An Efficient Direction of Arrival Estimation Algorithm for Sources with Intersecting Signature in the Time–Frequency Domain

Nabeel Ali Khan,Kwonhue Choi,Sadiq Ali

doi:10.3390/app11041849

Abstract

An efficient direction of arrival estimation method is proposed. The proposed algorithm accurately estimates the instantaneous frequency of signals received by multiple sensors (array of sensors/antennas). The estimated instantaneous frequency is then used to separate sources and estimate their direction of arrivals. Experimental results indicate that the proposed method achieves better performance than the existing methods both in terms of computational requirements and localization accuracy. It is also shown that the proposed method can work in under-determined situations.

Highlights

Estimation Algorithm for SourcesDirection of arrival (DOA) estimation is an important problem in many real-life applications such as MIMO communication and radar signal processing
Time–frequency (TF) Multiple SignalClassification (MUSIC) algorithm further enhances the MUSIC algorithm by employing spatial time–frequency distributions (STFDs) that allow the selection of high energy points in the TF domain improving the estimate of the covariance matrix [4,5,6,7,8,9]
Conventional time-domain MUSIC algorithm or its TF extension are only applicable in the over-determined scenario, i.e., when the number of sensors is higher than the number of sources

Summary

Introduction

Estimation Algorithm for SourcesDirection of arrival (DOA) estimation is an important problem in many real-life applications such as MIMO communication and radar signal processing. Time–frequency (TF) MUSIC algorithm further enhances the MUSIC algorithm by employing spatial time–frequency distributions (STFDs) that allow the selection of high energy points in the TF domain improving the estimate of the covariance matrix [4,5,6,7,8,9]. Conventional time-domain MUSIC algorithm or its TF extension are only applicable in the over-determined scenario, i.e., when the number of sensors is higher than the number of sources. STFDs can be employed to estimate DOA in under-determined scenarios, i.e., when the number of sensors is not higher than the number of sources.

Methods

Results

Conclusion