外辐射源雷达网络中的接收站自适应选择方法

由于带宽和能量的限制,外辐射源雷达网络在多目标跟踪过程中可能无法利用所有接收站数据,需要进行接收站的选择。基于势均衡多目标多伯努利滤波器,本文提出了一种新的接收站自适应选择方法。首先,对目标概率密度函数(probability density function, PDF)进行预测。其次,计算各接收站的理想量测,并利用该量测对预测PDF进行更新。最后,将目标数的后验期望估计及其方差分别作为目标函数,衡量不同接收站对PDF的更新质量,选择出最佳接收站。仿真实验利用多普勒频率对目标进行跟踪并在跟踪过程中完成接收站的选择,实验结果证明了所提方法的跟踪精度优于其他传统方法。

Adaptive receiver selection for passive radar network

Due to bandwidth and energy constraints of the passive radar network, the system may not be able to use measurements from all receivers and hence it is necessary to make receiver selection. Based on the multi-target multi-Bernoulli filter, a novel adaptive method for selecting receivers is proposed. First, the probability density function (PDF) of the target is predicted. Then, the ideal measurements for each receiver are estimated and used to update the predicted PDF. To measure the update quality of each receiver, the expected a posteriori estimate of the target number and its variance are used as the goal functions. Finally, the best receiver is selected using the proposed goal functions. Simulation results demonstrate more accurate tracking performances of the proposed method over others in tracking multiple targets via Doppler-shift measurements.