With the wide application of unmanned aerial vehicles (UAV) in industrial production, transportation, and entertainment, it is urgent to identify UAVs in time. Traditional UAV recognition mainly depends on wireless communication, which puts forward high requirements for a communication environment and has no way to deal with non-cooperative targets. Therefore, it is urgent to explore a UAV target recognition scheme based on perception. In this paper, aiming at the time series preprocessing method, a coding-based sequence preprocessing method is proposed. This method effectively improves the effect of the Deep Learning method in the identification task. In order to verify the ability of Deep Learning in radar time series data processing and the effectiveness of the proposed method, the Deep Learning method is used to analyze the radar signal time series of the target to realize the target recognition. Finally,considering the influence of micro-motion factors on UAV targets, the neural network is used to estimate UAV’s micro-motion parameters to enhance the ability of target recognition with the help of micro-motion information.

随着无人机在工业生产，运输和娱乐中的广泛应用，迫切需要及时识别无人机。传统的无人机识别主要依靠无线通信，这对通信环境提出了很高的要求，无法应对非合作目标。因此，迫切需要探索一种基于感知的无人机目标识别方案。针对时间序列预处理方法，提出了一种基于编码的序列预处理方法。该方法有效地提高了深度学习方法在识别任务中的效果。为了验证深度学习在雷达时间序列数据处理中的能力以及所提出方法的有效性，深度学习方法用于分析目标的雷达信号时间序列，以实现目标识别。最后，考虑微动因素对无人机目标的影响，利用神经网络估计无人机的微动参数，以借助微动信息增强目标识别能力。