Fig. 1. Frequency modulation type
Fig. 2. PRI modulation type
Fig. 3. Conventional radar signal classification
Fig. 4. CNN-based classification model
Fig. 5. RNN-based classification model
Fig. 6. Distribution of training data Freq/PRI
Fig. 7. Distribution of training data Freq/PRI modulation type
Fig. 8. Comparison of before and after application of frequency error
Fig. 9. Comparison of before and after application of pulse missing
Fig. 10. Change in cost value by model
Fig. 11. Comparison of No. 56~58 PRI characteristic
Fig. 12. Comparison of No. 32, 33 frequency characteristic
Table 1. Details of PDW
Table 2. Computer resource and library list
Table 3. Classification accuracy by model
참고문헌
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- Nedyalko Petrov et al., "Radar Emitter Signals Recognition and Classification with Feedforward Networks," Procedia Computer Science 22, pp. 1192-1200, 2013. https://doi.org/10.1016/j.procs.2013.09.206
- Chih-Min Lin, et al., “A Self-Organizing Interval Type-2 Fuzzy Neural Network for Radar Emitter Identification,” International Journal of Fuzzy Systems, Vol. 16, No. 1, pp. 20-30, 2014.
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- Dongqing Zhou, et al., "A Novel Radar Signal Recognition Method Based On a Deep Restricted Boltzmann Machine," Engineering Review, Vol. 37, Issue 2, pp. 165-171, 2017.
- Xavier Glorot, et al., "Understanding the Difficulty of Training Deep Feedforward Neural Networks," International Conference on Artificial Intelligence and Statistics PMLR 9, pp. 249-256, 2010.