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


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.

This is a preview of subscription content, access via your institution.


  1. 1

    Söderström T, Stoica P. System Identification. Upper Saddle River: Prentice Hall, 1989

    Google Scholar 

  2. 2

    Wang L Y, Zhao W X. System identification: new paradigms, challenges, and opportunities. Acta Autom Sin, 2013, 39: 933–942

    MathSciNet  Article  Google Scholar 

  3. 3

    Akyildiz I F, Su W, Sankarasubramaniam Y, et al. Wireless sensor networks: a survey. Comput Netw, 2002, 38: 393–422

    Article  Google Scholar 

  4. 4

    Hespanha J P, Naghshtabrizi P, Xu Y. A survey of recent results in networked control systems. Proc IEEE, 2007, 95: 138–1

    Article  Google Scholar 

  5. 5

    Guo J, Mu B, Wang L Y, et al. Decision-based system identification and adaptive resource allocation. IEEE Trans Automat Contr, 2017, 62: 2166–2179

    MathSciNet  Article  Google Scholar 

  6. 6

    Ma C Q, Li T, Zhang J F. Consensus control for leader-following multi-agent systems with measurement noises. J F. Consensus control for leader-following multi-agent systems with measurement noises. J Syst Sci Complex, 2010, 23: 35–49

    MathSciNet  Article  Google Scholar 

  7. 7

    Ma C Q, Zhang J F. On formability of linear continuous multi-agent systems. J Syst Sci Complex, 2012, 25: 13–29

    MathSciNet  Article  Google Scholar 

  8. 8

    Aström K J, Bernhardsson B M. Comparison of Riemann and Lebesgue sampling for first order stochastic systems. In: Proceedings of the 41st IEEE Conference on Decision and Control, Las Vegas, 2002

    Google Scholar 

  9. 9

    Wang A, Liao X, Dong T. Event-triggered gradient-based distributed optimisation for multi-agent systems with state consensus constraint. IET Control Theory Appl, 2018, 12: 1515–1519

    MathSciNet  Google Scholar 

  10. 10

    Shi D, Chen T, Shi L. On set-valued Kalman filtering and its application to event-based state estimation. IEEE Trans Automat Contr, 2015, 60: 1275–1290

    MathSciNet  Article  Google Scholar 

  11. 11

    Hetel L, Fiter C, Omran H, et al. Recent developments on the stability of systems with aperiodic sampling: an overview. Automatica, 2017, 76: 309–335

    MathSciNet  Article  Google Scholar 

  12. 12

    Wang A, Liao X, Dong T. Event-driven optimal control for uncertain nonlinear systems with external disturbance via adaptive dynamic programming. Neurocomputing, 2018, 281: 188–195

    Article  Google Scholar 

  13. 13

    Wang A, Dong T, Liao X. Event-triggered synchronization strategy for complex dynamical networks with the Markovian switching topologies. Neural Netw, 2016, 74: 52–57

    Article  Google Scholar 

  14. 14

    Yu Y G, Zeng Z W, Li Z K, et al. Event-triggered encirclement control of multi-agent systems with bearing rigidity. Sci China Inf Sci, 2017, 60: 110203

  15. 15

    Zheng C, Li L, Wang L Y, et al. How much information is needed in quantized nonlinear control? Sci China Inf Sci, 2018, 61: 092205

  16. 16

    Wang L Y, Zhang J F, Yin G G. System identification using binary sensors. IEEE Trans Automat Contr, 2003, 48: 1892–1907

    MathSciNet  Article  Google Scholar 

  17. 17

    Wang T, Tan J W, Zhao Y L. Asymptotically efficient non-truncated identification for FIR systems with binary-valued outputs. Sci China Inf Sci, 2018, 61: 129208

  18. 18

    Guo J, Wang L Y, Yin G, et al. Asymptotically efficient identification of FIR systems with quantized observations and general quantized inputs. Automatica, 2015, 57: 113–122

    MathSciNet  Article  Google Scholar 

  19. 19

    Zhao Y, Wang L Y, Yin G G, et al. Identification of Wiener systems with binary-valued output observations. Automatica, 2007, 43: 1752–1765

    MathSciNet  Article  Google Scholar 

  20. 20

    Guo J, Wang L Y, Yin G, et al. Identification of Wiener systems with quantized inputs and binary-valued output observations. Automatica, 2017, 78: 280–286

    MathSciNet  Article  Google Scholar 

  21. 21

    Casini M, Garulli A, Vicino A. Input design in worst-case system identification with quantized measurements. Automatica, 2012, 48: 2997–3007

    MathSciNet  Article  Google Scholar 

  22. 22

    Zhao Y, Bi W, Wang T. Iterative parameter estimate with batched binary-valued observations. Sci China Inf Sci, 2016, 59: 052201

  23. 23

    Goudjil A, Pouliquen M, Pigeon E. Identification of systems using binary sensors via support vector machines. In: Proceedings of IEEE 54th Annual Conference on Decision and Control, Osaka, 2015

  24. 24

    Chow Y S, Teicher H. Probability Theory: Independence, Interchangeability, Martingales. 2nd ed. New York: Springer- Verlag, 1997

    Google Scholar 

  25. 25

    Wang L Y, Yin G G. Asymptotically efficient parameter estimation using quantized output observations. Automatica, 2007, 43: 1178–1191

    MathSciNet  Article  Google Scholar 

Download references


This work was supported by National Natural Science Foundation of China (Grant No. 61773054).

Author information



Corresponding author

Correspondence to Jin Guo.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Guo, J., Diao, JD. Prediction-based event-triggered identification of quantized input FIR systems with quantized output observations. Sci. China Inf. Sci. 63, 112201 (2020).

Download citation

Keywords identification

  • FIR systems
  • prediction-based event-triggered communication
  • quantized observations
  • convergence performance