Partial-Nodes-Based Scalable $$H_{\infty }$$-Consensus Filtering with Censored Measurements
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Publisher
Springer International Publishing
Link
https://link.springer.com/content/pdf/10.1007/978-3-030-97075-8_6
Reference7 articles.
1. Allik, B., Miller, C., Piovoso, M., Zurakowski, R.: Estimation of saturated data using the Tobit Kalman filter. In: Proceedings of 2014 American Control Conference, Portland, Oregon, USA, pp. 4151–4156 (2014)
2. Allik, B., Miller, C., Piovoso, M., Zurakowski, R.: Nonlinear estimators for censored data: a comparison of the EKF, the UKF and the Tobit Kalman filter. In: Procedings of the American Control Conference, Chicago, IL, USA, pp. 5146–5151 (2015)
3. Allik, B., Miller, C., Piovoso, M., Zurakowski, R.: The Tobit Kalman filter: an estimator for censored measurements. IEEE Trans. Control Syst. Technol. 24(1), 365–371 (2016)
4. Han, F., Dong, H., Wang, Z., Li, G., Alsaadi, F.: Improved Tobit Kalman filtering for systems with random parameters via conditional expectation. Signal Process. 147, 35–45 (2018)
5. Li, J., Wei, G., Ding, D., Li, Y.: Set-membership filtering for discrete time-varying nonlinear systems with censored measurements under Round-Robin protocol. Neurocomputing 281, 20–26 (2018)
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