Resource Scheduling Method of Multi-sensor Cooperative Detection for Flying Targets
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摘要: 针对飞行目标机动性带来的多传感器协同探测资源调度动态性需求, 提出一种新的基于近端策略优化(Proximal policy optimization, PPO)与全连接神经网络结合的多传感器协同探测资源调度算法. 首先, 分析影响多传感器协同探测资源调度的复杂约束条件, 形成评价多传感器协同探测资源调度过程指标; 然后, 引入马尔科夫决策过程(Markov decision process, MDP)模拟多传感器协同探测资源调度过程, 并为提高算法稳定性, 将Adam算法与学习率衰减算法结合, 控制学习率调整步长; 最后, 基于改进近端策略优化与全卷积神经网络结合算法求解动态资源调度策略, 并通过对比实验表明该算法的优越性.Abstract: Aiming at the dynamic demand of multi-sensor cooperative detection resource scheduling brought by the maneuverability of flying targets, a new multi-sensor cooperative detection resource scheduling algorithm based on proximal policy optimization (PPO) and fully connected neural network is proposed. In this paper, we first build a constraint index model that affects the scheduling of multi-sensor cooperative detection resources. Next, we introduce the Markov decision process (MDP) to simulate the multi-sensor cooperative detection resource scheduling process, and in order to improve the stability of the algorithm, the Adam algorithm is combined with the learning rate attenuation algorithm to control the up-to-date step of learning rate. Finally, the optimal resource scheduling strategy is solved based on the improved proximal policy optimization and fully connected neural network algorithm, and the comparative experiment shows the superiority of the algorithm proposed in this paper.
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图 1 多传感器探测资源调度过程中复杂约束条件
Fig. 1 Complex constraints in the process of multi-sensor resources schedule
图 5 基于改进PPO-FCNN的多传感器协同探测资源调度算法训练示意图
Fig. 5 Training algorithm for multi-sensor cooperative detection resource scheduling based on improved PPO-FCNN
图 6 基于改进PPO-FCNN的多传感器协同探测资源动态调度算法流程
Fig. 6 Process of multi-sensor cooperative detection dynamic scheduling based on improved PPO-FCNN
图 8 面向不同传感器数量的不同算法训练效果
Fig. 8 Training effects of different algorithms for different sensor numbers
图 9 面向不同传感器数量的收敛时间对比
Fig. 9 Comparison of convergence time for different sensor numbers
表 1 各层次神经网络参数
Table 1 Parameters of neural network at various layers
层次名 隐元个数 激活函数 FCCN_1 100 ReLU FCCN_2 200 ReLU FCCN_3 200 Tanh FCCN_4 200 Tanh Softmax $num$ Softmax 
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表 2 仿真参数设置
Table 2 Simulation parameters
参数配置 数值 Actor学习率 0.0001 Critic学习率 0.0002 衰减因子 0.9 最小样本数 64 更新间隔 10 次 裁剪函数参数$\varepsilon$ 0.2
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表 3 飞行目标状态参数
Table 3 Parameters of flight target status
飞行目标参数 取值范围 横坐标$x_2^{(t)}$ 97 ~ 30 纵坐标$y_2^{(t)}$ 814 ~ 348 高度$z_2^{(t)}$ 168 ~ 400
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表 4 第$i$号探测设备状态参数
Table 4 Status parameters of No. $i$ detection equipment
第$i$号探测设备参数 取值 通视性$vis_{i,t}$ 1或0 最大探测范围$Dis_i^{Max}$ 0 ~ 400 最大可工作时长$Store_i^{Max}$ 20 最大切换次数$ht$ 12 优先级$pre_{i,t}$ 1或0
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表 5 传感器约束层次总排序表
Table 5 Hierarchical sorting summary for constraints of sensors
约束分类 权重 复杂约束 权重 $ {\alpha _1} $ $ {\alpha _2} $ $ {\beta _1} $ $ {\beta _2} $ $ {\beta _3} $ $ {\beta _4} $ $ {\beta _5} $ 单传感器约束 0.5 探测性能约束 0.5 0.4 0.4 0.2 0 0 0 探测效率约束 0.5 0 0 0.5 0.5 0 0 多传感器约束 0.5 关联约束 1.0 0 0 0 0 0 1.0 层次总排序 0.1 0.1 0.05 0.125 0.125 0.5
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表 6 随机一致性指标
Table 6 Random consistent index
$ n $ ${\rm{RI}}$ 1 0 2 0 3 0.58 4 0.90 5 1.12 6 1.24 7 1.32 8 1.41 9 1.45
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表 7 不同算法训练至收敛的迭代次数
Table 7 Iteration numbers of training to convergence for different algorithms
场景 改进PPO-FCNN PPO-FCNN DQN 遗传算法 面向10个传感器 10300 7133 38000 29000 面向15个传感器 10000 10712 42000 33000 面向20个传感器 10418 1935 26000 28000
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表 8 改进PPO-FCNN面向不同传感器数量的收敛时间幅度对比(%)
Table 8 Comparison of convergence time amplitude of improved PPO-FCNN for different sensor numbers (%)
场景 PPO-FCNN DQN 遗传算法 面向10个传感器 39.00 –68.90 –59.10 面向15个传感器 –0.06 –72.30 –62.90 面向20个传感器 4.10 –54.15 –56.30
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