Year: 2024
Type: article
Abstract: In this article, the Kalman filter design problem is investigated for linear discrete-time systems under binary encoding schemes. Under such a scheme, the local information is quantized into a bit string by the remote sensor based on a probabilistic quantizer, and then the bit string is transmitted via memoryless binary symmetric channels (BSCs). Due to the communication link noises, the bit flipping occurs in a random manner, and thus, the transmission of the bit string would suffer from specific bit-error rates. With the received bits, a recursive binary-encoding-based quantized Kalman filter is established in the approximate minimum mean-square error (MMSE) sense, which relies on the Gaussian approximation of the conditional probability density function at each iteration. Furthermore, the proposed estimator is shown to be of a Kalman-like type through performance analysis, which exhibits computational complexity comparable to the conventional Kalman filter. Subsequently, a posterior Cramér-Rao lower bound is derived for the proposed binary-encoding-based quantized Kalman filter. The effectiveness of the proposed estimator is demonstrated through numerical results.
Authors: Qinyuan Liu, Yao Nie, Zidong Wang, Hongli Dong, Changjun Jiang
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funders: Alexander von Humboldt-Stiftung, City University of Hong Kong, National Natural Science Foundation of China, Chinese Academy of Sciences, Harbin Institute of Technology, University of Hong Kong, Tongji University, Universität Duisburg-Essen, Northeast Petroleum University, Japan Society for the Promotion of Science, Institute of Automation, Chinese Academy of Sciences, National Key Research and Development Program of China, Fundamental Research Funds for the Central Universities
Awards: G5342982222, G828396987, G8923950675
OpenAlex ID: W4404238188