针对现有超宽带穿墙雷达时域波束成像分辨率低、旁瓣高以及干扰抑制能力弱等问题,提出利用稳健Capon波束形成对目标成像的方法。该方法基于目标回波模型首先补偿近场扩散损耗、墙体传播损耗和折射效应,实现天线阵列接收数据的配准,利用...针对现有超宽带穿墙雷达时域波束成像分辨率低、旁瓣高以及干扰抑制能力弱等问题,提出利用稳健Capon波束形成对目标成像的方法。该方法基于目标回波模型首先补偿近场扩散损耗、墙体传播损耗和折射效应,实现天线阵列接收数据的配准,利用稳健Capon波束成像得到良好的成像分辨率和更好的干扰抑制能力。利用时域有限差分(finite-difference time domain,FDTD)数值仿真和实验数据实现了隐藏目标的二维成像,验证了该方法的有效性。展开更多
当阵列误差存在时,Capon波束形成算法性能会急剧下降,特别是阵列输出信干噪比(signal to interfer-ence plus noise ratio,SINR)。对角加载可以减弱小特征值对应的噪声波束的影响,能有效改善阵列性能及方向图畸变,但加载值的确定是一个...当阵列误差存在时,Capon波束形成算法性能会急剧下降,特别是阵列输出信干噪比(signal to interfer-ence plus noise ratio,SINR)。对角加载可以减弱小特征值对应的噪声波束的影响,能有效改善阵列性能及方向图畸变,但加载值的确定是一个较为困难的问题。该算法根据加载值和采样协方差矩阵间的关系确定加载值,能自适应地根据采样数据确定加载值,在小快拍数和阵列误差存在情况下仍具有良好的鲁棒性,明显改善了阵列性能并减小了方向图畸变,且使零陷准确对准干扰方向。计算机仿真结果证实了此算法的鲁棒性。展开更多
A bi-capon beamforming （BCB） algorithm for multi-input multi-output （MIMO） radar is developed on the basis of correlation domain. By vectorizing the echo matrix and its transpose, the conventional capon cost funct...A bi-capon beamforming （BCB） algorithm for multi-input multi-output （MIMO） radar is developed on the basis of correlation domain. By vectorizing the echo matrix and its transpose, the conventional capon cost function is transformed into bi-capon quadratic functions. By calculating two lower dimensional weight vectors with sub-matrices of the correlation matrix, BCB can significantly decrease the computational complexity and the requirement of training samples. In the presence of short data records, BCB can achieve better interference suppression performance than fully adaptive capon algorithm. Simulation results are presented to demonstrate the effectiveness of the proposed method.展开更多
文摘针对现有超宽带穿墙雷达时域波束成像分辨率低、旁瓣高以及干扰抑制能力弱等问题,提出利用稳健Capon波束形成对目标成像的方法。该方法基于目标回波模型首先补偿近场扩散损耗、墙体传播损耗和折射效应,实现天线阵列接收数据的配准,利用稳健Capon波束成像得到良好的成像分辨率和更好的干扰抑制能力。利用时域有限差分(finite-difference time domain,FDTD)数值仿真和实验数据实现了隐藏目标的二维成像,验证了该方法的有效性。
文摘当阵列误差存在时,Capon波束形成算法性能会急剧下降,特别是阵列输出信干噪比(signal to interfer-ence plus noise ratio,SINR)。对角加载可以减弱小特征值对应的噪声波束的影响,能有效改善阵列性能及方向图畸变,但加载值的确定是一个较为困难的问题。该算法根据加载值和采样协方差矩阵间的关系确定加载值,能自适应地根据采样数据确定加载值,在小快拍数和阵列误差存在情况下仍具有良好的鲁棒性,明显改善了阵列性能并减小了方向图畸变,且使零陷准确对准干扰方向。计算机仿真结果证实了此算法的鲁棒性。
基金supported by the National Natural Science Foundation of China (60971111)the Natural Science Foundation of Shaanxi Province (2011JQ8041)
文摘A bi-capon beamforming （BCB） algorithm for multi-input multi-output （MIMO） radar is developed on the basis of correlation domain. By vectorizing the echo matrix and its transpose, the conventional capon cost function is transformed into bi-capon quadratic functions. By calculating two lower dimensional weight vectors with sub-matrices of the correlation matrix, BCB can significantly decrease the computational complexity and the requirement of training samples. In the presence of short data records, BCB can achieve better interference suppression performance than fully adaptive capon algorithm. Simulation results are presented to demonstrate the effectiveness of the proposed method.
