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在多基地独立观测估计的基础上,采用对状态矢量进行数据融合的方法对无源目标运动分析(以下简称ＴＭＡ)问题进行了研究。在双基地的条件下,讨论了当双基地的无源声呐匀采用容易得到的目标方位角、频率的数据测量为输入时的无源ＴＭＡ问题。利用伪线性方法或扩展Ｋａｌｍａｎ滤波方法作预处理后,将所得到的各状态矢量的预估计送入融合中心再进行数据融合,最后实现对目标的最终估计。计算机仿真结果表明:状态矢量融合的方法能够进一步提高对目标运动参数的估计精度,能有效地实现无源ＴＭＡ问题的估计。
Based on the multi-base independent observation and estimation, the problem of passive target motion analysis (TMA) is studied by data fusion of state vectors. Under bistatic conditions, we discuss the passive TMA problem when the bistatic passive sonar adopts easy-to-obtain target azimuth and the frequency data is measured as input. After using pseudo-linear method or extended Kalman filtering method for preprocessing, the obtained pre-estimation of each state vector is sent to the fusion center for data fusion, and finally the final estimation of the target is achieved. Computer simulation results show that the state vector fusion method can further improve the estimation accuracy of the target motion parameters and can effectively estimate the passive TMA problem.