由于多输入多输出(multiple input multiple output,MIMO)雷达的空域色噪声协方差矩阵通常为非对角矩阵,因此在色噪声下信号子空间与噪声子空间无法有效分离,从而致使传统算法无法有效估计目标角度.为此,首先利用信号协方差矩阵的低秩性和色噪声协方差矩阵的稀疏性来抑制空域色噪声.然后,根据MIMO雷达数据的内在多维结构特性,建立四阶张量CP(canonical or parallel factor analysis,CANDECOMP/PARAFAC)分解模型.针对传统交替最小二乘算法对数值病态性较为敏感而导致CP分解精度低的问题,利用张量因子矩阵之间的共轭关系来降低求解的病态敏感度,提高张量分解的稳健性.最后,利用最小二乘拟合法从因子矩阵的估计值中得到目标角度.仿真结果表明,所提算法能够对色噪声有效抑制并提高了角度估计的精度.
Angle Estimation for Multiple Input multiple Output Radar under Spatially Colored Noise
Since the spatially colored noise covariance matrix of multiple input multiple output(MIMO)radar is an off-diagonal matrix,the signal subspace and the noise subspace cannot be separated effectively under colored noise,which results in the problem that the traditional angle estimation algorithm cannot estimate the target angle correctly.Therefore,the low-rank property of the signal covariance matrix and the sparsity of the colored noise covariance matrix were utilized to suppress the spatially colored noise.Then,ac-cording to the multi-dimensional structure characteristics of MIMO radar data,the fourth-order tensor CP(canonical or parallel factor analysis,CANDECOMP/PARAFAC)decomposition model was established.To solve the problem that the traditional alternating least squares algorithm was sensitive to numerical ill-conditioning,which leads to the low precision of CP decomposition,the conjugate rela-tionship between tensor factor matrices was used to reduce the ill-conditioning sensitivity of the solution and improve the robustness of tensor decomposition.Finally,the least square fitting method was used to obtain the target angle from the estimation of the factor matri-ces.The simulation results indicated that our algorithm is capable of suppressing the colored noise adequately and improving the preci-sion of angle estimation.
spatially colored noiseMIMO radarlow rank and sparse decompositiontensor decompositionangle estimation
万方数据
维普
