This thesis is concerned with the development of a consistent, information-theoretic basis for understanding of coordination and cooperation decentralised multi-sensor multi-platform systems. Autonomous systems composed of multiple sensors and multiple platforms potentially have significant importance in applications such as defence, search and rescue mining or intelligent manufacturing. However, the effective use of multiple autonomous systems requires that an understanding be developed of the mechanisms of coordination and cooperation between component systems in pursuit of a common goal. A fundamental, quantitative, understanding of coordination and cooperation between decentralised autonomous systems is the main goal of this thesis. This thesis focuses on the problem of coordination and cooperation for teams of autonomous systems engaged in information gathering and data fusion tasks. While this is a subset of the general cooperative autonomous systems problem, it still encompasses a range of possible applications in picture compilation, navigation, searching and map building problems. The great advantage of restricting the domain of interest in this way is that an underlying mathematical model for coordination and cooperation can be based on the use of information-theoretic models of platform and sensor abilities. The information theoretic approach builds on the established principles and architecture previously developed for decentralised data fusion systems. In the decentralised control problem addressed in this thesis, each platform and sensor system is considered to be a distinct decision maker with an individual information-theoretic utility measure capturing both local objectives and the inter-dependencies among the decisions made by other members of the team. Together these information-theoretic utilities constitute the team objective. The key contributions of this thesis lie in the quantification and study of cooperative control between sensors and platforms using information as a common utility measure. In particular, * The problem of information gathering is formulated as an optimal control problem by identifying formal measures of information with utility or pay-off. * An information-theoretic utility model of coupling and coordination between decentralised decision makers is elucidated. This is used to describe how the information gathering strategies of a team of autonomous systems are coupled. * Static and dynamic information structures for team members are defined. It is shown that the use of static information structures can lead to efficient, although sub-optimal, decentralised control strategies for the team. * Significant examples in decentralised control of a team of sensors are developed. These include the multi-vehicle multi-target bearings-only tracking problem, and the area coverage or exploration problem for multiple vehicles. These examples demonstrate the range of non-trivial problems to which the theory in this thesis can be employed.

This thesis is concerned with the development of a consistent, information-theoretic basis for understanding of coordination and cooperation decentralised multi-sensor multi-platform systems. Autonomous systems composed of multiple sensors and multiple platforms potentially have significant importance in applications such as defence, search and rescue mining or intelligent manufacturing. However, the effective use of multiple autonomous systems requires that an understanding be developed of the mechanisms of coordination and cooperation between component systems in pursuit of a common goal. A fundamental, quantitative, understanding of coordination and cooperation between decentralised autonomous systems is the main goal of this thesis. This thesis focuses on the problem of coordination and cooperation for teams of autonomous systems engaged in information gathering and data fusion tasks. While this is a subset of the general cooperative autonomous systems problem, it still encompasses a range of possible applications in picture compilation, navigation, searching and map building problems. The great advantage of restricting the domain of interest in this way is that an underlying mathematical model for coordination and cooperation can be based on the use of information-theoretic models of platform and sensor abilities. The information theoretic approach builds on the established principles and architecture previously developed for decentralised data fusion systems. In the decentralised control problem addressed in this thesis, each platform and sensor system is considered to be a distinct decision maker with an individual information-theoretic utility measure capturing both local objectives and the inter-dependencies among the decisions made by other members of the team. Together these information-theoretic utilities constitute the team objective. The key contributions of this thesis lie in the quantification and study of cooperative control between sensors and platforms using information as a common utility measure. In particular, * The problem of information gathering is formulated as an optimal control problem by identifying formal measures of information with utility or pay-off. * An information-theoretic utility model of coupling and coordination between decentralised decision makers is elucidated. This is used to describe how the information gathering strategies of a team of autonomous systems are coupled. * Static and dynamic information structures for team members are defined. It is shown that the use of static information structures can lead to efficient, although sub-optimal, decentralised control strategies for the team. * Significant examples in decentralised control of a team of sensors are developed. These include the multi-vehicle multi-target bearings-only tracking problem, and the area coverage or exploration problem for multiple vehicles. These examples demonstrate the range of non-trivial problems to which the theory in this thesis can be employed.

