Bayesian forecasting and dynamic models (2nd ed.)
Bayesian forecasting and dynamic models (2nd ed.)
A Probabilistic Exclusion Principle for Tracking Multiple Objects
International Journal of Computer Vision
Principles of mobile communication (2nd ed.)
Principles of mobile communication (2nd ed.)
Simulation and the Monte Carlo Method
Simulation and the Monte Carlo Method
On sequential Monte Carlo sampling methods for Bayesian filtering
Statistics and Computing
Joint estimation and decoding of space-time Trellis codes
EURASIP Journal on Applied Signal Processing - Space-time coding and its applications - part I
Robust Full Bayesian Learning for Radial Basis Networks
Neural Computation
Bayesian detection and estimation of cisoids in colored noise
IEEE Transactions on Signal Processing
Multiuser detection of synchronous code-division multiple-accesssignals by perfect sampling
IEEE Transactions on Signal Processing
Delayed-pilot sampling for mixture Kalman filter with applicationin fading channels
IEEE Transactions on Signal Processing
A sequential Monte Carlo blind receiver for OFDM systems infrequency-selective fading channels
IEEE Transactions on Signal Processing
Monte Carlo smoothing with application to audio signal enhancement
IEEE Transactions on Signal Processing
An MCMC sampling approach to estimation of nonstationary hiddenMarkov models
IEEE Transactions on Signal Processing
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
Blind detection in MIMO systems via sequential Monte Carlo
IEEE Journal on Selected Areas in Communications
Extended object tracking using mixture Kalman filtering
NMA'06 Proceedings of the 6th international conference on Numerical methods and applications
| Hi-index | 0.00 |
The mixture Kalman filter is a general sequential Monte Carlo technique for conditional linear dynamic systems. It generates samples of some indicator variables recursively based on sequential importance sampling (SIS) and integrates out the linear and Gaussian state variables conditioned on these indicators. Due to the marginalization process, the complexity of the mixture Kalman filter is quite high if the dimension of the indicator sampling space is high. In this paper, we address this difficulty by developing a new Monte Carlo sampling scheme, namely, the multilevel mixture Kalman filter. The basic idea is to make use of the multilevel or hierarchical structure of the space from which the indicator variables take values. That is, we draw samples in a multilevel fashion, beginning with sampling from the highest-level sampling space and then draw samples from the associate subspace of the newly drawn samples in a lower-level sampling space, until reaching the desired sampling space. Such a multilevel sampling scheme can be used in conjunction with the delayed estimation method, such as the delayed-sample method, resulting in delayed multilevel mixture Kalman filter. Examples in wireless communication, specifically the coherent and noncoherent 16-QAM over flat-fading channels, are provided to demonstrate the performance of the proposed multilevel mixture Kalman filter.