System Identification with Quantized Observations

Wang, Le Yi; Yin, George; Zhang, Jifeng; Zhao, Yanlong

doi:10.1007/978-0-8176-4956-2

Cited by 182 publications

(121 citation statements)

References 63 publications

Supporting

Mentioning

121

Contrasting

Order By: Relevance

“…Some related results on identification, state estimation, and fault detection using binary or quantized outputs can be found in [17], [31], [33], [34], [35], [36]. In relation to the existing knowledge on observability of sampled systems, we refer the reader to standard textbooks on digital control systems; see, e.g., [7], [18], [24] for classical synchronized sampling schemes on linear systems, and [1], [11] on nonlinear systems.…”

mentioning

confidence: 99%

State Observability and Observers of Linear-Time-Invariant Systems Under Irregular Sampling and Sensor Limitations

Wang

Yin

et al. 2011

IEEE Trans. Automat. Contr.

Self Cite

View full text Add to dashboard Cite

Abstract-State observability and observer designs are investigated for linear-time-invariant systems in continuous time when the outputs are measured only at a set of irregular sampling time sequences. The problem is primarily motivated by systems with limited sensor information in which sensor switching generates irregular sampling sequences. State observability may be lost and the traditional observers may fail in general, even if the system has a full-rank observability matrix. It demonstrates that if the original system is observable, the irregularly sampled system will be observable if the sampling density is higher than some critical frequency, independent of the actual time sequences. This result extends Shannon's sampling theorem for signal reconstruction under periodic sampling to system observability under arbitrary sampling sequences. State observers and recursive algorithms are developed whose convergence properties are derived under potentially dependent measurement noises. Persistent excitation conditions are validated by designing sampling time sequences. By generating suitable switching time sequences, the designed state observers are shown to be convergent in mean square, with probability one, and with exponential convergence rates. Schemes for generating desired sampling sequences are summarized.

show abstract

mentioning

confidence: 99%

State Observability and Observers of Linear-Time-Invariant Systems Under Irregular Sampling and Sensor Limitations

Wang

Yin

et al. 2011

IEEE Trans. Automat. Contr.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Correlated noise processes (for example, stationary φ-mixing sequences) can be treated. We refer to the interested reader to our book [15] for further reading.…”

Section: Assumptionmentioning

confidence: 99%

Signal estimation with binary-valued sensors

Wang

Yin

et al. 2010

J Syst Sci Complex

View full text Add to dashboard Cite

This paper introduces several algorithms for signal estimation using binary-valued output sensing. The main idea is derived from the empirical measure approach for quantized identification, which has been shown to be convergent and asymptotically efficient when the unknown parameters are constants. Signal estimation under binary-valued observations must take into consideration of time varying variables. Typical empirical measure based algorithms are modified with exponential weighting and threshold adaptation to accommodate time-varying natures of the signals. Without any information on signal generators, the authors establish estimation algorithms, interaction between noise reduction by averaging and signal tracking, convergence rates, and asymptotic efficiency. A threshold adaptation algorithm is introduced. Its convergence and convergence rates are analyzed by using the ODE method for stochastic approximation problems.

show abstract

“…important topic (Bai & Reyland, 2008;Como, Fagnani, & Zampieri, 2010;Wang, Yin, Zhang, & Zhao, 2010). Most of the related research focuses on either the quantization or the communication uncertainty, and a lot of excellent results have been obtained (Casini, Garulli, & Vicino, 2011;Colinet & Juillard, 2010;Godoy, Goodwin, Agüero, Marelli, & Wigren, 2011;Høst-Madsen & Händel, 2000;Marelli, Godoy, & Goodwin, 2010;Wigren, 1998).…”

Section: Introductionmentioning

confidence: 95%