AR Model-Based Direction-of-Arrival Estimation of Coherent Signals in the Presence of Unknown Mutual Coupling

Zhi-Chao Sha,Yi-Yu Zhou,Zhang-Meng Liu,Zhi-Tao Huang

doi:10.1155/2013/386367

Abstract

This paper addresses the problem of direction-of-arrival (DOA) estimation of coherent signals in the presence of unknown mutual coupling, and an autoregression (AR) model-based method is proposed. The effects of mutual coupling can be eliminated by the inherent mechanism of the proposed algorithm, so the DOAs can be accurately estimated without any calibration sources. After the mixing matrix is estimated by independent component analysis (ICA), several parameter equations are established upon the mixing matrix. Finally, all DOAs of coherent signals are estimated by solving these equations. Compared with traditional methods, the proposed method has higher angle resolution and estimation accuracy. Simulation results demonstrate the effectiveness of the algorithm.

Highlights

Direction-of-arrival (DOA) estimation is very important in a variety of wireless communication applications, such as mobile communication, radar, and distributed sensor networks
Inspired by [19] and based on the estimation of the real steering vectors by independent component analysis (ICA), we develop a spatial AR model-based algorithm for coherent DOA estimation of uniform linear arrays (ULAs) in the presence of mutual coupling
Consider K narrowband non-Gaussian signals impinging on a uniform linear array (ULA) with M array elements, where the distance d between adjacent sensors is equal to half of the wavelength

Summary

Introduction

Direction-of-arrival (DOA) estimation is very important in a variety of wireless communication applications, such as mobile communication, radar, and distributed sensor networks. When the incident signals are highly correlated or coherent in the presence of unknown mutual coupling, the performance of conventional high-resolution DOA estimation methods will deteriorate significantly. Independent component analysis (ICA) has been utilized to solve the DOA estimation problem [16, 17] These methods can estimate the real steering vectors with unknown mutual coupling. Dai and Ye [18] propose an improved spatial smoothing algorithm for DOA estimation of coherent signals in the presence of unknown mutual coupling, but it significantly deteriorates while angle interval is not large enough or several groups of coherent signals coexist. Inspired by [19] and based on the estimation of the real steering vectors by ICA, we develop a spatial AR model-based algorithm for coherent DOA estimation of ULA in the presence of mutual coupling.

Data Model

Novel AR Model-Based DOA Estimation Algorithm

Simulation Experiment

Conclusion