crashen op Binnenhof
door Martijn Koolhoven lees voor
Emile Ratelband Foto: Peter Zonneveld
Het Novotel van waaraf het vliegtuigje werd gelanceerd, ligt tegenover het Binnenhof.
Foto: Google Maps
Een Twitter-kiekje van Abel Schooleman. Foto: http://tweetphoto.com/24876006
1.2.MODERNSENSORTECHNOLOGY
1
31.2.
M
ODERN
S
ENSOR
T
ECHNOLOGY
O
NEof the security means that play an important role in coping with (new) threats is sensor systems. An increasing number and an increasing variety of sensors is em-ployed in the modern world in various applications, environments, and ways in order to build the required situational awareness. The air-object of Figure1.1has to be first detected (i.e. assessing the situation) before there can be any reaction to its presence. Thus, there is a constant need to obtain the new required information. Information can be retrieved in numerous ways and deploying radars and cameras to monitor the en-vironment is one of them. Due to a constant drive for efficiency, a modern trend for sensors is that the existing systems and concepts (e.g. from civil and military domains) are merged into joint observation systems. The resulting systems can then fulfill differ-ent functions (e.g. detection, classification, electronic support measures) by retrieving different types of information.The capabilities of modern sensors vary, depending on the sensor type, and some consist of advanced technology that even allows to perform simultaneously multiple functions, such as tracking targets, identifying objects and guiding missiles [1,2]. Fig-ure1.2shows a selection of existing multi-functional sensor systems. An on-going pro-cess in sensor research and development is that the number of capabilities and settings of such sensors is increasing over time. This can be helpful for various current appli-cations, because it results in new functions and improved performances. In addition, when sensor technology is able to provide more options, then in theory it should be additionally possible to react to new threats and environmental conditions more easily. Therefore, this on-going process contributes to a solution for above discussed concern of changing circumstances within the security domain.
1
1.3.
T
HE
C
ONCEPT OF
S
ENSOR
R
ECONFIGURABILITY
I
Nthe Netherlands, the Sensor Technology Applied in Reconfigurable Systems (STARS) project has been initiated [3]. The project’s aim is to develop technology in such a way that the same sensors can be (re-)used for changing applications and within dynamic en-vironments. The idea is that, instead of developing dedicated systems for each particular scenario, reconfigurable sensors should be re-used to cope with yet unforeseen new sce-narios. The benefit of reconfigurability is that the lead time, which is the time before the required system is ready for operational commissioning, is reduced. The development cost of a reconfigurable system will be probably higher than of a conventional system, but it is expected that the total cost integrated over the system life cycle, including the additional cost of updates and feature upgrades, will be lower. Thus, reconfigurability further improves the capability to cope with changing operational conditions, to realize sensors that are future-proof, and to contribute to a sustainable security.The concept of sensor reconfigurability within the STARS project can be further ex-plained on the basis of sensor life cycle phases. Figure1.3depicts three phases: develop-ment, deploydevelop-ment, and operation. During the development phase, the reconfigurable building blocks are constructed and validated. These reconfigurable building blocks are combined, prepared, and positioned during the deployment phase. After deploy-ment, the sensor systems are in operation and able to execute tasks within the mission. (The development and operational phase are comparable with the more frequently used terms design-time and run/real-time, respectively.) Within each life cycle phase several decisions related to the systems are made. Any decision made early in the development or deployment phases of life cycle will reduce the number of options available for setting
people & experience reconfigurable building blocks & infrastructure model-based design & test
validated & deployable systems model-based integration & acceptance testing
development phase deployment phase
validated & deployable systems
operational phase
deployed sensor network environment mission operational tasks rules of engagement end-users factory, equipment & materials
1.4.PROBLEM OFOPTIMALRESOURCEALLOCATION
1
5 effector communica tion link UAV camera field of view identified threat flight plan 0 20 40 60 80 100 80 60 40 20 0 x [km] y [km]radar search volume
effector’s base station
antenna
field of view radarsearch beam identified threat 0 20 40 60 80 100 80 60 40 20 0 x [km] y [km] camera field of view reconfiguration original configuration objects being tracked
Figure 1.4: Reconfiguration of radar system resources after component failure.
observation parameters later on. The concept of reconfigurability postpones decisions that are traditionally made during design towards the operational phase: tailoring of the observation tasks can be postponed until the latest moment, when the need for infor-mation to be retrieved can be expressed in the most detailed way. Therefore, sensor reconfigurability enables fast response to any new need by adding extra reconfigurable resources that are available in the repository or re-setting deployed sensor systems.
