有源干扰条件下基于NSGA-II的雷达网优化部署方法

雷达网的部署状态直接影响着网络的覆盖能力，尤其是在雷达辐射轨迹受到外力干扰条件下，对网络节点的优化部署更具意义。雷达对部署的任务区域有一定的能量覆盖，不同的任务区也具有不同的重要级别，在不针对特定任务的前提下，实现全方位全纵深的预警能力，雷达网的部署起着至关重要的作用。本文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。文章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。

The Optimal Deployment of Radar Network based on NSGA-II under 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. Significantly, 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.