This thesis focuses on the problem of underwater target tracking with consideration for realistic conditions using active sonar. This thesis addresses the following specific problems: 1) underwater detection in three dimensional (3D) space using multipath detections and an uncertain sound speed profile in heavy clutter, 2) tracking a group of divers whose motion is dependent on each other using sonar detections corrupted by unknown structured background clutter, 3) extended target tracking (ETT) with a high-resolution sonar in the presence of multipath detection and measurement origin uncertainty. Unrealistic assumptions about the environmental conditions may degrade the performance of underwater tracking algorithms. Hence, underwater target tracking with realistic conditions is addressed by integrating the environment-induced uncertainties or constraints into the trackers. First, an iterated Bayesian framework is formulated using the ray-tracing model and an extension of the Maximum Likelihood Probabilistic Data Association (ML-PDA) algorithm to make use of multipath information. With the ray-tracing model, the algorithm can handle more realistic sound speed profile (SSP) instead of using the commonly-assumed constant velocity model or isogradient SSP. Also, by using the iterated framework, we can simultaneously estimate the SSP and target state in uncertain multipath environments. Second, a new diver dynamic motion (DDM) model is integrated into the Probability Hypothesis Density (PHD) to track the dependent motion diver targets. The algorithm is implemented with Gaussian Mixtures (GM) to ensure low computational complexity. The DDM model not only includes inter-target interactions but also the environmental influences (e.g., water flow). Furthermore, a log-Gaussian Cox process (LGCP) model is seamlessly integrated into the proposed filter to distinguish the target-originated measurement and false alarms. The final topic of interest is to address the ETT problem with multipath detections and clutter, which is practically relevant but barely addressed in the literature. An improved filter, namely MP-ET-PDA, with the classical probabilistic data association (PDA) filter and random matrices (RM) is proposed. The optimal estimates can be provided by MP-ET-PDA filter by considering all possible association events. To deal with the high computational load resulting from the data association, a Variational Bayesian (VB) clustering-aided MP-ET-PDA is proposed to provide near real-time processing capability. The traditional Cramer-Rao Lower Bound (CRLB), which is the inverse of the Fisher information matrix (FIM), quantifies the best achievable accuracy of the estimates. For the estimation problems, the corresponding theoretical bounds are derived for performance evaluation under realistic underwater conditions.

Doctor of Philosophy (PhD)

Thesis