基于张量四元数极化平滑的极化-DOA估计

为了解决相干信号的极化平滑算法在小快拍数和低信噪比条件下估计性能较差的问题，结合四元数的正交特性和协方差张量方法，提出了一种基于张量四元数的极化平滑多重信号分类（Multiple Signal Classification,MUSIC）解相干算法。首先，为了充分利用接收数据样本中的多维结构信息，建立了由张量四元数表示的柱面共形阵列极化平滑信号模型；其次，将平滑后的张量协方差矩阵通过高阶奇异值分解得到信号子空间；最后，通过极化秩亏MUSIC算法对入射相干信号分别进行二维波达方向（Direction of Arrival,DOA）估计和极化参数估计。仿真结果表明，该算法在小快拍数和低信噪比条件下具有更高的估计精度和分辨能力。

Joint Polarization-DOA Estimation Based on Tensor Quaternion Polarization Smoothing

In order to solve the problem that the estimation performance of the Polarization Smoothing Algorithm is relatively poor under the conditions of small snapshots and low signal-to-noise ratio(SNR),a polarization smoothing multiple signal classification(MUSIC) decoherence algorithm based on tensor quaternion is proposed by combining the orthogonal property of quaternion with covariance tensor method.Firstly,in order to take full use of the multi-dimensional structure information within the sampled received data,the polarization smoothing signal model of the cylindrical conformal array formulated by tensor quaternion is established.Secondly,the smoothed tensor covariance matrix is decomposed into signal subspace by higher order singular value decomposition(HOSVD).Finally,two-dimensional direction of arrival(DOA) estimation and polarization parameter estimation for incident coherent signals are performed by polarization rank deficient MUSIC algorithm.Simulation results show that the proposed algorithm performs better estimation accuracy and resolution under small snapshots and low SNR.