Blind System Identification in Noise Using a Dynamic-Based Estimator

In this work we consider the problem of blind system identification in noise driven by an independent and identically distributed (i.i.d) non-Gaussian signal generated from a deterministic nonlinear chaotic system. A new estimator for phase space volume (PSV) which is dynamic-based property chaos derived using maximum likelihood formulation. This novel PSV denoted as (ML-PSV). The Cramér Rao Lower Bound (CRLB) ML-PSV has also been derived. We have shown that mean square error estimate gradually approaches its CRLB asymptotically. An algorithm formulated applies objective function task autoregressive (AR) moving average (MA) models. proposed technique to improve performance at low signal-to-noise ratio (SNR) when both numeric symbolic signals. efficiency our method compared with conventional methods through simulations. Our further validated experimental evaluation based on software defined radio (SDR). Results show outperforms existing producing estimates SNR $\le20$ dB.