An Approach for DOA Estimation Based on Sparse Representation of Cumulans in Extended Array
Shuna Han · Electronics Optics & Control · 2015
The existing sparse reconstruction algorithms for DOA estimation cannot suppress noise and are not applicable under colored Gaussian noise, and the maximum number of sources that could be handled by these algorithms is smaller than the number of array elements. In order to solve these problems, a sparse representation model is constructed based on the fourth-order cumulant of received data, which suppresses the noise and realizes array extension by producing virtual array elements. And then, singular value decomposition is used upon the cumulant matrix to simplify the model. The simplified model not only reduces the scale of data, but also further suppresses noise. When the sparse representation model is solved by using weighted l1 norm algorithm, it's unnecessary to select the regularization parameter which balances reconstruction residual with the sparsity of solution. Theoretical analysis and experimental results show that the proposed algorithm is applicable under both the white or colored Gaussian noise, and the maximum number of sources that can be handled is larger than the number of array elements, with higher angle resolution.