稀疏L型阵中基于压缩感知的角度估计方法

利用二级Nested阵来构建稀疏L型阵列,针对此阵列,提出了基于压缩感知的角度估计方法。该方法通过计算接收数据的自相关协方差矩阵并向量化,然后进行重排序和去冗余,得到虚拟阵列的入射角信息。该虚拟阵列的长度远远大于实际物理阵列的长度,因而相比同物理阵元的均匀L型阵,阵列孔径和自由度明显增大。最后利用正交匹配追踪技术对虚拟阵列的l1范数约束问题进行求解,并完成二维角度的配对。计算机仿真表明,所提算法具有更高的信源分辨力,并且在高信噪比、高快拍数、大角度间隔条件下,具有更好的估计性能。

Angle estimation method based on compressed sensing in sparse L-shaped array

A two-level Nested array is employed to construct a sparse L-shaped array. For this array, an angle estimation method based on compressed sensing is proposed. This method calculates the autocorrelation covariance matrix of the received data and quantizes it, and then reorders and removes the redundancy to obtain the incidence angle information of the virtual array. The length of the virtual array is much larger than that of the actual physical array, so compared with the uniform L-shaped array with the same physical array element, the array aperture and degree of freedom have been greatly improved. Finally, the orthogonal matching pursuit technique is adopted to solve the l₁ norm constraint problem of the virtual array. Computer simulation shows that the proposed algorithm has higher source resolution and better estimation performance under the conditions of high signal-to-noise ratio, high snapshot number and large angle interval.