Kalman filtering: theory and practice
Kalman filtering: theory and practice
Estimation with Applications to Tracking and Navigation
Estimation with Applications to Tracking and Navigation
On sequential Monte Carlo sampling methods for Bayesian filtering
Statistics and Computing
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
IEEE Transactions on Signal Processing
Gaussian sum particle filtering
IEEE Transactions on Signal Processing
Adaptive Bayesian multiuser detection for synchronous CDMA withGaussian and impulsive noise
IEEE Transactions on Signal Processing
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
Marginalized particle filters for mixed linear/nonlinear state-space models
IEEE Transactions on Signal Processing
Particle filters for state estimation of jump Markov linear systems
IEEE Transactions on Signal Processing
Adaptive joint detection and decoding in flat-fading channels via mixture Kalman filtering
IEEE Transactions on Information Theory
Adaptive estimation in linear systems with unknown Markovian noise statistics
IEEE Transactions on Information Theory
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A generalized likelihood function model of a sampling importance resampling (SIR) particle filter (PF) has been derived for state estimation of a nonlinear system in the presence of non-stationary, non-Gaussian white measurement noise. The measurement noise is modeled by Gaussian mixture probability density function and the noise parameters are estimated by maximizing the log likelihood function of the noise model. This model is then included in the likelihood function of the SIR particle filter (PF) at each time step for online state estimation of the system. The performance of the proposed algorithm has been evaluated by estimating the states of (i) a non-linear system in the presence of non-stationary Rayleigh distributed noise and (ii) a radar tracking system in the presence of glint noise. The simulation results show that the proposed modified SIR PF offers best performance among the considered algorithms for these examples.