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.
Highlights
Improved Coordinated LateralUnmanned Aerial Vehicles (UAVs) are increasingly used in battlefield reconnaissance, suppressing enemy air defenses, attacking ground targets, and dominating battlespaces [1].The development of microcomputer technology has allowed modern UAVs to become comprehensive and intelligent [2]
The traditional nonlinear Kalman filter algorithms, such as extended Kalman filter (EKF) and unscented Kalman filter (UKF), can realize the state estimation of the ground target moving in constant velocity
Alouani explained that under the condition of random deviation, when the error covariance matrix of the unbiased filter of the two-step Kalman filter meets the limit of an algebraic condition, it is equivalent to the augmented state Kalman filter (ASKF), but this constraint is often not tenable in practical systems [8]
Summary
Unmanned Aerial Vehicles (UAVs) are increasingly used in battlefield reconnaissance, suppressing enemy air defenses, attacking ground targets, and dominating battlespaces [1]. It is of great significance to study autonomous target tracking methods to enhance the effectiveness of the UAV [5]. When the ground target moves in the turning model, the state estimation of the traditional nonlinear Kalman filter algorithm is inaccurate. Friedland proposed a two-step Kalman filter (TSKF), which can ensure the optimality of estimation for constant deviation [7]. OTSKF can still ensure the optimality of filter state estimation in Guidance Law. ISPRS Int. J. Proposed a UAV target tracking algorithm based on a navigation function, which only uses a range sensor. Wise et al [19] proposed an improved LVFG method, which allows multiple UAVs to reach the tracking position at the same time. A decentralized guidance strategy was proposed by Quintero et al [20] using model predict control for coordinated target tracking.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
