De-interleaving of superimposed quantized autoregressive processes

TitleDe-interleaving of superimposed quantized autoregressive processes
Publication TypeConference Paper
Year of Publication1996
AuthorsLogothetis, A., and V. Krishnamurthy
Conference NameAcoustics, Speech, and Signal Processing, 1996. ICASSP-96. Conference Proceedings., 1996 IEEE International Conference on
Pagination2994 -2997 vol. 5
Date Publishedmay.
Keywords1-bit quantized Gaussian AR processes, 1-bit quantized measurements, autoregressive processes, binary time series estimation, computer communications, deinterleaving, Gaussian noise, hidden Markov model, hidden Markov models, neural systems, noisy pulses, parameter estimates, parameter estimation, pulse train, quantisation (signal), radar detection, received signal, signal detection, superimposed quantized autoregressive processes, time series, white Gaussian noise

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


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