Robotic technology is advancing out of the laboratory and into the everyday world. This world is less ordered than the laboratory and requires an increased ability to identify, target, and track objects of importance. The Bayes filter is the ideal algorithm for tracking a single target and there exists a significant body of work detailing tractable approximations of it with the notable examples of the Kalman and Extended Kalman filter. Multiple target tracking also relies on a similar principle and the Kalman and Extended Kalman filter have multi-target implementations as well. Other method include the PHD filter and Multiple Hypothesis tracker. One issue is that these methods were formulated to only track one classification of target. With the increased need for robust perception, there exists a need to develop a target tracking algorithm that is capable of identifying and tracking targets of multiple classifications. This thesis examines two of these methods: the Probability Hypothesis Density (PHD) filter and the Multiple Hypothesis Tracker (MHT). A Matlab-based simulation of an office floor plan is developed and a simulation UGV equipped with a camera is set the task of navigating the floor plan and identifying targets. Results of these experiments indicated that both methods are mathematically capable of achieving this. However, there was a significant reliance on post-processing to verify the performance of each algorithm and filter out noisy sensor inputs indicating that specific multi-target multi-class implementations of each algorithm should be implemented with a detailed and more accurate sensor model. / Mechanical Engineering

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.

<div>The space around the Earth is becoming increasingly populated with a growth in number of launches and proliferation of debris. Currently, there are around 44,000 objects (with a minimum size of 10cm) orbiting the Earth as per the data made publicly available by the US strategy command (USSTRATCOM). These objects include active satellites and debris. The number of these objects are expected to increase rapidly in future from launches by companies in the private sector. For example, SpaceX is expected to deploy around 12000 new satellites in the LEO region to develop a space-based internet communication system. Hence in order to protect active space assets, tracking of all the objects is necessary. Probabilistic tracking methods have become increasingly popular for solving the multi-target tracking problem in Space Situational Awareness (SSA). This thesis studies one such technique known as the GM-PHD filter, which is an algorithm which estimates the number of objects and its states when non-perfect measurements (noisy measurements, false alarms) are available. For Earth orbiting objects, especially those in Geostationary orbits, ground based optical sensors are a cost-efficient way to gain information.In this case, the likelihood of gaining target-generated measurements depend on the probability of detection (p_D) of the target.An accurate modeling of this quantity is essential for an efficient performance of the filter. p_D significantly depends on the amount of light reflected by the target towards the observer. The reflected light depends on the relative position of the target with respect to the Sun and the observer, the shape, size and reflectivity of the object and the relative orientation of the object towards Sun and the observer. The estimation of the area and reflective properties of the object is in general, a difficult process. Uncontrolled objects, for example, start tumbling and no information regarding the attitude motion can be obtained. In addition, the shape can change because of disintegration and erosion of the materials. For the case of controlled objects, given that the object is stable, some information on the attitude can be obtained. But materials age in space which changes the reflective properties of the materials. Also, exact shape models for these objects are rare. Moreover,, area can never be estimated with optical measurements or any other measurements, as it is always albedo-area i.e., reflectivity times area that can be measured.</div><div> The purpose of this work is to design a variation of the GM-PHD filter which accounts for the uncertainty in p_D as the original GM-PHD filter designed by Vo and Ma assumes p_D as a constant. It is validated that the proposed method improves the filter performance when there is an uncertainty in area(hence uncertainty in p_D) of the targets. In the tested cases, the uncertainty in p_D was modeled as an uncertainty in area while assuming that the targets are spherical and that the reflectivity of the targets is constant. It is seen that a model mismatch in p_D affects the filter performance significantly and the proposed method improves the performance of the filter in all cases.</div>

Mapping stationary objects and tracking moving targets are essential for many autonomous functions in vehicles. In order to compute the map and track estimates, sensor measurements from radar, laser and camera are used together with the standard proprioceptive sensors present in a car. By fusing information from different types of sensors, the accuracy and robustness of the estimates can be increased. Different types of maps are discussed and compared in the thesis. In particular, road maps make use of the fact that roads are highly structured, which allows relatively simple and powerful models to be employed. It is shown how the information of the lane markings, obtained by a front looking camera, can be fused with inertial measurement of the vehicle motion and radar measurements of vehicles ahead to compute a more accurate and robust road geometry estimate. Further, it is shown how radar measurements of stationary targets can be used to estimate the road edges, modeled as polynomials and tracked as extended targets. Recent advances in the field of multiple target tracking lead to the use of finite set statistics (FISST) in a set theoretic approach, where the targets and the measurements are treated as random finite sets (RFS). The first order moment of a RFS is called probability hypothesis density (PHD), and it is propagated in time with a PHD filter. In this thesis, the PHD filter is applied to radar data for constructing a parsimonious representation of the map of the stationary objects around the vehicle. Two original contributions, which exploit the inherent structure in the map, are proposed. A data clustering algorithm is suggested to structure the description of the prior and considerably improving the update in the PHD filter. Improvements in the merging step further simplify the map representation. When it comes to tracking moving targets, the focus of this thesis is on extended targets, i.e., targets which potentially may give rise to more than one measurement per time step. An implementation of the PHD filter, which was proposed to handle data obtained from extended targets, is presented. An approximation is proposed in order to limit the number of hypotheses. Further, a framework to track the size and shape of a target is introduced. The method is based on measurement generating points on the surface of the target, which are modeled by an RFS. Finally, an efficient and novel Bayesian method is proposed for approximating the tire radii of a vehicle based on particle filters and the marginalization concept. This is done under the assumption that a change in the tire radius is caused by a change in tire pressure, thus obtaining an indirect tire pressure monitoring system. The approaches presented in this thesis have all been evaluated on real data from both freeways and rural roads in Sweden. / SEFS -- IVSS / VR - ETT

