Improving the automatic target recognition algorithm’s accuracy through an examination of the different time-frequency representations and data augmentation

Abstract

This research focuses on an improved automatic target recognition algorithm for solving the classification challenge of ground-moving targets from pulsed-Doppler radar. First, it was studied how decision-making intervals affect the proposed algorithm. Second, the altering of the data augmentation process was investigated. Third, a consideration of the three time-frequency signal representations and finally the use of different deep learning models for the classification issues were examined. It is proven that the proposed algorithm can efficiently recognize all targets enclosed in the publicly available RadEch dataset, with 4 s of radar echoes. When the decision-making time is only 1 s, a classification probability of 99.9% was obtained, which is an improvement related to the other research studies in this area. Furthermore, when the decision-making time is reduced 16 times the classification accuracy is reduced by only 1.3%. Moreover, the proposed algorithm was successful on another dataset enclosing ground-moving targets from comparable pulsed-Doppler radar.

Signal Preprocessing Technique With Noise-Tolerant for RF-Based UAV Signal Classification

“…To obtain higher classification accuracy, studies using the spectrogram have been recently proposed [28]- [30], [37]. The authors of [37] shared the dataset with [38], and obtained 100% accuracy by using the RGB values of the spectrogram and a CNN classifier. In [28], 99.9 % accuracy was obtained by extracting features from the PSD, which is the spectrogram value, through CNN transfer learning and classifying them using logistic regression.…”