Adaptive filter theory
Elements of information theory
Elements of information theory
On sequential Monte Carlo sampling methods for Bayesian filtering
Statistics and Computing
EURASIP Journal on Applied 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
Blind equalization of frequency-selective channels by sequential importance sampling
IEEE Transactions on Signal Processing - Part I
IEEE Transactions on Signal Processing
Gaussian sum particle filtering
IEEE Transactions on Signal Processing
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
A sequential Monte Carlo method for adaptive blind timing estimation and data detection
IEEE Transactions on Signal Processing - Part I
Resampling algorithms and architectures for distributed particle filters
IEEE Transactions on Signal Processing
Joint multiple target tracking and classification in collaborative sensor networks
IEEE Journal on Selected Areas in Communications
Extended Kalman and Particle Filtering for sensor fusion in motion control of mobile robots
Mathematics and Computers in Simulation
EURASIP Journal on Advances in Signal Processing
Distributed Markov Chain Monte Carlo kernel based particle filtering for object tracking
Multimedia Tools and Applications
An Efficient Particle Filter---based Tracking Method Using Graphics Processing Unit (GPU)
Journal of Signal Processing Systems
Hierarchical Resampling Algorithm and Architecture for Distributed Particle Filters
Journal of Signal Processing Systems
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Particle filtering methods are powerful tools for online estimation and tracking in nonlinear and non-Gaussian dynamical systems. They commonly consist of three steps: (1) drawing samples in the state-space of the system, (2) computing proper importance weights of each sample and (3) resampling. Steps 1 and 2 can be carried out concurrently for each sample, but standard resampling techniques require strong interaction. This is an important limitation, because one of the potential advantages of particle filtering is the possibility to perform very fast online signal processing using parallel computing devices. It is only very recently that resampling techniques specifically designed for parallel computation have been proposed, but little is known about the properties of such algorithms and how they compare to standard methods. In this paper, we investigate two classes of such techniques, distributed resampling with non-proportional allocation (DRNA) and local selection (LS). Namely, we analyze the effect of DRNA and LS on the sample variance of the importance weights; the distortion, due to the resampling step, of the discrete probability measure given by the particle filter; and the variance of estimators after resampling. Finally, we carry out computer simulations to support the analytical results and to illustrate the actual performance of DRNA and LS. Two typical problems are considered: vehicle navigation and tracking the dynamic variables of the chaotic Lorenz system driven by white noise.