Improving Deep CNN-Based Radar Target Classification Performance by Applying a Denoise Filter
| Autor: | Van-Tra Nguyen, Chi-Thanh Vu, Van-Sang Doan |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2024 |
| Předmět: | |
| Zdroj: | Journal of Electromagnetic Engineering and Science, Vol 24, Iss 2, Pp 198-205 (2024) |
| Druh dokumentu: | article |
| ISSN: | 2671-7255 2671-7263 |
| DOI: | 10.26866/jees.2024.2.r.220 |
| Popis: | This paper presents a novel method for removing noise from range-Doppler images by using a filter prior to conducting target classification using a deep neural network. Specifically, Kuan, Frost, and Lee filters are employed to eliminate speckle noise components from radar data images. Furthermore, a neural network that combines residual and inception blocks (RINet) is proposed. The RINet model is trained and tested on the RAD-DAR dataset—a collection of range-Doppler feature maps. The analysis results show that the application of a Lee filter with a window size of 7 in the RAD-DAR dataset demonstrates the most improvement in the model’s classification performance. On applying this noise filter to the dataset, the RINet model successfully classified radar targets, exhibiting a 4.51% increase in accuracy and a 14.07% decrease in loss compared to the classification results achieved for the original data. Furthermore, a comparison of the RINet model with the noise filtering solution with five other networks was conducted, the results of which show that the proposed model significantly outperforms the others. |
| Databáze: | Directory of Open Access Journals |
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