GPS Solutions最新文献

Detection of ionospheric disturbances with a sparse GNSS network in simulated near-real time Mw 7.8 and Mw 7.5 Kahramanmaraş earthquake sequence.

IF 4.5 1区地球科学

GPS Solutions Pub Date : 2025-01-01 Epub Date: 2025-01-18 DOI: 10.1007/s10291-024-01808-2

F Luhrmann, J Park, W-K Wong, L Martire, S Krishnamoorthy, A Komjáthy

{"title":"Detection of ionospheric disturbances with a sparse GNSS network in simulated near-real time Mw 7.8 and Mw 7.5 Kahramanmaraş earthquake sequence.","authors":"F Luhrmann, J Park, W-K Wong, L Martire, S Krishnamoorthy, A Komjáthy","doi":"10.1007/s10291-024-01808-2","DOIUrl":"10.1007/s10291-024-01808-2","url":null,"abstract":"On February 6, 2023 the Kahramanmaraş Earthquake Sequence caused significant ground shaking and catastrophic losses across south-central Türkiye and northwest Syria. These seismic events produced ionospheric perturbations detectable in Global Navigation Satellite System (GNSS) total electron content (TEC) measurements. This work aims to develop and incorporate a near-real-time (NRT) ionospheric disturbance detection method into JPL's GUARDIAN system. Our method uses a Long Short-Term Memory (LSTM) neural network to detect anomalous ionospheric behavior, such as co-seismic ionospheric disturbances among others. Our method detected an anomalous signature after the second <math><msub><mi>M</mi> <mi>w</mi></msub> </math> 7.5 earthquake at 10:24:48 UTC (13:24 local time) but did not alert after the first <math><msub><mi>M</mi> <mi>w</mi></msub> </math> 7.8 earthquake at 01:17:34 UTC (04:17 local time), which had a visible disturbance of smaller amplitude likely due to lower ionization levels at night and potentially the multi-source mechanism of the slip. Plain Language Summary Seismic activity, including the destructive Kahramanmaraş Earthquake Sequence on February 6, 2023 in the Republic of Türkiye, result in vertical ground displacement that cause atmospheric waves. These waves propagate upwards to the outer atmosphere, disturbing the ionospheric electron content. This disturbance impacts the signals broadcast by positioning satellites (such as GPS) and received by ground-based receivers. If the receiver position is known, the impact to these signals can be used to measure the electron density disturbance caused by these seismically-induced atmospheric waves. Such studies usually rely on being aware of the event a priori. Using deep learning neural networks, we instead aim to detect anomalous signals automatically. We propose to utilise this method to detect seismically-induced disturbances over a large geographical area. The detection method proposed in this paper successfully detected an anomalous event in the ionosphere approximately ten minutes after the second earthquake in the Kahramanmaraş Earthquake Sequence.Supplementary information: The online version contains supplementary material available at 10.1007/s10291-024-01808-2.","PeriodicalId":12788,"journal":{"name":"GPS Solutions","volume":"29 1","pages":"54"},"PeriodicalIF":4.5,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11839748/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143482106","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Performance of ambiguity-resolved detector for GNSS mixed-integer model.

IF 4.5 1区地球科学

GPS Solutions Pub Date : 2025-01-01 Epub Date: 2025-03-08 DOI: 10.1007/s10291-024-01806-4

Chengyu Yin, P J G Teunissen, C C J M Tiberius

{"title":"Performance of ambiguity-resolved detector for GNSS mixed-integer model.","authors":"Chengyu Yin, P J G Teunissen, C C J M Tiberius","doi":"10.1007/s10291-024-01806-4","DOIUrl":"10.1007/s10291-024-01806-4","url":null,"abstract":"Teunissen (J Geod 98(83):1-16, 2024) proposed the ambiguity-resolved (AR) detection theory for GNSS mixed-integer model validation. In this contribution, we study the performance of the AR detector through analysis and simulation experiments and compare it with the ambiguity-float (AF) and ambiguity-known (AK) detectors. We describe how the detectors can be implemented and how to evaluate their performance by computing the power as functions of the model misspecifications' size. We present two simulation experiments with single- and dual-frequency GPS models and demonstrate that the AR detector can provide a larger detection power than the AF detector, even if the success rate is not close to one. Then, we obtain power functions over 25 user locations with five observation models and 72 satellite geometries per location per model. We find that the AR detector increases the detection probability of ionosphere and troposphere delays by 47% and 60% on average when the success rate is larger than 97.5% and the level of significance is 0.01. We also find the AR detection power to be larger than that of the AF detector in case of multi-dimensional misspecifications.","PeriodicalId":12788,"journal":{"name":"GPS Solutions","volume":"29 2","pages":"76"},"PeriodicalIF":4.5,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11890412/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143596737","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Detection of slowly varying spoofing using weighted Kalman gain in GNSS/INS tightly coupled systems 在 GNSS/INS 紧密耦合系统中使用加权卡尔曼增益检测缓慢变化的欺骗行为

IF 4.9 1区地球科学

GPS Solutions Pub Date : 2024-01-01 DOI: 10.1007/s10291-023-01594-3

Xiaoqin Jin, Xiaoyu Zhang, Shoupeng Li, Shuaiyong Zheng