**5. Conclusions**

In this paper, an aircraft target classification algorithm is proposed based on weighted features fusion of multi-wave gates sparse echo data. Not only are the SL0 and OMP algorithms utilized to reconstruct the sparse echo data to solve the problem of low classification probability in this case, but also the amplitude deviation coefficient, time domain waveform entropy and frequency domain waveform entropy are extracted to classify aircraft targets according to the analysis of the micro-Doppler effect of echo data. The proposed algorithm works on the multi-wave gates echo data in weighted features fusion, rather than single wave gate echo data, which is helpful for reducing the number of cross-value features of different targets. Experimental results show that the proposed algorithm can improve the classification probability of reconstructed echo data obtained by the SL0 and OMP methods, and four wave gates echo data in weighted features fusion used to extract and fuse features and to both train and test the SVM model is the optimal wave gate number for target classification.

Although our method is effective in aircraft target-type classification, it can still be improved further. In the future, we not only study the aircraft target classification algorithm within unmanned aerial vehicle (UAV) types, but also verify the effectiveness of the algorithm in the actual radar equipment by conducting an experiment with measured data.

**Author Contributions:** Writing—original draft preparation, W.W.; methodology, Z.T. and Y.C.; writing—review and editing, W.W. and Y.C.; data curation, Y.Z. and Y.S.

**Funding:** This work was supported in part by the National Natural Science Foundation of China under Grant 61901514, and in part by Young Talent Program of Air Force Early Warning Academy under Grant TJRC425311G11.

**Acknowledgments:** The authors would like to thanks to the editor and anonymous reviewers for processing our manuscript.

**Conflicts of Interest:** The authors declare no conflict of interest.
