改进的MMPHD机动目标跟踪方法

基于序列蒙特卡罗方法的经典多模概率假设密度滤波方法及其各种衍生方法,在预测过程中依据多个并行的状态转移模型,通过将大量粒子散布到下一时刻目标所有可能出现的状态空间实现目标状态的捕获,造成计算量大、目标跟踪精度差。为此,提出一种改进的多模粒子概率假设密度机动目标跟踪方法。该方法利用最新量测信息估计目标运动模型概率及模型参数,并将估计得到的目标模型应用到粒子概率假设密度滤波方法的预测过程中生成预测粒子,从而将大部分粒子聚合在目标最可能出现的状态空间邻域中,实现粒子的有效利用。数值仿真表明,所提方法不仅显著地减少了目标丢失个数,而且提高了目标跟踪精度。

Improved MMPHD Method for Tracking Maneuvering Targets

The classical multiple-model probability hypothesis density filter and its extended methods are based on the sequential Monte Carlo method.For the purpose of capturing an interesting target state,they perform particle prediction based on multiple parallel single-target Markov transition models,and distribute a large number of particles throughout the state space in which the targets may appear.As a result,these classical methods show high computational complexity and poor performance for tracking maneuvering targets.This paper proposes an improved multiple-model probability hypothesis density filter for maneuvering target tracking which estimates the current target Markov transition model probability and model parameters by exploiting the last measurements,and performs particle prediction based on model probability.Consequently,most of the sampled particles will be distributed around the region of likely target appearance in the next frame.Numerical simulations demonstrate that the proposed method not only reduces the number of lost targets,but also improves the accuracy of maneuvering target tracking.