Randomized Multiple Model Multiple Hypothesis Tracking

This paper considers the data association problem for multi-target tracking. Multiple hypothesis tracking is a popular algorithm for solving this problem but it is NP-hard and is is quite complicated for a large number of targets or for tracking maneuvering targets. To improve tracking performance and enhance robustness, we propose a randomized multiple model multiple hypothesis tracking method, which has three distinctive advantages. First, it yields a randomized data association solution which maximizes the expectation of the logarithm of the posterior probability and can be solved efficiently by linear programming. Next, the state estimation performance is improved by the random coefficient matrices Kalman filter, which mitigates the difficulty introduced by randomized data association, i.e., where the coefficient matrices of the dynamic system are random. Third, the probability that the target follows a specific dynamic model is derived by jointly optimizing the multiple possible models and data association hypotheses, and it does not require prior mode transition probabilities. Thus, it is more robust for tracking multiple maneuvering targets. Simulations demonstrate the efficiency and superior results of the proposed algorithm over interacting multiple model multiple hypothesis tracking. keywords: Multi-target tracking; data association; random coefficient matrices Kalman filter; linear programming *This work was supported in part by the NSFC No. 61673282, U1836103 and the PCSIRT16R53. Haiqi Liu, Xiaojing Shen (corresponding author), Zhiguo Wang are with School of Mathematics, Sichuan University, Chengdu, Sichuan 610064, China. E-mail: haiqiliu0330@163.com, shenxj@scu.edu.cn, wangzg315@126.com; Junfeng Wang is with the School of Computer Science, Sichuan University, Chengdu, Sichuan 610064, China. E-mail:wangjf@scu.edu.cn. Fanqin Meng is with School of Aeronautics and Astronautics, Sichuan University, Chengdu, Sichuan 610064, China, also with School of Automation and Information Engineering, Sichuan University of Science and Engineering, Yibin, Sichuan 644000, China. E-mail: mengfanqin2008@163.com. P. K. Varshney is with the Department of Electrical Engineering and Computer Science, Syracuse University, Syracuse, NY 13244 USA. E-mail: varshney@syr.edu.

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