Reconfigurability is also beneficial when dealing with changes within the system it-self, such as component additions, removal and failures. An illustrative example is given in Figure1.4where a reconfigurable radar can cope with component failures within the multi-sensor station [4]. This station consists of a radar used to detect objects and a camera used to identify objects. Let us assume that the camera fails during the opera-tional phase. As a result, objects cannot be identified anymore, and the mission cannot be successfully completed. It is preferred to find as soon as possible a new configuration in order to continue the mission. Fortuitously, a camera-equipped drone is within range and available to participate in the mission. When a data link is established between the multi-sensor station and the drone, the detected targets can be cued to the camera for identification. Given this possibility and the assumption that the radar is reconfigurable (i.e. parameters can be set adaptively and/or new hardware can be easily added), then a solution can be quickly realized (i.e. during operation or deployment) by re-allocating a part of the radar antenna resources to the new communication capability. This demon-strates that system failures do not need to result in complete mission failure, but with reconfigurability in graceful degradation.
1.4.
P
ROBLEM OF
O
PTIMAL
R
ESOURCE
A
LLOCATION
M
ULTI-FUNCTIONALand reconfigurable sensors provide a wide range of options for the diverse available resources (e.g. time budget, antenna elements, computa-tional units) at different time instances. Such a variety of possibilities results in the need to make a control decision about how a sensor should be actually configured. In the above discussed case of radar reconfiguration, a trade-off between surveillance and1
communication capability has to be made, because the radar resources (e.g. time andenergy) are limited and now have to be shared with the task of communication. When we consider the three life cycle phases in Figure1.3, it is easy to generate many other questions: which building blocks should be used for the station?, where should we (re-)position the multi-sensor station?, which radar waveform should be transmitted for the surveillance task?, which modulation scheme should be selected for the communication task?, etc. Many of these control questions can be mapped to the problem of optimal re-source allocation.When reconfigurable sensor resources have to operate at their maximum potential, then it is evident that an automatic resource allocator is necessary to support overloaded end-users. The optimization of sensors is in practice too complex for end-users, because there are too many choices for humans to consider and they are too strongly related to advanced aspects of sensor technology. Furthermore, the changes in environment, threats and/or systems can happen too fast for an end-user to react to. Thus, utilizing modern sensors at their full capability requires an automatic resource manager, but re-alization of such a manager is a challenge itself. In the mean time, the constant advances in sensor research and development result in an even higher number of capabilities and aspects that have to be considered. Because of this, the challenge of developing a man-ager that can control these new systems is simultaneously increasing.
A required component for optimal resource allocation is an optimization algorithm (e.g. [5,6]). There exist mainly two fundamental types. The first one aims to maximize provided performance (e.g. detection coverage or tracking accuracy) of given limited resources (e.g. time budget within a sensor system or number of systems within a sen-sor depot). In this case, the problem can be mathematically matched with one of the many variants of the NP-hard Knapsack Problems [7,8]. The second type aims to min-imize resource usage while satisfying given performance requirements (e.g. [9–11]). In that case, the optimization can be compared with the NP-hard Traveling Salesman Prob-lem [12,13]. Both problem types are well-studied. This thesis focusses on the first type of optimization, because of several reasons. Firstly, in many cases the full resource bud-get is the one that is already fixed (e.g. available money at Ministry of Defence or time budget within this Universe). Secondly, in modern society it is hard to justify require-ments on an operational level that do not guarantee success in all cases (e.g. is a survival probability of 95% enough for the prime minister?). Finally, and very crucial, fixed per-formance requirements do not allow graceful degradation of system perper-formance, which is, as discussed in Section1.3, one of the key benefits of sensor reconfigurability.
