未知噪声统计下多模型概率假设密度粒子滤波算法

针对传统多目标概率假设密度滤波(PHD) 器在噪声先验统计未知或不准确时滤波精度下降甚至丢失目标的问题, 设计一种自适应多模型粒子PHD(MMPHD) 滤波算法. 该算法利用多模型近似思想, 推导出一种多模型概率假设密度估计器, 不仅能估计多目标状态, 而且能实时估计未知且时变的噪声参数, 并采用蒙特卡罗方法给出了MMPHD闭集解. 仿真实例表明, 所提出的算法具有应对噪声变化的自适应能力, 可有效提高目标跟踪精度.

Multiple-model probability hypothesis density filter for multi-target tracking without the statistics of noise parameters

When the prior noise statistic is unknown and time-varying, the conventional probability hypothesis density(PHD) filter declines in accuracy and loses targets. An adaptive multiple-model PHD(MMPHD) filter is proposed to estimate the states of targets and their noise variances. The unknown and time-varying noise is estimated based on multiple-models methods, and the MMPHD filter is designed to jointly estimate the target states and the statistics of the noise. The sequential Monte Carlo method is used to implement the MMPHD filter. Simulation results show that the proposed filter can accommodate the unknown measurement variances effectively and improve the estimation accuracy.