2011IEEE Transactions on Signal ProcessingRequires access

Direction-of-Arrival Estimation of Wideband Signals via Covariance Matrix Sparse Representation

Zhangmeng Liu, Zhitao Huang, Yiyu Zhou

Open publisher page 138 citations

Abstract

This paper focuses on direction-of-arrival (DOA) estimation of wideband signals, and a method named wideband covariance matrix sparse representation (W-CMSR) is proposed. In W-CMSR, the lower left triangular elements of the covariance matrix are aligned to form a new measurement vector, and DOA estimation is then realized by representing this vector on an over-complete dictionary under the constraint of sparsity. The a priori information of the incident signal number is not needed in W-CMSR, and no spectral decomposition or focusing is introduced. Simulation results demonstrate the satisfying performance of W-CMSR in wideband DOA estimation in various settings. Moreover, theoretical analysis and numerical examples show how many simultaneous signals can be separated by W-CMSR on typical array geometries, and that the half-wavelength spacing restriction in avoiding ambiguity can be relaxed from the highest to the lowest frequency of the incident wideband signals.

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What this paper is about

This paper focuses on direction-of-arrival (DOA) estimation of wideband signals, and a method named wideband covariance matrix sparse representation (W-CMSR) is proposed. In W-CMSR, the lower left triangular elements of the covariance matrix are aligned to form a new measurement vector, and DOA estimation is then realized by representing this vector on an over-complete dictionary under the constraint of sparsity. The a priori information of the incident signal number is not needed in W-CMSR, and no spectral decomposition or focusing is introduced. Simulation results demonstrate the satisfying performance of W-CMSR in wideband DOA estimation in various settings. Moreover, theoretical analysis and numerical examples show how many simultaneous signals can be separated by W-CMSR on typical array geometries, and that the half-wavelength spacing restriction in avoiding ambiguity can be relaxed from the highest to the lowest frequency of the incident wideband signals.

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Available abstract

This paper focuses on direction-of-arrival (DOA) estimation of wideband signals, and a method named wideband covariance matrix sparse representation (W-CMSR) is proposed. In W-CMSR, the lower left triangular elements of the covariance matrix are aligned to form a new measurement vector, and DOA estimation is then realized by representing this vector on an over-complete dictionary under the constraint of sparsity. The a priori information of the incident signal number is not needed in W-CMSR, and no spectral decomposition or focusing is introduced. Simulation results demonstrate the satisfying performance of W-CMSR in wideband DOA estimation in various settings. Moreover, theoretical analysis and numerical examples show how many simultaneous signals can be separated by W-CMSR on typical array geometries, and that the half-wavelength spacing restriction in avoiding ambiguity can be relaxed from the highest to the lowest frequency of the incident wideband signals.

Key concepts: Wideband, Covariance matrix, Direction of arrival, Algorithm, Covariance, Computer science, Sparse approximation, Sparse array

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