有源干扰条件下基于NSGA-Ⅱ的雷达网优化部署方法

在有源干扰条件下，雷达网部署直接影响着防区内指挥信息系统的预警监测能力。由于防区内由分散于不同位置，且重要度不同的责任区组成的，那么实现全方位全纵深的预警能力，将是雷达网部署的重要方面。根据覆盖系数和重叠系数为主要优化目标，基于NSGA-Ⅱ算法进行多目标优化。首先定义了覆盖系数和全局重叠系数两个指标，尤其是全局重叠系数打破了以往重叠系数的概念，从全局出发引导雷达网优化部署；同时，提出基于NSGA-Ⅱ的多目标优化部署算法，采用诱导跳跃、基因到位、诱导交叉等候选解生成方式，保持种群多样性，提高算法收敛性。实验表明，部署优化算法耗时较低，不同干扰源部署态势使网络节点部署产生较大差异，多样的候选解生成方法明显提高了算法的收敛速度。

Optimal Deployment of Radar Network Based on NSGA-Ⅱ under Active Jamming

The optimal deployment of overlay network, which involves constructing overlay model, extracting multi-object function and designing optimal deployment algorithm, is one of the important parts in overlay network research. Generally speaking, the ranges of overlay network are often irregular closed graphs because of multifarious barriers. Consequently, we hope to put forward a fast algorithm based on marginal fitting to overcome a mass of wasting when calculating cover area. Sig-nificantly, the optimal deployment of overlay network in real world should take more aspects into consideration, including cover area, detection probability, and threaten degree of near link path which are different to formers. More attention, the near link path is aimed to analyze the relationship between dispersed remain areas in order to find out the probability of being a relative closed path which is uncovered by the overlay network. Finally, we put forward an optimal deployment algorithm based on NSGA-Ⅱ to deal with this problem, within different genetic operators such as attractive jumping, gene reversing and attractive overlapping to generate new candidates, and using global temperature to control generation and selection of candidates as Simulated Annealing. The marginal fitting algorithm was found highly accurate and fast for calculating the cover area, and the near link path discovered a different novel request in deployment, and the optimal deployment algorithm was proved to suit for overlay network deployment.