Diffusion Nonlinear Estimation and Distributed UAV Path Optimization for Target Tracking with Intermittent Measurements and Unknown Cross-Correlations

Abstract

This paper focuses on distributed state estimation (DSE) and unmanned aerial vehicle (UAV) path optimization for target tracking. First, a diffusion cubature Kalman filter with intermittent measurements based on covariance intersection (DCKFI-CI) is proposed, to address state estimation with the existence of detection failure and unknown cross-correlations in the network. Furthermore, an alternative transformation of DCKFI-CI based on the information form is developed utilizing a pseudo measurement matrix. The performance of the proposed DSE algorithm is analyzed using the consistency and the bounded error covariance of the estimate. Additionally, the condition of the bounded error covariance is derived. In order to further improve the tracking performance, a UAV path optimization algorithm is developed by minimizing the sum of the trace of fused error covariance, based on the distributed optimization method. Finally, simulations were conducted to verify the effectiveness of the proposed algorithm.