A Rao-Blackwellised particle filter-based likelihood ratio approach to fault diagnosis for linear stochastic systems

2009 ◽  
Author(s):  
Ping Li ◽  
Ian Postlethwaite ◽  
Visakan Kadirkamanathan ◽  
Michael Z. Q. Chen
Keyword(s):  
Fault Diagnosis ◽  
Particle Filter ◽  
Likelihood Ratio ◽  
Stochastic Systems ◽  
Linear Stochastic Systems ◽  
Ratio Approach

Steady-State Marginalized Particle Filter for Attitude Estimation

2014 ◽  
Author(s):  
Yizhou Wang ◽  
Dennis Wai ◽  
Masayoshi Tomizuka
Keyword(s):  
Kalman Filter ◽  
Steady State ◽  
Particle Filter ◽  
Stochastic Systems ◽  
Linear Part ◽  
Linear Structure ◽  
Attitude Estimation ◽  
Computational Time ◽  
Unit Quaternions ◽  
Linear Stochastic Systems

A marginalized particle filter (MPF) is designed for attitude estimation problem. Unit quaternions are used to parameterize rotations. The linear structure in the gyroscope bias dynamics enables us to completely decouple its evolution from quaternion particles. We further show that the linear part of the proposed MPF reaches a steady state, similar to what Kalman filter does for controllable and observable linear stochastic systems. Although the steady-state MPF is similar to the particle filter in structure, it has two advantages: (i) the theoretical superiority of marginalizing linear substructure, and (ii) the reduction in total computational time. Numerical simulations are performed to demonstrated the performance of the proposed filter.

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