A QQ-Plot and its Application to Adaptive Recursive System Parameter Estimation

Because of the presence of sporadic high-intensity measurement noise (outliers),an adaptive algorithm for the robust estimation of parameters of linear dynamic discrete-time systems is proposed in this paper. first, the sorted data versus the normal quantiles is plotted, called QQ-plot. next the ε-contaminated normal distribution of noise is adopted. Then, a data classification procedure based on the QQ-plot approachcombined with the robustified data winsorization technique, is developed, the estimation of the unknown noise statistical parameters is solved. Moreover, an iterative procedure for estimating the contamination degree ε , which originated from an ML classification, is also proposed. Thus, an ε- contaminated noise distribution is estimated and, the suboptimal maximum likelihood criterion is defined, and the system-parameter estimation problem is solved robustly, using the proposed recursive robust parameter estimation scheme.