Target tracking is a broad subject area extensively studied in many engineering disciplines. In this thesis, target tracking implies the temporal estimation of target features such as the target's direction-of-arrival (DOA), the target's boundary pixels in a sequence of images, and/or the target's position in space. For multiple target tracking, we have introduced a new motion model that incorporates an acceleration component along the heading direction of the target. We have also shown that the target motion parameters can be considered part of a more general feature set for target tracking, e.g., target frequencies, which may be unrelated to the target motion, can be used to improve the tracking performance. We have introduced an acoustic multiple-target tracker using a flexible observation model based on an image tracking approach by assuming that the DOA observations might be spurious and that some of the DOAs might be missing in the observation set. We have also addressed the acoustic calibration problem from sources of opportunity such as beacons or a moving source. We have derived and compared several calibration methods for the case where the node can hear a moving source whose position can be reported back to the node. The particle filter, as a recursive algorithm, requires an initialization phase prior to tracking a state vector. The Metropolis-Hastings (MH) algorithm has been used for sampling from intractable multivariate target distributions and is well suited for the initialization problem. Since the particle filter only needs samples around the mode, we have modified the MH algorithm to generate samples distributed around the modes of the target posterior. By simulations, we show that this mode hungry algorithm converges an order of magnitude faster than the original MH scheme. Finally, we have developed a general framework for the joint state-space tracking problem. A proposal strategy for joint state-space tracking using the particle filters is defined by carefully placing the random support of the joint filter in the region where the final posterior is likely to lie. Computer simulations demonstrate improved performance and robustness of the joint state-space when using the new particle proposal strategy.

Design of nonlinear observers has received considerable attention since the early development of methods for linear state estimation. The most popular approach is the extended Kalman filter (EKF), that goes through significant degradation in the presence of nonlinearities, particularly if unmodeled dynamics are coupled to the process and the measurement. For uncertain nonlinear systems, adaptive observers have been introduced to estimate the unknown state variables where no priori information about the unknown parameters is available. While establishing global results, these approaches are applicable only to systems transformable to output feedback form. Over the recent years, neural network (NN) based identification and estimation schemes have been proposed that relax the assumptions on the system at the price of sacrificing on the global nature of the results. However, most of the NN based adaptive observer approaches in the literature require knowledge of the full dimension of the system, therefore may not be suitable for systems with unmodeled dynamics. We first propose a novel approach to nonlinear state estimation from the perspective of augmenting a linear time invariant observer with an adaptive element. The class of nonlinear systems treated here are finite but of otherwise unknown dimension. The objective is to improve the performance of the linear observer when applied to a nonlinear system. The approach relies on the ability of the NNs to approximate the unknown dynamics from finite time histories of available measurements. Next we investigate nonlinear state estimation from the perspective of adaptively augmenting an existing time varying observer, such as an EKF. EKFs find their applications mostly in target tracking problems. The proposed approaches are robust to unmodeled dynamics, including unmodeled disturbances. Lastly, we consider the problem of adaptive estimation in the presence of feedback control for a class of uncertain nonlinear systems with unmodeled dynamics and disturbances coupled to the process. The states from the adaptive EKF are used as inputs to the control law, which in target tracking usually takes the form of a guidance law. The applications of this approach lie in the areas of missile-target tracking, formation flight control and obstacle avoidance.

Unmanned Aerial Vehicles (UAVs) are capable of placing sensors at unique vantage points without endangering a pilot. Therefore, they are well suited to perform target tracking missions. However, performing the mission can be burdensome for the operator. To track a target, the operator must estimate the position of the target from the incoming video stream, update the orientation of the camera, and move the vehicle to an appropriate vantage point. The purpose of the research in this thesis is to provide a target tracking system that performs these tasks automatically in real-time. The first task, which receives the majority of the attention, is estimating the position of the target within the incoming video stream. Because of the inherent clutter in the imagery, the resulting probability distributions are typically non-Gaussian and multi-modal. Therefore, classical state estimation techniques, such as the Kalman filter and its variants are unacceptable solutions. The particle filter has become a popular alternative since it is able to approximate the multi-modal distributions using a set of samples, and it is used as part of this research. To improve the performance of the filter and manage the inherently large computational burden a neural network is used to estimate the performance of the particle filter. The filter parameters are then changed in response to the performance. Once the position of the target is estimated in the frame, it is projected on the ground using the camera orientation and vehicle attitude and input into a linear predictor. The output of the predictor is used to update the orientation of the camera and vehicle waypoints. Through offline, simulation, and flight testing, the approach is shown to provide a powerful visual tracking system for use onboard the GTMax unmanned research helicopter.

In solving target tracking problems, the Kalman filter (KF) is a systematic estimation algorithm. Whether the state of a moving target adapts to the changes in the observations depends on the model assumptions. The interacting multiple model (IMM) algorithm uses interaction of a bank of parallel KFs by updating associated model probabilities. Every parallel KF has its model probability adjusted by the dynamic system. For moving targets of different dynamic linear models, an IMM with two KFs generally performs well. In this thesis, in order to improve the performance of target tracking and state estimation, multi-sensor data fusion technique will be used. Same types of IMMs can be incorporated in the cooperative IMM-based algorithm. The IMM-based estimators exchange with each other the estimates, model robabilities and model transition probabilities. A distributed algorithm for multi-sensor tracking usually needs a fusion center that integrates decisions or estimates, but the proposed cooperative IMM-based algorithm does not use the architecture. Cooperative IMM estimator structures exchange weights and estimates on the platforms to avoid accumulation of errors. Performance of data fusion may degrade due to different kinds of undesirable environmental effects. The simulations show that an IMM estimator with smaller measurement noise level can be used to compensate the other IMM, which is affected by larger measurement noise. In addition, failure of a sensor will cause the problem that model probabilities can not be updated in the corresponding estimator. Kalman filters will not be able to perform state correction for the moving target. To tackle the problem, we can use the estimates from other IMM estimators by adjusting the corresponding weights and model probabilities. The simulations show that the proposed cooperative IMM structure effectively improve the tracking performance.

