{"title":"基于多级盒状粒子滤波的双无人机协同纯方位目标定位","authors":"Qiyuan Yin , Cheng Xu , Peng Zhou , Daqing Huang , Wenxiao Xu","doi":"10.1016/j.dsp.2025.105572","DOIUrl":null,"url":null,"abstract":"<div><div>This study addresses the challenge of enhancing the precision of maneuvering target localization using dual unmanned aerial vehicles (UAVs) equipped with angle-of-arrival (AOA) sensors. Traditional methods, operating in single-UAV mode, suffer from insufficient measurement dimensions, low measurement efficiency, and the highly nonlinear nature of angular measurement information. These factors impose stringent requirements on filter parameters, resulting in poor localization stability, complex parameter tuning, and significant limitations in practical applications. To tackle these issues, we propose a dual-UAV cooperative localization model based on box particle filtering. First, by reducing the dimensionality and unifying the original nonlinear measurement boxes, the computational efficiency of complex stochastic processes (CSP) is significantly improved. Second, a multi-level (ML) measurement box mechanism is designed, and through rigorous derivation, a method for calculating the weights of multi-level measurement boxes is defined. This mechanism not only effectively mitigates particle degradation during the filtering process but also further enhances the accuracy of measurement information. Finally, based on the multi-level box particle filtering model, we introduce an adaptive interval expansion (AIE) and adaptive adjustment method for maneuvering innovation. This approach leverages information generated by box particles to dynamically adjust the motion model parameters of maneuvering targets in real time, enabling the system to flexibly adapt to the high-mobility variations of adversarial targets. Extensive experimental results demonstrate that our model overcomes many shortcomings of traditional methods, providing an effective new approach for dual-UAV cooperative bearing-only target localization.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"168 ","pages":"Article 105572"},"PeriodicalIF":3.0000,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Dual-UAV cooperative bearing-only target localization based on multi-level box particle filter\",\"authors\":\"Qiyuan Yin , Cheng Xu , Peng Zhou , Daqing Huang , Wenxiao Xu\",\"doi\":\"10.1016/j.dsp.2025.105572\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>This study addresses the challenge of enhancing the precision of maneuvering target localization using dual unmanned aerial vehicles (UAVs) equipped with angle-of-arrival (AOA) sensors. Traditional methods, operating in single-UAV mode, suffer from insufficient measurement dimensions, low measurement efficiency, and the highly nonlinear nature of angular measurement information. These factors impose stringent requirements on filter parameters, resulting in poor localization stability, complex parameter tuning, and significant limitations in practical applications. To tackle these issues, we propose a dual-UAV cooperative localization model based on box particle filtering. First, by reducing the dimensionality and unifying the original nonlinear measurement boxes, the computational efficiency of complex stochastic processes (CSP) is significantly improved. Second, a multi-level (ML) measurement box mechanism is designed, and through rigorous derivation, a method for calculating the weights of multi-level measurement boxes is defined. This mechanism not only effectively mitigates particle degradation during the filtering process but also further enhances the accuracy of measurement information. Finally, based on the multi-level box particle filtering model, we introduce an adaptive interval expansion (AIE) and adaptive adjustment method for maneuvering innovation. This approach leverages information generated by box particles to dynamically adjust the motion model parameters of maneuvering targets in real time, enabling the system to flexibly adapt to the high-mobility variations of adversarial targets. Extensive experimental results demonstrate that our model overcomes many shortcomings of traditional methods, providing an effective new approach for dual-UAV cooperative bearing-only target localization.</div></div>\",\"PeriodicalId\":51011,\"journal\":{\"name\":\"Digital Signal Processing\",\"volume\":\"168 \",\"pages\":\"Article 105572\"},\"PeriodicalIF\":3.0000,\"publicationDate\":\"2025-09-10\",\"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/S1051200425005949\",\"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}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Digital Signal Processing","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1051200425005949","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}
Dual-UAV cooperative bearing-only target localization based on multi-level box particle filter
This study addresses the challenge of enhancing the precision of maneuvering target localization using dual unmanned aerial vehicles (UAVs) equipped with angle-of-arrival (AOA) sensors. Traditional methods, operating in single-UAV mode, suffer from insufficient measurement dimensions, low measurement efficiency, and the highly nonlinear nature of angular measurement information. These factors impose stringent requirements on filter parameters, resulting in poor localization stability, complex parameter tuning, and significant limitations in practical applications. To tackle these issues, we propose a dual-UAV cooperative localization model based on box particle filtering. First, by reducing the dimensionality and unifying the original nonlinear measurement boxes, the computational efficiency of complex stochastic processes (CSP) is significantly improved. Second, a multi-level (ML) measurement box mechanism is designed, and through rigorous derivation, a method for calculating the weights of multi-level measurement boxes is defined. This mechanism not only effectively mitigates particle degradation during the filtering process but also further enhances the accuracy of measurement information. Finally, based on the multi-level box particle filtering model, we introduce an adaptive interval expansion (AIE) and adaptive adjustment method for maneuvering innovation. This approach leverages information generated by box particles to dynamically adjust the motion model parameters of maneuvering targets in real time, enabling the system to flexibly adapt to the high-mobility variations of adversarial targets. Extensive experimental results demonstrate that our model overcomes many shortcomings of traditional methods, providing an effective new approach for dual-UAV cooperative bearing-only target localization.
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,