Prediction-based event-triggered identification of quantized input FIR systems with quantized output observations

Abstract

This paper addresses the identification of finite impulse response (FIR) systems withboth quantized and event-triggered observations.An event-triggered communication scheme for the binary-valued output quantization is introducedto save communication resources.Combining the empirical-measure-based identification techniqueand the weighted least-squares optimization,an algorithm is proposed to estimate the unknown parameterby full use of the received data and thenot-triggered condition.Under quantized inputs,it is shown that the estimate can strongly converge to the real valuesand the estimator is asymptotically efficient in terms of the Cramer-Rao lowerbound.Further, the limit of the average communication rate is derived and the tradeoff between this limitand the estimation performance is discussed.Moreover, the case of multi-threshold quantized observations is considered.Numerical examples are included to illustrate the obtained main results.