Pub Date : 2023-04-24DOI: 10.1109/PLANS53410.2023.10140018
T. Nguyen, Charles H. Lee, Yinwei Chen, S. Behseta, Dan Shen, Gen-yong Chen, John Nguyen, Xiwen Kang, K. Pham
{"title":"Innovative Multicarrier Broadband Waveforms for Future GNSS Applications - A System Overview","authors":"T. Nguyen, Charles H. Lee, Yinwei Chen, S. Behseta, Dan Shen, Gen-yong Chen, John Nguyen, Xiwen Kang, K. Pham","doi":"10.1109/PLANS53410.2023.10140018","DOIUrl":"https://doi.org/10.1109/PLANS53410.2023.10140018","url":null,"abstract":"","PeriodicalId":94036,"journal":{"name":"IEEE/ION Position Location and Navigation Symposium : [proceedings]. IEEE/ION Position Location and Navigation Symposium","volume":"459 1","pages":"952-967"},"PeriodicalIF":0.0,"publicationDate":"2023-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76967075","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-01Epub Date: 2023-06-08DOI: 10.1109/plans53410.2023.10139997
Swapnil Sayan Saha, Yayun Du, Sandeep Singh Sandha, Luis Antonio Garcia, Mohammad Khalid Jawed, Mani Srivastava
Inertial navigation provides a small footprint, low-power, and low-cost pathway for localization in GPS-denied environments on extremely resource-constrained Internet-of-Things (IoT) platforms. Traditionally, application-specific heuristics and physics-based kinematic models are used to mitigate the curse of drift in inertial odometry. These techniques, albeit lightweight, fail to handle domain shifts and environmental non-linearities. Recently, deep neural-inertial sequence learning has shown superior odometric resolution in capturing non-linear motion dynamics without human knowledge over heuristic-based methods. These AI-based techniques are data-hungry, suffer from excessive resource usage, and cannot guarantee following the underlying system physics. This paper highlights the unique methods, opportunities, and challenges in porting real-time AI-enhanced inertial navigation algorithms onto IoT platforms. First, we discuss how platform-aware neural architecture search coupled with ultra-lightweight model backbones can yield neural-inertial odometry models that are 31-134× smaller yet achieve or exceed the localization resolution of state-of-the-art AI-enhanced techniques. The framework can generate models suitable for locating humans, animals, underwater sensors, aerial vehicles, and precision robots. Next, we showcase how techniques from neurosymbolic AI can yield physics-informed and interpretable neural-inertial navigation models. Afterward, we present opportunities for fine-tuning pre-trained odometry models in a new domain with as little as 1 minute of labeled data, while discussing inexpensive data collection and labeling techniques. Finally, we identify several open research challenges that demand careful consideration moving forward.
