New Assignment-Based Data Association for Tracking Move-Stop-Move Targets

In this paper we present a new assignment-based algorithm for data association in tracking ground targets employing evasive move-stop-move maneuvers using Moving Target Indicator (MTI) reports obtained from an airborne sensor. To avoid detection by the MTI sensor, the targets deliberately stop for some time before moving again. The sensor does not detect a target when the latter's radial velocity (along the line-of-sight from the sensor) falls below a certain Minimum Detectable Velocity (MDV). Even in the absence of moue-stop-move maneuvers, the detection has a less-than-unity probability $(P_{D} < 1)$ due to obscuration and thresholding. Then, it is of interest, when a target is not detected, to develop a systematic technique that can distinguish between lack of detection due to $P_{D}<1$ and lack of detection due to a stop (or a near stop). In this paper, we develop a novel “two-dummy” assignment approach for move-stop-move targets that consider the problem indata association as well as in filtering. Typically, in assignment-based data association $a$ “dummy” measurement is usedto denotethe non-detection event. The use of the standard single-dummy assignment, which does not handle move-stop-move motion explicitly, can result in broken tracks. The new algorithm proposed in this paper handles the evasive mone-stop-moue motion by introducing a second dummy measurement to represent non-detection due to the MDV. Using this two-dummy data association algorithm, the track corresponding to a move-stop-move target is kept “alive” even during missed detections due to MDV. Results on typical move-stop-move scenarios show that the new algorithm results in about 6% track breakage compared with 100% breakage with the standard single-dummy algorithm.