De-interleaving of superimposed quantized autoregressive processes

We consider the de-interleaving of N independent autoregressive (AR) processes from 1-bit quantized measurements. De-interleaving has applications in radar and signal detection. Other possible applications are computer communications and neural systems. The received signal (pulse train) is the super...

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Bibliographic Details
Published in1996 IEEE International Conference on Acoustics, Speech, and Signal Processing Conference Proceedings Vol. 5; pp. 2994 - 2997 vol. 5
Main Authors Logothetis, A., Krishnamurthy, V.
Format Conference Proceeding
LanguageEnglish
Published IEEE 1996
Subjects
Application software
Autoregressive processes
Computer applications
Gaussian noise
Hidden Markov models
Parameter estimation
Quantum computing
Radar applications
Signal detection
Signal processing
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ISBN9780780331921
0780331923
ISSN1520-6149
DOI10.1109/ICASSP.1996.550184

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Summary:We consider the de-interleaving of N independent autoregressive (AR) processes from 1-bit quantized measurements. De-interleaving has applications in radar and signal detection. Other possible applications are computer communications and neural systems. The received signal (pulse train) is the superposition of N 1-bit quantized Gaussian AR processes observed in white Gaussian noise. The aim is to identify which sources are responsible for the observed noisy pulses. Furthermore, it is desired to obtain parameter estimates for the N sources. The proposed algorithm, (subject to model assumptions) optimally combines hidden Markov model and binary time series estimation techniques.
ISBN:9780780331921
0780331923
ISSN:1520-6149
DOI:10.1109/ICASSP.1996.550184