On sequential Monte Carlo sampling methods for Bayesian filtering
Statistics and Computing
Delayed-pilot sampling for mixture Kalman filter with applicationin fading channels
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
Particle filtering equalization method for a satellite communication channel
EURASIP Journal on Applied Signal Processing
Brief paper: Distribution function tracking filter design using hybrid characteristic functions
Automatica (Journal of IFAC)
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In many situations, there is a need to approximate a sequence of probability measures over a growing product of finite spaces. Whereas it is in general possible to determine analytic expressions for these probability measures, the number of computations needed to evaluate these quantities grows exponentially thus precluding real-time implementation. Sequential Monte Carlo techniques (SMC), which consist in approximating the flow of probability measures by the empirical distribution of a finite set of particles, are attractive techniques for addressing this type of problems. In this paper, we present a simple implementation of the sequential importance sampling/resampling (SISR) technique for approximating these distributions; this method relies on the fact that, the space being finite, it is possible to consider every offspring of the trajectory of particles. The procedure is straightforward to implement, and well-suited for practical implementation. A limited Monte Carlo experiment is carried out to support our findings.