# What is the estimator used in this paper for system identification?

Paper : System Identification using Symbolic Chaotic Sequence download link proposes EM-UKS estimator for system identification of a linear FIR channel when excited by non-linear input.

In Fig 3 of this paper there is a comparative plot of system identification of FIR model

$y[n] = H^T x[n] + w[n]$ where $x[n]$ is a chaotic input and $w[n]$ is AWGN with unknown variance.

Performance is compared using CRLB vs MSE when the FIR system is driven by (a) chaotic deterministic numerical input, (b) chaotic quantized input (called as symbolic input), (c) filtered Gaussian input, and (d) PRBS input.

Q1: What is the estimation technique when the input is white Gaussian and PRBS ? Is it Ordinary Least Square as they mention that they are comparing with the CRLB of non-blind. So, OLS is a non-blind technique.

This is how I implemented when the input is white noise:

 h = [1    0.65   -0.2];  % MA channel coefficients
x = randn(1000,2);  % white noise input
u = filter(h,1,x);
y = awgn(u,10,'measured'); % adding noise w of SNR =10dB
X = [y,ones(n,1)];
[b,C]=lscov(X,y)


Q2: I am not sure how to implement using PRBS. Can somebody please provide the code?

Q3: Is the system identification using EM-UKS when the input is chaotic and symbolic proposed by the Authors blind or semi-blind and why?