Fast DOA estimation algorithm based on an orthogonal projection and noise pseudo-eigenvector in forward model

This paper presents a new fast direction of arrival (DOA) estimation technique, using both the projection spectrum and the eigenspectrum. First, the rough DOA range is selected using the projection spectrum; then, a linear matrix equation, formed by a forward-only data model, is used to acquire a noise pseudo-eigenvector. Finally, the fine DOA estimation is obtained from an eigenspectrum approach based on the noise pseudo-eigenvector. Without the need to form the covariance matrix from a block of array data and without prior knowledge of the number of incoming signals, reduced complexity is achieved, in contrast to conventional subspace-based algorithms. Moreover, it also handles the coherent or uncorrelated signals well. Since the new approach can reduce computational complexity while maintaining similar resolution capability, it may provide wider application prospects in real time DOA estimation when contrasted to other comparable methods.