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INS/GNSS integration using recurrent fuzzy wavelet neural networks / Parisa Doostdar in GPS solutions, vol 24 n° 1 (January 2020)  

Public [article]  inGPS solutions > vol 24 n° 1 (January 2020) Titre : INS/GNSS integration using recurrent fuzzy wavelet neural networks Type de document : Article/Communication Auteurs : Parisa Doostdar, Auteur ; Jafar Keighobadi, Auteur ; Mohammad Ali Hamed, Auteur Année de publication : 2020 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] classification floue [Termes descripteurs IGN] données GNSS [Termes descripteurs IGN] filtre de Kalman [Termes descripteurs IGN] GNSS-INS [Termes descripteurs IGN] interruption du signal [Termes descripteurs IGN] ondelette [Termes descripteurs IGN] réseau neuronal artificiel [Termes descripteurs IGN] réseau neuronal récurrent [Termes descripteurs IGN] vitesse [Vedettes matières IGN] Traitement de données GNSS Résumé : (Auteur) In recent years, aided navigation systems through combining inertial navigation system (INS) with global navigation satellite system (GNSS) have been widely applied to enhance the position, velocity, and attitude information of autonomous vehicles. In order to gain the accuracy of the aided INS/GNSS in GNSS gap intervals, a heuristic neural network structure based on the recurrent fuzzy wavelet neural network (RFWNN) is applicable for INS velocity and position error compensation purpose. During frequent access to GNSS data, the RFWNN should be trained as a highly precise prediction model equipped with the Kalman filter algorithm. Therefore, the INS velocity and position error data are obtainable along with the lost intervals of GNSS signals. For performance assessment of the proposed RFWNN-aided INS/GNSS, real flight test data of a small commercial unmanned aerial vehicle (UAV) were conducted. A comparison of test results shows that the proposed NN algorithm could efficiently provide high-accuracy corrections on the INS velocity and position information during GNSS outages. Numéro de notice : A2020-019 Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s10291-019-0942-z date de publication en ligne : 23/12/2019 En ligne : https://doi.org/10.1007/s10291-019-0942-z Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94458 [article]

Kalman-filter-based undifferenced cycle slip estimation in real-time precise point positioning / Pan Li in GPS solutions, vol 23 n° 4 (October 2019)  

Public [article]  inGPS solutions > vol 23 n° 4 (October 2019) Titre : Kalman-filter-based undifferenced cycle slip estimation in real-time precise point positioning Type de document : Article/Communication Auteurs : Pan Li, Auteur ; Xinyuan Jiang, Auteur ; Xiaohong Zhang, Auteur ; et al., Auteur Année de publication : 2019 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie spatiale [Termes descripteurs IGN] filtre de Kalman [Termes descripteurs IGN] glissement de cycle [Termes descripteurs IGN] interruption du signal [Termes descripteurs IGN] mesurage de phase [Termes descripteurs IGN] modèle mathématique [Termes descripteurs IGN] positionnement cinématique [Termes descripteurs IGN] positionnement ponctuel précis [Termes descripteurs IGN] précision du positionnement [Termes descripteurs IGN] station GLONASS [Termes descripteurs IGN] station GPS [Termes descripteurs IGN] temps réel Résumé : (Auteur) Global navigation satellite system (GNSS) precise point positioning (PPP) requires continuous carrier-phase observations to achieve a solution of high precision. Precisely correcting cycle slips caused by signal interruptions is crucial for recovering the data continuity. Most of the existing approaches usually employ only data of one epoch after the interruption for real-time cycle slip processing. In this study, we propose to introduce and estimate cycle slip parameters together with standard PPP parameters, such as position, ionospheric delay, and ambiguities in the case that possible cycle slips are detected, using a Kalman-filter-based procedure with the undifferenced and uncombined PPP model. The integer search strategy is used to fix cycle slips. To reduce the probability of wrong integer fixing, a strict integer validation threshold is suggested. As a result, it is not easy to fix all cycle slips with only one epoch of data. Our approach can be easily extended to use multi-epoch observations to enhance the cycle slip estimation. Once the cycle slips are correctly determined, continuous PPP can be achieved instantaneously. This new approach is tested and validated with three groups of experiments using GPS and GLONASS stations operated by the International GNSS Service from DOY 1–10, 2017, and a real vehicle kinematic data. Numerous experimental results showed that the proposed method can correctly fix the cycle slips for more than 99.5% of epochs suffering from re-convergence. On average, this method takes observation information from about 1.5–2.5 epochs to fix cycle slips and realize rapid re-convergence. Consequently, positioning performance is significantly improved. Numéro de notice : A2019-334 Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s10291-019-0894-3 date de publication en ligne : 16/07/2019 En ligne : https://doi.org/10.1007/s10291-019-0894-3 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93423 [article]

