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SCIENTIA SINICA Informationis, Volume 50 , Issue 5 : 704-717(2020) https://doi.org/10.1360/SSI-2019-0106

Prediction of unmanned aerial vehicle target intention under incomplete information

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  • ReceivedMay 21, 2019
  • AcceptedJun 17, 2019
  • PublishedApr 24, 2020

Abstract

The complexity and uncertainty of actual air combat and the unknown information of some air combat bring great challenges to unmanned aerial vehicle (UAV) air combat target intention prediction. In this paper, we examine the problem of air combat intention prediction under incomplete information, and present an air combat target intention prediction model based on long-short-term memory (LSTM) with incomplete information. The model adopts a hierarchical method to establish the feature set of air combat target intention prediction, encodes the information of air combat to time series features, encapsulates expert knowledge into labels, and introduces the method of fitting cubic sample interpolation function and filling average value to repair incomplete data. Also, we used the adaptive moment estimation (Adam) optimization algorithm to accelerate the training speed of the model to effectively prevent local optimum. Finally, the simulation results show that the proposed model can effectively predict the target intention of UAVs in air combat.


Funded by

装备预研中国电科联合基金开放课题(6141B08231110a)

装备预研重点实验室基金项目(61425040104)


References

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  • Figure 1

    Intention representation and prediction process of air combat target

  • Figure 2

    Intention prediction workflow of UAV air combat

  • Figure 3

    (Color online) Air combat situation. (a) Advantage of our side; (b) advantage of the enemy;protect łinebreak (c) neutrality between the two sides; (d) balance of power between the two sides

  • Figure 4

    Characteristic description tree chart of UAV air combat intention prediction

  • Figure 5

    Framework of UAV air combat intention prediction model

  • Figure 6

    Flow chart of air combat data repairing

  • Figure 7

    Intention coding and pattern analysis

  • Figure 8

    Fitting curve of distance in the case that some distance values are missing at some time points

  • Figure 9

    (Color online) Recognition accuracy of training set and test set

  • Figure 10

    (Color online) Comparison of different recognition models

  • Table 1   Correspondence of air combat situation and target intention
    Air combat situation Most possible target intention Secondary possible operational intention
    Advantage of the enemy Attack Surveillance
    Advantage of our side Defense Penetration, electronic interference
    Balance of power between the two sides Feint Attack, defense, electronic interference
    Neutrality between the two sides Reconnaissance Electronic interference
  •   

    Algorithm 1 基于Adam算法的LSTM参数调节

    Require:步长$\alpha~$, 矩估计的指数衰减率${\beta~_1}$和${\beta~_2}$, 小常数$\varepsilon~$, 训练集和标签集, 待训练参数集合$\Theta~$, 当前时刻的累积误差${L^{(t)}}$;

    Output:训练完成的参数集合$\Theta~$;

    for $\Theta~$中的每个$\theta~$

    初始化参数$\theta~$, 初始化一阶和二阶矩变量${m_0}$, ${v_0}$, 初始化时间步长$t~=~0$;

    while ${\theta~_t}$没有达到停止准则 do

    从训练集中采包含$m$个样本$X' = \left\{ {{x_1},{x_2}, \ldots ,{x_m}} \right\}$和其对应的${\hat y_{x'}}$标签集合;

    计算梯度: ${g_{t~+~1}}~=~\frac{1}{m}{\nabla~_\theta~}\sum\nolimits_i~{L\left(~{f\left(~{{x_i};{\theta~_t}}~\right),{y_{{x_i}}}}~\right)}~$;

    $t~=~t~+~1$;

    更新有偏一阶矩估计: ${m_t}{\rm{~=~}}{\beta~_1}~\cdot~{m_{t~-~1}}~+~\left(~{1~-~{\beta~_1}}~\right)~\cdot~{g_t}$;

    更新有偏二阶矩估计: ${v_t}~=~{\beta~_2}~\cdot~{v_{t~-~1}}~+~\left(~{1~-~{\beta~_2}}~\right)~\cdot~g_t^2$;

    修正一阶矩的偏差: ${\hat~m_t}~=~{m_t}/\left(~{1~-~\beta~_1^t}~\right)$;

    修正二阶矩的偏差: ${\hat~v_t}~=~{v_t}/\left(~{1~-~\beta~_2^t}~\right)$;

    更新参数: ${\theta~_t}~=~{\theta~_{t~-~1}}~-~\alpha~~\cdot~{\hat~m_t}/\left(~{\sqrt~{{{\hat~v}_t}}~~+~\varepsilon~}~\right)$;

    end while

    end for

  • Table 2   Distance between two sides under a certain battle intention
    Time point Distance between two sides (km) Time point Distance between two sides (km) Time point Distance between two sides (km)
    1 6.14 5 4.76 9 5.77
    2 5.90 6 4.67 10 5.88
    3 5.72 7 4.97 11 6.08
    4 5.32 8 5.47 12 6.25
  • Table 3   Correspondence of data missing percent and model recognition accuracy (%)
    Data missing Model recognition Data missing Model recognition
    0 94.12 40 84.85
    10 93.53 50 73.76
    20 92.78 60 60.57
    30 90.69 70 46.67
  • Table 4   Recognition accuracy on different training sets and test sets (%)
    Recognition rate of Test recognition Recognition rate of Test recognition
    training set rate training set rate
    98.16 94.30 98.62 93.95
    98.12 93.91 98.06 93.89
    98.54 94.11 98.10 94.06
    98.57 93.91 98.12 94.26
    98.47 94.12 98.42 94.21

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