Detection of abrupt changes: theory and application
Detection of abrupt changes: theory and application
Automatica (Journal of IFAC)
Robust model-based fault diagnosis for dynamic systems
Robust model-based fault diagnosis for dynamic systems
On sequential Monte Carlo sampling methods for Bayesian filtering
Statistics and Computing
Fault Diagnosis: Models, Artificial Intelligence, Applications
Fault Diagnosis: Models, Artificial Intelligence, Applications
Architectures for efficient implementation of particle filters
Architectures for efficient implementation of particle filters
Neuro-fuzzy networks and their application to fault detection of dynamical systems
Engineering Applications of Artificial Intelligence
Analysis of parallelizable resampling algorithms for particle filtering
Signal Processing
Resampling algorithms for particle filters: a computational complexity perspective
EURASIP Journal on Applied Signal Processing
A new class of particle filters for random dynamic systems with unknown statistics
EURASIP Journal on Applied Signal Processing
Adaptive threshold computation for CUSUM-type procedures in change detection and isolation problems
Computational Statistics & Data Analysis
A Multithreaded Framework for Sequential Monte Carlo Methods on CPU/FPGA Platforms
ARC '09 Proceedings of the 5th International Workshop on Reconfigurable Computing: Architectures, Tools and Applications
Particle filtering: the need for speed
EURASIP Journal on Advances in Signal Processing
Mobile robot self-diagnosis with a bank of adaptive particle filters
ICAIS'11 Proceedings of the Second international conference on Adaptive and intelligent systems
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
Resampling algorithms and architectures for distributed particle filters
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
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Fault diagnosis is one of the most challenging problems, which have to be solved if one considers real-life applications of mobile robots. In this paper, we present a particle filtering-based approach combined with the negative log-likelihood test to address the fault detection task. The major disadvantage of the method is its high computational burden closely related to the number of particles used, which can be computationally too expensive to be processed online by the onboard computer of the robot. In order to address this problem, a solution, in which a part of computations are delegated to an external parallel computing environment such as a computer cluster, is presented. The proposed methods of parallelizing particle filters are aimed at improving their performance in terms of efficiency, estimation error and execution time, which are vital factors in an online setup. To depict the performance benefits of the presented methods, they are confronted with some other existing approaches in a series of experiments.