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

Andrew Logothetis; Vikram Krishnamurthy; H. Vincent Poor

doi:10.1016/s0165-1684(97)00029-7

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

Andrew Logothetis, Vikram Krishnamurthy, H. Vincent Poor

Research output: Contribution to journal › Article › peer-review

2 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 language	English (US)
Pages (from-to)	273-292
Number of pages	20
Journal	Signal Processing
Volume	58
Issue number	3
DOIs	https://doi.org/10.1016/s0165-1684(97)00029-7
State	Published - 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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10.1016/s0165-1684(97)00029-7

Cite this

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title = "Estimation of 1-bit quantized time-series with Markov regime",

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.",

keywords = "Autoregressive process, Binary time series, Expectation maximization algorithm, Hidden Markov models, Parameter estimation",

author = "Andrew Logothetis and Vikram Krishnamurthy and Poor, {H. Vincent}",

note = "Funding Information: Dans cet article nous Btudions le probleme consistant a des-entrelacer un nombre fini de sources paramhriques et stochatiques. Les sources sont mod&lides comme des processus autor&essifs (AR) indkndants. En nous basant sur une politique de passage markovienne, nous supposons que les diffkrentes sources transmettent des signaux sur le mgme canal. Le rkcepteur enregistre la version quantifike b 1 bit du signal transmis et cherche B identifier la sequence des sources actives. Une fois que la sequence des source a Ct&i dentifibe, les cara&ristiques (paramktres) de chaque source l Corresponding author. Tel.: 613 9344 6714; fax: 613 9344 6678; e-mail: [email protected]. {\textquoteright} Partially supported by the Australian Telecommunications and Electronics Research Board (ATERB), Australian Research Council (ARC) and the Co-operative Research Centre for Sensor Signal and Information Processing (CSSIP). Copyright: Copyright 2020 Elsevier B.V., All rights reserved.",

year = "1997",

month = may,

doi = "10.1016/s0165-1684(97)00029-7",

language = "English (US)",

volume = "58",

pages = "273--292",

journal = "Signal Processing",

issn = "0165-1684",

publisher = "Elsevier B.V.",

number = "3",

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TY - JOUR

T1 - Estimation of 1-bit quantized time-series with Markov regime

AU - Logothetis, Andrew

AU - Krishnamurthy, Vikram

AU - Poor, H. Vincent

N1 - Funding Information: Dans cet article nous Btudions le probleme consistant a des-entrelacer un nombre fini de sources paramhriques et stochatiques. Les sources sont mod&lides comme des processus autor&essifs (AR) indkndants. En nous basant sur une politique de passage markovienne, nous supposons que les diffkrentes sources transmettent des signaux sur le mgme canal. Le rkcepteur enregistre la version quantifike b 1 bit du signal transmis et cherche B identifier la sequence des sources actives. Une fois que la sequence des source a Ct&i dentifibe, les cara&ristiques (paramktres) de chaque source l Corresponding author. Tel.: 613 9344 6714; fax: 613 9344 6678; e-mail: [email protected]. ’ Partially supported by the Australian Telecommunications and Electronics Research Board (ATERB), Australian Research Council (ARC) and the Co-operative Research Centre for Sensor Signal and Information Processing (CSSIP). Copyright: Copyright 2020 Elsevier B.V., All rights reserved.

PY - 1997/5

Y1 - 1997/5

N2 - 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.

AB - 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.

KW - Autoregressive process

KW - Binary time series

KW - Expectation maximization algorithm

KW - Hidden Markov models

KW - Parameter estimation

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DO - 10.1016/s0165-1684(97)00029-7

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SN - 0165-1684

VL - 58

SP - 273

EP - 292

JO - Signal Processing

JF - Signal Processing

IS - 3

ER -

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

Abstract

All Science Journal Classification (ASJC) codes

Keywords

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