2013Unpublished venueRequires access

An improved algorithm for spatial spectrum estimation in array signal processing

Lei Tao, Rongyu Fang, Zhihui Qiu

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Abstract

Direction of Arrival (DOA) estimation based on array signal processing is the main content of spatial spectrum estimation. Multiple Signal Classification (MUSIC) is the most classical super-resolution spatial spectrum estimation method. Under ideal condition, the algorithm can precisely estimate the DOA of uncorrelated signals. However, the performance of MUSIC algorithm will degrade seriously or even fail in the coherent source signal estimation. In the condition that many artificial signals have cyclostationary characteristics which use the target signal information, circular cross correlation MUSIC algorithm can further improve the quality of signal processing and have better noise suppressing property and resolution. But it is restricted to the cyclic correlation signal resolution. Therefore, this paper proposes an improved circular cross correlation MUSIC algorithm. Simulation results show that the performance of the improved circular cross correlation MUSIC algorithm is superior to the conventional MUSIC algorithm and circular cross correlation MUSIC algorithm in noise suppressing and signal selectivity.

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What this paper is about

Direction of Arrival (DOA) estimation based on array signal processing is the main content of spatial spectrum estimation. Multiple Signal Classification (MUSIC) is the most classical super-resolution spatial spectrum estimation method. Under ideal condition, the algorithm can precisely estimate the DOA of uncorrelated signals. However, the performance of MUSIC algorithm will degrade seriously or even fail in the coherent source signal estimation. In the condition that many artificial signals have cyclostationary characteristics which use the target signal information, circular cross correlation MUSIC algorithm can further improve the quality of signal processing and have better noise suppressing property and resolution. But it is restricted to the cyclic correlation signal resolution. Therefore, this paper proposes an improved circular cross correlation MUSIC algorithm. Simulation results show that the performance of the improved circular cross correlation MUSIC algorithm is superior to the conventional MUSIC algorithm and circular cross correlation MUSIC algorithm in noise suppressing and signal selectivity.

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Available abstract

Direction of Arrival (DOA) estimation based on array signal processing is the main content of spatial spectrum estimation. Multiple Signal Classification (MUSIC) is the most classical super-resolution spatial spectrum estimation method. Under ideal condition, the algorithm can precisely estimate the DOA of uncorrelated signals. However, the performance of MUSIC algorithm will degrade seriously or even fail in the coherent source signal estimation. In the condition that many artificial signals have cyclostationary characteristics which use the target signal information, circular cross correlation MUSIC algorithm can further improve the quality of signal processing and have better noise suppressing property and resolution. But it is restricted to the cyclic correlation signal resolution. Therefore, this paper proposes an improved circular cross correlation MUSIC algorithm. Simulation results show that the performance of the improved circular cross correlation MUSIC algorithm is superior to the conventional MUSIC algorithm and circular cross correlation MUSIC algorithm in noise suppressing and signal selectivity.

Key concepts: Cyclostationary process, Algorithm, SIGNAL (programming language), Direction of arrival, Computer science, Noise (video), Multiple signal classification, Cross-correlation

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