<p> In this thesis, we address the problem of sensor management with particular application to using unmanned aerial vehicles (U AV s) for multi target tracking. Also, we present a decision based approach for controlling information flow in decentralized multi-target multi-sensor data fusion.</p> <p> Considering the problem of sensor management for multitarget tracking, we study the problem of decision based control of a group of UAVs carrying out surveillance over a region that includes a number of moving targets. The objective is to maximize the information obtained and to track as many targets as possible with the maximum possible accuracy. Uncertainty in the information obtained by each UAV regarding the location of the ground targets are addressed in the problem formulation. We propose an altered version of a classical Value Iteration algorithm, one of the most commonly used techniques to calculate the optimal policy for Markov Decision Processes (MDPs) based on Dynamic Element Matching (DEM) algorithms. DEM algorithms, widely used for reducing harmonic distortion in Digital-to-Analog converters, are used as a core element in the modified algorithm. We introduce and demonstrate a number of new performance metrics, to verify the effectiveness of an MDP policy, especially useful for quantifying the impact of the modified DEM-based Value Iteration algorithm on an MDP policy. Also, we introduce a multi-level hierarchy of MDPs controlling each of the UAV s. Each level in the hierarchy solves a problem at a different level of abstraction. Simulation results are presented on a representative multisensor-multitarget tracking problem showing a significant improvement in performance compared to the classical algorithm. The proposed method demonstrated robust performance while guaranteeing polynomial computational complexity.</p> <p> Decentralized multisensor-multitarget tracking has numerous advantages over singlesensor or single-platform tracking. In this thesis, we present a solution for one of the main problems in decentralized tracking, namely, distributed information transfer and fusion among the participating platforms. We present a decision mechanism for collaborative distributed data fusion that provides each platform with the required data for the fusion process while substantially reducing redundancy in the information flow in the overall system. We consider a distributed data fusion system consisting of platforms that are decentralized, heterogenous, and potentially unreliable. The proposed approach, which is based on Markov Decision Processes with introduced hierarchial structure will control the information exchange and data fusion process. The information based objective function is based on the Posterior Cramer-Rao lower bound and constitutes the basis of a reward structure for Markov decision processes which are used, together with decentralized lookup substrate, to control the data fusion process. We analyze three distributed data fusion algorithms - associated measurement fusion, tracklet fusion and track-to-track fusion. The thesis also provides a detailed analysis of communication and computational load in distributed tracking algorithms. Simulation examples demonstrate the operation and the performance results of the system.</p> <p> In this thesis, we also present the development of a multisensor-multitarget tracking testbed for simulating large-scale distributed scenarios, capable of handling multiple, heterogeneous sensors, targets and data fusion methods</p>. / Thesis / Doctor of Philosophy (PhD)

As a result of recent technological advances in modernized sensor sets and sensor platforms, sensor management combined with sensor platform path planning are studied to conduct intelligence, surveillance and reconnaissance (ISR) operations in novel ways. This thesis addresses the path planning and sensor management for aerial vehicles to cover areas of interest (AOIs), scan objects of interest (OOIs) and/or track multiple detected targets in surveillance missions. The problems in this thesis, which include 1) the spatio-temporal coordination of sensor platforms to observe AOIs or OOIs, 2) the optimal sensor geometry and path planning for localization and tracking of targets in a mobile three-dimensional (3D) space, and 3) the scheduling of sensors working in different (i.e., active and passive) modes combined with path planning to track targets in the presence of jammers, emerge from real-world demands and scenarios. The platform path planning combined with sensor management is formulated as optimization problems with problem-dependent performance evaluation metrics and constraints. Firstly, to cover disjoint AOIs over an extended time horizon using multiple aerial vehicles for persistent surveillance, a joint multi-period coverage path planning and temporal scheduling, which allows revisiting in a single-period path, is formulated as a combinatorial optimization with novel objective functions. Secondly, to use a group of unmanned aerial vehicles (UAVs) cooperatively carrying out search-and-track (SAT) in a mobile 3D space with a number of targets, a joint path planning and scanning (JPPS) is formulated based on the predictive information gathered from the search space. The optimal 3D sensor geometry for target localization is also analyzed with the objective to minimize the estimation uncertainty under constraints on sensor altitude, sensor-to-sensor and sensor-to-target distances for active or passive sensors. At last, to accurately track targets in the presence of jammers broadcasting wide-band noise by taking advantage of the platform path planning and the jammer's information captured by passive sensors, a joint path planning and active-passive scheduling (JPPAPS) strategy is developed based on the predicted tracking performance at the future time steps in a 3D contested environment. The constraints on platform kinematic, flyable area and sensing capacity are included in these optimization problems. For these multisensor path planning and decision making, solution techniques based on the genetic algorithm are developed with specific chromosome representations and custom genetic operators using either the non-dominated sorting multiobjective optimization (MOO) architecture or the weighted-sum MOO architecture. Simulation results illustrate the performance and advantage of the proposed strategies and methods in real-world surveillance scenarios. / Thesis / Doctor of Philosophy (PhD)

<p>In this work an investigation of the benefits and problems of implementing a tracker using sensor management is done. The tracker is implemented in a fusion node in a distributed radar simulator provided by Ericsson Microwave. To investigate this, a literature study of sensor fusion and sensor management is first done, after which a practical study is chosen as method. The fusion method presented in this work is then tested so that tests of sensor management, which depend upon implemented sensor fusion, can be trusted. Sensor management is tested by letting the system track a specific target in the simulated environment. The system is tested to see what impact the delay in the distributed environment has on the implemented system’s capability to track an object. Two different scenarios are chosen to test the system, where a scenario in this thesis is a fly-by of two aircrafts in the terrain covered by the radar sensors. To test the actual correctness of the system, three dimensional coordinates of the objects are used and Euclidian distance between the original value and the fused value is used as an error measurement. The results are then displayed in a series of graphs and tables.</p><p>The results show that the chosen fusion algorithm works well with the unsynchronized data. The delay simulated in the system creates a great uncertainty where the object will be, but the presented prediction algorithm manages to find good estimates of the new positions of the object tracked. Loss of data however forces the system to use less information when estimating positions which leads to loss of track. Even though there is a long time delay the presented prediction algorithm can track the object for a period of time, until it looses track due to loss of data. It is also concluded that a system that manages to track an object using a narrow tracking beam is able to track more objects simultaneously using the same radar sensors.</p>

