<p>This thesis considers the problem of tracking multiple static or moving targets with one single pan/tilt-camera with a limited field of view. The objective is to minimize both the time needed to pan and tilt the camera's view between the targets and the total position uncertainty of all targets. To solve this problem, several planning methods have been developed and evaluated by Monte Carlo simulations and real world experiments. If the targets are moving and their true positions are unknown, both their current and future positions need to be estimated in order to calculate the best sensor trajectory. When dealing with static and known targets the problem is reduced to a deterministic optimization problem.</p><p>The planners have been tested through experiments using a real camera mounted above a car track using toy cars as targets. An algorithm has been developed to detect the cars and associate the detections with the correct target.</p><p>The Monte Carlo simulations show that, in the case of static targets, there is a huge advantage to arrange the targets into groups to be able to view more than one target at the time. In the case of moving targets with estimated positions it can be concluded that if the objective is to minimize the error in the position estimation the best planning choice is to always move to the target with the highest position uncertainty.</p>

We consider sensor data scheduling for remote state estimation. Due to constrained communication energy and bandwidth, a sensor needs to decide whether it should send the measurement to a remote estimator for further processing. We propose an event-based sensor data scheduler for linear systems and derive the corresponding minimum squared error estimator. By selecting an appropriate eventtriggering threshold, we illustrate how to achieve a desired balance between the sensor-to-estimator communication rate and the estimation quality. Simulation examples are provided to demonstrate the theory. / <p>QC 20130318</p>

This thesis considers the problem of tracking multiple static or moving targets with one single pan/tilt-camera with a limited field of view. The objective is to minimize both the time needed to pan and tilt the camera's view between the targets and the total position uncertainty of all targets. To solve this problem, several planning methods have been developed and evaluated by Monte Carlo simulations and real world experiments. If the targets are moving and their true positions are unknown, both their current and future positions need to be estimated in order to calculate the best sensor trajectory. When dealing with static and known targets the problem is reduced to a deterministic optimization problem. The planners have been tested through experiments using a real camera mounted above a car track using toy cars as targets. An algorithm has been developed to detect the cars and associate the detections with the correct target. The Monte Carlo simulations show that, in the case of static targets, there is a huge advantage to arrange the targets into groups to be able to view more than one target at the time. In the case of moving targets with estimated positions it can be concluded that if the objective is to minimize the error in the position estimation the best planning choice is to always move to the target with the highest position uncertainty.

Designing local controllers for networked systems is challenging, because in these systems each local controller can often access only part of the overall information on system parameters and sensor measurements. Traditional control design cannot be easily applied due to the unconventional information patterns, communication network imperfections, and design procedure complexities. How to control large-scale systems is of immediate societal importance as they appear in many emerging applications, such as intelligent transportation systems, smart grids, and energy-efficient buildings. In this thesis, we make three contributions to the problem of designing networked controller under information asymmetries and limitations. In the first contribution, we investigate how to design local controllers to optimize a cost function using only partial knowledge of the model governing the system. Specifically, we derive some fundamental limitations in the closed-loop performance when the design of each controller only relies on local plant model information. Results are characterized in the structure of the networked system as well as in the available model information. Both deterministic and stochastic formulations are considered for the closed-loop performance and the available information. In the second contribution of the thesis, we study decision making in transportation systems using heterogeneous routing and congestion games. It is shown that a desirable global behavior can emerge from simple local strategies used by the drivers to choose departure times and routes. Finally, the third contribution is a novel stochastic sensor scheduling policy for ad-hoc networked systems, where a varying number of control loops are active at any given time. It is shown that the policy provides stochastic guarantees for the network resources dynamically allocated to each loop. / <p>QC 20140221</p>

abstract: Modern measurement schemes for linear dynamical systems are typically designed so that different sensors can be scheduled to be used at each time step. To determine which sensors to use, various metrics have been suggested. One possible such metric is the observability of the system. Observability is a binary condition determining whether a finite number of measurements suffice to recover the initial state. However to employ observability for sensor scheduling, the binary definition needs to be expanded so that one can measure how observable a system is with a particular measurement scheme, i.e. one needs a metric of observability. Most methods utilizing an observability metric are about sensor selection and not for sensor scheduling. In this dissertation we present a new approach to utilize the observability for sensor scheduling by employing the condition number of the observability matrix as the metric and using column subset selection to create an algorithm to choose which sensors to use at each time step. To this end we use a rank revealing QR factorization algorithm to select sensors. Several numerical experiments are used to demonstrate the performance of the proposed scheme. / Dissertation/Thesis / Doctoral Dissertation Applied Mathematics 2015

