报告简介：

This talk addresses the challenges of remote state estimation with measurement compression and packet loss in a hidden Markov channel. The sensor measurements are compressed to reduce communication load and transmitted over a lossy channel, where packet loss depends on the channel state, which is partially observed. We propose an estimation framework that integrates measurement compression, channel-state-dependent packet loss, and decompression error. Using conditional expectation and Lyapunov-based techniques, we derive conditions for mean-square boundedness of the estimation error. An optimization strategy is introduced to minimize the error bound, improving accuracy. Simulations show how compression quality and channel-state observation impact performance.

报告人简介：

王子栋，现任英国伦敦Brunel University讲席教授，欧洲科学院院士，欧洲科学与艺术院院士，IEEE Fellow，International Journal of Systems Science主编，Neurocomputing主编。多年来从事控制理论、机器学习、生物信息学等方面研究，在SCI刊物上发表国际论文六百余篇。现任或曾任十二种国际刊物的主编、副编辑或编委。曾任旅英华人自动化及计算机协会主席、东华大学长江学者讲座教授、清华大学国家级专家。