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An algorithm to assist the robust filter for tightly coupled RTK/INS navigation system / Zun Niu in Remote sensing, vol 14 n° 10 (May-2 2022)  

Public [article]  inRemote sensing > vol 14 n° 10 (May-2 2022) . - n° 2449 Titre : An algorithm to assist the robust filter for tightly coupled RTK/INS navigation system Type de document : Article/Communication Auteurs : Zun Niu, Auteur ; Guangchen Li, Auteur ; Fugui Guo, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 2449 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Navigation et positionnement [Termes IGN] C++ [Termes IGN] centrale inertielle [Termes IGN] erreur de positionnement [Termes IGN] filtre de Kalman [Termes IGN] implémentation (informatique) [Termes IGN] matrice de covariance [Termes IGN] positionnement cinématique en temps réel [Termes IGN] précision du positionnement [Termes IGN] rapport signal sur bruit [Termes IGN] valeur aberrante Résumé : (auteur) The Real-Time Kinematic (RTK) positioning algorithm is a promising positioning technique that can provide real-time centimeter-level positioning precision in GNSS-friendly areas. However, the performance of RTK can degrade in GNSS-hostile areas like urban canyons. The surrounding buildings and trees can reflect and block the Global Navigation Satellite System (GNSS) signals, obstructing GNSS receivers’ ability to maintain signal tracking and exacerbating the multipath effect. A common method to assist RTK is to couple RTK with the Inertial Navigation System (INS). INS can provide accurate short-term relative positioning results. The Extended Kalman Filter (EKF) is usually used to couple RTK with INS, whereas the GNSS outlying observations significantly influence the performance. The Robust Kalman Filter (RKF) is developed to offer resilience against outliers. In this study, we design an algorithm to improve the traditional RKF. We begin by implementing the tightly coupled RTK/INS algorithm and the conventional RKF in C++. We also introduce our specific implementation in detail. Then, we test and analyze the performance of our codes on public datasets. Finally, we propose a novel algorithm to improve RKF and test the improvement. We introduce the Carrier-to-Noise Ratio (CNR) to help detect outliers that should be discarded. The results of the tests show that our new algorithm’s accuracy is improved when compared to the traditional RKF. We also open source the majority of our code, as we find there are few open-source projects for coupled RTK/INS in C++. Researchers can access the codes at our GitHub. Numéro de notice : A2022-401 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.3390/rs14102449 Date de publication en ligne : 20/05/2022 En ligne : https://doi.org/10.3390/rs14102449 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100704 [article]