Data Association Algorithms for Multisensor-Multitarget Tracking

Abstract

In this thesis, the data association problem in multisensor-multitarget tracking is explored. Algorithms that improve data association performance by eliminating sensor biases or utilizing available domain knowledge are proposed.

Sensor calibration and data association are two essential steps in multisensor-multitarget tracking systems to correct local measurements using estimated sensor biases and to associate measurements from different sensors. The problem of multitarget localization using time difference of arrival (TDOA) measurements at multiple unsynchronized sensors under measurement origin uncertainty is considered. A novel joint multidimensional association algorithm for multisensor synchronization is proposed. This algorithm is extended to a multiframe case to ensure the observability of unknown parameters consisting of target positions and sensor clock offsets. To improve the proposed algorithm's efficiency, a gating method and a multidimensional plus sequential two-dimensional association approach are developed. The Cram\'er-Rao lower bound for this problem is derived as a performance benchmark. Numerical results show that the proposed algorithm outperforms the algorithms that address sensor calibration and data association separately in terms of correct association rate and target position and sensor clock bias estimation accuracies.

Exploring and exploiting domain knowledge can improve tracking performance, especially in the context of on-road target tracking. Due to traffic rules and limited lane capacity, on-road targets tend to move in an orderly manner along the centerline of each lane of the roads except for occasional lane changes. A novel sequence-aided 2D assignment (SA-2DA) algorithm, which integrates the target position sequence information into data association by utilizing this information in evaluating the probability of association hypothesis, is proposed. The sequence information is further exploited within the joint probabilistic data association (JPDA) framework, making it suitable for high false alarm rate or high association ambiguity scenarios, and within the tracking framework consisting of the interacting multiple model (IMM) estimator and the JPDA algorithm, making it suitable for tracking maneuvering targets. The uncertainty in target position sequence due to target lane-changing behavior is addressed by two strategies: a) The multiple-hypothesis method combined with the modeling of target lane-changing behavior as a homogeneous Markov chain; b) The track segment association algorithm. The posterior Cram\'er-Rao lower bound is derived for tracking multitarget along a multi-lane road. Numerical results show that the proposed algorithms (i.e., SA-2DA, SA-JPDA and SA-IMMJPDA) achieve better track accuracy and consistency than the existing multitarget tracking algorithms (i.e., standard 2DA, JPDA and IMMJPDA)) that do not make use of target position sequence information.