Les systèmes de surveillance modernes doivent coordonner leurs stratégies d’observation pour améliorer l’information obtenue lors de leurs futures mesures afin d’estimer avec précision les états des objets d’intérêt (emplacement, vitesse, apparence, etc.). Par conséquent, la gestion adaptative des capteurs consiste à déterminer les stratégies de mesure des capteurs exploitant les informations a priori afin de déterminer les actions de détection actuelles. L’une des applications la plus connue de la gestion des capteurs est le suivi multi-objet, qui fait référence au problème de l’estimation conjointe du nombre d’objets et de leurs états ou trajectoires à partir de mesures bruyantes. Cette thèse porte sur les stratégies de gestion des capteurs en temps réel afin de résoudre le problème du suivi multi-objet dans le cadre de l’approche RFS labélisée. La première contribution est la formulation théorique rigoureuse du filtre mono-capteur LPHD avec son implémentation Gaussienne. La seconde contribution est l’extension du filtre LPHD pour le cas multi-capteurs. La troisième contribution est le développement de la méthode de gestion de capteurs basée sur la minimisation du risque Bayes et formulée dans les cadres POMDP et LRFS. En outre, des analyses et des simulations des approches de gestion de capteurs existantes pour le suivi multi-objets sont fournies / Modern surveillance systems must coordinate their observation strategies to enhance the information obtained by their future measurements in order to accurately estimate the states of objects of interest (location, velocity, appearance, etc). Therefore, adaptive sensor management consists of determining sensor measurement strategies that exploit a priori information in order to determine current sensing actions. One of the most challenging applications of sensor management is the multi-object tracking, which refers to the problem of jointly estimating the number of objects and their states or trajectories from noisy sensor measurements. This thesis focuses on real-time sensor management strategies formulated in the POMDP framework to address the multi-object tracking problem within the LRFS approach. The first key contribution is the rigorous theoretical formulation of the mono-sensor LPHD filter with its Gaussian-mixture implementation. The second contribution is the extension of the mono-sensor LPHD filter for superpositional sensors, resulting in the theoretical formulation of the multi-sensor LPHD filter. The third contribution is the development of the Expected Risk Reduction (ERR) sensor management method based on the minimization of the Bayes risk and formulated in the POMDP and LRFS framework. Additionally, analyses and simulations of the existing sensor management approaches for multi-object tracking, such as Task-based, Information-theoretic, and Risk-based sensor management, are provided.

<p>In this thesis we consider the problem of managing an array of sensors in order to track multiple targets in the presence of clutter in centralized, distributed and decentralized architectures. As a result of recent technological advances, a large number of sensors can be deployed and used for multitarget tracking purposes. Even though a large number of sensors are available, due to frequency, power and other physical limitations, only a few of them can be used at anyone time. The problem is then to select sensor subsets that should be used by fusion centers at each measurement time step in order to optimize the tracking performance subject to their operational constraints.</p> <p>In general, sensor management is performed based on the predicted tracking performance at the future time steps. In this thesis, the Posterior Cntmer-Rao lower bound (PCRLB), which provides a measure of the optimal achievable accuracy of target state estimation, is used as the performance measure. We derive the multitarget PCRLB and show the existence of a multitarget information reduction matrix (IRM), which can be calculated off-line in most cases. First, the sensor subset selection problem for centralized architecture is considered for two different scenarios: (i) fixed and known llumber of targets; (ii) varying number of targets. Then, in the distributed architecture, in addition to assigning sensor subsets to local fusion centers (LFCs), the transmission frequencies and powers of active sensors need to be assigned. In this thesis, we assume that the transmission power of the sensors will be software controllable within certain lower and upper limits. Finally, we consider the decentralized architecture in which there is no central fusion center (CFC), each fusion center (FC) communicates only with the neighboring FCs, and communications are restricted. In this case, each FC has to decide which sensors should to be used by itself at each measurement time step by considering which sensors may be used by neighboring FCs.</p> <p>We give the optimal formulations for all of the above problems. Finding the optimal solutions to the above problems in real time is very hard in large scale scenarios. We present algorithms to find suboptimal solutions in real time. Simulation results illustrate the performance of the algorithms, both in terms of their real-time capability for large scale problems and the resulting estimation accuracy.</p> / Doctor of Philosophy (PhD)

The purpose of information acquisition for data and information fusion is to provide relevant and timely information. The acquired information is integrated (or fused) to estimate the state of some environment. The success of information acquisition can be measured in the quality of the environment state estimates generated by the data and information fusion process. In this thesis, we introduce and set out to characterise the concept of large-scale information acquisition. Our interest in this subject is justified both by the identified lack of research on a holistic view on data and information fusion, and the proliferation of networked sensors which promises to enable handy access to a multitude of information sources. We identify a number of properties that could be considered in the context of large-scale information acquisition. The sensors used could be large in number, heterogeneous, complex, and distributed. Also, algorithms for large-scale information acquisition may have to deal with decentralised control and multiple and varying objectives. In the literature, a process that realises information acquisition is frequently denoted sensor management. We, however, introduce the term perception management instead, which encourages an agent perspective on information acquisition. Apart from explictly inviting the wealth of agent theory research into the data and information fusion research, it also highlights that the resource usage of perception management is constrained by the overall control of a system that uses data and information fusion. To address the challenges posed by the concept of large-scale information acquisition, we present a framework which highlights some of its pertinent aspects. We have implemented some important parts of the framework. What becomes evident in our study is the innate complexity of information acquisition for data and information fusion, which suggests approximative solutions. We, furthermore, study one of the possibly most important properties of large-scale information acquisition, decentralised control, in more detail. We propose a recurrent negotiation protocol for (decentralised) multi-agent coordination. Our approach to the negotiations is from an axiomatic bargaining theory perspective; an economics discipline. We identify shortcomings of the most commonly applied bargaining solution and demonstrate in simulations a problem instance where it is inferior to an alternative solution. However, we can not conclude that one of the solutions dominates the other in general. They are both preferable in different situations. We have also implemented the recurrent negotiation protocol on a group of mobile robots. We note some subtle difficulties with transferring bargaining solutions from economics to our computational problem. For instance, the characterising axioms of solutions in bargaining theory are useful to qualitatively compare different solutions, but care has to be taken when translating the solution to algorithms in computer science as some properties might be undesirable, unimportant or risk being lost in the translation. / QC 20100903

In underwater tracking and surveillance, the active towed array sonar presents a way of discovering and tracking adversarial submerged targets that try to stay hidden. The configuration consist of listening and emitting hydrophones towed behind a ship. Moreover, it has inherent limitations, and the characteristics of sound in the ocean are complex. By varying the pulse form emitted and the trajectory of the ship the measurement accuracy may be improved. This type of optimization constitutes a sensor management problem. In this thesis, a model of the tracking scenario has been constructed derived from Cramér-Rao bound analyses. A model predictive control approach together with information measures have been used to optimize a filter's estimated state of the target. For the simulations, the MATLAB environment has been used. Different combinations of decision horizons, information measures and variations of the Kalman filter have been studied. It has been found that the accuracy of the Extended Kalman filter is too low to give consistent results given the studied information measures. However, the Unscented Kalman filter is sufficient for this purpose.