Figure1.5depicts on the left a knapsack problem. The objective is to find the set of items that fits in the bag and maximizes the value of the total content. When considering resource allocation, the items can be seen as tasks and the volume of the bag as the avail-able resource. A solution is feasible if the selected items fit in the bag. A solution is opti-mal if there is no other feasible solution that has a higher total value. Finding a solution that is feasible is relatively easy. Finding a solution that is optimal requires substantially more effort. In practice, the required effort is unfortunately not linearly proportional with the problem size, which is the fundamental feature of all NP-hard problems.
The knapsack problem is normally formulated with discrete options, but potentially the items can even change their exact size and value. Such a situation is definitely the
1.4.PROBLEM OFOPTIMALRESOURCEALLOCATION
1
7?
15 kg
27 kg 12 kg 15 kg more knapsacks if multiple resources are considered 12 kg €4 3 kg €2 8 kg €10 2 kg €2 1 kg €1Figure 1.5: Illustration of the (Multiple) Knapsack Problem [SeeAcknowledgment].
case for controlling reconfigurable sensors. For instance, after making a (discrete) deci-sion that a target has to be tracked, a (continuous) decideci-sion has to be made about how well the target will be tracked. The track quality normally depends on the amount of resources (e.g. time and energy) that are spent on the target.
We may also expect more than one (type of ) resource. In this case, the resource al-location problem can be compared to a Multiple Knapsack Problem, as illustrated in Figure1.5, or formulated as a distributed constraint optimization problem (e.g. [14,15]). Beside the fact that this results in more controllable parameters for the optimization al-gorithm, the estimation of the item value becomes more complex, because the added value of putting an item in a specific bag can depend on the content of the other bags. In the end, the optimization problem can be formulated as follows:
maximize K X k=1 vk(xk) subject to ∀r ∈ {1,...,R} : K X k=1 wr(xk) ≤ Wr where xk∈ Xk (1.1)
where K is the number of tasks (items), R is the number of resources (knapsacks), vk(xk)
is the value of executing task (including item) k with configuration xk, wr(xk) is the
amount (weight) required of resource r for executing task k with configuration xk, Wr
is the total amount available at resource r (the weight a knapsack can carry), and xkis
the configuration within the available ones in set Xkfor task k. In this formulation, the
decision to execute task k, and if so, the decision which configuration for task k is used are simultaneously made by controlling xk, that also specifies which (multiple) resources
r are involved. Because the value function is usually non-linear and non-convex, a
lo-cal optimum does not have to be a global optimum (see AppendicesBandC), and it is unfortunately hard to verify if a solution is optimal.
In any case, considering a single or many (types of ) resources, dealing with discrete or continuous (task) options, it requires normally too much computational time to find the global optimal solution and to verify it. Therefore, approximation algorithms, that make a trade-off between solution optimality and computational speed, are frequently used for above mentioned optimization problems. Although approximation algorithms do not always find the theoretical optimal solution, the provided solutions are already a significant improvement to manual optimization. Besides, the end-user rather wants a sub-optimal solution in time, than no solution or a global optimal solution too late.
1
trends. Firstly, it is expected that the sensors become ever more capable and adaptable.The challenge of optimal resource allocation will probably grow due to three modern Secondly, the same sensors will probably be used simultaneously for many (new) oper-ational tasks such as traffic control, search & rescue, crowd control, weather alarm, air defense, and ground pursuit. Finally, future surveillance systems will probably consist of ever higher number of sensors and different types of sensors: large-scale heterogeneous sensor networks. These trends of extended sensor reconfigurability, broader mission de-scription, and increased network sizes imply a huge resource allocation problem. All the resources (e.g. antennas, computational units and time budget) within each system have to be controlled in such a way that the numerous requested tasks are executed, and ide-ally, in an optimal manner. Moreover, technical trade-offs between capabilities within the systems should be made automatically. Such ideal utilization of the resources is cur-rently a long way off. It is therefore essential that we invest in resource management research in order to exploit (at least a few of ) the future possibilities.1.5.
