基于GGIW-PMB的衍生扩展目标跟踪

针对标准的扩展目标泊松多伯努利(Poisson multi-Bernoulli, PMB)滤波器难以有效跟踪衍生目标的问题,提出一种改进的PMB跟踪算法.算法采用随机矩阵法对扩展目标外形和尺寸建模,在滤波预测阶段利用多假设模型对衍生事件进行预测,得到多个伽玛高斯逆威沙特(gamma Gaussian inverse Wishart, GGIW)预测假设分量,最后在滤波更新阶段对预测分量更新得到扩展目标的运动状态和扩展形状估计.仿真结果表明,与标准的PMB滤波算法相比,所提算法有效改善衍生扩展目标的跟踪性能.

Spawning Extended Target Tracking Based on GGIW-PMB

The standard Poisson multi-Bernoulli (PMB) filter for extended targets can hardly track spawning targets effectively. To resolve this problem, this study proposes an improved PMB tracking algorithm. The algorithm uses a random matrix method to model shapes and dimensions of extended targets and adopts a multi-hypothesis model to predict spawning targets in the filtering prediction stage and obtain multiple hypothetical components of gamma Gaussian inverse Wishart (GGIW). Finally, it updates the predicted components in the filtering update stage to estimate the motion state and expansion shapes of extended targets. Simulations show that the proposed algorithm has better tracking performance for spawning extended targets in comparison with the standard PMB filtering algorithm.