Mushtaq Ahmad , Xiaofei Zhang , Farman Ali , Xin Lai
{"title":"Enhanced DOD and DOA estimations in coprime MIMO radar: Modified matrix pencil method","authors":"Mushtaq Ahmad , Xiaofei Zhang , Farman Ali , Xin Lai","doi":"10.1016/j.dsp.2024.104977","DOIUrl":null,"url":null,"abstract":"<div><div>Recent research indicates that coprime multiple-input multiple-output (MIMO) radar systems enhance target detection and parameter estimation capabilities due to their unique array configurations. However, despite these advantages, effectively managing scenarios with both coherent and uncorrelated targets requires a delicate balance between computational efficiency and performance accuracy. In this paper, we propose an innovative approach for the joint estimation of the direction of departure (DOD) and direction of arrival (DOA) in coprime MIMO radar systems capable of effectively handling both coherent and uncorrelated targets. We first construct an extended virtual uniform rectangular array (URA) by employing array interpolation, which enhances the system's resolution capabilities. Next, we apply a low-rank structured matrix recovery technique to tackle inherent rank deficiency issues in coherent targets. This approach allows us to estimate the parameters of these targets accurately. We use the full-rank covariance matrix to directly apply the modified matrix pencil (MMP) method for estimating DOD and DOA. This dual-faceted approach automatically pairs estimated parameters without separating processing paths for coherent and uncorrelated targets. Comprehensive simulations indicate the effectiveness of our proposed algorithm in managing mixed target scenarios. It achieves high estimation accuracy and resolution while maintaining computational efficiency.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"159 ","pages":"Article 104977"},"PeriodicalIF":2.9000,"publicationDate":"2025-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Digital Signal Processing","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1051200424006018","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
Abstract
Recent research indicates that coprime multiple-input multiple-output (MIMO) radar systems enhance target detection and parameter estimation capabilities due to their unique array configurations. However, despite these advantages, effectively managing scenarios with both coherent and uncorrelated targets requires a delicate balance between computational efficiency and performance accuracy. In this paper, we propose an innovative approach for the joint estimation of the direction of departure (DOD) and direction of arrival (DOA) in coprime MIMO radar systems capable of effectively handling both coherent and uncorrelated targets. We first construct an extended virtual uniform rectangular array (URA) by employing array interpolation, which enhances the system's resolution capabilities. Next, we apply a low-rank structured matrix recovery technique to tackle inherent rank deficiency issues in coherent targets. This approach allows us to estimate the parameters of these targets accurately. We use the full-rank covariance matrix to directly apply the modified matrix pencil (MMP) method for estimating DOD and DOA. This dual-faceted approach automatically pairs estimated parameters without separating processing paths for coherent and uncorrelated targets. Comprehensive simulations indicate the effectiveness of our proposed algorithm in managing mixed target scenarios. It achieves high estimation accuracy and resolution while maintaining computational efficiency.
期刊介绍:
Digital Signal Processing: A Review Journal is one of the oldest and most established journals in the field of signal processing yet it aims to be the most innovative. The Journal invites top quality research articles at the frontiers of research in all aspects of signal processing. Our objective is to provide a platform for the publication of ground-breaking research in signal processing with both academic and industrial appeal.
The journal has a special emphasis on statistical signal processing methodology such as Bayesian signal processing, and encourages articles on emerging applications of signal processing such as:
• big data• machine learning• internet of things• information security• systems biology and computational biology,• financial time series analysis,• autonomous vehicles,• quantum computing,• neuromorphic engineering,• human-computer interaction and intelligent user interfaces,• environmental signal processing,• geophysical signal processing including seismic signal processing,• chemioinformatics and bioinformatics,• audio, visual and performance arts,• disaster management and prevention,• renewable energy,