S
ENSOR
M
ANAGEMENT IN THE
O
PEN
L
ITERATURE
S
OMEoperational multi-functional sensors are (partly) controlled by automatic sensor management systems. Thus, one may conclude that sensor management is already “done”. Nonetheless, it is debatable if it is done optimally. The decisions that are made automatically during operation are frequently based on pre-defined rules, flowcharts and other heuristics. Selection of an ‘optimal’ system configuration based on some ap-proach or criteria is not the problem, but it is difficult to provide a convincing and scien-tific argument why such management is (non-)optimal. A related issue is that it is hard to make trade-offs between performances of multiple tasks, because there is no clear definition of the higher objective for the sensor managers. Although probably not all sensor management solutions are published, because it is strategically important that the opponent or competitor does not know how resources are employed, an overview of the solutions described in the open literature is given below.Because the field of sensor management covers many aspects, there are many for-malizations of sensor management problems (e.g. [16–19]). Some attempts have been made to structure all of them with a hierarchical method (e.g. [20,21]). This results in several functional management levels where various decisions are made separately. Two examples are shown in Figure1.6. The scheduling of sensing tasks such as search, tracking and/or identification (e.g. [17,22]) can also be executed by introducing several levels of tasks (e.g. [20,23]): measurement tasks (e.g. radar pulse/burst) are executed within the “short-term” time intervals and sensing tasks (e.g. target tracking) within the “long-term” time interval. Each smaller problem is preferably defined in such a way that the solutions of each smaller problem together result in the same solution as solving the original full scale problem at once. Design processes are in general methodologi-cally similar, because many designers are working in parallel, not aware of what their colleagues are doing precisely, but just the specification of the input, output and control interfaces is sufficient. In fact, the original task of management as depicted in Figure1.3 is already broken down into a hierarchical system of smaller problems, namely: devel-opment, deployment, and operational phase.
1.5.SENSORMANAGEMENT IN THEOPENLITERATURE
1
9 Mission Planning Resource Deployment Resource Planning Sensor Scheduling Sensor Control A hierarchical division Level 4 Level 3 Level 2 Level 1 Level 0 Action Planning Resource Allocation Actuator Scheduling Signal Generation Actuator Front-ends Another hierarchical division Level 3 Level 2 Level 1 Level 0 Physical LevelFigure 1.6: Two different functional architectures proposals for hierarchical resource management [20,21].
category of approaches that can be discussed is heuristics. In this thesis the term heuris-tic is referring to methods based on if-then programs, flowcharts, greedy/myopic strate-gies, fuzzy logic, (knowledge-based) look-up tables and rules of thumb (e.g. [17,24,25]). Such solutions are frequently used, for instance, when processing power is limited. Al-though the term sometimes has a negative connotation for scientists, heuristics can be very fast - and even literally optimal - for a limited set of problems. However, its key dis-advantage is that it is questionable if an exhaustive validation of the heuristic methods is feasible for problems that were not considered during the design. Therefore, heuristics are not preferred to be combined with the concept of reconfigurability.
A better method to solve optimization problems is by defining an objective function and maximize or minimize its output [26]. Such an objective function then quantifies performance with an ordinal (or cardinal) number, and allows to unambiguously com-pare optimality of solutions. In this way, the optimization goal is mathematically clear and the algorithm is more robust against changing circumstances, which is crucial when considering reconfigurability.
