A Particle Filtering Approach for Fault Detection and Isolation of UAV IMU Sensors: Design, Implementation and Sensitivity Analysis
Sensor fault detection and isolation (SFDI) is a fundamental topic in unmanned aerial vehicle (UAV) development, where attitude estimation plays a key role in flight control systems and its accuracy is crucial for UAV reliability. In commercial drones with low maximum take-off weights, typical redun...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-04-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/9/3066 |
| _version_ | 1797535978161700864 |
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| author | Egidio D’Amato Vito Antonio Nardi Immacolata Notaro Valerio Scordamaglia |
| author_facet | Egidio D’Amato Vito Antonio Nardi Immacolata Notaro Valerio Scordamaglia |
| author_sort | Egidio D’Amato |
| collection | DOAJ |
| description | Sensor fault detection and isolation (SFDI) is a fundamental topic in unmanned aerial vehicle (UAV) development, where attitude estimation plays a key role in flight control systems and its accuracy is crucial for UAV reliability. In commercial drones with low maximum take-off weights, typical redundant architectures, based on triplex, can represent a strong limitation in UAV payload capabilities. This paper proposes an FDI algorithm for low-cost multi-rotor drones equipped with duplex sensor architecture. Here, attitude estimation involves two 9-DoF inertial measurement units (IMUs) including 3-axis accelerometers, gyroscopes and magnetometers. The SFDI algorithm is based on a particle filter approach to promptly detect and isolate IMU faulted sensors. The algorithm has been implemented on a low-cost embedded platform based on a Raspberry Pi board. Its effectiveness and robustness were proved through experimental tests involving realistic faults on a real tri-rotor aircraft. A sensitivity analysis was carried out on the main algorithm parameters in order to find a trade-off between performance, computational burden and reliability. |
| first_indexed | 2024-03-10T11:53:03Z |
| format | Article |
| id | doaj.art-68493ed8efe84f4cb167d991c0f0f431 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T11:53:03Z |
| publishDate | 2021-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-68493ed8efe84f4cb167d991c0f0f4312023-11-21T17:32:05ZengMDPI AGSensors1424-82202021-04-01219306610.3390/s21093066A Particle Filtering Approach for Fault Detection and Isolation of UAV IMU Sensors: Design, Implementation and Sensitivity AnalysisEgidio D’Amato0Vito Antonio Nardi1Immacolata Notaro2Valerio Scordamaglia3Dipartimento di Scienze e Tecnologie, Universitá degli Studi di Napoli “Parthenope”, 80143 Napoli, ItalyDipartimento di Ingegneria dell’Informazione, delle Infrastrutture e dell’Energia Sostenibile, Universitá degli Studi “Mediterranea” di Reggio Calabria, 89122 Reggio Calabria, ItalyDipartimento di Ingegneria, Universitá degli Studi della Campania “L.Vanvitelli”, 81031 Aversa, ItalyDipartimento di Ingegneria dell’Informazione, delle Infrastrutture e dell’Energia Sostenibile, Universitá degli Studi “Mediterranea” di Reggio Calabria, 89122 Reggio Calabria, ItalySensor fault detection and isolation (SFDI) is a fundamental topic in unmanned aerial vehicle (UAV) development, where attitude estimation plays a key role in flight control systems and its accuracy is crucial for UAV reliability. In commercial drones with low maximum take-off weights, typical redundant architectures, based on triplex, can represent a strong limitation in UAV payload capabilities. This paper proposes an FDI algorithm for low-cost multi-rotor drones equipped with duplex sensor architecture. Here, attitude estimation involves two 9-DoF inertial measurement units (IMUs) including 3-axis accelerometers, gyroscopes and magnetometers. The SFDI algorithm is based on a particle filter approach to promptly detect and isolate IMU faulted sensors. The algorithm has been implemented on a low-cost embedded platform based on a Raspberry Pi board. Its effectiveness and robustness were proved through experimental tests involving realistic faults on a real tri-rotor aircraft. A sensitivity analysis was carried out on the main algorithm parameters in order to find a trade-off between performance, computational burden and reliability.https://www.mdpi.com/1424-8220/21/9/3066fault detection and isolationparticle filterUAV fault detectionFDIfault tolerant attitude estimationduplex attitude estimation architecture |
| spellingShingle | Egidio D’Amato Vito Antonio Nardi Immacolata Notaro Valerio Scordamaglia A Particle Filtering Approach for Fault Detection and Isolation of UAV IMU Sensors: Design, Implementation and Sensitivity Analysis Sensors fault detection and isolation particle filter UAV fault detection FDI fault tolerant attitude estimation duplex attitude estimation architecture |
| title | A Particle Filtering Approach for Fault Detection and Isolation of UAV IMU Sensors: Design, Implementation and Sensitivity Analysis |
| title_full | A Particle Filtering Approach for Fault Detection and Isolation of UAV IMU Sensors: Design, Implementation and Sensitivity Analysis |
| title_fullStr | A Particle Filtering Approach for Fault Detection and Isolation of UAV IMU Sensors: Design, Implementation and Sensitivity Analysis |
| title_full_unstemmed | A Particle Filtering Approach for Fault Detection and Isolation of UAV IMU Sensors: Design, Implementation and Sensitivity Analysis |
| title_short | A Particle Filtering Approach for Fault Detection and Isolation of UAV IMU Sensors: Design, Implementation and Sensitivity Analysis |
| title_sort | particle filtering approach for fault detection and isolation of uav imu sensors design implementation and sensitivity analysis |
| topic | fault detection and isolation particle filter UAV fault detection FDI fault tolerant attitude estimation duplex attitude estimation architecture |
| url | https://www.mdpi.com/1424-8220/21/9/3066 |
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