An interval-amplitude algorithm for deinterleaving stochastic pulse train sources

TitleAn interval-amplitude algorithm for deinterleaving stochastic pulse train sources
Publication TypeJournal Article
Year of Publication1998
AuthorsLogothetis, A., and V. Krishnamurthy
JournalSignal Processing, IEEE Transactions on
Pagination1344 -1350
Date Publishedmay.
Keywordsautoregressive processes, binary time series estimation, communication constraints, fast sensor, Gaussian channels, Gaussian communication channel, hidden Markov model, hidden Markov models, HMM, independent sources, interval-amplitude algorithm, maximum likelihood estimation, maximum likelihood parameter estimates, noise, observed noisy pulses, pulse trains deinterleaving, quantisation (signal), quantized Gaussian AR sources, recorded signals, sampling, sequences, signal sampling, spectral analysis, spectral estimation, stochastic parameterized sources, stochastic pulse train sources, time series

We consider the deinterleaving of pulse trains transmitted by N independent sources. The deinterleaving problem considered has applications in spectral estimation, where N (known number) stochastic parameterized sources are sampled using a fast sensor recording the sign of the signal from each source. Due to communication constraints, the recorded signals-pulse trains or sequences of zeros and ones-are superimposed and transmitted through a single Gaussian communication channel. The aim of this paper is to estimate the parameters that characterize the sources and identify those sources that are responsible for the observed noisy pulses. Our proposed algorithm, subject to modeling assumptions, optimally combines hidden Markov model and binary time series estimation techniques and yields maximum likelihood parameter estimates of the sources


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