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2018•2018 International Computers, Signals and Systems Conference (ICOMSSC)Requires access

An Improved MUSIC Algorithm Based on Subspace Projection Method

Qiang Shu, Yuwen Wang, Hanjing Li, Haolong Wu

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Abstract

In this paper, an improved MUSIC algorithm based on subspace projection is proposed to improve the resolution of the MUSIC algorithm under the low signal to noise ratio and the small snapshot number. The algorithm makes full use of the noise subspace information and uses the new eigenvalue correction method to weighting the noise subspace to obtain the new spatial spectrum function. By searching its maximum value, the direction of DOA estimation can be obtained. Finally, through simulation and data analysis, the feasibility and effectiveness of the algorithm are verified.

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What this paper is about

In this paper, an improved MUSIC algorithm based on subspace projection is proposed to improve the resolution of the MUSIC algorithm under the low signal to noise ratio and the small snapshot number. The algorithm makes full use of the noise subspace information and uses the new eigenvalue correction method to weighting the noise subspace to obtain the new spatial spectrum function. By searching its maximum value, the direction of DOA estimation can be obtained. Finally, through simulation and data analysis, the feasibility and effectiveness of the algorithm are verified.

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Available abstract

In this paper, an improved MUSIC algorithm based on subspace projection is proposed to improve the resolution of the MUSIC algorithm under the low signal to noise ratio and the small snapshot number. The algorithm makes full use of the noise subspace information and uses the new eigenvalue correction method to weighting the noise subspace to obtain the new spatial spectrum function. By searching its maximum value, the direction of DOA estimation can be obtained. Finally, through simulation and data analysis, the feasibility and effectiveness of the algorithm are verified.

Key concepts: Subspace topology, Snapshot (computer storage), Multiple signal classification, Algorithm, Computer science, Weighting, Signal subspace, Noise (video)

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