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TitleMultiple classification without model order estimation
Publication TypeConference Paper
Year of Publication2012
AuthorsAcharyya, R., and Scott N.L.
Conference NameProgram Book - OCEANS 2012 MTS/IEEE Yeosu: The Living Ocean and Coast - Diversity of Resources and Sustainable Activities
Date Published2012
KeywordsArray processing, Covariance matrix, Digital simulation, Direction of arrival, Direction of arrival estimation, Discrete points, Eigen decomposition, Eigenanalysis, Eigenvalues and eigenfunctions, Localisation, MBES, Model order estimation, Multiple Classification, MUSIC, MUSIC algorithms, Noise subspace, Oceanography, pseudo-spectrum, Relative strength, Signal to noise ratio, SNR values, Sonar, Sonar data, Spectrum Analysis, Sub-space methods, Super resolution, Underwater acoustics, Wavelet analysis
AbstractA novel approach to weighting eigen-subspaces is proposed which leads to a pseudo-spectrum with super-resolution performance similar to the MUSIC algorithm without the need for model-order detection. The method is shown to be applicable to a type of problem encountered in active carrier-wave sonar direction of arrival estimation where the SNR is high and the targets are discrete point targets and few in number. The method forms a covariance matrix with subsequent eigendecomposition as in MUSIC. Similarly, a pseudo-spectrum is formed as a reciprocal of a weighted reconstruction. Weights are used for all eigenvectors, contrasting with MUSIC & weighted subspace methods which typically use only the noise subspace. Further the weights proposed here include functions based on the nature of each individual eigenvector which can be related to the sparsity of the components of the eigenvector. The presented results of digital simulation demonstrate a performance comparable to MUSIC & MUSIC MDL for a range of signal/target positions, relative strengths & SNR values. Further, application of the algorithm to the real sonar data shows promising improvement in target localisation. © 2012 IEEE.

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