Reduced-Complexity Direction of Arrival Estimation Using Real-Valued Computation with Arbitrary Array Configurations

Feng-Gang Yan,Shuai Liu,Yi Shen,Ming Jin,Jun Wang

doi:10.1155/2018/3284619

Abstract

A low-complexity algorithm is presented to dramatically reduce the complexity of the multiple signal classification (MUSIC) algorithm for direction of arrival (DOA) estimation, in which both tasks of eigenvalue decomposition (EVD) and spectral search are implemented with efficient real-valued computations, leading to about 75% complexity reduction as compared to the standard MUSIC. Furthermore, the proposed technique has no dependence on array configurations and is hence suitable for arbitrary array geometries, which shows a significant implementation advantage over most state-of-the-art unitary estimators including unitary MUSIC (U-MUSIC). Numerical simulations over a wide range of scenarios are conducted to show the performance of the new technique, which demonstrates that with a significantly reduced computational complexity, the new approach is able to provide a close accuracy to the standard MUSIC.

Highlights

Direction-of-arrival (DOA) estimation is a fundamental array processing problem with many applications, for example, radar, sonar, wireless communication, and smart antenna design [1, 2]
The multiple signal classification (MUSIC) algorithm is able to provide super resolution DOA estimation for closely spaced sources and has an easy implementation with arbitrary array configuration [7]. Such advantages are archived at the expense of huge complexity which is mainly caused by an involved eigenvalue decomposition (EVD) step and a tremendous spectral search step, both are generally implemented with complex-valued computations
Numerical simulations with 500 Monte Carlo trials are conducted to demonstrate the performance of the proposed method on a linear array composed of M = 10 sensors, where L = 2 sources are considered throughout the simulations

Summary

Introduction

Direction-of-arrival (DOA) estimation is a fundamental array processing problem with many applications, for example, radar, sonar, wireless communication, and smart antenna design [1, 2] This topic has been extensively studied during the past four decades, resulting in many efficient and accurate algorithms including multiple signal classification (MUSIC) [3], maximum likelihood (ML) [4], subspace fitting (SF) [5], minimum norm (MN) [6], and their derivations. Among those methods, the MUSIC algorithm is able to provide super resolution DOA estimation for closely spaced sources and has an easy implementation with arbitrary array configuration [7].

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Results

Conclusion