Decomposition of geodetic time series: A combined simulated annealing algorithm and Kalman filter approach / Feng Ming in Advances in space research, vol 64 n°5 (1 September 2019)  

Public [article]  inAdvances in space research > vol 64 n°5 (1 September 2019) . - pp Titre : Decomposition of geodetic time series: A combined simulated annealing algorithm and Kalman filter approach Type de document : Article/Communication Auteurs : Feng Ming, Auteur ; Yuanxi Yang, Auteur ; Anmin Zeng, Auteur ; Bin Zhao, Auteur Année de publication : 2019 Article en page(s) : pp Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] algorithme du recuit simulé [Termes descripteurs IGN] bruit (théorie du signal) [Termes descripteurs IGN] bruit blanc [Termes descripteurs IGN] bruit rose [Termes descripteurs IGN] données GPS [Termes descripteurs IGN] filtre de Kalman [Termes descripteurs IGN] méthode du maximum de vraisemblance (estimation) [Termes descripteurs IGN] série temporelle [Termes descripteurs IGN] variation saisonnière [Vedettes matières IGN] Traitement de données GNSS Numéro de notice : A2019-399 Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.asr.2019.05.049 date de publication en ligne : 10/06/2019 En ligne : https://doi.org/10.1016/j.asr.2019.05.049 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93519 [article]

Robust M–M unscented Kalman filtering for GPS/IMU navigation / Cheng Yang in Journal of geodesy, vol 93 n° 8 (August 2019)  

Public [article]  inJournal of geodesy > vol 93 n° 8 (August 2019) . - pp 1093 - 1104 Titre : Robust M–M unscented Kalman filtering for GPS/IMU navigation Type de document : Article/Communication Auteurs : Cheng Yang, Auteur ; Wenzhong Shi, Auteur ; Wu Chen, Auteur Année de publication : 2019 Article en page(s) : pp 1093 - 1104 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Navigation et positionnement [Termes descripteurs IGN] coefficient de corrélation [Termes descripteurs IGN] erreur de mesure [Termes descripteurs IGN] erreur de modèle [Termes descripteurs IGN] filtre de Kalman [Termes descripteurs IGN] GNSS-INS [Termes descripteurs IGN] matrice de covariance [Termes descripteurs IGN] méthode du maximum de vraisemblance (estimation) [Termes descripteurs IGN] méthode robuste [Termes descripteurs IGN] modèle non linéaire Résumé : (auteur) In this paper, a robust unscented Kalman filter (UKF) based on the generalized maximum likelihood estimation (M-estimation) is proposed to improve the robustness of the integrated navigation system of Global Navigation Satellite System and Inertial Measurement Unit. The UKF is a variation of Kalman filter by which the Jacobian matrix calculation in a nonlinear system state model is not necessary. The proposed robust M–M unscented Kalman filter (RMUKF) applies the M-estimation principle to both functional model errors and measurement errors. Hence, this robust filter attenuates the influences of disturbances in the dynamic model and of measurement outliers without linearizing the nonlinear state space model. In addition, an equivalent weight matrix, composed of the bi-factor shrink elements, is proposed in order to keep the original correlation coefficients of the predicted state unchanged. Furthermore, a nonlinear error model is used as the dynamic equation to verify the performance of the proposed RMUKF with a simulation and field test. Compared with the conventional UKF, the impacts of measurement outliers and system disturbances on the state estimation are both controlled by RMUKF. Numéro de notice : A2019-370 Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s00190-018-01227-5 date de publication en ligne : 22/01/2019 En ligne : https://doi.org/10.1007/s00190-018-01227-5 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93455 [article]