The defined objective functions are frequently task-driven and related to task exe-cution performance. When a sensing task, such as dedicated target tracking, has to be executed by a sensor, like a phased array radar, then the task performance is referred to with terms as track quality, quality-of-service, etc. Moreover, such quality-driven per-formance measures in sensor management are usually sensor-driven, namely strongly focused on sensing characteristics. Popular measures are (cumulative) detection prob-ability (e.g. [27–29]), tracking accuracy (e.g. [23,30–32]), measures in information the-ory (e.g. [31,33–37]), power, signal-to-noise-ratio and/or energy levels (e.g. [38–41]), or other qualities (e.g. [42–45]). Abstract technical quality metrics are also used in non-sensor domains, for instance, to measure communication performance: packet/bit (er-ror) rates (e.g. [46,47]), signal (interference) levels (e.g. [48–50]) or throughput/latency (e.g. [49,51]). A more generic method is to measure the probability that a task is success-fully executed (e.g. [52,53]). In the end, above proposed measures describe how well a
1
task is executed from a technical perspective, but a mathematical relation between thesemeasures and actual operational impact for end-users is not given. The management challenge is already revealed when a single task has to be optimally executed by a system, but resources should also be optimized when many tasks have to be executed. If the available resources are limited and each individual task would pre-fer to claim all resources, then a method is required to trade-off between the tasks. This challenge exists for a set of homogeneous tasks (e.g. multiple targets that need to be tracked), but it also exists for a set of heterogeneous tasks (e.g. surveillance together with communication task). A dominant approach is based on a principle of summing up all task-quality/utilities multiplied with a task-priority/weight into a single additive total-utility function (e.g. [23,28,42–44,54]). Such an approach is not solely used for sensor management, but it is also found in other domains: computer processors (e.g. [55]) and communication systems (e.g. [56]). The exact employed terms may differ, but these ap-proaches can be captured in the following function:UQ= K
X
k=1
pkUT(QoSk(xk), k) (1.2)
where pkis priority (or weight), QoSkis the Quality-of-Service (or just quality) for task k
as function of configuration xk, and function UT transforms a quality QoSkinto a
util-ity value in a common domain. A function UT (e.g. [23,26,28]) is sometimes absent,
but required for a consistent summation if different types of quality measures are used (e.g. detection probability is not comparable with tracking accuracy). Note, when fixed requirements are defined for these characteristics (e.g. UT is based on a heaviside step
function), this blocks the possibility of graceful performance degradation and trade-off mechanisms. Eventually, it is assumed in current literature that when one of above func-tions is maximized the utility for the end-user is also maximized.
When we consider the Knapsack Problem, as discussed in Section1.4, with equation (1.2), then the value of adding a flexible item in a knapsack is defined as:
vk(xk) = pkUT(QoSk(xk), k) (1.3)
This explicitly shows the following assumptions. Firstly, it is assumed that a list of K system tasks already exists, their priorities pk are already defined, and that the
perfor-mance QoSkcan be adapted with xk. In fact, this approach does not solve the problem
of defining the optimization objective, but it creates new problems, namely: definition of tasks, priorities and qualities. Secondly, utility is the sum of all task-qualities/utilities multiplied with their task-priorities/weights. Thus, utility is not, for instance, the biggest of the qualities/utilities. Note, this utility makes only sense if all task-qualities/utilities are defined in the same domain, also for heterogeneous tasks (e.g. object search together with target tracking). Thirdly, priorities/weights that indicate the (relative) importance of tasks should be defined. For instance, task A is 1.875 times as important as task B. Considering the priority values, they may be assumed to be provided by end-users or based on automatic threat assessment (e.g. [57–59]). Finally, it is assumed that QoSk
and/or UT can be computed for every task and task’s type. To conclude, above approach
1.6.SELECTEDCHALLENGE INRESOURCEMANAGEMENT
1
11or relation given that the multiplication of a task-utility UT with its priority pkequals the
marginal utility for end-users.
Another approach in sensor management defines optimization goals based on vari-ants of risks and costs. At least the term “risk” is more frequently used by end-users in military domain, and has therefore better foundation and is more meaningful [6,58]. Af-ter such a parameAf-ter has been defined, the management goal is to minimize this risk or cost. However, in several cases sensing and/or resource characteristics are again incor-porated: risk of losing track [23], probability of localization error [32], misclassification [60], and cost of weapon deployment [61]. Although they can be important for mission success, they do not specify this success as such.
To summarize, the majority of current optimization methods are driven by the idea that task performances and/or sensing characteristics have to be optimized. This is probably caused by a teleological type of reasoning as further explained in AppendixA. Although the optimal allocation of sensing tasks is claimed by maximizing the above util-ity metrics, it is not shown, or at least in many cases there is no concrete relation given, that this utility corresponds to the utility perceived by end-users. For example, is it the goal of end-users really to have qualitative information provided by sensors? Beside the fact that end-users often misunderstand many of these abstract technical characteristics (e.g. entropy in information theory), they also do not imply mission success. As a result, the traditional type of optimization does not necessarily lead to an optimal allocation of resources from a mission success point of view.
