New algorithm for fast direction-of-arrival estimation using the shrinking signal subspace and the noise pseudo-eigenvector

This study presents a new direction-of-arrival (DOA) estimation technique with application to the uniform linear sensor array for rapidly varying signals in the uncorrelated or coherent case. Unlike the conventional subspace-based algorithms, it need not form the covariance matrix using a block of the array data, a complicated computational burden and a prior knowledge of the number of incoming signals. This approach is based on the technologies of the shrinking signal subspace and the noise pseudo-eigenvector. In contrast with Kim's method, our purposed algorithm can improve its performance of resolution capabilities but requires less computational complexity. Computer simulation results that demonstrate the resolution performance of the proposed algorithm are included. Since the algorithm is simple and can maintain resolution capabilities as compared to Kim's method, it may be used on its own to provide a wider application prospect in real-time DOA estimation or be used as good initialisation for more complex and more high-resolution DOA algorithms.