On the detectability of mis-modeled biases in the network-derived positioning corrections and their user impact / Amir Khodabandeh in GPS solutions, vol 23 n° 3 (July 2019)  

Public [article]  inGPS solutions > vol 23 n° 3 (July 2019) Titre : On the detectability of mis-modeled biases in the network-derived positioning corrections and their user impact Type de document : Article/Communication Auteurs : Amir Khodabandeh, Auteur ; Jinling Wang, Auteur ; Chris Rizos, Auteur ; Ahmed El-Mowafy, Auteur Année de publication : 2019 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Navigation et positionnement [Termes descripteurs IGN] correction [Termes descripteurs IGN] filtre de Kalman [Termes descripteurs IGN] inférence statistique [Termes descripteurs IGN] intégrité des données [Termes descripteurs IGN] modèle linéaire [Termes descripteurs IGN] positionnement cinématique en temps réel [Termes descripteurs IGN] positionnement par GNSS [Termes descripteurs IGN] positionnement ponctuel précis [Termes descripteurs IGN] précision du positionnement [Termes descripteurs IGN] Receiver Autonomous Integrity Monitoring [Termes descripteurs IGN] traitement de données GNSS Résumé : (auteur) High-precision single-receiver positioning requires the provision of reliable network-derived corrections. Care must therefore be exercised to continuously check the quality of the corrections and to detect the possible presence of mis-modeled biases in the network data. In network-RTK or its state-space implementation, PPP-RTK, quality control of the solutions is executed in two separate phases: the network component and the user component. Once confidence in the network-derived solutions is declared, a subset of the solutions is sent as corrections to a single-receiver user, thereby allowing the user to separately check the integrity of his network-aided model. In such a two-step integrity monitoring procedure, an intermediate step is missing, the integrity monitoring of the corrections themselves. It is the goal of this contribution to provide a quality control procedure for GNSS parameter solutions at the correction level, and to measure the impact a missed detection bias has on the (ambiguity-resolved) user position. New detection test statistics are derived with which the single-receiver user can check the overall validity of the corrections even before applying them to his data. A small-scale network of receivers is utilized to provide numerical insights into the detectability of mis-modeled biases using the proposed detectors and to analyze the impact of such biases on the user positioning performance. Numéro de notice : A2019-195 Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s10291-019-0863-x date de publication en ligne : 14/05/2019 En ligne : https://doi.org/10.1007/s10291-019-0863-x Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92649 [article]

Real-time sea-level monitoring using Kalman filtering of GNSS-R data / Joakim Strandberg in GPS solutions, vol 23 n° 3 (July 2019)  

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An improved robust Kalman filtering strategy for GNSS kinematic positioning considering small cycle slips / Wanke Liu in Advances in space research, vol 63 n° 9 (1 May 2019)  

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Real-time GPS satellite orbit and clock estimation based on OpenMP / Kaifa Kuang in Advances in space research, vol 63 n° 8 (15 April 2019)  

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BIM-Tracker: A model-based visual tracking approach for indoor localisation using a 3D building model / Debaditya Acharya in ISPRS Journal of photogrammetry and remote sensing, vol 150 (April 2019)  

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On the assimilation of absolute geodetic dynamic topography in a global ocean model: impact on the deep ocean state / Alexey Androsov in Journal of geodesy, vol 93 n° 2 (February 2019)  

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On constrained integrated total Kalman filter for integrated direct geo-referencing / Vahid Mahboub in Survey review, vol 51 n° 364 (January 2019)  

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Differential positioning based on the orthogonal transformation algorithm with GNSS multi-system / Xiao Liang in GPS solutions, vol 22 n° 3 (July 2018)  

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GPS receiver phase biases estimable in PPP-RTK networks : dynamic characterization and impact analysis / Baocheng Zhang in Journal of geodesy, vol 92 n° 6 (June 2018)  

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Design and implementation of a model predictive observer for AHRS / Jafar Keighobadi in GPS solutions, vol 22 n° 1 (January 2018)  

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Tight integration of INS/Stereo VO/Digital map for land vehicle navigation / Fei Liu in Photogrammetric Engineering & Remote Sensing, PERS, vol 84 n° 1 (January 2018)  

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