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Performance Analyses of a RAIM Algorithm for Kalman Filter with GPS and NavIC Constellations

This paper evaluates the performance of an integrity monitoring algorithm of global navigation satellite systems (GNSS) for the Kalman filter (KF), termed KF receiver autonomous integrity monitoring (RAIM). The algorithm checks measurement inconsistencies in the range domain and requires Schmidt KF...

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Autor principal: Bhattacharyya, Susmita
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8708194/
https://www.ncbi.nlm.nih.gov/pubmed/34960531
http://dx.doi.org/10.3390/s21248441
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author Bhattacharyya, Susmita
author_facet Bhattacharyya, Susmita
author_sort Bhattacharyya, Susmita
collection PubMed
description This paper evaluates the performance of an integrity monitoring algorithm of global navigation satellite systems (GNSS) for the Kalman filter (KF), termed KF receiver autonomous integrity monitoring (RAIM). The algorithm checks measurement inconsistencies in the range domain and requires Schmidt KF (SKF) as the navigation processor. First, realistic carrier-smoothed pseudorange measurement error models of GNSS are integrated into KF RAIM, overcoming an important limitation of prior work. More precisely, the error covariance matrix for fault detection is modified to capture the temporal variations of individual errors with different time constants. Uncertainties of the model parameters are also taken into account. Performance of the modified KF RAIM is then analyzed with the simulated signals of the global positioning system and navigation with Indian constellation for different phases of aircraft flight. Weighted least squares (WLS) RAIM used for comparison purposes is shown to have lower protection levels. This work, however, is important because KF-based integrity monitors are required to ensure the reliability of advanced navigation methods, such as multi-sensor integration and vector receivers. A key finding of the performance analyses is as follows. Innovation-based tests with an extended KF navigation processor confuse slow ramp faults with residual measurement errors that the filter estimates, leading to missed detection. RAIM with SKF, on the other hand, can successfully detect such faults. Thus, it offers a promising solution to developing KF integrity monitoring algorithms in the range domain. The modified KF RAIM completes processing in time on a low-end computer. Some salient features are also studied to gain insights into its working principles.
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spelling pubmed-87081942021-12-25 Performance Analyses of a RAIM Algorithm for Kalman Filter with GPS and NavIC Constellations Bhattacharyya, Susmita Sensors (Basel) Article This paper evaluates the performance of an integrity monitoring algorithm of global navigation satellite systems (GNSS) for the Kalman filter (KF), termed KF receiver autonomous integrity monitoring (RAIM). The algorithm checks measurement inconsistencies in the range domain and requires Schmidt KF (SKF) as the navigation processor. First, realistic carrier-smoothed pseudorange measurement error models of GNSS are integrated into KF RAIM, overcoming an important limitation of prior work. More precisely, the error covariance matrix for fault detection is modified to capture the temporal variations of individual errors with different time constants. Uncertainties of the model parameters are also taken into account. Performance of the modified KF RAIM is then analyzed with the simulated signals of the global positioning system and navigation with Indian constellation for different phases of aircraft flight. Weighted least squares (WLS) RAIM used for comparison purposes is shown to have lower protection levels. This work, however, is important because KF-based integrity monitors are required to ensure the reliability of advanced navigation methods, such as multi-sensor integration and vector receivers. A key finding of the performance analyses is as follows. Innovation-based tests with an extended KF navigation processor confuse slow ramp faults with residual measurement errors that the filter estimates, leading to missed detection. RAIM with SKF, on the other hand, can successfully detect such faults. Thus, it offers a promising solution to developing KF integrity monitoring algorithms in the range domain. The modified KF RAIM completes processing in time on a low-end computer. Some salient features are also studied to gain insights into its working principles. MDPI 2021-12-17 /pmc/articles/PMC8708194/ /pubmed/34960531 http://dx.doi.org/10.3390/s21248441 Text en © 2021 by the author. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Bhattacharyya, Susmita
Performance Analyses of a RAIM Algorithm for Kalman Filter with GPS and NavIC Constellations
title Performance Analyses of a RAIM Algorithm for Kalman Filter with GPS and NavIC Constellations
title_full Performance Analyses of a RAIM Algorithm for Kalman Filter with GPS and NavIC Constellations
title_fullStr Performance Analyses of a RAIM Algorithm for Kalman Filter with GPS and NavIC Constellations
title_full_unstemmed Performance Analyses of a RAIM Algorithm for Kalman Filter with GPS and NavIC Constellations
title_short Performance Analyses of a RAIM Algorithm for Kalman Filter with GPS and NavIC Constellations
title_sort performance analyses of a raim algorithm for kalman filter with gps and navic constellations
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8708194/
https://www.ncbi.nlm.nih.gov/pubmed/34960531
http://dx.doi.org/10.3390/s21248441
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