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

• Published in: Science China. Information sciences Vol. 63; no. 1; p. 112201
• Main Authors: Guo, Jin, Diao, Jing-Dong
• Format: Journal Article
• Language: English
• Published: Beijing Science China Press 01.01.2020; Springer Nature B.V
• Subjects: Algorithms; Automation; Communication; Computer Science; Design; Energy consumption; Impulse response; Information Systems and Communication Service; Lower bounds; Optimization; Parameter estimation; Parameter identification; Research Paper; Sensors; quantized observations; prediction-based event-triggered communication; FIR systems; convergence performance
• ISSN: 1674-733X; 1869-1919
• DOI: 10.1007/s11432-018-9845-6

This paper addresses the identification of finite impulse response (FIR) systems with both quantized and event-triggered observations. An event-triggered communication scheme for the binary-valued output quantization is introduced to save communication resources. Combining the empirical-measure-based identification technique and the weighted least-squares optimization, an algorithm is proposed to estimate the unknown parameter by full use of the received data and the not-triggered condition. Under quantized inputs, it is shown that the estimate can strongly converge to the real values and the estimator is asymptotically efficient in terms of the Cram’er-Rao lower bound. Further, the limit of the average communication rate is derived and the tradeoff between this limit and 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.