Proceedings ArticleDOI
Adaptive HMM filters for signals in noisy fading channels
Iain B. Collings,John B. Moore +1 more
- pp 305-308
TLDR
The demodulation scheme presented can be applied to both digital M-ary differential phase shift keyed (MDPSK) and analog frequency modulated (FM) signals and can easily be generalised for other transmission schemes, such as continuous phase modulate (CPM) signals.Abstract:
Kalman filtering (KF) and hidden Markov model (HMM) signal processing techniques are coupled to demodulate signals transmitted through noisy fading channels. The demodulation scheme presented can be applied to both digital M-ary differential phase shift keyed (MDPSK) and analog frequency modulated (FM) signals. Adaptive state and parameter estimation algorithms are devised based on the assumption that the transmission channel introduces time-varying gain and phase changes, modelled by a stochastic linear system, and has additive Gaussian noise. Our technique is to use an HMM filter, for signal estimation, coupled with a KF, for channel parameter tracking. The approach taken can easily be generalised for other transmission schemes, such as continuous phase modulated (CPM) signals. >read more
Citations
More filters
Journal ArticleDOI
Adaptive estimation of HMM transition probabilities
Jason J. Ford,John B. Moore +1 more
TL;DR: This paper presents new schemes for recursive estimation of the state transition probabilities for hidden Markov models (HMMs) via extended least squares (ELS) and recursive state prediction error (RSPE) methods.
Journal ArticleDOI
On adaptive HMM state estimation
Jason J. Ford,John B. Moore +1 more
TL;DR: New online adaptive hidden Markov model (HMM) state estimation schemes are developed, based on extended least squares (ELS) concepts and recursive prediction error (RPE) methods.
Journal Article
On adaptive HMM state estimation
Jason J. Ford,John B. Moore +1 more
TL;DR: In this paper, new online adaptive hidden Markov model (HMM) state estimation schemes are developed, based on extended least squares (ELS) concepts and recursive prediction error (RPE) methods.
Dissertation
Markov Modeling of Third Generation Wireless Channels
TL;DR: The aim of this thesis is to find efficient algorithms that can model third generation wireless channels in a discrete sense using different Markov models, and their results are tested and validated.
Proceedings ArticleDOI
Optimal HMM filtering and decision feedback equalisation for differential encoded transmission systems
Jason J. Ford,John B. Moore +1 more
TL;DR: Conditional hidden Markov model filters and conditional Kalman filters are coupled together to improve demodulation of differential encoded signals in noisy fading channels and the techniques developed can be extended to other forms of differential modulation.
References
More filters
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A tutorial on hidden Markov models and selected applications in speech recognition
TL;DR: In this paper, the authors provide an overview of the basic theory of hidden Markov models (HMMs) as originated by L.E. Baum and T. Petrie (1966) and give practical details on methods of implementation of the theory along with a description of selected applications of HMMs to distinct problems in speech recognition.
Journal ArticleDOI
Maximum likelihood sequence estimation of CPM signals transmitted over Rayleigh flat-fading channels
J.H. Lodge,M.L. Moher +1 more
TL;DR: A method for the sequential updating of log- likelihood functions for maximum-likelihood sequence estimation is presented and it is shown that, in a general case, this method can be implemented using Kalman filtering techniques.
Journal ArticleDOI
Adaptive processing techniques based on hidden Markov models for characterizing very small channel currents buried in noise and deterministic interferences.
TL;DR: The signal processing technique is capable of characterizing the signal characteristics quite accurately even when the amplitude of currents is as small as 5-10 fA, and a technique is provided by which channel currents originating from the sum of two or more independent single channels are decomposed so that each process can be separately characterized.
Journal ArticleDOI
Recursive Prediction Error Techniques for Adaptive Estimation of Hidden Markov Models
TL;DR: In contrast to the off-line Expectation Maximisation (EM) algorithm, the on-line schemes have significantly reduced memory requirements, and with appropriate forgetting, can track slowly varying HMM parameters in an asymptotically efficient manner.