Jingchao You , Zhikun Chen , Xue Liu , Zhibin Chen
{"title":"Parameter tracking method for polarization-sensitive arrays based on the generalized labeled multi-Bernoulli filter","authors":"Jingchao You , Zhikun Chen , Xue Liu , Zhibin Chen","doi":"10.1016/j.dsp.2025.105361","DOIUrl":null,"url":null,"abstract":"<div><div>Current methods for the joint estimation of direction of arrival (DOA) and polarization parameters are generally optimized for stationary scenarios. In dynamic environments where signal sources move rapidly, these methods frequently encounter estimation errors. To overcome this challenge, we introduce a novel approach for the joint tracking of DOA and polarization parameters in dynamic scenarios. This method utilizes a polarization-sensitive array to capture incoming wave signals and employs the generalized labeled multi-Bernoulli (GLMB) framework to update the DOA and polarization parameters. Initially, an enhanced MUSIC function is deployed as a pseudo-likelihood function to improve particle distribution in areas of high-likelihood. Subsequently, a novel measurement separation (NMSS) strategy is developed to create a one-to-one correspondence between measurements and signal sources. The implementation of this algorithm through sequential Monte Carlo (SMC) techniques aims to approximate the posterior density accurately. Simulation results indicate that our proposed method surpasses existing algorithms, particularly in environments characterized by low signal-to-noise ratios (SNR) and limited snapshots.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"166 ","pages":"Article 105361"},"PeriodicalIF":2.9000,"publicationDate":"2025-06-03","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/S1051200425003835","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
Current methods for the joint estimation of direction of arrival (DOA) and polarization parameters are generally optimized for stationary scenarios. In dynamic environments where signal sources move rapidly, these methods frequently encounter estimation errors. To overcome this challenge, we introduce a novel approach for the joint tracking of DOA and polarization parameters in dynamic scenarios. This method utilizes a polarization-sensitive array to capture incoming wave signals and employs the generalized labeled multi-Bernoulli (GLMB) framework to update the DOA and polarization parameters. Initially, an enhanced MUSIC function is deployed as a pseudo-likelihood function to improve particle distribution in areas of high-likelihood. Subsequently, a novel measurement separation (NMSS) strategy is developed to create a one-to-one correspondence between measurements and signal sources. The implementation of this algorithm through sequential Monte Carlo (SMC) techniques aims to approximate the posterior density accurately. Simulation results indicate that our proposed method surpasses existing algorithms, particularly in environments characterized by low signal-to-noise ratios (SNR) and limited snapshots.
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,