被动虚拟阵列的CRB界及最大似然估计方法研究

CRB and maximum likelihood estimation for passive synthetic array

摘要:

针对利用机载运动平台对窄带微波信号进行侦测的背景，研究了被动虚拟阵列(PASA)对窄带微波信号的参数估计性能。在考虑方向角、频率和幅度均为未知参数的条件下，推导了方向角估计的克拉美劳界(CRB)的表达式，同时给出了PASA合成孔径长度的选取方案。另外，本文给出了PASA对方位角估计的最大似然(ML)估计算法。研究表明，随着合成孔径长度和信噪比的增加，ML估计误差可以很快地收敛于CRB，但存在阈值效应。计算机仿真结果验证了本文研究结果的正确性。

Abstract:

In this article, performance of estimation of signal parameters in PAssive Synthetic Array (PASA) is studied, focusing on the background of airborne platform intercepting narrowband microwave signals. Cramer-Rao Bound(CRB) of bearing is derived in case that none of bearing, frequency and amplitude is known. And a synthetic aperture selecting method is given to achieve the desired estimation precision. In addition, Maximum Likelihood(ML) estimator is derived for PASA. It is demonstrated that ML estimating error can converge to CRB quickly with increasing synthetic aperture length and Signal to Noise Ratio(SNR), but a threshold effect exists. The result is validated by computer simulation.