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Intelligent target following strategy design for UAV against multi-threats
QI Xiaoming, WEI Ruixuan , ZHOU Kai
Aerospace Engineering College, Air Force Engineering University, Xi'an 710038, China
Received: 2015-05-11; Accepted: 2015-08-06; Published online: 2015-10-14 15:39
Foundation items: National Natural Science Foundation of China (61573373);Aeronautical Science Foundation of China (20135896027)
Corresponding author. Tel.: 17082452676 E-mail: 2856402009@qq.com
Abstract: With the development of integrative combat, the task will be converted into target following mode when unmanned aerial vehicle (UAV) finishes searching ground moving target. Complex combat environment is considered, and the problem of UAV following moving target in the condition of multi-threats source are studied. An intelligent target following strategy based on decision trees is proposed in order to ensure the security of UAV and the precision of target following. Firstly, threat probability map (TPM) model was established. Secondly, the problem how to solve the minimum of TPM was researched. Then, the different rules were generated on the basis of the different priorities for object tasks by geometric method; the complete decision trees were established; the heading and speed commands of UAV were generated for different rules. Finally, simulation results demonstrate the validity of the proposed method.
Key words: threat modeling     unmanned aerial vehicle (UAV)     gradient descent     rules     decision trees     target following     intelligent navigation

1 威胁概率图模型

 图 1 多威胁区域的风险度示意图 Fig. 1 Schematic of risk degree of multi-threats area
 图 2 风险度在二维平面投影图 Fig. 2 Project of risk degree on two dimension plat
2 基于决策树的智能目标跟随策略设计 2.1 基本集合元素定义

 图 3 UAV与运动目标的位置和航向示意图 Fig. 3 Schematic of position and heading of UAV and moving target

 图 4 VPR和VFPR示意图 Fig. 4 Schematic of VPR and VFPR

 图 5 UAV的安全航向范围 Fig. 5 Safe heading range of UAV
2.2 生成决策树

UAV在多威胁区域中执行目标跟随任务时,除了考虑跟随精度外,还需考虑受损及安全性等问题,因此,UAV在自主跟随过程中需要同时满足以下3种目标:①避免受限区域;②保持与目标之间的距离;③UAV损伤程度最小。根据3种目标的不同优先级以及UAV与目标之间的几何关系设计了不同的决策条件和规则状态并生成决策树,进而生成UAV的期望航向角及期望速度指令。

 图 6 UAV智能目标跟随策略决策树 Fig. 6 Decision trees of UAV intelligent target following strategy

 策略状态 决策条件1 决策条件2 决策条件3 决策条件4 ① NTA N Y Y ② NTA N Y N ③ NTA N N N/A ④ NTA Y N/A N/A ⑤ LTA N Y Y ⑥ LTA N Y N ⑦ LTA N N N/A ⑧ LTA Y N/A N/A ⑨ HTA N/A N/A N/A
2.3 不同策略条件下UAV航向/速度指令设计

 图 7 惯性坐标系与局部坐标系示意图 Fig. 7 Schematic of inertia and local coordinates

 图 8 局部坐标系下UAV与近似圆之间的位置误差示意图 Fig. 8 Schematic of position error between UAV and proximity circle in local coordinate

3 仿真验证与分析

1) 目标的初始位置为(38,5),初始速度为50 m/s,初始航向角为π/2。

2) UAV的初始位置为(39,2),初始速度为100 m/s,初始航向角为2π/3。

3) UAV的最小和最大飞行速度分别为50 m/s和180 m/s,最大和最小加速度分别为3 m/s2和-3 m/s2,最大转弯角速率为π/12 rad/s,制导更新周期Ts=3 s。

4) UAV机载传感器探测范围为2 km,近似圆范围为1.5 km。

5) 受限区边界概率密度f2=0.02,航向角偏差范围ψHDC*=π/4。

6) UAV与近似圆之间的远近程度参数为:δtg=0.05,δtp=0.20。

7) 指令速度比例控制增益参数为:Ks=0.5,Ke=0.000 5。

8) 测量噪声的标准偏差为0.05 km。

9) 加速目标的运动方程为

 图 9 基于离散点寻优法的加速目标跟随仿真 Fig. 9 Following simulation for acceleration target based on discrete point optimization method
 图 10 基于专家规则的智能加速目标跟随仿真 Fig. 10 Following simulation for intelligent acceleration target based on expert rules

10) 复杂运动目标的运动方程如下。

 图 11 基于离散点寻优法的复杂运动目标跟随仿真 Fig. 11 Follwing simulation for complex moving target based on discrete point optimization method
 图 12 基于专家规则的智能复杂运动目标跟随仿真 Fig. 12 Following simulation for intelligent complex moving target based on expert rules
4 结 论

1) 在原有威胁概率图模型基础上,根据UAV、运动目标以及所提出的任务优先级设计出不同规则条件下UAV飞行航向及速度指令。

2) 采用了一种基于专家系统规则的智能决策方法,并在不同的规则下采用模型预测控制算法中的滚动时域预测方法进行决策。

3) 将该方法应用于UAV对地面加速目标和复杂运动目标跟随任务中,根据不同规则实时改变UAV的速度和航向控制指令,使UAV受到的损伤概率达到最小,并较好地跟随此2种目标,进一步验证了本文所提方法的有效性。

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#### 文章信息

QI Xiaoming, WEI Ruixuan, ZHOU Kai

Intelligent target following strategy design for UAV against multi-threats

Journal of Beijing University of Aeronautics and Astronsutics, 2016, 42(4): 780-788.
http://dx.doi.org/10.13700/j.bh.1001-5965.2015.0296