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Estimation of Direction of Arrival Based on MSWF Without Reference Signal

Jun Li

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Abstract

The current subspace algorithm for direction of arrival(DOA) estimation based on multi-stage wiener filter(MSWF) has low computational complexity,but it relies heavily on the prior knowledge of a reference signal,which is often unavailable.In this paper,starting from solving a linear prediction(LP) problem using MSWF,we propose a new DOA estimation algorithm with low computational complexity.The new algorithm combines the LP method with the subspace method,and does not require the reference signal.Further,the new algorithm is shown to be stable and has a good estimation performance,even in the case of low signal to noise ratio(SNR) or incorrect source number estimation.Simulation results are provided to verify our conclusion.

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What this paper is about

The current subspace algorithm for direction of arrival(DOA) estimation based on multi-stage wiener filter(MSWF) has low computational complexity,but it relies heavily on the prior knowledge of a reference signal,which is often unavailable.In this paper,starting from solving a linear prediction(LP) problem using MSWF,we propose a new DOA estimation algorithm with low computational complexity.The new algorithm combines the LP method with the subspace method,and does not require the reference signal.Further,the new algorithm is shown to be stable and has a good estimation performance,even in the case of low signal to noise ratio(SNR) or incorrect source number estimation.Simulation results are provided to verify our conclusion.

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Available abstract

The current subspace algorithm for direction of arrival(DOA) estimation based on multi-stage wiener filter(MSWF) has low computational complexity,but it relies heavily on the prior knowledge of a reference signal,which is often unavailable.In this paper,starting from solving a linear prediction(LP) problem using MSWF,we propose a new DOA estimation algorithm with low computational complexity.The new algorithm combines the LP method with the subspace method,and does not require the reference signal.Further,the new algorithm is shown to be stable and has a good estimation performance,even in the case of low signal to noise ratio(SNR) or incorrect source number estimation.Simulation results are provided to verify our conclusion.

Key concepts: Signal subspace, Direction of arrival, Algorithm, Computational complexity theory, Computer science, Subspace topology, SIGNAL (programming language), Noise (video)

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