{"title":"Inertial Navigation on Extremely Resource-Constrained Platforms: Methods, Opportunities and Challenges.","authors":"Swapnil Sayan Saha, Yayun Du, Sandeep Singh Sandha, Luis Antonio Garcia, Mohammad Khalid Jawed, Mani Srivastava","doi":"10.1109/plans53410.2023.10139997","DOIUrl":"10.1109/plans53410.2023.10139997","url":null,"abstract":"<p><p>Inertial navigation provides a small footprint, low-power, and low-cost pathway for localization in GPS-denied environments on extremely resource-constrained Internet-of-Things (IoT) platforms. Traditionally, application-specific heuristics and physics-based kinematic models are used to mitigate the curse of drift in inertial odometry. These techniques, albeit lightweight, fail to handle domain shifts and environmental non-linearities. Recently, deep neural-inertial sequence learning has shown superior odometric resolution in capturing non-linear motion dynamics without human knowledge over heuristic-based methods. These AI-based techniques are data-hungry, suffer from excessive resource usage, and cannot guarantee following the underlying system physics. This paper highlights the unique methods, opportunities, and challenges in porting real-time AI-enhanced inertial navigation algorithms onto IoT platforms. First, we discuss how platform-aware neural architecture search coupled with ultra-lightweight model backbones can yield neural-inertial odometry models that are 31-134× smaller yet achieve or exceed the localization resolution of state-of-the-art AI-enhanced techniques. The framework can generate models suitable for locating humans, animals, underwater sensors, aerial vehicles, and precision robots. Next, we showcase how techniques from neurosymbolic AI can yield physics-informed and interpretable neural-inertial navigation models. Afterward, we present opportunities for fine-tuning pre-trained odometry models in a new domain with as little as 1 minute of labeled data, while discussing inexpensive data collection and labeling techniques. Finally, we identify several open research challenges that demand careful consideration moving forward.</p>","PeriodicalId":94036,"journal":{"name":"IEEE/ION Position Location and Navigation Symposium : [proceedings]. IEEE/ION Position Location and Navigation Symposium","volume":"2023 ","pages":"708-723"},"PeriodicalIF":0.0,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10512424/pdf/nihms-1928657.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41180729","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.1109/PLANS53410.2023.10140011
F. Graas
{"title":"Doppler Processing for Satellite Navigation","authors":"F. Graas","doi":"10.1109/PLANS53410.2023.10140011","DOIUrl":"https://doi.org/10.1109/PLANS53410.2023.10140011","url":null,"abstract":"","PeriodicalId":94036,"journal":{"name":"IEEE/ION Position Location and Navigation Symposium : [proceedings]. IEEE/ION Position Location and Navigation Symposium","volume":"27 1","pages":"365-371"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73895921","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.1109/PLANS53410.2023.10140131
A. Cuenca, H. Moncayo
{"title":"Q-Learning Model Covariance Adaptation of Rao-Blackwellized Particle Filtering in Airborne Geomagnetic Navigation","authors":"A. Cuenca, H. Moncayo","doi":"10.1109/PLANS53410.2023.10140131","DOIUrl":"https://doi.org/10.1109/PLANS53410.2023.10140131","url":null,"abstract":"","PeriodicalId":94036,"journal":{"name":"IEEE/ION Position Location and Navigation Symposium : [proceedings]. IEEE/ION Position Location and Navigation Symposium","volume":"16 1","pages":"143-149"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88303701","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-01-01DOI: 10.1109/PLANS46316.2020.9109889
Yanyan Wang, Ya Zhang, Kai Wang, Zhuo Wang, Jiachong Chang, Dingjie Xu
{"title":"Research on multi-model adaptive hull deformation measurement algorithm","authors":"Yanyan Wang, Ya Zhang, Kai Wang, Zhuo Wang, Jiachong Chang, Dingjie Xu","doi":"10.1109/PLANS46316.2020.9109889","DOIUrl":"https://doi.org/10.1109/PLANS46316.2020.9109889","url":null,"abstract":"","PeriodicalId":94036,"journal":{"name":"IEEE/ION Position Location and Navigation Symposium : [proceedings]. IEEE/ION Position Location and Navigation Symposium","volume":"54 1","pages":"718-722"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86154510","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Research on pedestrian location based on dual MIMU/magnetometer/ultrasonic module","authors":"Qiuying Wang, Zheng Guo, Minghui Zhang, Xufei Cui, Hui Wu, Li Jia","doi":"10.1109/PLANS.2018.8373428","DOIUrl":"https://doi.org/10.1109/PLANS.2018.8373428","url":null,"abstract":"","PeriodicalId":94036,"journal":{"name":"IEEE/ION Position Location and Navigation Symposium : [proceedings]. IEEE/ION Position Location and Navigation Symposium","volume":"76 1","pages":"565-570"},"PeriodicalIF":0.0,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73026605","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-01-01DOI: 10.1109/PLANS.2018.8373357
