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 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