A reason for the complexity on the definition of the objective function is to prove the correctness of the definition. In fact, an objective function would be the method to judge if a solution is correct. If someone just states “optimality is optimal, because my definition of optimality says so”, then this is circular reasoning and not convincing. To clarify, an objective function should not describe how something is done or what is currently achieved (i.e. descriptive), but it should describe what ideally should be done or achieved (i.e. normative). The former one is more easily realized with, for example, empirical and/or statistical research, but the latter one requires more fundamental ef-forts. To conclude, there is still a need to further research the definition of the objective functions for sensor management.
1.6.
S
ELECTED
C
HALLENGE IN
R
ESOURCE
M
ANAGEMENT
T
HEutilization of multi-functional sensors at their full potential is in many aspects not realized yet and is a very challenging problem. Beside the fact that it is very hard to manage currently available functions, an expected upcoming challenge is that future systems become more reconfigurable, consist of more (sub-)systems and able to provide a wide range of new capabilities. With the current sensor management technology we simply cannot utilize these sensors at their full potential.The key drawback of current design and control approaches is that systems are usu-ally optimized from a technical perspective (e.g. improve detection coverage), but this is not necessarily what end-users need. A reason for this mismatch is that it is easier to only focus on fixed technical design requirements (e.g. minimal detection range, track-ing accuracy, minimal number of tracks). In this way, the design process does not have to deeply consider mission aspects and the final goals of the end-users. Therefore, it is
rel-1
atively easy to measure if the design requirements are satisfied, and if the performanceis increased, at least from a technical point of view. It is clear that this development pro-cess results in a system, but also that it is not nepro-cessarily optimal considering the actual mission of the end-users. Additionally, fixed design requirements, that have to be sat-isfied in any case, do not allow graceful degradation of performance and can therefore block less, but still useful, reconfiguration solutions.A more preferred option is to develop and configure systems in such a way that they contribute as much as possible to the actual goals of the end-user’s mission. Therefore, the question that this study addresses is:
How can reconfigurable sensing resources be optimally managed within an end-user’s mission?
The realization of a mission-driven resource management solution, in which the op-timization is directly driven by mission objectives, is a huge challenge. The challenge should not be underestimated because of two reasons. Firstly, it is very hard to conceive a mathematical definition of what end-users actually want during the mission. Secondly, it is challenging to convey these end-user goals all the way down to system parameters, because the mathematical link between these two can become complex.
The following three aspects are considered beyond the scope of this thesis: devel-opment of a new (reconfigurable) sensor system, develdevel-opment of a new sensor perfor-mance model, and development of a new optimization algorithm. If necessary, this the-sis assumes that these three components are already available.
1.7.
R
ESEARCH
A
PPROACH
T
HEresearch approach is explained by discussing three aspects. The resulting struc-ture of this thesis is depicted in Figure1.7.1.7.1.
C
ROSS-
DISCIPLINARYThe developed management solution should be generic, and thus, not solely limited to specific sensor applications and not depend on a specific level of reconfigurability. A cross-disciplinary approach allows to verify and maximize the applicability and consis-tency (e.g. methodology, terminology) of this research.
Within this research several opportunities occurred to discuss sensor technology in the context of realistic missions with various end-users from the safety, security, and defense domain. The interaction with end-users and the employed terminology is dis-cussed in Chapter2. This research’s goal was to find what end-users consider important and analyze how sensors can contribute to this (i.e. top-down thinking). In contrast, the aim was to evade a method that is driven by technology itself (i.e bottom-up thinking) and then pushed towards the end-user.
Thus, the proposed approach aims to contribute to the result of the end-user’s mis-sion. In this perspective the approach is based on consequentialism and
utilitarian-ism [62]. These are theories, mostly discussed in relation to ethical dilemmas, for making the optimal decision, as thoroughly explained in AppendixA. In addition, the hypothe-sis of expected-utility [63,64], which is well-known in economics, is used in Chapter2to mathematically define the objective function for automatic optimization.
1.7.RESEARCHAPPROACH
1
13Chapter 1: How can reconfigurable sensing resources be optimally managed within an end-user’s mission?
