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摘要:
针对飞行区航空器单一监视源存在监视精度低、位置跳变的问题，提出一种基于Transformer和注意力机制的航空器监视数据融合方法。利用Transformer的编码器结构分别对各监视源数据进行特征提取，通过注意力机制对不同监视源赋予权重值，经过全连接网络进行回归计算，获得最终的融合结果。选取场面监视雷达(SMR)和广播式自动相关监视(ADS-B)系统的监视数据作为融合源，多点定位(MLAT)数据作为真实标签，实验结果表明：所提方法有效降低了单一监视源的监视误差，且融合效果优于基于注意力机制的长短期记忆网络、循环神经网络和扩展卡尔曼滤波融合方法，平均绝对误差分别提升了2.81%、16.73%和35.80%。
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关键词:
- 数据融合 /
- Transformer /
- 注意力机制 /
- 场面监视雷达 /
- 广播式自动相关监视
Abstract:An aircraft surveillance data fusion method based on a Transformer and attention mechanism is proposed to address the issues of low monitoring accuracy and position jump in a single surveillance source for aircraft in the flight area. Prior to assigning weight values to various surveillance sources via the attention mechanism, features are first extracted from each surveillance source data using the Transformer’s encoder structure. Finally, regression calculations are performed through a fully connected network to obtain the final fusion result. The multilateration (MLAT) data are employed as actual tags, while the surveillance data from the autonomous dependent surveillance-broadcast (ADS-B) system and the surface movement radar (SMR) are chosen as fusion sources. The experimental results show that the proposed method effectively reduces the surveillance error of a single surveillance source, and the fusion effect is better than that of the long short-term memory network based on the attention mechanism, recurrent neural network, and extended Kalman filter fusion methods. The mean absolute error is increased by 2.81%, 16.73% and 35.80% respectively.
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图 4 航空器直线滑行时模型融合结果
Figure 4. Model fusion results during aircraft straight-line taxiing
表 1 Trans-Attention融合模型参数设置
Table 1. Trans-Attention fusion model parameter settings
学习率 隐藏层
丢失率训练
轮次多头注意
力个数Encode层
深度批量
大小0.001 0.1 600 6 2 048 8 
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表 2 基于Trans-Attention融合方法结果数据评估
Table 2. Data evaluation based on Trans-Attention fusion method results
数据来源 MSE/m RMSE/m MAE/m Trans-Attention 4.2811 2.0691 2.0595 ADS-B 14.4971 3.8075 4.3479 SMR1 36.3145 6.0262 8.2769 SMR2 47.4476 6.8882 10.6579
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表 3 EKF融合方法参数设置
Table 3. EKF fusion method parameter settings
参数类型 参数值 初始化状态协方差矩阵${{\boldsymbol{P}}_0}$ $ \left[ {\begin{array}{*{20}{c}} {{{1.0}}}&{{{0}}}&{{{0}}}&{{{0}}} \\ {{{0}}}&{{{1.0}}}&{{{0}}}&{{{0}}} \\ {{{0}}}&{{{0}}}&{{{1.0}}}&{{{0}}} \\ {{{0}}}&{{{0}}}&{{{0}}}&{{{1.0}}} \end{array}} \right] $ 过程噪声协方差${\boldsymbol{Q}}_0$ $\left[ {\begin{array}{*{20}{c}} {0.000\;1}&{0}&{0}&{0}&{0} \\ {0}&{0.000\;1}&{0}&{0}&{0} \\ {0}&{0}&{0.000\;09}&{0}&{0} \\ {0}&{0}&{0}&{0.001}&{0} \\ {0}&{0}&{0}&{0}&{0.001} \end{array}} \right]$ SMR1的测量协方差矩阵${{\boldsymbol{R}}_1}$ $\left[ {\begin{array}{*{20}{c}} {6.25}&{0}&{0} \\ {0}&{0.001\;6}&{0} \\ {0}&{0}&{6.25} \end{array}} \right]$ SMR2的测量协方差矩阵${{\boldsymbol{R}}_2}$ $\left[ {\begin{array}{*{20}{c}} {4.622\;5}&{0}&{0} \\ {0}&{0.000\;9}&{0} \\ {0}&{0}&{4.625\;5} \end{array}} \right]$ ADS-B的测量协方差矩阵${{\boldsymbol{R}}_3}$ $\left[ {\begin{array}{*{20}{c}} {2.25}&{0} \\ {0}&{2.25} \end{array}} \right]$
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表 4 RNN融合方法参数设置
Table 4. RNN fusion method parameter settings
学习率 隐藏层丢失率 训练轮次 批量大小 RNN层数 0.001 0.1 300 8 2
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表 5 LSTM-Attention融合方法参数设置
Table 5. LSTM-Attention fusion method parameter settings
学习率 隐藏层丢失率 训练轮次 批量大小 LSTM层数 0.001 0.1 300 8 2
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表 6 不同融合方法结果数据评估
Table 6. Data evaluation form for results of different fusion methods
方法 MSE/m RMSE/m MAE/m 本文方法 4.2811 2.0691 2.0595 EKF 9.9745 3.1582 3.2078 LSTM-Attention 4.3775 2.0922 2.1190 RNN 4.9697 2.2293 2.4732
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