Unmanned Aerial Vehicle Target Tracking Based on OTSCKF and Improved Coordinated Lateral Guidance Law

Abstract

This paper proposes an approach of target tracking of a ground target for UAVs using Optimal Two-Stage Cubature Kalman Filter and Improved Coordinated Lateral Guidance Law. Firstly, the Optimal Two-Stage Cubature Kalman Filter (OTSCKF) is proposed to estimate the target motion. The OTSCKF combines two-stage filtering technology with CKF to improve the estimation accuracy. Secondly, to keep a constant distance between the UAV and the target, a new guidance law based on the lateral turning equation is proposed and its asymptotic stability is proven. On this basis, a distributed tracking algorithm is designed to balance the phase difference and achieve cooperation among multi-UAVs. Thirdly, numerical experiments are performed for the tracking problems of moving targets and the results verify the effectiveness of the proposed guidance algorithm.

Visual image design of the internet of things based on AI intelligence

“…However, because a UAV must fly at a high height in order to obtain a broad observation view, the objects that are photographed frequently have a small pixel size, and the categories that they fall into are subject to a great deal of ambiguity. Many advanced computer vision applications rely on visual image detection, including autonomous driving, face identification and recognition, and activity recognition [ 3 ]. Significant improvement has been made in recent years, but these algorithms prioritize detection in generic circumstances above those taken by drones.…”