Efficient Data Association for Move-Stop-Move Target Tracking

摘要

In this paper, we present an efficient data association algorithm for tracking ground targets that perform movestop- move maneuvers using ground moving target indicator (GMTI) radar. A GMTI radar does not detect the targets whose radial velocity falls below a certain minimum detectable velocity. Hence, to avoid detection enemy targets deliberately stop for some time before moving again. When targets perform move-stop-move maneuvers, a missed detection of a target by the radar leads to an ambiguity as to whether it is because the target has stopped or due to the probability of detection being less than one. A solution to track move-stop-move target tracking is based on the variable structure interacting multiple model (VS-IMM) estimator in an ideal scenario ( single target tracking with no false measurements) has been proposed. This solution did not consider the data association problem. Another solution, called two-dummy solution, considered the data association explicitly and proposed a solution based on the multiframe assignment algorithm. This solution is computationally expensive, especially when the scenario is complex (e.g., high target density) or when one wants to perform high dimensional assignment. In this paper, we propose an efficient multiframe assignment-based solution that considers the second dummy measurement as a real measurement than a dummy. The proposed algorithm builds a less complex assignment hypothesis tree, and, as a result, is more efficient in terms of computational resource requirement.