Automatica (Journal of IFAC)
Kalman filtering: theory and practice
Kalman filtering: theory and practice
Sensor validation using dynamic belief networks
UAI '92 Proceedings of the eighth conference on Uncertainty in Artificial Intelligence
Extending expectation propagation for graphical models
Extending expectation propagation for graphical models
IEEE Transactions on Signal Processing
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
Monte Carlo smoothing with application to audio signal enhancement
IEEE Transactions on Signal Processing
Particle filtering based likelihood ratio approach to faultdiagnosis in nonlinear stochastic systems
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Design of Intelligent Acceleration Schedules for Extending the Life of Aircraft Engines
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Hybrid estimation of complex systems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Adaptive joint detection and decoding in flat-fading channels via mixture Kalman filtering
IEEE Transactions on Information Theory
Visual tracking by fusing multiple cues with context-sensitive reliabilities
Pattern Recognition
Particle filter with multimode sampling strategy
Signal Processing
On-line anomaly detection and resilience in classifier ensembles
Pattern Recognition Letters
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Sensor fault detection and identification (FDI) is a process of detecting and validating sensor's fault status. Because FDI guarantees system reliable performance, it has received much attention recently. In this paper, we address the problem of online sensor fault identification and validation. For a physical sensor validation system, it contains transitions between sensor normal and faulty states, change of system parameters, and a fusion of noisy readings. A common dynamic state-space model with continuous state variables and observations cannot handle this problem. To circumvent this limitation, we adopt a Markov switch dynamic state-space model to simulate the system: we use discrete-state variables to model sensor states and continuous variables to track the change of the system parameters. Problems in Markov switch dynamic state-space model can be well solved by particle filters, which are popularly used in solving problems in digital communications. Among them, mixture Kalman filter (MKF) and stochastic M-algorithm (SMA) have very good performance, both in accuracy and efficiency. In this paper, we plan to incorporate these two algorithms into the sensor validation problem, and compare the effectiveness and complexity of MKF and SMA methods under different situations in the simulation with an existing algorithm-- interactive multiple models.