abstract: Research on developing new algorithms to improve information on brain functionality and structure is ongoing. Studying neural activity through dipole source localization with electroencephalography (EEG) and magnetoencephalography (MEG) sensor measurements can lead to diagnosis and treatment of a brain disorder and can also identify the area of the brain from where the disorder has originated. Designing advanced localization algorithms that can adapt to environmental changes is considered a significant shift from manual diagnosis which is based on the knowledge and observation of the doctor, to an adaptive and improved brain disorder diagnosis as these algorithms can track activities that might not be noticed by the human eye. An important consideration of these localization algorithms, however, is to try and minimize the overall power consumption in order to improve the study and treatment of brain disorders. This thesis considers the problem of estimating dynamic parameters of neural dipole sources while minimizing the system's overall power consumption; this is achieved by minimizing the number of EEG/MEG measurements sensors without a loss in estimation performance accuracy. As the EEG/MEG measurements models are related non-linearity to the dipole source locations and moments, these dynamic parameters can be estimated using sequential Monte Carlo methods such as particle filtering. Due to the large number of sensors required to record EEG/MEG Measurements for use in the particle filter, over long period recordings, a large amounts of power is required for storage and transmission. In order to reduce the overall power consumption, two methods are proposed. The first method used the predicted mean square estimation error as the performance metric under the constraint of a maximum power consumption. The performance metric of the second method uses the distance between the location of the sensors and the location estimate of the dipole source at the previous time step; this sensor scheduling scheme results in maximizing the overall signal-to-noise ratio. The performance of both methods is demonstrated using simulated data, and both methods show that they can provide good estimation results with significant reduction in the number of activated sensors at each time step. / Dissertation/Thesis / M.S. Electrical Engineering 2012

Les travaux développés dans cette thèse portent sur la "co-conception diagnostic / ordonnancement des mesures dans un système contrôlé en réseau" qui est un sujet multidisciplinaire nécessitant des compétences en théorie du contrôle et en théorie des réseaux. La thèse a pour but de développer, dans le contexte des systèmes contrôlés en réseau, une approche de co-conception qui intègre de façon coordonnée les caractéristiques qui expriment la performance du diagnostic des défauts et les paramètres de l'ordonnancement temps-réel des messages. L'intérêt de cette approche coordonnée réside essentiellement dans la minimisation des ressources nécessaires pour atteindre la performance du diagnostic requise, minimisation qui prend tout son sens dans le contexte des systèmes embarqués. Nous nous sommes intéressés plus particulièrement à l'étude des problèmes liés à l'élaboration d'algorithmes de diagnostic efficaces et adaptés aux caractéristiques de l'application de façon tout en prenant en compte différents types de contraintes liées au réseau. En conjonction avec ces algorithmes, deux ensembles de techniques d'ordonnancement des mesures ont été développés : - ordonnancement hors ligne - ordonnancement évènementiel en ligne Pour l'ordonnancement hors ligne, les séquences de communication sont conçues en amont, préalablement à la mise en oeuvre de l'algorithme de diagnostic (implémentation). D'autre part, nous proposons aussi des techniques d'ordonnancement en ligne basées sur l'échantillonnage évènementiel développé au cours de la dernière décennie. Au contraire de la plupart des recherches en automatique classique, considérant que l'échantillonnage des signaux continus est réalisé d'une manière périodique, les mesures dans cette approche sont transmises si et seulement si la condition de transmission (évènement) est vérifiée / The works developed in this thesis deal with 'fault diagnosis and sensor scheduling co-design' in networked control system. This multidisciplinary subject requires theoretical knowledge in both fault diagnosis and communication networks. Our contribution consists in developing a co-design approach that integrates in the same framework the characteristics of fault diagnosis performance and real time sensor scheduling. The main benefit of this approach is minimizing the required network resources for attending acceptable fault diagnosis performances. We are interested in the development of more efficient and more adapted for real time implementation fault diagnosis algorithms while taking into account different types of communication constraints. In conjunction with these algorithms, two sets of sensor scheduling techniques are used : - Off-line scheduling - On-line scheduling (event triggered sampling) For off-line scheduling, the communication sequences are designed before the implementation of the diagnostic algorithm. In this context, we proposed several techniques for scheduling with different spatial and temporal complexity and adapted to different operating condition for the detection and the isolation of faults based on the information provided by the selected communication sequences. Moreover, we deal also with on-line scheduling techniques based on the event triggered sampling developed during the last decade. In This approach measurement packets are transmitted if and only if the transmission condition (event) is verified. This saves resources provided by the network while maintaining acceptable performance of fault diagnosis. The objective of these algorithms is to minimize the number of transmitted information which means less energy consumption and has a major importance in wireless networked control systems

Design trade-offs between estimation performance, processing delay and communication cost for a sensor scheduling problem is discussed. We consider a heterogeneous sensor network with two types of sensors: the first type has low-quality measurements, small processing delay and a light communication cost, while the second type is of high quality, but imposes a large processing delay and a high communication cost. Such a heterogeneous sensor network is common in applications, where for instance in a localization system the poor sensor can be an ultrasound sensor while the more powerful sensor can be a camera. Using a time-periodic Kalman filter, we show how one can find an optimal schedule of the sensor communication. One can significantly improve estimation quality by only using the expensive sensor rarely. We also demonstrate how simple sensor switching rules based on the Riccati equation drives the filter into a stable time-periodic Kalman filter. ￜ 2008 IEEE. / <p>QC 20110224</p>