基于FT_SSIM和ICAGA_CNN在小样本场景下雷达动作识别方法研究

Research on radar action recognition method based on FT_SSIM and ICAGA_CNN in small sample scenes

针对基于小样本场景下人体动作识别出现训练效果差、过拟合现象以及传统对抗生成网络收敛速度慢、对计算机性能要求高等问题,从数据增强及超参数优化方面提出了解决方案。首先,搭建AWR1243雷达数据采集平台,对采集的回波信号进行预处理。其次,利用STFT进行时频分析以及新提出的FT_SSIM算法进行数据增强。再者,利用提出的ICAGA_CNN进行分类识别,并与传统的数据增强算法、超参数优化进行实验对比。为了验证该算法具有一定的泛化能力,先后选择了在公开KTH人体动作数据集以及利用雷达的实测数据进行验证。实验结果表明,一方面,提出算法有效避免了小样本场景下过拟合的发生,降低了传统数据增强对计算机性能的要求,加快了收敛的速度;另一方面,其具有更好的识别精度,平均识别率达到98.5%。这也说明了提出的算法在小样本场景下雷达动作识别具有很好的表现。

Aiming at the problems of poor training effect,over-fitting phenomenon in human action recognition based on small sample scenarios,slow convergence speed of traditional confrontation generation network,and high requirements for computer performance,this paper proposed solutions from data enhancement and hyperparameter optimization.Firstly,it built the AWR1243 radar data acquisition platform to preprocess the collected echo signals.Secondly,it used STFT for time-frequency analysis and the newly proposed FT_SSIM algorithm for data enhancement.Furthermore,it used the proposed ICAGA_CNN for classification and recognition,and compared the experiments with traditional data enhancement algorithms and hyperparameter optimization.In order to verify the proposed algorithm that has a certain generalization ability,the public KTH human body motion data set and the actual measurement data of radar were used for verification.Experimental results show that,on the one hand,the proposed algorithm effectively avoids the occurrence of over-fitting in small sample scenarios,reduced the requirements of traditional data enhancement on computer performance,and accelerated the speed of convergence.On the other hand,the proposed algorithm has better recognition accuracy,with an average recognition rate of 98.5%.This also shows that the proposed algorithm has a good performance in radar action recognition in small sample scenarios.