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.
翻译:本文考虑了多目标跟踪的数据关联问题。 多假设跟踪是解决这一问题的流行算法,但对于大量目标或跟踪操纵目标而言,它非常复杂,非常复杂。为了改进跟踪性能和增强稳健性,我们提出了一个随机的多模型多假设跟踪方法,该方法有三个独特的优势。首先,它产生一个随机数据关联解决方案,最大限度地实现对事后概率对数的期望,并且可以通过线性编程有效解决。接下来,通过随机系数矩阵 Kalman 过滤器改进了国家估算性能,这缓解了随机数据关联带来的困难,即动态系统的系数矩阵是随机的。第三,该目标遵循特定动态模型的概率是通过联合优化多种可能的模型和数据关联假设而得出的,而不需要事先模式过渡概率。因此,它对于跟踪多重调整目标来说更加有力。模拟显示了拟议算法在互动多个模型多假设跟踪方面的效率和优异结果。