Moving-Horizon Estimation for Multi-Sensor Systems Under Probabilistic Caching Mechanism: A Co-Design Scheme

Abstract

In practice, the cache, capable of storing frequently accessed data, is widely deployed in edge servers to guarantee quick retrieval and improve overall system performance. In this paper, the moving-horizon state estimation problem is investigated for a class of multi-sensor systems under the effects of limited caching capacity and sensor resolution. The measurement information collected by multiple sensors is first transmitted to an edge server for state estimation purposes and then stored in the cache for future use. To accommodate the limited caching capacity, the probabilistic caching mechanism (PCM) is harnessed to manage the cached content, under which only a portion of the measurement information is probabilistically selected and retained in the cache. By solving the least-squares optimization problem, a novel moving-horizon state estimator is proposed under the PCM. Sufficient conditions are derived to guarantee that the estimation error is exponentially ultimately bounded in the mean-square sense. To improve the estimation accuracy, the parameters of both the estimator and the PCM are jointly designed by addressing a constrained optimization problem with the assistance of the particle swarm optimization method. Finally, two examples are given to showcase the effectiveness of the proposed algorithm.