基于leg-by-leg机动的两级采样被动跟踪方法

针对被动声呐方位-频率观测情况下粒子滤波检测前跟踪算法中高维采样效率低的问题，该文提出一种利用leg-by-leg机动可观测性特点的两级采样方法。首先，对leg-by-leg机动的可观测性进行分析；然后，建立极坐标系下的目标运动状态模型，以粒子相对观测站的距离和法向速度均匀分布为准则，提出将极坐标系下的目标状态向量映射至直角坐标系的方法；最后，为改善滤波收敛性，提出根据粒子的空间分布特征自适应地调整过程噪声协方差矩阵。仿真结果表明，对于典型的水下目标跟踪场景，所提方法可使滤波收敛率增大约47.6%，距离估计误差减小约329 m，滤波收敛时间缩短约450 s。

Passive Tracking Method with Two-hierarchy Sampling Based on Leg-by-leg Maneuver

According to the low sampling efficiency of particle filter track before detecting in high dimension state space with bearing-frequency measurements of passive sonar, a two-hierarchy sampling method based on the observability of leg-by-leg maneuver is proposed. Firstly, the observability of leg-by-leg maneuver is analyzed. Secondly, the target motion model in polar coordinate system is build. Based on the uniform distribution of the distance and normal velocity of particles relative to the observation station, the method of mapping the target state vector in polar coordinate system to rectangular coordinate system is proposed. Finally, in order to improve the convergence of the filter, the covariance matrix of process noise is adaptively adjusted according to the spatial distribution of particle. Simulation results show that, compared with the traditional method, the proposed method can increase the filter convergence rate by about 47.6%, reduce the distance estimation error by about 329 m and reduce the convergence time by about 450 s.