基于多级盒状粒子滤波的双无人机协同纯方位目标定位

IF 3 3区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC
Qiyuan Yin , Cheng Xu , Peng Zhou , Daqing Huang , Wenxiao Xu
{"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 ,&nbsp;Cheng Xu ,&nbsp;Peng Zhou ,&nbsp;Daqing Huang ,&nbsp;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 ,&nbsp;Cheng Xu ,&nbsp;Peng Zhou ,&nbsp;Daqing Huang ,&nbsp;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}
引用次数: 0

摘要

本研究解决了利用双无人机装备到达角传感器来提高机动目标定位精度的难题。传统方法在单无人机模式下工作,存在测量尺寸不足、测量效率低、角度测量信息高度非线性等问题。这些因素对滤波器参数提出了严格的要求,导致定位稳定性差,参数调整复杂,在实际应用中受到很大限制。针对这些问题,提出了一种基于盒状粒子滤波的双无人机协同定位模型。首先,通过降维和统一原始非线性测量盒,显著提高了复杂随机过程(CSP)的计算效率;其次,设计了多级测量箱机构,并通过严格推导,确定了多级测量箱权重的计算方法;该机制不仅有效减轻了滤波过程中粒子的降解,而且进一步提高了测量信息的准确性。最后,在多层盒状粒子滤波模型的基础上,引入了机动创新的自适应区间展开(AIE)和自适应调整方法。该方法利用盒状粒子产生的信息,实时动态调整机动目标的运动模型参数,使系统能够灵活适应敌对目标的高机动性变化。大量实验结果表明,该模型克服了传统方法的诸多不足,为双无人机协同纯方位目标定位提供了一种有效的新方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Digital Signal Processing
Digital Signal Processing 工程技术-工程:电子与电气
CiteScore
5.30
自引率
17.20%
发文量
435
审稿时长
66 days
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,
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信