基于侦收价值的短波协同侦收资源调度算法
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摘要
针对现有调度方法未充分考虑目标先验信息导致调度缺乏指导性的问题,提出基于信号侦收价值的短波协同侦收资源调度算法,提高了资源调度性能,缓解了短波侦收中各侦收设备各自为战,缺乏整体性,指导性的情况。从截获的宽频段无线电数据中提取信号部分特征构建侦收价值评估模型,定义侦收效益的概念,以实现最大侦收效益和为调度目标,结合约束条件建立短波协同侦收资源调度模型并对其求解,以调度成功率和实现价值率来反映调度算法对重要任务的调度性能。实验结果表明,在相同条件下,相比传统调度算法,本文算法提高了侦收效益,增强了调度性能。
Aiming at the problem that the existing scheduling methods do not take full account of the priori information of the signals and lead to the lack of guidance in the task of High Frequency(HF)wave reception,this paper presented a resource algorithm based on the reception value of target signals.According to the target signal property,the reception value evaluation model was constructed.The conception of optimal benefit for HF wave reception was presented and taken as the objective function according to the constraint conditions.And scheduling success rate and realization value rate was taken to reflect the execution of scheduling algorithm.The experimental results show that compared with the traditional priority scheduling algorithm,the proposed algorithm not only improves the efficiency of reception,but also enhances the scheduling performance.
Aiming at the problem that the existing scheduling methods do not take full account of the priori information of the signals and lead to the lack of guidance in the task of High Frequency(HF)wave reception,this paper presented a resource algorithm based on the reception value of target signals.According to the target signal property,the reception value evaluation model was constructed.The conception of optimal benefit for HF wave reception was presented and taken as the objective function according to the constraint conditions.And scheduling success rate and realization value rate was taken to reflect the execution of scheduling algorithm.The experimental results show that compared with the traditional priority scheduling algorithm,the proposed algorithm not only improves the efficiency of reception,but also enhances the scheduling performance.
引文
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[2]Guo Songtao,Xiao Bin,Yang Yuanyuan,et al.EnergyEfficient Dynamic Offloading and Resource Scheduling in Mobile Cloud Computing[C]//The 35th Annual IEEE International Conference on Computer Communication.US:IEEE,2016:1-9.
[3]Yu Rong,Zhong Weifeng,Xie Shengli,et al.QoS Differential Scheduling in Cognitive-Radio-Based Smart Grid Networks:An Adaptive Dynamic Programming Approach[J].IEEE Transactions on Neural Networks and Learning Systems,2016,27(2):435-443.
[4]Hiroyuki Chishiro.RT-Seed:Real-Time Middleware for Semi-Fixed-Priority Scheduling[C]//2016IEEE 19th International Symposium on Real-Time Distributed Computing.US:IEEE,2016:124-133.
[5]Bruggen Georg von der,Chen JianJia,Huang WenHung.Schedulability and Optimization Analysis for NonPreemptive Static Priority Scheduling Based on Task Utilization and Blocking Factors[C]//The 27th Euromicro Conference on Real-Time Systems.US:IEEE,2015:90-101.
[6]Lee Jinkyu.Time-Reversibility for Real-Time Scheduling on Multiprocessor Systems[J].IEEE Transactions on Parallel and Distributed Systems,2017,28(1):230-243.
[7]郝才勇,彭忠宝.短波监测覆盖范围分析[J].中国无线电,2015,30(12):56-58.
[8]张浩为,谢军伟,盛川.综合优先级规划下的相控阵雷达自适应调度方法[J].兵工学报,2016,37(11):2163-2169.
[9]Lu JB,Xiao H,Xi ZM,et al.Multifunction phased array radar resourcemanagement:real-time scheduling algorithm[J].Journal of Computational Information Systems.2011,7(2):385-393.
[10]Miranda S L C,Baker C J,Woodbridge K,et al.Comparison of scheduling algorithms for multifunction radar[J].IET Radar,Sonar&Navigation,2007,6(1):414-424.