Abstract
In multiple detection target tracking environments, PDA-based algorithms such as multiple detection joint integrated probabilistic data association (MD-JIPDA) utilize the measurement partition method to generate measurement cells. Thus, one-to-many track-to-measurements associations can be realized. However, in this structure, the number of joint data association events grows exponentially with the number of measurement cells and the number of tracks. MD-JIPDA is plagued by large increases in computational complexity when targets are closely spaced or move cross each other, especially in multiple detection scenarios. Here, the multiple detection Markov chain joint integrated probabilistic data association (MD-MC-JIPDA) is proposed, in which a Markov chain is used to generate random data association sequences. These sequences are substitutes for the association events. The Markov chain process significantly reduces the computational cost since only a few association sequences are generated while keeping preferable tracking performance. Finally, MD-MC-JIPDA is experimentally validated to demonstrate its effectiveness compared with some of the existing multiple detection data association algorithms.
Highlights
Target tracking and information fusion techniques have achieved more attention in recent years due to their wide applications in both military and civilian domains [1,2,3,4,5,6]
The measurement cell selection for track t in ηεt j+1 is only related to the selection of track t in ηεt j based on the transition matrix, which is the core of the proposed Markov chain sequences
The MD-MC-joint integrated probabilistic data association (JIPDA) algorithm is proposed for multiple detection multitarget tracking
Summary
Target tracking and information fusion techniques have achieved more attention in recent years due to their wide applications in both military and civilian domains [1,2,3,4,5,6]. Since JIPDA suffers from a heavy computational load, a suboptimal method is proposed in [17], called linear multitarget integrated probabilistic data association (LM-IPDA) In this algorithm, after track t selects various measurements, the measurement generated by the target being tracked by another track is treated as additional clutter for track t. Due to the applications of high resolution sensors and some special kinds of radars such as over-the-horizon-radar (OTHR), multiple detection target tracking generally attracts more attention from the research community [9,21,22,23,24,25,26] For such multiple detection situations, the widely used point target assumption is relaxed and the data association process needs to assign multiple measurements to one track, which leads to the association complexity exponentially increasing compared to the single detection case.
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