针对传统的航迹融合算法高度依赖先验信息的问题,文中提出了一种基于多阶差分损失全卷积网络的航迹融合算法。融合中心首先对各局部航迹进行时空配准和航迹关联;然后通过全卷积结构设计,避免了传统卷积神经网络模型中由于全连接层的使用导致参数量大、训练难度大的问题;最后通过计算航迹及其一、二阶差分的加权损失,实现了更高精度的融合结果。消融实验表明文中提出的航迹融合算法模型小、收敛性强、精度高、运算时间适中。仿真实验表明,文中算法不需要先验信息,当噪声参数无法准确估计时,算法融合精度优于方差加权融合算法和扩维卡尔曼滤波融合算法。实验结果证实了所提算法的有效性和可行性。

To tackle the issue of previous track fusion algorithms′ heavy reliance on prior information, a track fusion algorithm based on fully convolutional network with multi-order difference loss is proposed. The various local tracks are subjected to spatiotemporal registration and track association at the fusion center. Through the design of the fully convolutional structure, the problems of large amount of parameters and difficult training caused by the use of fully connected layers in the traditional convolutional neural network model are avoided. Higher-precision fusion results are obtained by calculating the weighted loss of the track and its first- and second-order differences. The ablation experiments show that the track fusion algorithm proposed in this paper has a small model, strong convergence, high precision and moderate computing time. Simulation results confirmed that the algorithm proposed in this paper does not require any previous information. The proposed algorithm's fusion accuracy is better than the variance weighted fusion algorithm and the Kalman filter fusion algorithm when the noise parameters cannot be accurately estimated. The experimental results confirm the effectiveness and feasibility of the algorithm proposed in this paper.

TN953+.7,TP183

国家自然科学基金资助项目(61790552)