Yipeng Ning, Wengang Sang, Guobiao Yao, Jingxue Bi, Shida Wang
{"title":"GNSS/MIMU与改进的多状态ZUPT/DZUPT约束紧密耦合,用于拒绝GNSS环境中的陆地车辆","authors":"Yipeng Ning, Wengang Sang, Guobiao Yao, Jingxue Bi, Shida Wang","doi":"10.1080/19479832.2020.1829718","DOIUrl":null,"url":null,"abstract":"ABSTRACT A GNSS/INS integrated navigation system has been intensively developed and widely applied in multiple areas. It can provide high accuracy position, velocity and attitude for vehicle with appropriate data fusion algorithm. However, the overall performance of a low-cost GNSS/MEMS IMU frequently degrades in shaded environment. The traditional constraints GNSS/MIMU algorithm based on zero-velocity detection can effectively increase positioning performance, but easily be susceptible to false detection. This article aims to improve a ZUPT/DZUPT constraints model to improve the accuracy of navigation solutions during satellites signal blockages for different motion states. Firstly, we present a tightly coupled strategy to integrate GPS/BDS and INS by applying EKF. Then, a compositive static zero-velocity detection scheme is carried out by using the Vondrak low pass filter, GNSS/INS calculated velocity and the original data of INS. Meanwhile, a dynamic ZUPT constraint model is also constructed based on the motion characteristics of vehicle. An vehicle test was performed to validate the new algorithm. The results indicate that proposed method can effectively improve the success rate of zero-velocity detection. When the satellite signal is interrupted for 120 s, the position and velocity accuracy of the vehicle are improved by 74.7%~ 96% and 47%~ 86.2% respectively.","PeriodicalId":46012,"journal":{"name":"International Journal of Image and Data Fusion","volume":"12 1","pages":"226 - 241"},"PeriodicalIF":1.8000,"publicationDate":"2020-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/19479832.2020.1829718","citationCount":"6","resultStr":"{\"title\":\"GNSS/MIMU tightly coupled integrated with improved multi-state ZUPT/DZUPT constraints for a Land vehicle in GNSS-denied enviroments\",\"authors\":\"Yipeng Ning, Wengang Sang, Guobiao Yao, Jingxue Bi, Shida Wang\",\"doi\":\"10.1080/19479832.2020.1829718\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"ABSTRACT A GNSS/INS integrated navigation system has been intensively developed and widely applied in multiple areas. It can provide high accuracy position, velocity and attitude for vehicle with appropriate data fusion algorithm. However, the overall performance of a low-cost GNSS/MEMS IMU frequently degrades in shaded environment. The traditional constraints GNSS/MIMU algorithm based on zero-velocity detection can effectively increase positioning performance, but easily be susceptible to false detection. This article aims to improve a ZUPT/DZUPT constraints model to improve the accuracy of navigation solutions during satellites signal blockages for different motion states. Firstly, we present a tightly coupled strategy to integrate GPS/BDS and INS by applying EKF. Then, a compositive static zero-velocity detection scheme is carried out by using the Vondrak low pass filter, GNSS/INS calculated velocity and the original data of INS. Meanwhile, a dynamic ZUPT constraint model is also constructed based on the motion characteristics of vehicle. An vehicle test was performed to validate the new algorithm. The results indicate that proposed method can effectively improve the success rate of zero-velocity detection. When the satellite signal is interrupted for 120 s, the position and velocity accuracy of the vehicle are improved by 74.7%~ 96% and 47%~ 86.2% respectively.\",\"PeriodicalId\":46012,\"journal\":{\"name\":\"International Journal of Image and Data Fusion\",\"volume\":\"12 1\",\"pages\":\"226 - 241\"},\"PeriodicalIF\":1.8000,\"publicationDate\":\"2020-10-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1080/19479832.2020.1829718\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Image and Data Fusion\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1080/19479832.2020.1829718\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"REMOTE SENSING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Image and Data Fusion","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/19479832.2020.1829718","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"REMOTE SENSING","Score":null,"Total":0}
GNSS/MIMU tightly coupled integrated with improved multi-state ZUPT/DZUPT constraints for a Land vehicle in GNSS-denied enviroments
ABSTRACT A GNSS/INS integrated navigation system has been intensively developed and widely applied in multiple areas. It can provide high accuracy position, velocity and attitude for vehicle with appropriate data fusion algorithm. However, the overall performance of a low-cost GNSS/MEMS IMU frequently degrades in shaded environment. The traditional constraints GNSS/MIMU algorithm based on zero-velocity detection can effectively increase positioning performance, but easily be susceptible to false detection. This article aims to improve a ZUPT/DZUPT constraints model to improve the accuracy of navigation solutions during satellites signal blockages for different motion states. Firstly, we present a tightly coupled strategy to integrate GPS/BDS and INS by applying EKF. Then, a compositive static zero-velocity detection scheme is carried out by using the Vondrak low pass filter, GNSS/INS calculated velocity and the original data of INS. Meanwhile, a dynamic ZUPT constraint model is also constructed based on the motion characteristics of vehicle. An vehicle test was performed to validate the new algorithm. The results indicate that proposed method can effectively improve the success rate of zero-velocity detection. When the satellite signal is interrupted for 120 s, the position and velocity accuracy of the vehicle are improved by 74.7%~ 96% and 47%~ 86.2% respectively.
期刊介绍:
International Journal of Image and Data Fusion provides a single source of information for all aspects of image and data fusion methodologies, developments, techniques and applications. Image and data fusion techniques are important for combining the many sources of satellite, airborne and ground based imaging systems, and integrating these with other related data sets for enhanced information extraction and decision making. Image and data fusion aims at the integration of multi-sensor, multi-temporal, multi-resolution and multi-platform image data, together with geospatial data, GIS, in-situ, and other statistical data sets for improved information extraction, as well as to increase the reliability of the information. This leads to more accurate information that provides for robust operational performance, i.e. increased confidence, reduced ambiguity and improved classification enabling evidence based management. The journal welcomes original research papers, review papers, shorter letters, technical articles, book reviews and conference reports in all areas of image and data fusion including, but not limited to, the following aspects and topics: • Automatic registration/geometric aspects of fusing images with different spatial, spectral, temporal resolutions; phase information; or acquired in different modes • Pixel, feature and decision level fusion algorithms and methodologies • Data Assimilation: fusing data with models • Multi-source classification and information extraction • Integration of satellite, airborne and terrestrial sensor systems • Fusing temporal data sets for change detection studies (e.g. for Land Cover/Land Use Change studies) • Image and data mining from multi-platform, multi-source, multi-scale, multi-temporal data sets (e.g. geometric information, topological information, statistical information, etc.).