Parameter estimation of quantized DARMA systems using weighted least squares

This paper is concerned with parameter estimate of deterministic autoregressive moving average (DARMA) systems with uniform quantized output observations. By designing system input signals, the recursive least-squares algorithm with designed weights is proved to have convergence properties under the uniform output signal quantizer. The authors analyse the properties of the size of quantization error, which implies that the convergence properties can be achieved when the quantization error satisfies some conditions. A numerical example is supplied to demonstrate the theoretical results.