{"title":"低可观测性条件下 GNSS-蜂窝机会信号的融合定位","authors":"Tian Jin;Pei Zhang;James Chakwizira;Yuchen Wang","doi":"10.1109/TIM.2024.3485434","DOIUrl":null,"url":null,"abstract":"Cellular signals of opportunity (CSOPs) can be utilized as a backup positioning system for global navigation satellite systems (GNSSs) in urban. Current studies on the fusion of GNSS-CSOPs require sufficient signals. However, in low-observability environments where the total number of visible signals does not exceed four, the existing GNSS-CSOPs pseudorange fusion methods lack robust strategies and have poor performance. In addition, GNSS-CSOPs are not spatiotemporally synchronized and have not yet been considered by current studies. Moreover, GNSS-CSOPs pseudorange measurements noise exhibit differences under the kinematic receiver, and this challenge has not been well dealt with by the existing weighting methods. To address the above-mentioned issues, a novel fusion positioning system based on an iterated extended Kalman filter (IEKF) for GNSS-CSOP is formulated under low observability, and the degree of observability is analyzed. In this system, the influence of spatiotemporal asynchronicity is addressed. Then, a Helmert unit variance estimation (HUVE) algorithm is proposed to obtain the measurement weights in GNSS-CSOPs fusion. Moreover, a double Dog-leg incremental estimation (DDIE) algorithm is proposed to enhance convergence when solving under low observability. In field tests, results show that the positioning accuracy of the proposed system can reach approximately 6.5 m under low observability. Compared with other state-of-the-art studies, such as weighting with power-of-signal, quasi-Newton (QN), Levenberg-Marquardt (LM), and Dog-leg methods, the performance of the proposed system has been improved by 62.4%, 82.4%, 72.9%, and 64.7%, respectively. This study presents a novel fusion positioning strategy for the GNSS-CSOP in low-observability environments.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":null,"pages":null},"PeriodicalIF":5.6000,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Fusion Positioning of GNSS-Cellular Signals of Opportunity Under Low Observability\",\"authors\":\"Tian Jin;Pei Zhang;James Chakwizira;Yuchen Wang\",\"doi\":\"10.1109/TIM.2024.3485434\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Cellular signals of opportunity (CSOPs) can be utilized as a backup positioning system for global navigation satellite systems (GNSSs) in urban. Current studies on the fusion of GNSS-CSOPs require sufficient signals. However, in low-observability environments where the total number of visible signals does not exceed four, the existing GNSS-CSOPs pseudorange fusion methods lack robust strategies and have poor performance. In addition, GNSS-CSOPs are not spatiotemporally synchronized and have not yet been considered by current studies. Moreover, GNSS-CSOPs pseudorange measurements noise exhibit differences under the kinematic receiver, and this challenge has not been well dealt with by the existing weighting methods. To address the above-mentioned issues, a novel fusion positioning system based on an iterated extended Kalman filter (IEKF) for GNSS-CSOP is formulated under low observability, and the degree of observability is analyzed. In this system, the influence of spatiotemporal asynchronicity is addressed. Then, a Helmert unit variance estimation (HUVE) algorithm is proposed to obtain the measurement weights in GNSS-CSOPs fusion. Moreover, a double Dog-leg incremental estimation (DDIE) algorithm is proposed to enhance convergence when solving under low observability. In field tests, results show that the positioning accuracy of the proposed system can reach approximately 6.5 m under low observability. Compared with other state-of-the-art studies, such as weighting with power-of-signal, quasi-Newton (QN), Levenberg-Marquardt (LM), and Dog-leg methods, the performance of the proposed system has been improved by 62.4%, 82.4%, 72.9%, and 64.7%, respectively. This study presents a novel fusion positioning strategy for the GNSS-CSOP in low-observability environments.\",\"PeriodicalId\":13341,\"journal\":{\"name\":\"IEEE Transactions on Instrumentation and Measurement\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":5.6000,\"publicationDate\":\"2024-10-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Transactions on Instrumentation and Measurement\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10731872/\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Instrumentation and Measurement","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/10731872/","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
Fusion Positioning of GNSS-Cellular Signals of Opportunity Under Low Observability
Cellular signals of opportunity (CSOPs) can be utilized as a backup positioning system for global navigation satellite systems (GNSSs) in urban. Current studies on the fusion of GNSS-CSOPs require sufficient signals. However, in low-observability environments where the total number of visible signals does not exceed four, the existing GNSS-CSOPs pseudorange fusion methods lack robust strategies and have poor performance. In addition, GNSS-CSOPs are not spatiotemporally synchronized and have not yet been considered by current studies. Moreover, GNSS-CSOPs pseudorange measurements noise exhibit differences under the kinematic receiver, and this challenge has not been well dealt with by the existing weighting methods. To address the above-mentioned issues, a novel fusion positioning system based on an iterated extended Kalman filter (IEKF) for GNSS-CSOP is formulated under low observability, and the degree of observability is analyzed. In this system, the influence of spatiotemporal asynchronicity is addressed. Then, a Helmert unit variance estimation (HUVE) algorithm is proposed to obtain the measurement weights in GNSS-CSOPs fusion. Moreover, a double Dog-leg incremental estimation (DDIE) algorithm is proposed to enhance convergence when solving under low observability. In field tests, results show that the positioning accuracy of the proposed system can reach approximately 6.5 m under low observability. Compared with other state-of-the-art studies, such as weighting with power-of-signal, quasi-Newton (QN), Levenberg-Marquardt (LM), and Dog-leg methods, the performance of the proposed system has been improved by 62.4%, 82.4%, 72.9%, and 64.7%, respectively. This study presents a novel fusion positioning strategy for the GNSS-CSOP in low-observability environments.
期刊介绍:
Papers are sought that address innovative solutions to the development and use of electrical and electronic instruments and equipment to measure, monitor and/or record physical phenomena for the purpose of advancing measurement science, methods, functionality and applications. The scope of these papers may encompass: (1) theory, methodology, and practice of measurement; (2) design, development and evaluation of instrumentation and measurement systems and components used in generating, acquiring, conditioning and processing signals; (3) analysis, representation, display, and preservation of the information obtained from a set of measurements; and (4) scientific and technical support to establishment and maintenance of technical standards in the field of Instrumentation and Measurement.