In this work an investigation of the benefits and problems of implementing a tracker using sensor management is done. The tracker is implemented in a fusion node in a distributed radar simulator provided by Ericsson Microwave. To investigate this, a literature study of sensor fusion and sensor management is first done, after which a practical study is chosen as method. The fusion method presented in this work is then tested so that tests of sensor management, which depend upon implemented sensor fusion, can be trusted. Sensor management is tested by letting the system track a specific target in the simulated environment. The system is tested to see what impact the delay in the distributed environment has on the implemented system’s capability to track an object. Two different scenarios are chosen to test the system, where a scenario in this thesis is a fly-by of two aircrafts in the terrain covered by the radar sensors. To test the actual correctness of the system, three dimensional coordinates of the objects are used and Euclidian distance between the original value and the fused value is used as an error measurement. The results are then displayed in a series of graphs and tables. The results show that the chosen fusion algorithm works well with the unsynchronized data. The delay simulated in the system creates a great uncertainty where the object will be, but the presented prediction algorithm manages to find good estimates of the new positions of the object tracked. Loss of data however forces the system to use less information when estimating positions which leads to loss of track. Even though there is a long time delay the presented prediction algorithm can track the object for a period of time, until it looses track due to loss of data. It is also concluded that a system that manages to track an object using a narrow tracking beam is able to track more objects simultaneously using the same radar sensors.

This thesis has investigated how the energy consumption can be reduced in a mobile sensor unit by using a dynamic measurement scheme. This was done by developing a scheme based on inspiration from existing works in related areas and on techniques found in literature. The developed scheme was then implemented on a mobile sensor unit and tests were conducted where the energy consumed by the scheme was measured. This was compared to a static baseline approach in order to evaluate the efficiency of the scheme. The results showed that on the platform used in this thesis the developed scheme can reduce the energy consumption in a typical scenario by 4.7% or 6.7% depending on which sensors are used. A conclusion drawn is that the platform has a major impact on how effective the scheme can be.

No description available.

<p>By purposefully utilising sensors, for instance by a datafusion system, the state of some system-relevant environmentmight be adequately assessed to support decision-making. Theever increasing access to sensors o.ers great opportunities,but alsoincurs grave challenges. As a result of managingmultiple sensors one can, e.g., expect to achieve a morecomprehensive, resolved, certain and more frequently updatedassessment of the environment than would be possible otherwise.Challenges include data association, treatment of con.ictinginformation and strategies for sensor coordination.</p><p>We use the term information acquisition to denote the skillof a data fusion system to actively acquire information. Theaim of this thesis is to instructively situate that skill in ageneral context, explore and classify related research, andhighlight key issues and possible future work. It is our hopethat this thesis will facilitate communication, understandingand future e.orts for information acquisition.</p><p>The previously mentioned trend towards utilisation of largesets of sensors makes us especially interested in large-scaleinformation acquisition, i.e., acquisition using many andpossibly spatially distributed and heterogeneous sensors.</p><p>Information acquisition is a general concept that emerges inmany di.erent .elds of research. In this thesis, we surveyliterature from, e.g., agent theory, robotics and sensormanagement. We, furthermore, suggest a taxonomy of theliterature that highlights relevant aspects of informationacquisition.</p><p>We describe a function, perception management (akin tosensor management), which realizes information acquisition inthe data fusion process and pertinent properties of itsexternal stimuli, sensing resources, and systemenvironment.</p><p>An example of perception management is also presented. Thetask is that of managing a set of mobile sensors that jointlytrack some mobile targets. The game theoretic algorithmsuggested for distributing the targets among the sensors proveto be more robust to sensor failure than a measurement accuracyoptimal reference algorithm.</p><p><b>Keywords:</b>information acquisition, sensor management,resource management, information fusion, data fusion,perception management, game theory, target tracking</p>

This thesis is a part of the SIREOS project at Swedish Defence Research Agency which aims at developing a sensor system consisting of infrared and video sensors and an integrated navigation system. The sensor system is placed in a camera gimbal and will be used on moving platforms, e.g. UAVs, for surveillance and reconnaissance. The gimbal is a device that makes it possible for the sensors to point in a desired direction. In this thesis the sensor pointing problem is studied. The problem is analyzed and a system design is proposed. The major blocks in the system design are gimbal trajectory planning and gimbal motion control. In order to develop these blocks, kinematic and dynamic models are derived using techniques from robotics. The trajectory planner is based on the kinematic model and can handle problems with mechanical constraints, kinematic singularity, sensor placement offset and reference signal transformation. The gimbal motion controller is tested with two different control strategies, PID and LQ. The challenge is to perform control that responds quickly, but do not excite the damping flexibility too much. The LQ-controller uses a linearization of the dynamic model to fulfil these requirements.