模糊推理优化的抗遮挡PMBM跟踪算法

Anti-occlusion PMBM tracking algorithm optimized by fuzzy inference

目标遮挡是多扩展目标跟踪中的常见问题,当目标之间的距离较近或传感器的扫描范围内存在未知障碍物时,就会出现目标被部分或全部遮挡的现象,从而导致对目标的漏估。针对现有的泊松多伯努利混合滤波算法在遮挡场景下不能稳定跟踪的问题,提出了融入模糊推理的高斯过程-泊松多伯努利混合滤波算法。首先,在随机集目标跟踪框架下根据不同的遮挡场景给出了对应的扩展目标遮挡模型;在此基础上对高斯过程-泊松多伯努利混合滤波器的状态空间进行扩维,通过加入可变检测概率的方式将遮挡对目标状态的影响考虑到算法滤波步骤中;最后构建了可以估计目标遮挡概率的模糊推理系统,并将其与高斯过程-泊松多伯努利混合滤波算法结合,借助模糊系统的描述能力和泊松多伯努利混合滤波器良好的跟踪性能,实现遮挡场景下对目标的准确估计。仿真实验结果表明,所提算法在目标遮挡场景下的跟踪性能优于现有的泊松多伯努利混合滤波算法。

Target occlusion is a common problem in multiple extended target tracking.When the distance between targets is close or there are unknown obstacles within the scanning range of the sensor,the phenomenon of partial or complete occlusion of the target will occur,resulting in underestimation of the target quantity.Aiming at the problem that the existing Poisson multi-Bernoulli mixture(PMBM)filtering algorithms cannot perform stable tracking in occlusion scenarios,this paper proposes a GP-PMBM algorithm incorporating fuzzy inference.First,based on the random set target tracking framework,the corresponding extended target occlusion model is given according to different occlusion scenarios.On this basis,the state space of the GP-PMBM filter is expanded,and the influence of occlusion on the target state is taken into account in the filtering steps of the algorithm by adding variable detection probability.Finally,a fuzzy inference system that can estimate the target occlusion probability is constructed and combined with the GP-PMBM algorithm,and the accurate estimation of the target in occlusion scenarios is achieved with the help of the description ability of the fuzzy system and the good tracking performance of the PMBM filter.Simulation results show that the tracking performance of the proposed algorithm in target occlusion scenarios is better than that of the existing PMBM filtering algorithms.