A novel harmonic retrieval algorithm is proposed in this paper based on Hopfield's neural network. Frequencies can be retrieved with high accuracy and high resolution under low signal to noise ratio (SNR). Amplitudes and phases in harmonic signals can also be estimated roughly by an energy constrained linear projection approach as proposed in the algorithm. Only no less than 2q neurons are necessary in order to detect harmonic siglnals with q different frequencies, where q denotes the number of different frequencies in harmonic signals. Experimental simulations show fast convergence and stable solution in spite of low signal to noise ratio can be obtained using the proposed algorithm.

A high resolution iterative algorithm for estimating the direction-of-arrival of multiple wide band sources is proposed in this paper. For equally spaced array structure, two Unitary Transform based approaches are proposed in frequency domain for signal subspace processing in both coherent multipath and incoherent environment. Given a priori knowledge of the initial estimates of DOA, with proper spatial prefiltering to separate multiple groups of closely spaced sources, our proposed algorithm is shown to have high resolution capability even in coherent multipath environment without reducing the angular resolution, compared with the use of subarray. Compared with the conventional algorithm, the performance by the proposed algorithm is shown by the simulations to be improved under low Signal to Noise Ratio (SNR) while the performance is not degraded under high SNR. Moreover the computation burden involved in the eigencomputation is largely reduced by introducing the Pesudo-Hermitian matrix approximation.