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Object classification on noise-reduced and augmented micro-doppler radar spectrograms

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Abstract

The classification of targets is one of the most challenging tasks in radar signal processing. Classifying a target can help radar operators figure out the nature of the target, such as its source and activity. However, it is very difficult to find the labeled data necessary to develop radar target classification models. Generating a radar dataset is an expensive and time-consuming process. To address these issues, we propose a noise reduction method that can be applied to micro-Doppler radar datasets. This method is carried out by averaging the spectrogram of each class in the RadEch micro-Doppler radar datasets and subtracting pixel by pixel from each sample. RadEch dataset has also been augmented with traditional and learning-based data augmentation methods. The learning-based data augmentation method was carried out by using Generative Adversarial Networks. Raw spectrograms, augmented spectrograms and noise-reduced spectrograms have been classified using 5-layer Convolutional Neural Network, VGG-16, and VGG-19. Classification results are compared with state-of-the-art studies. Comparison results show that the classification on noise-reduced spectrogram performs better than current state-of-the-art methods.

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Acknowledgements

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. We are grateful to Assoc. Prof. Radim Burget who has participated in the study and provided significant insights and supports.

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Authors and Affiliations

  1. Institute of Science and Engineering, Başkent University, 06790, Ankara, Turkey

    Alperen Erdoğan

  2. Department of Electrical and Electronics Engineering, Başkent University, 06790, Ankara, Turkey

    Selda Güney

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Erdoğan, A., Güney, S. Object classification on noise-reduced and augmented micro-doppler radar spectrograms. Neural Comput & Applic 35, 429–447 (2023). https://doi.org/10.1007/s00521-022-07776-3

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