Abstract
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.
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© 2006 Springer-Verlag Berlin Heidelberg
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Li, D., Gao, S., Wang, F., Meng, F. (2006). Direction of Arrival Estimation Based on Minor Component Analysis Approach. In: King, I., Wang, J., Chan, LW., Wang, D. (eds) Neural Information Processing. ICONIP 2006. Lecture Notes in Computer Science, vol 4233. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893257_58
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DOI: https://doi.org/10.1007/11893257_58
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46481-5
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