The problem that two cooperative missiles intercept a maneuvering target while imposing a desired relative geometry is investigated in the paper. Firstly, a three-dimensional (3D) estimation model for cooperative target tracking is proposed and the observability of the missile-target range measurement is analyzed. In order to enhance the estimation performance, a two-level cooperative interception guidance architecture is proposed which combines a coordination algorithm with a novel 3D fixed-time convergent guidance law considering line of sight (LOS) angle constraints, such that the desired relative geometry can be imposed quickly and steadily by a dynamic strategy. The effectiveness and superiority of the proposed guidance law is evidenced through the numerical simulations comparing with other guidance laws.

Abstract– –In this paper, a collaborative relay tracking method based on information fusion is proposed for UAVs. Target matching is performed by combining the target's spatio-temporal information with its appearance and other auxiliary cues. Only the motion information and a compressed image of the target are used for target handover. Specifically, to determine the spatio-temporal transfer probability of the target after entering the blind area of visual field, this paper proposes a spatio-temporal correlation model construction method to predict the spatio-temporal similarity. Besides, Siam-LGNet is proposed in this paper for target appearance matching to overcome the effects of various view angles, rotations, scale changes and other factors on the images. This effectively ensures the accuracy of target matching and lowers the model complexity. Additionally, similarities obtained from multiple cues are fused by D-S evidence theory to compensate for the uncertainty of single cue. Finally, the effectiveness of the proposed method is verified in simulation experiments and physical experiments. This paper fills a gap in the field of UAVs relay tracking. The proposed scheme is scalable and lays the foundation for subsequent research on UAVs relay tracking problems.

Fast and accurate threat evaluation (TE) of incoming air targets has a great influence on air defense. In this paper, two new generalized intuitionistic fuzzy soft set (GIFSS) methods are proposed for threat evaluation of air targets. Firstly, the threat evaluation index system is reasonably constructed by analyzing the relative kinematics between the targets and assets, apart from that between the targets and interceptors, which is more reasonable and practical. Secondly, after the threat indexes (TI) are properly obtained, two new aggregation operators for GIFSS are put forward based on the generalized $\lambda$-Shapley Choquet integral. The proposed operators not only depict the correlations among the evaluation index but also consider the importance of them globally. Finally, the effectiveness and superiority of the proposed methods are verified through a numerical simulation including four air targets in different index systems.

This paper proposes a novel hybrid data-driven modeling method for missiles. Based on actual flight test data, the missile hybrid model is established by combining neural networks and the mechanism modeling method, considering the uncertainties and nonlinear factors in missiles. This method can avoid the problems in missile mechanism modeling and traditional data-driven modeling, and can also provide a solution for nonlinear dynamic system modeling problems in offline usage scenarios. Finally, the feasibility of the proposed method and the credibility of the established model are verified by simulation experiments and statistical analysis.

In recent years, correlation filtering has been widely used in the field of UAV target tracking for its high efficiency and good robustness, even on a common CPU. However, the existing correlation filter-based tracking methods still have major problems when dealing with challenges such as fast moving targets, camera shake, and partial occlusion in UAV scenarios. Furthermore, the lack of reasonable attention mechanism for distortion information as well as background information prevents the limited computational resources from being used for the part of the object most severely affected by interference. In this paper, we propose the spatial attention aberration repressed correlation filter, which models the aberrations, makes full use of the spatial information of aberrations and assigns different attentions to them, and can better cope with these challenges. In addition, we propose a mechanism for the intermittent learning of the global context to balance the efficient use of limited computational resources and cope with various complex scenarios. We also tested the mechanism on challenging UAV benchmarks such as UAVDT and Visdrone2018, and the experiments show that SAARCF has better performance than state-of-the-art trackers.