Quantized identification of ARMA systems with colored measurement noise

This paper studies the identification of ARMA systemswith coloredmeasurement noises using finite-level quantized observations. Comparedwith the case under colorless noises, this problem ismore challenging. Our approach is to jointly design an adaptive quantizer and a recursive estimator to identify system parameters. Specifically, the quantizer uses the latest estimate to adjust its thresholds, and the estimator is updated by using quantized observations. To accommodate the temporal correlations of quantization errors andmeasurement noises, we construct a second-order statistics equivalent system, fromwhich the original ARMA system is identified. The associated identifiability problem and convergence are analyzed as well. Finally, numerical simulations are performed to demonstrate the effectiveness of the proposed algorithm. © 2015 Elsevier Ltd. All rights reserved.