低快拍下基于稀疏重构的大电磁矢量传感器阵列多维参数联合估计

大尺寸电磁矢量传感器(electro magnetic vector sensor, EMVS)比小尺寸EMVS辐射效率更高, 研究其参数估计算法有助于推动EMVS的实装化应用。针对大尺寸EMVS阵列研究了低快拍下参数估计问题, 提出基于稀疏重构的波达方向(direction of arrival, DOA)和极化参数联合估计算法。首先构造仅含DOA信息的低维块稀疏表示, 然后采用块正交匹配追踪算法恢复块稀疏信号, 最后利用得到恢复后的信号反推出DOA和极化参数估计值。仿真表明了该算法在低快拍下参数估计的有效性, 且计算量和精度均优于现有基于稀疏重构的小尺寸EMVS参数估计算法。

Joint multi-dimensional parameters estimation for large-sized electromagnetic vector sensor array based on sparse reconstruction in limited snapshots

The radiation efficiency of the large-sized electromagnetic vector sensor(EMVS)is higher than that of small-sized EMVS.The study of its parameter estimation algorithm is helpful to promote the practical application of EMVS.The problem of parameter estimation in low snapshots is studied for large-sized EMVS array.A joint estimation algorithm of direction of arrival(DOA)and polarization parameters based on sparse reconstruction is proposed.Firstly,a low-dimensional block sparse representation with only DOA information is constructed.Then,the block orthogonal matching pursuit algorithm is used to recover the block sparse signal.Finally,the DOA and polarization parameters are estimated by the recovered signal.Simulation shows the effectiveness of the algorithm in limited snapshots,and the computation and accuracy of the proposed algorithm are better than that of the parameter estimation algorithm of the existing small-sized EMVS based on the sparse reconstruction.