This thesis addresses the problem of tracking multiple ground targets whose motion is dependent on one another. Multiple approaches which integrate the social force based motion model into different filtering algorithms are proposed. The social force concept has previously been used to model pedestrian motion where the interactions among pedestrians are described using social forces. First, the social force based motion model integrated into the Probability Hypothesis Density (PHD) framework is proposed. Two different implementations, namely, the Sequential Monte Carlo (SMC) technique and the Gaussian Mixture (GM) technique, are derived to implement the proposed Social Force PHD (SF-PHD) filter in ground target tracking scenarios. Next, a social-force-based motion model integrated into the stacked Kalman filter (stacked SF-KF) is developed and its multiple model (stacked IMM-SF-KF) variant is derived. Then, the assumption used in the proposed algorithms, that the actual values of the social force parameters are known, is not valid at all times and the assumption is relaxed. Hence, simultaneous parameter estimation techniques for the social force parameters during the tracking are proposed. Three approaches based on the state augmentation method, the Expectation Maximization (EM) method and the maximum likelihood method are derived. The maximum likelihood method can be implemented offline or online, depending on the requirement. The traditional Posterior Cramer Rao Lower Bound (PCRLB), which is the inverse of the Fisher information matrix, gives a bound on the optimal achievable accuracy of the estimated state of a target with independent motion. Subsequently, a modified performance measure based on the PCRLB for targets whose motion is dependent on each other is derived to validate the performance of the proposed algorithms. Finally, the PCRLB that accounts for unknown interactions is derived to validate the proposed simultaneous parameter estimation techniques. Simulated and real data are used to show the performance of the proposed algorithms and simultaneous parameter estimation techniques compared to the algorithms in the literature. / Thesis / Doctor of Philosophy (PhD) / This thesis addresses the problem of tracking multiple ground targets whose motion is dependent on one another. In target tracking literature, it is commonly assumed that a target’s motion follows a nearly constant velocity, constant turn or a constant acceleration model independent of the motion of other targets. But the actual behavior of a ground target may be more intricate than that and it is often affected by the motion of other targets, obstacles in the surrounding and its intended destination. Hence, a more sophisticated motion modeling technique, which integrates the various factors that affect the motion of ground targets, is needed. In this thesis, multiple approaches which integrate the social force based motion model into different filtering algorithms are proposed. The social force concept has previously been used to model pedestrian motion where the interactions among pedestrians are described using social forces. First, the social force based motion model integrated into the Probability Hypothesis Density (PHD) framework is proposed. Two different implementations, namely, the Sequential Monte Carlo (SMC) technique and the Gaussian Mixture (GM) technique, are derived to implement the proposed Social Force PHD (SF-PHD) filter in ground target tracking scenarios. Next, a social-force-based motion model integrated into the stacked Kalman filter (stacked SF-KF) is developed and its multiple model (stacked IMM-SF-KF) variant is derived. Then, the assumption used in the proposed algorithms, that the actual values of the social force parameters are known, is not valid at all times and the assumption is relaxed. Hence, simultaneous parameter estimation techniques for the social force parameters during the tracking are proposed. Three approaches based on the state augmentation method, the Expectation Maximization (EM) method and the maximum likelihood method are derived. The maximum likelihood method can be implemented offline or online, depending on the requirement. The traditional Posterior Cramer Rao Lower Bound (PCRLB), which is the inverse of the Fisher information matrix, gives a bound on the optimal achievable accuracy of the estimated state of a target with independent motion. Subsequently, a modified performance measure based on the PCRLB for targets whose motion is dependent on each other is derived to validate the performance of the proposed algorithms. Finally, the PCRLB that accounts for unknown interactions is derived to validate the proposed simultaneous parameter estimation techniques. Simulated and real data are used to show the performance of the proposed algorithms and simultaneous parameter estimation techniques compared to the algorithms in the literature.

