融合分割点检测的在线机动轨迹识别方法. (Chinese)

An online maneuver trajectory recognition method based on segmentation point detection. (English)

To enhance the precision of aircraft maneuver recognition method and improve the real-time of the recognition process, considering the dynamic and temporal nature of tactical maneuver trajectory, through fusing maneuver trajectory segmentation point detection method, an online maneuver trajectory recognition method based on the Mahalanobis distance-based dynamic time warping network is proposed. Firstly, in order to prevent the trained segmentation recognition model from overfitting, the flight parameters of the maneuver trajectory are converted into the maneuver trajectory feature parameters by extracting the maneuver trajectory features, and the maneuver library including 21 maneuver trajectory units is constructed. Secondly, in order to quickly split the maneuver trajectory units, a method is introduced that combines support vector machines and Mahalanobis distance: a maneuver trajectory segmentation point detection method based on Mahalanobis Distance Support Vector Machine. Then, in order to improve the accuracy of maneuver trajectory unit recognition, a method is proposed that combines dynamic time warping based on improved Mahalanobis distance and convolutional neural networks: a maneuver trajectory unit recognition method based on Mahalanobis distance measurement in dynamic time warping neural networks. Finally, by fusing the segmentation point detection model and the trajectory unit recognition model, an online maneuvering trajectory recognition platform is constructed, and the simulation analysis using three maneuver trajectory conditions data is carried out. The experimental results show that the proposed method can detect the segmentation point of the aircraft unit in real time, and the accuracy of the segmentation detection can reach 97. 0%. Compared with other maneuver trajectory recognition methods, the proposed method not only meets realtime requirements, but also has high recognition accuracy exceeding 90%. The results validate the effectiveness and real-time performance of the online maneuver recognition model proposed in this research. [ABSTRACT FROM AUTHOR]

为提高飞机机动识别方法精准度, 提升识别过程的实时性, 考虑到战术机动轨迹的动态性以及时序性, 融合机动轨迹 分割点检测方法, 提出一种基于马氏距离度量动态时间规整神经网络的在线机动轨迹识别方法。 首先, 为防止训练的分割识 别模型过拟合, 通过提取机动轨迹特征, 将机动轨迹飞行参数转化为机动轨迹特征参数, 同时构建了包括 21 种机动轨迹单元 的机动库; 然后, 为快速分割机动轨迹单元, 结合支持向量机和马氏距离, 提出了一种基于马氏距离支持向量机的机动轨迹分 割点检测方法; 其次, 为提高机动轨迹单元识别精度, 结合基于改进马氏距离的动态时间规整与卷积神经网络, 提出一种基于 马氏距离度量动态时间规整神经网络的机动轨迹单元识别方法; 最后, 通过融合分割点检测模型与轨迹单元识别模型, 构建 在线机动轨迹识别平台, 并利用 3 种机动轨迹工况数据对该方法进行仿真分析。 仿真结果表明, 提出的方法能实时检测飞机 机动轨迹单元分割点, 且在分割检测的准确率达到 97. 0%; 与其他机动轨迹识别方法相比较, 本研究提出的方法不仅满足实 时性, 且具有较高的识别精度, 达到 90% 以上, 从而验证了本研究提出的在线机动识别模型的有效性与实时性。 [ABSTRACT FROM AUTHOR]

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