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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MDPI AG
2021-12-01
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| Online Access: | https://www.mdpi.com/1424-8220/21/24/8441 |
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| author | Susmita Bhattacharyya |
| author_facet | Susmita Bhattacharyya |
| author_sort | Susmita Bhattacharyya |
| collection | DOAJ |
| 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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| id | doaj.art-f623c1e09fe94767b9714a6bdde8d45e |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T03:09:13Z |
| publishDate | 2021-12-01 |
| publisher | MDPI AG |
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| spelling | doaj.art-f623c1e09fe94767b9714a6bdde8d45e2023-11-23T10:31:30ZengMDPI AGSensors1424-82202021-12-012124844110.3390/s21248441Performance Analyses of a RAIM Algorithm for Kalman Filter with GPS and NavIC ConstellationsSusmita Bhattacharyya0Department of Aerospace Engineering, Indian Institute of Technology Kharagpur, Kharagpur 721302, IndiaThis 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.https://www.mdpi.com/1424-8220/21/24/8441Kalman filterGNSSRAIMSchmidt Kalman filterweighted least squares |
| spellingShingle | Susmita Bhattacharyya Performance Analyses of a RAIM Algorithm for Kalman Filter with GPS and NavIC Constellations Sensors Kalman filter GNSS RAIM Schmidt Kalman filter weighted least squares |
| 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 | Kalman filter GNSS RAIM Schmidt Kalman filter weighted least squares |
| url | https://www.mdpi.com/1424-8220/21/24/8441 |
| work_keys_str_mv | AT susmitabhattacharyya performanceanalysesofaraimalgorithmforkalmanfilterwithgpsandnavicconstellations |