基于轨迹泊松多伯努利混合滤波器的浅海匹配场连续跟踪方法

Shallow sea matching field continuous tracking method based on trajectory Poisson multi-Bernoulli hybrid filter

匹配场跟踪方法依据模糊度函数时间序列中声源位置移动的连续性和伪峰位置的无序性,可实现水下声源轨迹跟踪.然而,受到浅海空时起伏波导环境和声源复杂运动场景的双重影响,已有匹配场跟踪方法易出现轨迹中断、交叉混叠和虚假轨迹等现象,导致不连续的轨迹跟踪结果.针对这一问题,本文基于轨迹泊松多伯努利混合滤波器,利用模糊度函数中峰值位置距离似然和峰值幅度似然的一致性,提出一种匹配场连续跟踪方法.该方法应用于SWellEx-96实验数据并由线性规划准则度量跟踪性能,结果表明:相比已有匹配场跟踪和基于随机有限集的多目标跟踪方法,所提方法实现了两个水下运动声源轨迹连续跟踪,其中,轨迹状态随机有限集的建模方式以及在轨迹空间内执行预测和更新步骤,可以防止在未持续发声且数量未知的声源跟踪过程中出现轨迹中断和混叠现象;结合模糊度函数峰值位置和幅度信息执行数据关联步骤,可抑制虚假轨迹.

In the shallow water waveguide,matched field tracking methods use the continuity of the peak position of the moving source and the disorder of pseudo-peaks on the sequential ambiguity surfaces to track the underwater source trajectory.However,owing to the dual influence of the space-time fluctuating shallow water waveguide environment and the complex sources motion scene,the existing matching field tracking methods are prone to track interruption,switches and false track phenomena,leading to discontinuous tracking results.Using the consistency between the peak position distance likelihood and the peak amplitude likelihood of sequential ambiguity surfaces,a continuous matched field tracking method is proposed based on the trajectory Poisson multi-Bernoulli mixture filter in this paper.The proposed method is applied to SWellEx-96 experimental data,and the tracking performance is measured by the linear programming metric.The results show that compared with the existing matching field tracking method and multi-target tracking method via random finite set,the proposed method achieves continuous tracking and accurate quantity estimation of moving sources trajectory.Among them,the prediction step and updating step in the trajectory space can avoid the phenomenon of trajectory interruption and switches in unvoiced periods.