The accuracy of target location and the coverage range of sensor network are two factors that affect each other in target tracking. When the flocking sensor network has a larger coverage area, it can increase the range of detecting target and the scope of environmental information. The network can also pass the information to a query source or other sensors which do not belong to the flocking network. However, the accuracy of measurements at sensors may be affected by the distances between the target and the sensors. We use mobile sensors as agents in flocking algorithm for target tracking. Every mobile sensor exchanges information with its neighbors, and keeps an appropriate separation distance with neighbors to maintain flocking. Flocking algorithm is a distributed control method for mobile sensor which can catch up the target and maintain flocking formation. In the thesis, we derive the cost function based on the accuracy of target positioning and range coverage. The proposed adaptive flocking algorithm combines the amount of information and the distance changes between neighbors based on the cost function. Each mobile sensor adaptively adjusts distance separation with all its neighbors within communication range. Sensors closer to the target shortens the separation distance between neighbors, therefore they will move toward the target and obtain better measurement. Kalman-consensus information filter is used for target positioning. The accuracy of target position can therefore be improved in the overall network. On the other hand, the sensors located far from the target will widen the distance separation between neighbors to expand the overall network area. In the thesis, we use Kalman-consensus information filter to estimate the state of a target, and use adaptive flocking algorithm for maintaining the formation of mobile sensors. Simulations show that adaptive flocking algorithm effectively improves location accuracy while maintaining approximate generally same coverage area when compared with other methods.

In this thesis the radar target tracking problem in Bayesian estimation framework is studied. Traditionally, linear or linearized models, where the uncertainty in the system and measurement models is typically represented by Gaussian densities, are used in this area. Therefore, classical sub-optimal Bayesian methods based on linearized Kalman filters can be used. The sequential Monte Carlo methods, i.e. particle filters, make it possible to utilize the inherent non-linear state relations and non-Gaussian noise models. Given the sufficient computational power, the particle filter can provide better results than Kalman filter based methods in many cases. A survey over relevant radar tracking literature is presented including aspects as estimation and target modeling. In various target tracking related estimation applications, particle filtering algorithms are presented.

In this thesis a number of improvements have been established for specific methods which utilize sequential Monte Carlo (SMC), aka. Particle filtering (PF) techniques. The first problem is the Bayesian multi-target tracking (MTT) problem for which we propose the use of non-parametric Bayesian models that are based on time varying extension of Dirichlet process (DP) models. The second problem studied in this thesis is an important application area for the proposed DP based MTT method / the tracking of vocal tract resonance frequencies of the speech signals. Lastly, we investigate SMC based parameter estimation problem of nonlinear non-Gaussian state space models in which we provide a performance improvement for the path density based methods by utilizing regularization techniques.

Sonar is the traditional method of underwater target detection and tracking. However, using traditional sonar arrays may be difficult and impractical in some mission-critical scenarios as they require a ship or a submersible to be mounted on or towed by. Alternatively, Underwater Acoustic Sensor Networks (UW-ASN) offer a promising solution approach. In this thesis, a target tracking algorithm for UW-ASN, Three-Dimensional Underwater Target Tracking (3DUT) is presented. The objective of 3DUT is to collaboratively accomplish accurate tracking of underwater targets with minimum energy expenditure. Based on the time-of-arrival (ToA) of the echoes from the target after transmitting acoustic pulses from the sensors, the ranges of the nodes to the target are determined, and trilateration is used to obtain the location of the target. The location and the calculated velocity of the target are then exploited to achieve tracking. In order to realize energy-effective target tracking, 3DUT incorporates a new target movement-based duty cycle mechanism. To avoid rapid depletion of energy resources of boundary nodes due to continuous surveillance, 3DUT employs an adaptive procedure to find, designate, and activate new boundary nodes. Performance evaluation shows that 3DUT is a promising alternative to the traditional sonar based target tracking approaches especially for on-demand surveillance applications.

In this study Track Before Detect (TBD) approach will be analysed for tracking of dim moving targets. First, a radar setup is presented in order to introduce the radar range equation and signal models. Then, preliminary information is given about particle filters. As the main algorithm of this thesis, a multi-model particle filter method is developed in order to solve the non-linear non-Gaussian Bayesian estimation problem. Probability of target existence and RMS estimation accuracy are defined as the performance parameters of the algorithm for very low SNR targets. Simulation results are provided and performance analysis is presented as a conclusion.

Short range thrusting projectiles are one of the various threats against armored vehicles and helicopters on the battlefield. Developing a countermeasure for this kind of projectiles is very crucial since they are vast in number and easy to operate on the battlefield. A countermeasure may consist of fire point prediction of the projectile and attack the launcher of it, or it may be the impact point prediction of the projectile and apply a hard-kill counter measure on its way to the ally target. For both of the countermeasure concepts, dynamics and parameters of the projectile must be estimated precisely. In this thesis, dynamic models for thrusting and ballistic flight modes of thrusting projectile are obtained. Three different tracking filters are suggested for precise tracking of the projectiles and their estimation performances are compared. These filters are the Extended Kalman Filter (EKF), the Particle Filter (PF) and the Marginalized Particle Filter (MPF).