Identification for FIR Systems With Scheduled Binary-Valued Observations

This paper studies the identification of finite impulse response (FIR) systems whose output observations are subject to both the binary-valued quantization and the scheduling scheme. By utilizing the statistical property of the system noise and the scheduling policy, an empirical-measure-based identification algorithm is proposed. Under periodical inputs, it is proved that the estimation from the algorithm can converge to the real parameters. The mean-square convergence rate of the estimation error is established, based on which and the Cramer-Rao lower bound, the asymptotic efficiency of algorithm is proved. Moreover, the communication rate is derived and the input design problem is discussed. A numerical example is given to illustrate the main results obtained.