Fault Tolerant Particle Filtering Using IMM-Based Rao-Blackwellization
I. M. Rapoport, Yaakov Oshman · AIAA Guidance, Navigation, and Control Conference and Exhibit · 2004
Fault tolerant particle flltering of a system with nonlinear dynamics and fault-prone scalar measurement channels is addressed, where each measurement channel is characterized by an additive measurement error generated by a linear scalar hybrid system. Particle flltering is an emerging method, which exploits the recent advances in computer technology by using simulation-based techniques to represent probability density functions in nonlinear, non-Gaussian systems. Since, in the system under investigation, the overall state vector includes both the main states of the system and the parameters of the measurement channels, its size can be prohibitively large for e‐cient application of ordinary particle flltering, due to the required number of particles. The Rao-Blackwellization technique is adopted herein, allowing to estimate just the main system states using a reduced-size set of particles. The parameters of each measurement channel are estimated by a separate IMM scalar fllter. A numerical example is presented, where four fault-prone rate gyros are used to estimate the angular velocity of a spacecraft and the fault parameters of the measurement channels. The new state estimator is compared with two implementations of the ordinary particle fllter, which difier by the number of particles used to represent the state distribution. The results demonstrate the superiority of the proposed algorithm over the ordinary particle fllters in terms of computational e‐ciency, which translates, in this case, to better accuracy and stability.