The traditional time-frequency analysis (TFA) techniques are instruments for target classification, which can reflect the feature of the target in the time-frequency domain. However, it will lead to a serious decrease in the recognition accuracy as the decline of the sampling rate. To ease this problem, in this article, the sparse time-frequency feature analysis (STFFA) is implemented for aircraft classification, and the genetic algorithm is adopted to solve the sparse problem quickly for saving time. Firstly, the sparse time-frequency decomposition and recovery signal are obtained by matching pursuit. Then, three novel kinds of aircraft features are extracted from the sparse time-frequency decomposition, which are sparse recovery time-frequency entropy (SRTFE), frequency entropy by time, and first-order sparse time-frequency moment. Thus, the feature combination modes based on the three features are applied to realize the classification of aircraft and compared with the traditional TFA techniques. Besides, a support vector machine is also used to classify the three kinds of aircraft. The accuracy and efficiency of the STFFA method have been investigated by employing the parametric model data and electromagnetic scattering model simulated trials. Furthermore, in contrast to the traditional TFA instrument, our method can reach a recognition accuracy of more than 90% from the numerical experiment results, which demonstrates that the feature extraction by sparse time-frequency analysis improves the accuracy of aircraft classification under a low sampling rate.

中文翻译：

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低采样率和短观测时间飞机目标分类的稀疏时频分析

传统的时频分析（TFA）技术是目标分类的工具，可以在时频域中反映目标的特征。但是，随着采样率的下降，会导致识别准确率的严重下降。为了缓解这个问题，本文对飞机分类实现了稀疏时频特征分析（STFFA），并采用遗传算法快速解决稀疏问题，以节省时间。首先通过匹配追踪得到稀疏时频分解恢复信号。然后，从稀疏时频分解中提取了三种新的飞机特征，它们是稀疏恢复时频熵（SRTFE）、时间频率熵和一阶稀疏时频矩。因此，应用基于三种特征的特征组合方式实现飞机的分类，并与传统的TFA技术进行比较。此外，还使用支持向量机对三种飞行器进行分类。采用参数模型数据和电磁散射模型模拟试验研究了STFFA方法的准确性和效率。此外，与传统的TFA仪器相比，我们的方法从数值实验结果可以达到90%以上的识别准确率，这表明稀疏时频分析的特征提取提高了低采样下飞机分类的准确性。速度。