Markov chain Monte Carlo methods with applications to signal processing
Signal Processing - Special section on Markov Chain Monte Carlo (MCMC) methods for signal processing
On sequential Monte Carlo sampling methods for Bayesian filtering
Statistics and Computing
A survey of convergence results on particle filtering methods forpractitioners
IEEE Transactions on Signal Processing
Particle filters for positioning, navigation, and tracking
IEEE Transactions on Signal Processing
Blind adaptive multiuser detection
IEEE Transactions on Information Theory
Performance comparison of EKF and particle filtering methods for maneuvering targets
Digital Signal Processing
Analysis of parallelizable resampling algorithms for particle filtering
Signal Processing
Erratum to "a new class of particle filters for random dynamic systems with unknown statistics"
EURASIP Journal on Applied Signal Processing
EURASIP Journal on Applied Signal Processing
Frequency-selective and nonlinear channel estimation with unknown noise statistics
IEEE Communications Letters
Sensor selection for target tracking in binary sensor networks using particle filter
Sarnoff'10 Proceedings of the 33rd IEEE conference on Sarnoff
Robust tracking algorithm for wireless sensor networks based on improved particle filter
Wireless Communications & Mobile Computing
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In recent years, particle filtering has become a powerful tool for tracking signals and time-varying parameters of random dynamic systems. These methods require a mathematical representation of the dynamics of the system evolution, together with assumptions of probabilistic models. In this paper, we present a new class of particle filtering methods that do not assume explicit mathematical forms of the probability distributions of the noise in the system. As a consequence, the proposed techniques are simpler, more robust, and more flexible than standard particle filters. Apart from the theoretical development of specific methods in the new class, we provide computer simulation results that demonstrate the performance of the algorithms in the problem of autonomous positioning of a vehicle in a 2-dimensional space.