基于高斯数据增广的小样本数据无人机编队类型抗扰识别

Anti-disturbance Identification of UAV Formation Types Based on Gaussian Data Augmentation with Small Sample Data

近年来,随着无人机的应用越来越广泛,对无人机编队类型进行准确识别具有重要意义。基于此,对小样本数据下无人机编队类型识别问题进行了研究。首先,对目前常用的各种无人机编队类型识别方法进行了介绍,针对小样本数据无人机编队类型抗扰识别面临的主要问题进行了分析;其次,对所提方法涉及的理论背景进行了研究;然后,对所提无人机编队类型抗扰识别算法进行了详细阐述;最后以带有噪声的菱形无人机编队的位置坐标作为输入数据,使用所提方法进行仿真实验,仿真实验结果表明,以参数均方误差作为队形识别的准确度指标,所提方法可将均方误差由45.1降低至0.46,显著提高了无人机机群特征聚类的精度与鲁棒性,证明了所提方法在小样本数据无人机编队类型抗扰识别问题上的有效性。

In recent years,with the increasing application of drones,accurate identification of drone formation types is of great significance.This article studies the problem of identifying drone formation types under small sample data.Firstly,various commonly used methods for identifying drone formation types are introduced,and the main problems faced by anti-interference recognition of drone formation types in small sample data are analyzed.Secondly,the theoretical background of the proposed method is studied.Then,the anti-interference recognition algorithm for drone formation types proposed in this article is elaborated in detail.Finally,the position coordinates of a diamond shaped drone formation with noise were used as input data for simulation experiments using the proposed method.The simulation results showed that using parameter mean square error as the accuracy indicator for formation recognition,the proposed method can reduce the error from 45.1 to 0.46,significantly improving the accuracy and robustness of drone cluster feature clustering,and proved the effectiveness of the proposed method in anti-interference identification of UAV formation types with small sample data.