Abstract
In order to deal with the new threat of low altitude slow small (LSS) targets in air defense operations and provide support for LSS target interception decision, we propose a simple and reliable LSS target threat assessment method. Based on the detection capability of LSS targets and their threat characteristics, this paper proposes a threat evaluation factor and threat degree quantization function in line with the characteristics of LSS targets. LSS targets not only have the same threat characteristics as traditional air targets but also have the unique characteristics of flexible mobility and dynamic mission planning. Therefore, we use analytic hierarchy process (AHP) and information entropy to determine the subjective and objective threat factor weights of LSS targets and use the optimization model to combine them to obtain more reliable evaluation weights. Finally, the effectiveness and credibility of the proposed method are verified by experimental simulation.
Highlights
Threat assessment is an estimate of the lethality of enemy forces and the degree of threat to us [1]
Taking the typical low altitude slow small (LSS) target of an unmanned aerial vehicle (UAV) swarm as an example, it can be seen from the UAV combat concept, development planning and research projects published in recent years [2,3,4,5,6], that a UAV swarm is composed of dozens to hundreds of UAVs with low cost, small size and limited functions equipped with a variety of mission loads
The objective weight is calculated based on the method of information entropy, and the information entropy of evaluation indexes is calculated according to formula (9), and the following is obtained: H = [0.958 0.992 0.955 0.923 0.946 0.963]
Summary
Threat assessment is an estimate of the lethality of enemy forces and the degree of threat to us [1]. The subjective weighting methods commonly used in target threat estimation need large-scale auxiliary systems and a priori knowledge base. The research on UAV swarm warfare is still in the preliminary stage, and it is difficult to obtain accurate evaluation results based on such methods. The entropy method determines the weight according to the index variability, and the weight is adjusted in real-time, which can sensitively find the “dissimilarity” in the cluster, highlight the high threat targets that need to be paid attention to in the cluster, and can be suitable for the threat assessment of LSS targets. AHP and entropy method are introduced into LSS target threat assessment, and an optimization model is established to optimize and fuse the weights determined by them to obtain a more reliable assessment weight.
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