EFFECTIVE DATA ASSOCIATION ALGORITHMS FOR MULTITARGET TRACKING

Abstract

In multitarget tracking scenarios with high false alarm rate and low target detection probability, data association plays a key role in resolving measurement origin uncertainty. The measurement origin uncertainty becomes worse when there are multiple detection per scan from the same target. This thesis proposes efficient data association algorithms for multitarget tracking under these conditions. For a multiple detection scenario, this thesis presents a novel Multiple-Detection Probabilistic Data Association Filter (MD-PDAF) and its multitarget version, Multiple-Detection Joint Probabilistic Data Association Filter (MD-JPDAF). The algorithms are capable of handling multiple detection per scan from target in the presence of clutter and missed detection. The algorithms utilize the multiple-detection pattern, which accounts for many-to-one measurement set-to-track association rather than one-to-one measurement-to-track association, in order to generate multiple detection association events. In addition, a Multiple Detection Posterior Cramer-Rao Lower Bound (MD-PCRLB) is derived in order to evaluate the performance of the proposed filters with theoretical bound. With respect to instantaneous track update, a continuous 2-D assignment for multitarget tracking with rotating radars is proposed. In this approach, the full scan is divided into sectors, which could be as small as a single detection, depending on the scanning rate, sparsity of targets and required track state update speed. The measurement-to-track association followed by filtering and track state update is done dynamically while sweeping from one region to another. As a result, a continuous track update, limited only by the inter-measurement interval, becomes possible. Finally, a new measurement-level fusion algorithm is proposed for a heterogeneous sensors network. In the proposed method, a maritime scenario, where radar measurements and Automatic identification System (AIS) messages are available, is considered. The fusion algorithms improve the estimation accuracy by assigning multiple AIS IDs to a track in order to resolve the AIS ID-to-track association ambiguity. In all cases, the performance of the proposed algorithms is evaluated with a Monte Carlo simulation experiment.