Estimation of 1-bit quantized time-series with Markov regime

Andrew Logothetis, Vikram Krishnamurthy, H. Vincent Poor

Research output: Contribution to journalArticle

1 Scopus citations

Abstract

In this paper we consider de-interleaving a finite number of stochastic parametric sources. The sources are modeled as independent autoregressive (AR) processes. Based on a Markovian switching policy, we assume that the different sources transmit signals on the same single channel. The receiver records the 1-bit quantized version of the transmitted signal and aims to identify the sequence of active sources. Once the source sequence has been identified, the characteristics (parameters) of each source are estimated. We formulate the parametric pulse train de-interleaving problem as a 1-bit quantized Markov modulated AR series. The algorithm proposed in this paper combines Hidden Markov Model (HMM) and Binary Time Series (BTS) estimation techniques. Our estimation scheme generalizes Kedem's (1980) binary time series algorithm for linear time series to Markov modulated time series.

Original languageEnglish (US)
Pages (from-to)273-292
Number of pages20
JournalSignal Processing
Volume58
Issue number3
DOIs
StatePublished - May 1997

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering

Keywords

  • Autoregressive process
  • Binary time series
  • Expectation maximization algorithm
  • Hidden Markov models
  • Parameter estimation

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