Jamming resources allocation ( JRA) is an essential task in clustered combat environments. Especially in cases with multiple threats, efficient JRA is imperative for protecting key assets. In this study, multiple distributed outboard jammers protecting multiple targets against a multi-missile threat are considered, where the function of JRA includes jamming object selection and jammer power allocation. To address this problem, first, a scheme to evaluate the jamming effectiveness of the radar seeker under cooperative jamming from multiple jammers is proposed. Considering power allocation, a mixed-integer programing model for the optimisation of JRA is further established. A two-track simultaneous update binary particle swarm algorithm (TS-BPSO) is then devised to obtain the solutions of the JRA. Finally, the effectiveness of the proposed method for multiple jammers in the multiple missiles scenario is evaluated through simulations. The simulation results suggest that the distributed cooperative jamming resource allocation is affected by the incoming orientation of the missile and the relative position of the jammers and the targets. The findings of the study enable evaluating the use of jammer formations and provide insights into developing effective jamming strategies. K E Y W O R D S distributed outboard jammers, jamming effectiveness assessment, jamming resources allocation, multi-missile threat, TS-BPSO This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
To address the data storage, management, analysis, and mining of ship targets, the object-oriented method was employed to design the overall structure and functional modules of a ship trajectory data management and analysis system (STDMAS). This paper elaborates the detailed design and technical information of the system’s logical structure, module composition, physical deployment, and main functional modules such as database management, trajectory analysis, trajectory mining, and situation analysis. A ship identification method based on the motion features was put forward. With the method, ship trajectory was first partitioned into sub-trajectories in various behavioral patterns, and effective motion features were then extracted. Machine learning algorithms were utilized for training and testing to identify many types of ships. STDMAS implements such functions as database management, trajectory analysis, historical situation review, and ship identification and outlier detection based on trajectory classification. STDMAS can satisfy the practical needs for the data management, analysis, and mining of maritime targets because it is easy to apply, maintain, and expand.
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