LS-Based Parameter Estimation of DARMA Systems with Uniformly Quantized Observations

Abstract This paper is concerned with the parameter estimation of deterministic autoregressive moving average (DARMA) systems with quantization data. The estimation algorithms adopted here are the least squares (LS) and the forgetting factor LS, and the signal quantizer is of uniform, that is, with...
Ausführliche Beschreibung

Abstract This paper is concerned with the parameter estimation of deterministic autoregressive moving average (DARMA) systems with quantization data. The estimation algorithms adopted here are the least squares (LS) and the forgetting factor LS, and the signal quantizer is of uniform, that is, with uniform quantization error. The authors analyse the properties of the LS and the forgetting factor LS, and establish the boundedness of the estimation errors and a relationship of the estimation errors with the size of quantization error, which implies that the smaller the quantization error is, the smaller the estimation error is. A numerical example is given to demonstrate theorems. Ausführliche Beschreibung