Direction of Arrival Estimation Based on Minor Component Analysis Approach
Many high resolution DOA estimation algorithms like MUSIC and ESPRIT estimation are based on the sub-space concept and require the eigen-decomposition of the input correlation matrix. As quantities of computation of eigen-decomposition, it is unsuitable for real time processing. An algorithm for noise subspace estimation based on minor component analysis is proposed. These algorithms are based on anti-Hebbian learning neural network and contain only relatively simple operations, which are stable, convergent, and have self-organizing properties. Finally a method of real-time parallel processing is proposed, and data processing can be finished at end time of sampling. Simulations show that the proposed algorithm has an analogy performance with the MUSIC algorithm.
KeywordsAnalogy Performance Signal Subspace Noise Subspace Music Algorithm Arrival Estimation
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