Yuan Changshun, Wang Jun, Lei Peng, Bi Yanxian, Sun Zhongsheng
School of Electronic and Information Engineering, Beihang University, Beijing 100191, China.
Sensors (Basel). 2015 Dec 4;15(12):30385-402. doi: 10.3390/s151229804.
Knowledge of the clutter rate is of critical importance in multi-target Bayesian tracking. However, estimating the clutter rate is a difficult problem in practice. In this paper, an improved multi-Bernoulli filter based on random finite sets for multi-target Bayesian tracking accommodating non-linear dynamic and measurement models, as well as unknown clutter rate, is proposed for radar sensors. The proposed filter incorporates the amplitude information into the state and measurement spaces to improve discrimination between actual targets and clutters, while adaptively generating the new-born object random finite sets using the measurements to eliminate reliance on prior random finite sets. A sequential Monte-Carlo implementation of the proposed filter is presented, and simulations are used to demonstrate the proposed filter's improvements in estimation accuracy of the target number and corresponding multi-target states, as well as the clutter rate.
在多目标贝叶斯跟踪中，杂波率的知识至关重要。然而，在实际中估计杂波率是一个难题。本文针对雷达传感器，提出了一种基于随机有限集的改进多贝努利滤波器，用于多目标贝叶斯跟踪，该滤波器适用于非线性动态和测量模型以及未知杂波率。所提出的滤波器将幅度信息纳入状态空间和测量空间，以提高实际目标与杂波之间的区分度，同时利用测量值自适应地生成新生目标随机有限集，从而消除对先验随机有限集的依赖。给出了所提出滤波器的序贯蒙特卡罗实现方法，并通过仿真验证了所提出滤波器在目标数量估计精度、相应的多目标状态以及杂波率方面的改进。
