Recursive bayesian estimation using gaussian sums
Automatica (Journal of IFAC)
Optimal recursive estimation with uncertain observation
IEEE Transactions on Information Theory
Learning with a probabilistic teacher
IEEE Transactions on Information Theory
Rao-Blackwellized particle filter for multiple target tracking
Information Fusion
Robotics and Autonomous Systems
Brief paper: A detection-estimation scheme for state estimation in switching environments
Automatica (Journal of IFAC)
Brief paper: Detection and estimation for abruptly changing systems
Automatica (Journal of IFAC)
On identification and adaptive estimation for systems with interrupted observations
Automatica (Journal of IFAC)
Hybrid metaheuristic particle filters for stochastic volatility estimation
Proceedings of the 14th annual conference on Genetic and evolutionary computation
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This paper deals with the state estimation for the systems under measurement noise whose mean and covariance change with Markov transition probabilities. The minimum variance estimate for the state involves consideration of a prohibitively large number of sequences, so that the usual computation method becomes impractical. In the algorithm proposed here, the estimate is calculated with a relatively small number of sequences sampled at random from the set of a large number of sequences. The average risk of the algorithm is shown to converge to the optimal average risk as the number of sampled sequences increases. An ideal sampling probability yielding a very fast convergence is found. The probability is approximated in a minimum mean squared sense by a probability according to which sequences can be sampled sequentially and with great ease. This policy of determination of sampling probability makes it possible to design practical and efficient algorithms. Digital simulation results show a good performance of the proposed algorithm.