Conference
Kalman Smoothing for better RFID Landslide Monitoring
| العنوان: | Kalman Smoothing for better RFID Landslide Monitoring |
|---|---|
| المؤلفون: | Charléty, Arthur, Michel, Olivier, J.J., Le Breton, Mathieu |
| المساهمون: | Institut des Sciences de la Terre (ISTerre), Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Savoie Mont Blanc (USMB Université de Savoie Université de Chambéry )-Centre National de la Recherche Scientifique (CNRS)-Université Gustave Eiffel-Observatoire des Sciences de l'Univers de Grenoble (Fédération OSUG)-Université Grenoble Alpes (UGA), GIPSA Pôle Géométrie, Apprentissage, Information et Algorithmes (GIPSA-GAIA), Grenoble Images Parole Signal Automatique (GIPSA-lab), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP), Université Grenoble Alpes (UGA), Géolithe Alpes, Groupe Géolithe, RISQID, CNRS |
| المصدر: | EUSIPCO 2023 - 31st European Signal Processing Conference ; https://hal.science/hal-04208382 ; EUSIPCO 2023 - 31st European Signal Processing Conference, Sep 2023, Helsinki, Finland ; https://eusipco2023.org/ |
| بيانات النشر: | CCSD |
| سنة النشر: | 2023 |
| المجموعة: | Université Grenoble Alpes: HAL |
| مصطلحات موضوعية: | RFID Kalman Landslides Sensor Fusion, RFID, Kalman, Landslides, Sensor Fusion, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, [INFO.INFO-NI]Computer Science [cs]/Networking and Internet Architecture [cs.NI], [SDU.STU.GM]Sciences of the Universe [physics]/Earth Sciences/Geomorphology, [SPI.GCIV.RISQ]Engineering Sciences [physics]/Civil Engineering/Risques |
| جغرافية الموضوع: | Helsinki, Finland |
| الوصف: | International audience ; The use of Radio-Frequency Identification (RFID) in Earth Sciences has been growing in the recent years, notably for landslide monitoring using phase-of-arrival localization schemes. In this article, an Extended Kalman Filtering approach is presented to exploit RFID phase data for landslide displacement monitoring. The filtering is based on a stochastic Langevin equation for the state-space model, introducing a heuristic coupling based on the mechanical continuity of the landslide material. This helps correct measurement biases and deal with missing data in the tracking of multiple tags. The Kalman state covariance matrix is a useful indicator of the tags localization quality. It can be exploited to discriminate true displacements from multipathinduced artifacts. Phase unwrapping is performed implicitly through the state model. |
| نوع الوثيقة: | conference object |
| اللغة: | English |
| الاتاحة: | https://hal.science/hal-04208382 https://hal.science/hal-04208382v1/document https://hal.science/hal-04208382v1/file/20230613081455_931661_1507.pdf |
| Rights: | info:eu-repo/semantics/OpenAccess |
| رقم الانضمام: | edsbas.E5F146D7 |
| قاعدة البيانات: | BASE |
| الوصف غير متاح. |