Recursive Filtering With Measurement Fading: A Multiple Description Coding Scheme

• 成果类型: Article
• 署名作者: Wang, Licheng; Wang, Zidong; Shen, Bo; Wei, Guoliang
• 署名单位: University of Shanghai for Science & Technology; Brunel University; Donghua University; University of Shanghai for Science & Technology
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2020.3034196
• 发表日期: 2021
• 页码: 5144-5159
• 关键词: Fading channels; Decoding; encoding; Stochastic systems; Random variables; Mathematical model; communication networks; Fading channels; multiple description coding (MDC); nonlinear stochastic systems; packet dropout; recursive filtering

摘要

In this article, the recursive filtering problem is studied for a class of discrete-time nonlinear stochastic systems subject to fading measurements. In order to facilitate the data transmission in a resource-constrained communication network, the multiple description coding scheme is adopted to encode the fading measurements into two descriptions with the identical importance. Two independent Bernoulli distributed random variables are introduced to govern the occurrences of the packet dropouts in two channels from the encoders to the decoders. The channel fading phenomenon is characterized by the Mth-order Rice fading model whose coefficients are mutually independent random variables obeying certain probability distributions. The purpose of the problem addressed is to design a recursive filter, such that in the simultaneous presence of the stochastic noises, the channel fading and the data coding-decoding mechanism, an upper bound of the filtering error variance is obtained and then minimized at each time step. In virtue of the Riccati difference equation technique and the stochastic analysis approach, the explicit form of the desired filter parameters is derived by solving a sequence of coupled algebraic Riccati-like difference equations. Finally, a simulation experiment is provided to show the applicability of the developed filtering scheme.