Frequency tracking of nonsinusoidal periodic signals in noise
Signal Processing
Fast particle smoothing: if I had a million particles
ICML '06 Proceedings of the 23rd international conference on Machine learning
Mean time to loose lock of phase tracking by particle filtering
Signal Processing - Special section: Distributed source coding
Posterior Cramer-Rao bounds for discrete-time nonlinear filtering
IEEE Transactions on Signal Processing
A Bayesian approach to tracking multiple targets using sensorarrays and particle filters
IEEE Transactions on Signal Processing
Optimal Particle Filters for Tracking a Time-Varying Harmonic or Chirp Signal
IEEE Transactions on Signal Processing - Part I
Marginalized particle filters for mixed linear/nonlinear state-space models
IEEE Transactions on Signal Processing
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The problem of estimating the frequencies and phases of two superimposed pulse-like, repetitive signals is considered. A method of solving this problem based on Bayesian (i.e., sequential Monte-Carlo or particle) filtering is presented. The particular problem motivating the technique is estimation of the rate of maternal and foetal heartbeats, although the technique is applicable to other domains. Aspects of the problem leading to the solution are discussed, and the performance of the proposed technique is presented on both simulated signals and real heart signals. The resulting frequency estimator has very similar functionality to a phase-locked loop, and so the performance measure used is the time to lose phase lock. The estimator is also analysed in terms of the posterior Cramer-Rao bound on the phase estimation accuracy.