A. Matthews
{"title":"The operation and mechanization of the hemispherical resonator gyroscope","authors":"A. Matthews","doi":"10.1109/PLANS.2018.8373357","DOIUrl":"https://doi.org/10.1109/PLANS.2018.8373357","url":null,"abstract":"","PeriodicalId":94036,"journal":{"name":"IEEE/ION Position Location and Navigation Symposium : [proceedings]. IEEE/ION Position Location and Navigation Symposium","volume":"148 1","pages":"7-14"},"PeriodicalIF":0.0,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77844482","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-01-01DOI: 10.1109/PLANS.2018.8373462
Qiuying Wang, Minghui Zhang, Zheng Guo, Hui Wu
{"title":"Integrated navigation method using marine inertial navigation system and star sensor based on model predictive filtering","authors":"Qiuying Wang, Minghui Zhang, Zheng Guo, Hui Wu","doi":"10.1109/PLANS.2018.8373462","DOIUrl":"https://doi.org/10.1109/PLANS.2018.8373462","url":null,"abstract":"","PeriodicalId":94036,"journal":{"name":"IEEE/ION Position Location and Navigation Symposium : [proceedings]. IEEE/ION Position Location and Navigation Symposium","volume":"15 1","pages":"850-857"},"PeriodicalIF":0.0,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84625793","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-04-11DOI: 10.1109/PLANS.2016.7479710
R. Hang, Zhang Lei, Liu Shuo, Liu Feng
Mitigating carrier phase multipath errors continue to be a significant challenge for Real Time Kinematic (RTK) using Global Navigation Satellite Systems (GNSS). As the multipath error is dependent on the operational environment and therefore cannot be mitigated by differencing techniques, we propose a Multiple Carrier Correlators based carrier phase multipath mitigation technique (MCC) for the high precise positioning in the environment with multipath. In the method, two extra carrier correlators at the phases of pi/4 and −pi/4 are introduced and a maximum likelihood estimator is developed based on these correlator outputs to estimate the carrier phase of the line-of-sight signal. A Monte Carlo simulation is carried out to evaluate the carrier phase measuring accuracy of the proposed method under multipath. The experiments using real GNSS signal data are also conducted in a certain multipath scenario and an urban canyon area with random multipath. The results show the proposed method can greatly improve the performance of Real Time Kinematic positioning in the situation with multipath and outperforms the traditional multicorrelator based carrier phase multipath mitigation method.
{"title":"Multiple carrier correlators based carrier phase multipath mitigation technique for real time kinematic","authors":"R. Hang, Zhang Lei, Liu Shuo, Liu Feng","doi":"10.1109/PLANS.2016.7479710","DOIUrl":"https://doi.org/10.1109/PLANS.2016.7479710","url":null,"abstract":"Mitigating carrier phase multipath errors continue to be a significant challenge for Real Time Kinematic (RTK) using Global Navigation Satellite Systems (GNSS). As the multipath error is dependent on the operational environment and therefore cannot be mitigated by differencing techniques, we propose a Multiple Carrier Correlators based carrier phase multipath mitigation technique (MCC) for the high precise positioning in the environment with multipath. In the method, two extra carrier correlators at the phases of pi/4 and −pi/4 are introduced and a maximum likelihood estimator is developed based on these correlator outputs to estimate the carrier phase of the line-of-sight signal. A Monte Carlo simulation is carried out to evaluate the carrier phase measuring accuracy of the proposed method under multipath. The experiments using real GNSS signal data are also conducted in a certain multipath scenario and an urban canyon area with random multipath. The results show the proposed method can greatly improve the performance of Real Time Kinematic positioning in the situation with multipath and outperforms the traditional multicorrelator based carrier phase multipath mitigation method.","PeriodicalId":94036,"journal":{"name":"IEEE/ION Position Location and Navigation Symposium : [proceedings]. IEEE/ION Position Location and Navigation Symposium","volume":"33 1","pages":"263-271"},"PeriodicalIF":0.0,"publicationDate":"2016-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85436938","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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