A Novel Approach for Kalman Filter Tuning for Direct and Indirect Inertial Navigation System/Global Navigation Satellite System Integration
This work presents an innovative approach for tuning the Kalman filter in INS/GNSS integration, combining states from the inertial navigation system (INS) and data from the Global Navigation Satellite System (GNSS) to enhance navigation accuracy. The INS uses measurements from accelerometers and gyr...
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MDPI AG
2024-11-01
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| Online Access: | https://www.mdpi.com/1424-8220/24/22/7331 |
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| author | Adalberto J. A. Tavares Jr. Neusa M. F. Oliveira |
| author_facet | Adalberto J. A. Tavares Jr. Neusa M. F. Oliveira |
| author_sort | Adalberto J. A. Tavares Jr. |
| collection | DOAJ |
| description | This work presents an innovative approach for tuning the Kalman filter in INS/GNSS integration, combining states from the inertial navigation system (INS) and data from the Global Navigation Satellite System (GNSS) to enhance navigation accuracy. The INS uses measurements from accelerometers and gyroscopes, which are subject to uncertainties in scale factor, misalignment, non-orthogonality, and bias, as well as temporal, thermal, and vibration variations. The GNSS receiver faces challenges such as multipath, temporary signal loss, and susceptibility to high-frequency noise. The novel approach for Kalman filter tuning involves previously performing Monte Carlo simulations using ideal data from a predetermined trajectory, applying the inertial sensor error model. For the indirect filter, errors from inertial sensors are used, while, for the direct filter, navigation errors in position, velocity, and attitude are also considered to obtain the process noise covariance matrix Q. This methodology is tested and validated with real data from Castro Leite Consultoria’s commercial platforms, PINA-F and PINA-M. The results demonstrate the efficiency and consistency of the estimation technique, highlighting its applicability in real scenarios. |
| format | Article |
| id | doaj-art-e56b1a2194ad4804bca1cd2cc084a8b1 |
| institution | OA Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
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| series | Sensors |
| spelling | doaj-art-e56b1a2194ad4804bca1cd2cc084a8b12025-08-20T01:54:08ZengMDPI AGSensors1424-82202024-11-012422733110.3390/s24227331A Novel Approach for Kalman Filter Tuning for Direct and Indirect Inertial Navigation System/Global Navigation Satellite System IntegrationAdalberto J. A. Tavares Jr.0Neusa M. F. Oliveira1Electronic Engineering Division, Aeronautics Institute of Technology, São José dos Campos 12228-900, BrazilElectronic Engineering Division, Aeronautics Institute of Technology, São José dos Campos 12228-900, BrazilThis work presents an innovative approach for tuning the Kalman filter in INS/GNSS integration, combining states from the inertial navigation system (INS) and data from the Global Navigation Satellite System (GNSS) to enhance navigation accuracy. The INS uses measurements from accelerometers and gyroscopes, which are subject to uncertainties in scale factor, misalignment, non-orthogonality, and bias, as well as temporal, thermal, and vibration variations. The GNSS receiver faces challenges such as multipath, temporary signal loss, and susceptibility to high-frequency noise. The novel approach for Kalman filter tuning involves previously performing Monte Carlo simulations using ideal data from a predetermined trajectory, applying the inertial sensor error model. For the indirect filter, errors from inertial sensors are used, while, for the direct filter, navigation errors in position, velocity, and attitude are also considered to obtain the process noise covariance matrix Q. This methodology is tested and validated with real data from Castro Leite Consultoria’s commercial platforms, PINA-F and PINA-M. The results demonstrate the efficiency and consistency of the estimation technique, highlighting its applicability in real scenarios.https://www.mdpi.com/1424-8220/24/22/7331Kalman filter tuningINS/GNSS integrationinertial sensor error model |
| spellingShingle | Adalberto J. A. Tavares Jr. Neusa M. F. Oliveira A Novel Approach for Kalman Filter Tuning for Direct and Indirect Inertial Navigation System/Global Navigation Satellite System Integration Sensors Kalman filter tuning INS/GNSS integration inertial sensor error model |
| title | A Novel Approach for Kalman Filter Tuning for Direct and Indirect Inertial Navigation System/Global Navigation Satellite System Integration |
| title_full | A Novel Approach for Kalman Filter Tuning for Direct and Indirect Inertial Navigation System/Global Navigation Satellite System Integration |
| title_fullStr | A Novel Approach for Kalman Filter Tuning for Direct and Indirect Inertial Navigation System/Global Navigation Satellite System Integration |
| title_full_unstemmed | A Novel Approach for Kalman Filter Tuning for Direct and Indirect Inertial Navigation System/Global Navigation Satellite System Integration |
| title_short | A Novel Approach for Kalman Filter Tuning for Direct and Indirect Inertial Navigation System/Global Navigation Satellite System Integration |
| title_sort | novel approach for kalman filter tuning for direct and indirect inertial navigation system global navigation satellite system integration |
| topic | Kalman filter tuning INS/GNSS integration inertial sensor error model |
| url | https://www.mdpi.com/1424-8220/24/22/7331 |
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