Research On The Technology For Multi-target Tracking Based On FISST Theory

Target tracking technique is a vital research topic for a long time, especially in the military and civilian areas. This technique has important application value and broad development prospects. Since the past ten years, Finite set statistics (FISST) theory has been beginning to be used for target tracking widely. Due to its demonstrated intuitive description to the problem and rigorous derivation, the FISST theory has attracted many research interests. Restricted to the computational intractability of the optimal Bayes filter in the FISST framework, some scholars have proposed many approximate algorithms based on the FISST theory for tracking multiple targets, such as the probability hypothesis density (PHD) filter, the Cardinalized probability hypothesis density (CPHD) filter, etc. Nevertheless, their computational complexity is still high in dense clutter environment. On the other hand, multi-target tracking algorithms based on the FISST theory always fail to give the track-value of the estimated targets.In this paper, based on the in-depth study on the PHD and CPHD filters, we give the improved algorithm for particle PHD filter in a dense cluttered environment, multiple model extension of the CPHD filter and improved tracks-value estimation algorithm for the PHD filter. The specific research contents are as follows: When the clutter is dense, the computational complexity of approximation particle algorithm for the PHD filter is too high. Inspired by the idea of threshold in traditional tracking algorithm, we propose an improved particle algorithm. The proposed algorithm reduces the amount of computation by adding threshold to filter the clutter. The experimental results show that, the improved particle PHD filter improves the filtering efficiency, without affecting the filtering results.Aiming at the tracking problem of multiple maneuvering targets, this paper presents a multiple model CPHD filter. The introduced filter combines the traditional multiple model method and the CPHD filter. After giving detailed theoretical derivation, we achieve the particle approximation method of the multiple model CPHD filter. Experimental results show that, the filter can provide superior performance for tracking multiple maneuvering targets.Finally, to account for the point that multi-target tracking algorithms based on the FISST theory can not give the tracks, we propose an improved track-value estimation method by adding the label to particles. Compared to the existing methods, the improved method treats complex scenes better, as spawning targets, target crossings and/or clutter. Experimental results validate the above conclusions.