基于雷达量测时空特征的航迹起始方法

日益拥挤、复杂的空域环境，使得确定真实目标航迹的起始尤为主要。现有关于雷达目标航迹起始的研究大多只考虑了实时性或起始率中的一种，难以在强杂波环境下完成快速而准确的航迹起始。本文提出一种适用于强杂波环境的基于深度学习和雷达量测时空(DLTS)特征的航迹起始算法。该算法首先从雷达量测组合中筛选出候选集，并从中提取出量测组合的时序变化和空间分布向量，作为一维卷积神经网络(1DCNN)和门控循环单元(GRU)混合模型的输入，获得量测组合的时间和空间维度特征，再将二者合并得到时空特征。最后对经过自注意力处理的时空特征进行真假航迹分类，完成航迹起始。在仿真实验中，DLTS算法在强杂波环境下能够在时间损耗与逻辑法相近的情况下有效提高真假航迹起始率性能。

A track initiation algorithm based on temporal-spatial characteristics of radar measurement

The increasingly crowded and complex airspace environment makes it necessary to determine the initiation of the true target track. Most existing research on radar target track initiation only considers one of real-time or initiation rate, and it is difficult to complete fast and accurate track initiation in a strong clutter environment. In this paper, a track initiation algorithm is proposed based on Deep Learning and Temporal-Spatial(DLTS) characteristics of radar measurement suitable for strong clutter environment. The algorithm first selects the candidate set from the radar measurement combinations, and next extracts the temporal change vector and spatial distribution vector of the measurement combination, and uses them as the input of the One-dimensional Convolutional Neural Network(1DCNN) and Gated Recurrent Unit(GRU) hybrid model to obtain the time dimensional characteristics and space dimensional characteristics of the measurement combination, then merge the two to get the temporal-spatial characteristics. Finally, the true and false tracks are classified with the temporal-spatial characteristics processed by self-attention, and the track initiation is completed. The simulations show that DLTS algorithm can effectively improve the performance of the true and false track initiation rate when the time loss is similar to that of the logic method in the strong clutter environment.