Chapter 2: Definition of Optimality from the Mission Perspective
Chapter 8: What are the accomplished results of this research and recommendations for future work?
analyzing limitations and mitigating them developing mission-driven objective and concepts
Chapter 6: Revision of Optimality Definition with Prospect Theory
Chapter 3: Concept of Operational Tasks for Reconfigurable Sensors Chapter 4: Quality Metric for Heterogeneous
Operational Tasks
Chapter 5: Automatic Resource Management during Operational Phase
Chapter 7: Inclusion of Operational Uncertainty with Subjective Logic
Figure 1.7: The structure of this thesis.
It is frequently assumed that sensors should just search, classify, identify, track, trieve accurate information, and/or create “situational awareness”. In contrast, this re-search also considers the functions after sensing by incorporating the Observe, Orient,
Decide, and Act (OODA) loop [65]. This loop has been proposed originally in the military domain and is further discussed in Chapter2. A larger volume (e.g. more detected ob-jects) or higher accuracy (e.g. lower position estimation error) of provided information is in itself not always better for the mission. More information may confuse or overload end-users and extra accurate information may be unnecessary to intercept objects.
To conclude, this thesis investigates what the consequence of sensing is for the end-user’s mission, and employs this knowledge to optimally control the sensors. In the end, Chapter2 forms a basis for this thesis by outlining the discussions with end-users of sensing systems, the mission-driven objective function definition, and the generic sys-tem architecture.
1.7.2.
E
ND-
USER ORIENTEDEventually, the developed solution should be beneficial for end-users and these benefits should also be understood by them. To demonstrate and explain the developed mission-driven concept to end-users, several experimental resource management tools were de-veloped to automatically deploy, allocate, and configure reconfigurable sensor systems
1
during fictional deployment and operational phases. The final goal was to retrieve end-users feedback and validate the mission-driven concept from their perspective. It was proposed to demonstrate the results of the research on a relatively high level with the concept of operational tasks to create a context in which the optimization results can be discussed with end-users. Simultaneously, a hierarchical resource management structure, as discussed in Section1.5and further elaborated in Chapter3, is used to over-come the intractability of managing sensors and to evade some traditional sensor man-agement problems (e.g. the exact scheduling of radar beams on a pulse to pulse basis) in the experimental resource management tools. Chapter4proposes a probabilistic-based quality metric, that can be used for different types of tasks and linked to mission suc-cess, for extra end-user insight. Chapter5demonstrates the developed solution within a fictional large event during operational phase, evaluates analytically and numerically the obtained improvement, and discusses end-user feedback.A selection of the employed optimization algorithms can be found in AppendicesB andC. Although the experimental tools do not (yet) make all control decisions that are known or possible, it is important to note that a limited number of controllable parame-ters does not restrict the generality of objective functions. The developed objective func-tion can in principle be used for these control decisions and others.
1.7.3.
F
ALSIFY AND CORRECTThe boundaries of applicability and validity of the developed mission-driven solution are identified by self-critically analyzing its limitations from different scientific disci-plines (i.e. cross-disciplinary). This verification is inspired by the reasoning of Popper’s
falsifiability [66]: it is not possible to prove the correctness of a theory (i.e. hypothe-sis), but only its incorrectness can be proven if the theory is falsifiable and incorrect (i.e. false). The term “falsifiable” does not mean that something is made false, but instead, if it is false, then it can be shown. Falsifiability can also be used as a criterion: a theory is genuinely scientific if, and only if, it is falsifiable. The developed objective function, which defines ‘optimality’, is a falsifiable hypothesis. It is false when the optimal, accord-ing to the objective function, allocation is actually not optimal.
Because an objective function is normative instead of descriptive, as discussed in Section1.5, and further discussed in Chapter6and AppendixA, criticism can only be based on consistent arguments. (Empirical evidence can be used to falsify only descrip-tive theories.) If the criticism is successful for some cases, then it is known that the de-veloped objective function is not accurate for these ones.
