基于调度效益最大化的多功能组网认知雷达资源优化调度算法

在组网认知雷达中,针对多目标多任务(如搜索、跟踪与成像等)按优先级进行资源调度时易造成目标任务丢失的问题,提出基于调度效益最大化的多功能组网认知雷达资源优化调度算法。该算法将搜索与跟踪任务的时间窗考虑到目标函数中,通过目标任务的重要性(优先级)和有效性(时间窗)两个因素的加权来表示雷达对目标任务的调度效益,根据调度效益最大准则建立并利用遗传算法求解资源调度模型。对仿真结果分析表明,该方法能够提高组网认知雷达的整体效能。

Optimal Scheduling Algorithm for Multi-Functional Network Cognitive Radar Resources Based on Maximizing Scheduling Benefits

In network cognitive radar, aiming at the problem that resource scheduling according to priority for multi-target and multi-task (such as searching, tracking and imaging) can easily lead to the loss of target tasks, an Optimal scheduling algorithm for multi-functional network cognitive radar resources based on maximizing scheduling benefits is proposed. The algorithm takes the time window of the search and tracking task into consideration in the objective function, and the scheduling benefit of the radar to the target task is represented by the weighting of the importance (priority) and validity (time window) of the target task. According to the maximum scheduling benefit criterion, a resource scheduling model is established and solved by genetic algorithm. The simulation experiment analyzes the scheduling sequence diagram and performance indicators, and verifies that the method can improve the overall performance of the network cognitive radar.