Continuous-Time Channel Gain Control for Minimum-Information Kalman-Bucy Filtering

• 成果类型: Article
• 署名作者: Tanaka, Takashi; Zinage, Vrushabh; Ugrinovskii, Valery; Skoglund, Mikael
• 署名单位: University of Texas System; University of Texas Austin; University of New South Wales Sydney; Royal Institute of Technology
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2024.3395477
• 发表日期: 2024
• 页码: 7255-7262
• 关键词: mutual information; Random variables; Channel estimation; Gain control; Time-varying channels; Kalman filters; Information filters; Continuous time systems; Information theory; networked control systems; optimal control

摘要

We consider the problem of estimating a continuous-time Gauss-Markov source process observed through a vector Gaussian channel with an adjustable channel gain matrix. Specifically, for various (generally time-varying) choices channel gain matrices, we study the tradeoff relationship between 1) the mean-square estimation error attainable by the classical Kalman-Bucy filter, and 2) the mutual information between the source process and its Kalman-Bucy estimate. We then formulate a novel optimal channel gain control problem where the objective is to control the channel gain matrix strategically to minimize the weighted sum of these two performance metrics. To develop insights into the optimal solution, we first consider the problem of controlling a time-varying channel gain over a finite time interval. A necessary optimality condition is derived based on Pontryagin's minimum principle. For a scalar system, we show that the optimal channel gain is a piecewise constant signal with at most two switches. We also consider the problem of designing the optimal time-invariant gain to minimize the average cost over an infinite time horizon. A novel semidefinite programming heuristic is proposed and the exactness of the solution is discussed.