Efficient wideband signals’ direction of arrival estimation method with unknown number of signals

Yaoping Zeng,Guangyue Lu

doi:10.1177/1550147716676557

Abstract

This article focuses on the direction of arrival estimation of wideband signals, and an efficient method based on test of orthogonality of frequency subspaces is proposed. Using suitable coefficient m to weight and inverse the covariance matrix, the proposed method can obtain estimated noise subspace with unknown number of signals. Unlike conventional subspace estimation with heavy complex computations, the proposed method reduces the amount of computation by real matrix multiplication, and through only half of the total angular field-of-view. It decreases the computational load of spectrum peak search to half. Moreover, compared with the existing real-valued techniques suitable for only the centro-symmetrical array, the new method can be applied to arbitrary array. The simulation and experimental data results are provided to demonstrate the effectiveness and performance of the proposed method.

Highlights

Source localization using wideband signals has been widely studied in multiple areas with very diverse application such as radar, communication, and microphone array systems,[1,2] and a variety of techniques have been proposed to handle the wideband signal localization problem
coherent signal subspace method (CSSM) is effective in low signal-to-noise ratio (SNR) cases, while it is sensitive to the initial values and poor initial values can result in bigger biased estimates
We propose an efficient direction of arrival (DOA) estimation method for wideband signals with unknown number of signals, which can overcome the aforementioned shortcomings of the existing algorithm

Summary

Introduction

Source localization using wideband signals has been widely studied in multiple areas with very diverse application such as radar, communication, and microphone array systems,[1,2] and a variety of techniques have been proposed to handle the wideband signal localization problem. We can obtain the estimated noise subspace by reversing the weighted covariance matrix through suitable coefficient m with no prior information of the source number.

Results

Conclusion