An Improved KF-RBF Based Estimation Algorithm for Coverage Control with Unknown Density Function

Abstract

This paper investigates the coverage control for a group of agents, where the density function over the given region is unknown and time-varying. A cost function, depending on the density function and a certain metric, is provided to evaluate the performance of coverage network. Then, while considering the sampling noise, a novel estimation algorithm is developed to approximate the density function based on the Kalman filter (KF) and the Radial Basis Function (RBF) neural network. Compared with the other estimation algorithms, a novel sampling regulation mechanism is designed to improve the estimation performance and reduce the computational load. On this basis, a coverage control scheme with estimated density function is proposed to drive the agents to the optimal deployment. Moreover, the stability and performance of proposed coverage control system are strictly analyzed. Finally, numerical simulation is provided to illustrate the effectiveness of proposed approaches.