We address the problem of Vandermonde constrained CANDECOMP/PARAFAC (CP) tensor decomposition in application to angle estimation for bistatic multiple-input multiple output (MIMO) radar under spatially colored noise. By exploiting the temporally uncorrelated characteristic of colored noise, a new denoising scheme based on the temporally smoothed cross-correlation approach is presented. Then, after rearranging the smoothed cross-correlation matrix into a fourth-order tensor, a Vandermonde constrained CP tensor decomposition model is formulated, which fully exploits the multidimensional structure of the array measurement and the Vandermonde structure of the factor matrices. To solve this model, an efficient constrained alternating least squares (ALS) algorithm is developed to decompose the Vandermonde factor matrices. Finally, joint estimates of direction of departure (DOD) and direction of arrival (DOA) are obtained by the generators of the Vandermonde factor matrices. Compared with the state-of-the-art approaches, the proposed method can achieve better angle estimation performance by jointly using the temporally smoothed cross-correlation denoising operation and enforcing the Vandermonde structure information in the tensor decomposition. Numerical simulation results verify the effectiveness and improvement of our algorithm.

中文翻译：

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空间有色噪声下双基地MIMO雷达基于Vandermonde约束CP张量分解的角度估计

我们解决了 Vandermonde 约束 CANDECOMP/PARAFAC (CP) 张量分解在空间有色噪声下双基地多输入多输出 (MIMO) 雷达角度估计中的应用问题。通过利用有色噪声的时间不相关特性，提出了一种基于时间平滑互相关方法的新去噪方案。然后，将平滑后的互相关矩阵重新排列为四阶张量后，建立了范德蒙约束CP张量分解模型，该模型充分利用了阵列测量的多维结构和因子矩阵的范德蒙德结构。为了求解该模型，开发了一种高效的约束交替最小二乘 (ALS) 算法来分解 Vandermonde 因子矩阵。最后，由范德蒙因子矩阵的生成器获得出发方向 (DOD) 和到达方向 (DOA) 的联合估计。与最先进的方法相比，所提出的方法可以通过联合使用时间平滑互相关去噪操作和在张量分解中强制使用范德蒙德结构信息来实现更好的角度估计性能。数值模拟结果验证了算法的有效性和改进性。