<div>Multi-target tracking (MTT) is the problem to simultaneously estimate the number of targets and their states or trajectories. Numerous techniques have been developed for over 50 years, with a multitude of applications in many fields of study; however, there are two most widely used approaches to MTT: i) data association-based traditional algorithms; and ii) finite set statistics (FISST)-based data association free Bayesian multi-target filtering algorithms. Most data association-based traditional filters mainly use a statistical or simple model of the feature without explicitly considering the correlation between the target behavior</div><div>and feature characteristics. The inaccurate model of the feature can lead to divergence of the estimation error or the loss of a target in heavily cluttered and/or low signal-to-noise ratio environments. Furthermore, the FISST-based data association free Bayesian multi-target filters can lose estimates of targets frequently in harsh environments mainly</div><div>attributed to insufficient consideration of uncertainties not only measurement origin but also target's maneuvers.</div><div>To address these problems, three main approaches are proposed in this research work: i) new feature models (e.g., target dimensions) dependent on the target behavior</div><div>(i.e., distance between the sensor and the target, and aspect-angle between the longitudinal axis of the target and the axis of sensor line of sight); ii) new Gaussian mixture probability hypothesis density (GM-PHD) filter which explicitly considers the uncertainty in the measurement origin; and iii) new GM-PHD filter and tracker with jump Markov system models. The effectiveness of the analytical findings is demonstrated and validated with illustrative target tracking examples and real data collected from the surveillance radar.</div>

The multi-target tracking problem essentially involves the recursive joint estimation of the state of unknown and time-varying number of targets present in a tracking scene, given a series of observations. This problem becomes more challenging because the sequence of observations is noisy and can become corrupted due to miss-detections and false alarms/clutter. Additionally, the detected observations are indistinguishable from clutter. Furthermore, whether the target(s) of interest are point or extended (in terms of spatial extent) poses even more technical challenges. An approach known as random finite sets provides an elegant and rigorous framework for the handling of the multi-target tracking problem. With a random finite sets formulation, both the multi-target states and multi-target observations are modelled as finite set valued random variables, that is, random variables which are random in both the number of elements and the values of the elements themselves. Furthermore, compared to other approaches, the random finite sets approach possesses a desirable characteristic of being free of explicit data association prior to tracking. In addition, a framework is available for dealing with random finite sets and is known as finite sets statistics. In this thesis, advanced signal processing techniques are employed to provide enhancements to and develop new random finite sets based multi-target tracking algorithms for the tracking of both point and extended targets with the aim to improve tracking performance in cluttered environments. To this end, firstly, a new and efficient Kalman-gain aided sequential Monte Carlo probability hypothesis density (KG-SMC-PHD) filter and a cardinalised particle probability hypothesis density (KG-SMC-CPHD) filter are proposed. These filters employ the Kalman- gain approach during weight update to correct predicted particle states by minimising the mean square error between the estimated measurement and the actual measurement received at a given time in order to arrive at a more accurate posterior. This technique identifies and selects those particles belonging to a particular target from a given PHD for state correction during weight computation. The proposed SMC-CPHD filter provides a better estimate of the number of targets. Besides the improved tracking accuracy, fewer particles are required in the proposed approach. Simulation results confirm the improved tracking performance when evaluated with different measures. Secondly, the KG-SMC-(C)PHD filters are particle filter (PF) based and as with PFs, they require a process known as resampling to avoid the problem of degeneracy. This thesis proposes a new resampling scheme to address a problem with the systematic resampling method which causes a high tendency of resampling very low weight particles especially when a large number of resampled particles are required; which in turn affect state estimation. Thirdly, the KG-SMC-(C)PHD filters proposed in this thesis perform filtering and not tracking , that is, they provide only point estimates of target states but do not provide connected estimates of target trajectories from one time step to the next. A new post processing step using game theory as a solution to this filtering - tracking problem is proposed. This approach was named the GTDA method. This method was employed in the KG-SMC-(C)PHD filter as a post processing technique and was evaluated using both simulated and real data obtained using the NI-USRP software defined radio platform in a passive bi-static radar system. Lastly, a new technique for the joint tracking and labelling of multiple extended targets is proposed. To achieve multiple extended target tracking using this technique, models for the target measurement rate, kinematic component and target extension are defined and jointly propagated in time under the generalised labelled multi-Bernoulli (GLMB) filter framework. The GLMB filter is a random finite sets-based filter. In particular, a Poisson mixture variational Bayesian (PMVB) model is developed to simultaneously estimate the measurement rate of multiple extended targets and extended target extension was modelled using B-splines. The proposed method was evaluated with various performance metrics in order to demonstrate its effectiveness in tracking multiple extended targets.