Particle filtering
Djuric, P.M.; Kotecha, J.H.; Jianqui Zhang; Yufei Huang; Ghirmai, T.; Bugallo, M.F.; Miguez, J.
Signal Processing Magazine, IEEE
Volume 20, Issue 5, Sep 2003 Page(s): 19 - 38
Digital Object Identifier 10.1109/MSP.2003.1236770
Summary: Recent developments have demonstrated that particle filtering is an emerging and powerful methodology, using Monte Carlo methods, for sequential signal processing with a wide range of applications in science and engineering. It has captured the attention of many researchers in various communities, including those of signal processing, statistics and econometrics. Based on the concept of sequential importance sampling and the use of Bayesian theory, particle filtering is particularly useful in dealing with difficult nonlinear and non-Gaussian problems. The underlying principle of the methodology is the approximation of relevant distributions with random measures composed of particles (samples from the space of the unknowns) and their associated weights. First, we present a brief review of particle filtering theory; and then we show how it can be used for resolving many problems in wireless communications. We demonstrate its application to blind equalization, blind detection over flat fading channels, multiuser detection, and estimation and detection of space-time codes in fading channels.
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