Wan Fuhai, Xu Jingwei, Zhang Zhenrong
School of Computer and Electronic Information, Guangxi University, Nanning 530004, China.
National Lab of Radar Signal Processing, Xidian University, Xi'an 710071, China.
Sensors (Basel). 2022 Feb 14;22(4):1479. doi: 10.3390/s22041479.
Frequency diverse array (FDA)-multiple-input multiple-output (MIMO) radars can generate a range-angle two-dimensional transmit steering vector (SV), which is capable of suppressing mainbeam deceptive jamming in the transmit-receive frequency domain by utilizing additional degrees of freedom (DOFs) in the range dimension. However, when there are target SV mismatch, covariance matrix estimation error and target contamination, the jamming suppression performance degrades severely. In this paper, a robust adaptive beamforming algorithm for anti-jammer application based on covariance matrix reconstruction is proposed in FDA-MIMO radar. In this method, the residual noise is further determined by using the spatial power spectrum estimation approach, which results in improved estimation accuracy of the signal covariance matrix and the desired target SV. The jamming SV is obtained from vectors in the intersection of two subspaces (namely, the signal-jamming subspace derived from the sample covariance matrix (SCM) and the jamming subspace generated from the jamming covariance matrix) by an alternating projection algorithm. Furthermore, the jamming power is obtained by exploiting the orthogonality between the different SVs. With the obtained parameters of target and jamming, the optimal adaptive beamformer weight vector is calculated. Simulation results demonstrate that the proposed algorithm can cope with the mainbeam deceptive jamming suppression under various model mismatches and has excellent performance over a wide range of signal-to-noise ratios (SNRs).
频率分集阵列（FDA）-多输入多输出（MIMO）雷达能够生成距离-角度二维发射导向矢量（SV），通过利用距离维度中的额外自由度（DOF），该矢量能够在收发频域中抑制主波束欺骗性干扰。然而，当存在目标SV失配、协方差矩阵估计误差和目标污染时，干扰抑制性能会严重下降。本文提出了一种基于协方差矩阵重构的FDA-MIMO雷达抗干扰应用的鲁棒自适应波束形成算法。在该方法中，利用空间功率谱估计方法进一步确定残余噪声，从而提高了信号协方差矩阵和期望目标SV的估计精度。通过交替投影算法从两个子空间（即由样本协方差矩阵（SCM）导出的信号-干扰子空间和由干扰协方差矩阵生成的干扰子空间）的交集中的矢量获得干扰SV。此外，利用不同SV之间的正交性获得干扰功率。利用获得的目标和干扰参数，计算最优自适应波束形成器权重矢量。仿真结果表明，该算法能够在各种模型失配情况下应对主波束欺骗性干扰抑制，并且在很宽的信噪比（SNR）范围内具有优异的性能。
