Risk‐sensitive filtering for nonlinear Markov jump systems on the basis of particle approximation

DOI10.1002/acs.1284zbMath1417.93320OpenAlexW1493878376MaRDI QIDQ4908475

Fei Liu, Xiaoli Luan, Shunyi Zhao

Publication date: 6 March 2013

Published in: International Journal of Adaptive Control and Signal Processing (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1002/acs.1284

zbMATH Keywords

particle approximationnonlinear Markov jump systemsrisk-sensitive filterreference probability technique

Mathematics Subject Classification ID

Filtering in stochastic control theory (93E11) Sensitivity (robustness) (93B35) Control/observation systems with incomplete information (93C41) Stochastic systems in control theory (general) (93E03)

Related Items (4)

Improved minimum entropy filtering for continuous nonlinear non-Gaussian systems using a generalized density evolution equation ⋮ State estimation for nonlinear discrete-time systems with Markov jumps and nonhomogeneous transition probabilities ⋮ State estimation for jump Markov nonlinear systems of unknown measurement data covariance ⋮ Moving horizon estimation for Markov jump systems

Cites Work

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• Risk-sensitive filtering, prediction and smoothing for discrete-time singular systems
• Risk-sensitive filtering for jump Markov linear systems
• Particle-method-based formulation of risk-sensitive filter
• The interacting multiple model algorithm for systems with Markovian switching coefficients
• Risk-sensitive filtering and smoothing via reference probability methods
• Fixed-order robust H/sub /spl infin// filter design for Markovian jump systems with uncertain switching probabilities
• Measure Theory and Filtering

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