Sparse time-frequency analysis for aircraft target classification with low sampling rate and short observation time

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.