Distributed observers for pose estimation in the presence of inertial sensory soft faults

• Verfasst von: Sadeghzadeh Nokhodberiz, Nargess [VerfasserIn]
• Verfasst von: Poshtan, Javad [VerfasserIn]
• Verfasst von: Wagner, Achim [VerfasserIn]
• Verfasst von: Nordheimer, Eugen [VerfasserIn]
• Verfasst von: Badreddin, Essameddin [VerfasserIn]
• Titel: Distributed observers for pose estimation in the presence of inertial sensory soft faults
• Verf.angabe: Nargess Sadeghzadeh-Nokhodberiz, Javad Poshtan, Achim Wagner, Eugen Nordheimer, Essameddin Badreddin
• E-Jahr: 2014
• Jahr: 20 May 2014
• Umfang: 13 S.
• Fussnoten: Gesehen am 11.04.2019
• Titel Quelle: Enthalten in: Instrumentation, Systems, and Automation SocietyISA transactions
• Ort Quelle: Amsterdam [u.a.] : Elsevier, 1989
• Jahr Quelle: 2014
• Band/Heft Quelle: 53(2014), 4, Seite 1307-1319
• ISSN Quelle: 1879-2022
• DOI: doi:10.1016/j.isatra.2014.04.002
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: Kalman Filtering
• Sach-SW: Large Scale Systems
• Sach-SW: MEMS IMU
• Sach-SW: Particle Filtering
• Sach-SW: Photogrammetry
• Sach-SW: Pose Estimation
• Sach-SW: Sensor fault diagnosis
• Sach-SW: Sensor fusion
• Sach-SW: System decomposition
• Sach-SW: Vision based navigation
• K10plus-PPN: 1663113858

Abstract

Distributed Particle-Kalman Filter based observers are designed in this paper for inertial sensors (gyroscope and accelerometer) soft faults (biases and drifts) and rigid body pose estimation. The observers fuse inertial sensors with Photogrammetric camera. Linear and angular accelerations as unknown inputs of velocity and attitude rate dynamics, respectively, along with sensory biases and drifts are modeled and augmented to the moving body state parameters. To reduce the complexity of the high dimensional and nonlinear model, the graph theoretic tearing technique (structural decomposition) is employed to decompose the system to smaller observable subsystems. Separate interacting observers are designed for the subsystems which are interacted through well-defined interfaces. Kalman Filters are employed for linear ones and a Modified Particle Filter for a nonlinear non-Gaussian subsystem which includes imperfect attitude rate dynamics is proposed. The main idea behind the proposed Modified Particle Filtering approach is to engage both system and measurement models in the particle generation process. Experimental results based on data from a 3D MEMS IMU and a 3D camera system are used to demonstrate the efficiency of the method.