Gaussian-Beta Filters with Unknown Probability of Measurement Loss

• Published in: IEEE access Vol. 10; p. 1
• Main Authors: Zhang, Guanghua, Lian, Feng, Zeng, Linghao, Fu, Na, Dai, Shasha, Liu, Xinqiang
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Data loss; Density measurement; Extraterrestrial measurements; Gaussian processes; Gaussian-Beta filter; Loss measurement; measurement loss; Noise measurement; Pollution measurement; Random variables; Reliability analysis; State estimation; State space models; State-space methods; State-space model; threshold technology; Time measurement
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2022.3217791

Data loss is ubiquitous in practical engineering applications due to communication delay or congestion. Data loss rate is a key metric to evaluate the reliability of state estimation. To jointly estimate system state and data loss rate, we propose a class of Gaussian-Beta filters for linear and moderate nonlinear Gaussian state-space models with unknown probability of measurement loss. In the filters, the arrival of the measurement at each time is formulated as a binary random variable, which is determined by the classical threshold technology. In addition, the hidden state and the unknown probability of measurement loss are modeled as a product of Gaussian and Beta distributions, and the form remains unchanged through recursive operations. Simulation results verify the effectiveness of the proposed Gaussian-Beta filters compared with the existing filtering algorithms.