Abstract:

The MUSIC algorithm with subspace orthogonal characteristics has an excellent super resolution performance, but it needs to eigendecompose the spatial covariance matrix, which leads to a great computational cost. To reduce the computational complexity, a fast subspace algorithm is proposed. Making use of the characteristic of signal eigenvalue being larger than noise eigenvalue, this method approximates the signal subspace or noise sub-space through the high order power of the spatial covariance matrix or the inverse one to avoid the eigendecompsition. After obtaining the noise sub-space, it is capable to get the DOA by a MUSIC algorithm. The simulation result shows \that the method achieves the performance of the MUSIC algorithm while reducing the computational cost.