Beside proving criticism, supplementary solutions are provided based on the new insights to mitigate the discovered drawbacks. Chapter 6 analyses the rationality of the developed concept and mitigates the irrationality with Prospect Theory [67]. The uncertainty of expectations and models is analyzed and incorporated with Subjective Logic [68] in Chapter7. AppendixAcan be seen as additional self-criticism focussed on consequentialism and utilitarianism, and uses the Categorical Imperative [69] to incor-porate that some actions are categorically wrong.
This thesis is concluded in Chapter8by explaining the impact of the research results and discussing possibilities for future work.
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2
D
EFINITION OF
O
PTIMALITY FROM
THE
M
ISSION
P
ERSPECTIVE
There is nothing so useless as doing efficiently that which should not be done at all.
Peter Drucker
If there is more than one management decision available to take, then it is preferred to select the optimal one. To do this non-accidentally and consistently, it is required to in-vestigate what ‘optimal’ means and how this can be defined in mathematical terms for automatic optimization. In other words, the actual value vk(xk) in (1.1) of adding an
item in a knapsack has to be determined. Part of the approach is to participate in many discussions with end-users about sensors within the context of realistic scenarios. These activities are explained in Section2.1and the resulting terminology is outlined in Sec-tion2.2. Section2.3presents a solution that mathematically defines optimality from a mission point of view, and discusses the properties of the resulting mission-driven objec-tive function. Section2.4discusses general system architectures to further clarify the con-trast between ‘what can be done’ and ‘what has to be done’. The benefits of this proposed objective function are outlined in Section2.5.
Parts of this chapter have been published at International Conference on Information Fusion (2014) [1], and submitted to IEEE Transactions on Aerospace and Electronic Systems [2] and IEEE Systems Journal [3].
2
2.1.
I
NTERACTION WITH
E
ND
-
USERS
T
HROUGHOUTthis research there were many opportunities to discuss sensor technol-ogy and mission goals with end-users from the safety, security, and defense domain. Several serious gaming events were organized where end-users could interactively par-ticipate in fictional missions. This encouraged them to do mission planning and sensor utilization within a realistic context, and provided us a unique platform to investigate what is important for end-users.The formulated goal, as discussed in Section1.6, was that sensors should be directly optimized from an end-user’s mission point of view. The mission objectives should not be first manually, together with end-users, translated to intermediate fixed technical re-quirements for the sensing systems. Such manual translation would delay the manage-ment of resources and disable graceful sensor performance degradation.
In practice, the employed functionality is influenced by the available means, but in theory, the required functionality has nothing to do with it. Therefore, the aim remains to focus on what is actually needed for end-users (‘what has to be done’) instead of de-veloping new functionality for reconfigurable radars (‘what can be done’).
The employed strategy was to observe how end-users behave and react during dif-ferent types of (changing) missions and scenarios. Additionally, end-users have been asked about their motives. In contrast, there was no static questionnaire for formal inter-views in which potentially too general and/or politically correct answers would be given. The retrieved knowledge is used to develop a resource management concept. In return, the developed concept was demonstrated to the end-users (more than 30) in follow-up meetings for feedback and verification.
2.2.
D
ISCUSSION ON
T
ERMINOLOGY
I
Norder to avoid confusion, the terms that are employed in this thesis are defined, explained and related to each other below.The end-user is the person who uses the system to accomplish his/her assigned mis-sion. The user is the person who bought the system. The system consists of many com-ponents such as effectors (e.g. helicopters, humans) and sensors (e.g. radars, humans), and decision makers (e.g. police officers). A (sub)system can contribute to the mission by executing tasks. An example of a task for sensor systems is object search. Resources are the means that are needed to execute tasks and should be understood in a broad sense, it includes the systems themselves, but also, for example, time and power bud-gets. A mission-driven resource management process optimizes a mission-driven
objec-tive function, and as a result, allocates and controls resources in such a way that they
contribute as much as possible to achieving mission success.
The mission is considered fully successful when everything that has to happen hap-pens and everything that is not allowed to happen does not happen. Thus, all specified
goals are achieved. Threats are general events that can block the achievement of a goal.
A scenario is a specific story that can unfold during the mission. Therefore, a scenario can be threatening. For instance, an air object flies a specific trajectory and causing damage to an important building. Opportunities are general events that can enable the achievement of a goal. For example, a fugitive that had just robbed a bank results in the
