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)
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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
| 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 |
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$t~=~0$; |
|
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: ${\theta~_t}~=~{\theta~_{t~-~1}}~-~\alpha~~\cdot~{\hat~m_t}/\left(~{\sqrt~{{{\hat~v}_t}}~~+~\varepsilon~}~\right)$; |
|
| 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 |
| 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 |
| 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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