Abstract
With the fast development of the new radar emitter in the complex system and multi-modulation mode, the signal detection and classification based on Deep learning technology has received significant attention. When a new signal category is added, the existing deep learning network often needs to retrain network parameters, so it fails to quickly form the recognition ability of the new signal. The paper puts forward a kind of incremental learning recognition method for radar emitter based on minimum sample distance, by using contrastive predictive encoder and residual neural network of radar emitter time domain signal processing to extract the signal feature vector. We use the minimum sample distance classifier to improve radar emitter recognition network, and introduce the distillation loss and classification loss. In this way, the proposed network can simultaneously retain the features of the original dataset and learn the features of the new dataset. After the identification task, some samples from all the datasets were selected to construct a new dataset for the next incremental task. The feasibility of the proposed method is verified via the experiment of type 10 navigation radar signal. Compared with the retraining of the network, the training speed of our proposed incremental learning method is faster and the memory consumption is smaller.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Huang, J., Lei, Y.: Radio fingerprint extraction based on marginal fisher deep autoencoders. Wirel. Pers. Commun. 103(4), 2729–2742 (2018)
Dong, X., Cheng, S., Yang, J., et al.: Radar specific emitter recognition based on DBN feature extraction. J. Phys. Conf. Ser. 1176(3), 032025 (2019). IOP Publishing
Ding, L., Wang, S., Wang, F., et al.: Specific emitter identification via convolutional neural networks. IEEE Commun. Lett. 22(12), 2591–2594 (2018)
Pu, Y., Liu, T., Guo, J., et al.: Radar emitter signal recognition based on convolutional neural network and coordinate transformation of ambiguity function main ridge. Acta Armamentarii 42(8), 1680–1689 (2021)
Yu, H., Yan, X., Liu, S., et al.: Radar emitter multi-label recognition based on residual network. Defence Technol. 18(3), 410–417 (2021)
Wong, L.J., Headley, W.C., Michaels, A.J.: Specific emitter identification using convolutional neural network-based IQ imbalance estimators. IEEE Access 7, 33544–33555 (2019)
Li, X., Liu, Z., Huang, Z., et al.: Radar emitter classification with attention-based multi-RNNs. IEEE Commun. Lett. 24(9), 2000–2004 (2020)
Zhang, J., Su, Q., Tang, B., et al.: DPSNet: multitask learning using geometry reasoning for scene depth and semantics. IEEE Trans. Neural Netw. Learn. Syst. (2021). https://doi.org/10.1109/TNNLS.2021.3107362
Hao, Z., Liu, H., Liu, Z., et al.: An algorithm of emitter identification with incremental neural network learning. In: Proceedings of the 14th National Conference on Signal Processing, pp. 562–565, China (2009)
Pfülb, B., Gepperth, A.: A comprehensive, application-oriented study of catastrophic forgetting in DNNs. In: ICLR 2019 International Conference on Learning Representations, pp. 1–14. Open Review, New Orleans (2019)
Pérez-Rúa, J.-M., Zhu, X., Hospedales, T.M., Xiang, T.: Incremental few-shot object detection. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 13843–13852. IEEE, Seattle (2020)
Cermelli, F., Mancini, M., Rota Bulò, S., Ricci, E., Caputo, B.: Modeling the background for incremental learning in semantic segmentation. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 9230–9239. IEEE, Seattle (2020)
Zhang, J., Su, Q., Wang, C., et al.: Monocular 3D vehicle detection with multi-instance depth and geometry reasoning for autonomous driving. Neurocomputing 403, 182–192 (2020)
Polikar, R., Upda, L., Upda, S., et al.: Learn++: an incremental learning algorithm for supervised neural networks. IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.) 31(4), 497–508 (2001)
Li, Z., Hoiem, D.: Learning without forgetting. IEEE Trans. Pattern Anal. Mach. Intell. 40(12), 2935–2947 (2017)
Rebuffi, S., Kolesnikov, A., Sperl, G., Lampert, C.H.: iCaRL: incremental classifier and representation learning. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5533–5542. IEEE, Honolulu (2017)
Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning representations by back-propagating errors. Nature 323(9), 533–536 (1986)
Van den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding. arXiv e-prints, arXiv:1807.03748 (2018)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778. IEEE, Las Vegas (2016)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 Chinese Institute of Command and Control
About this paper
Cite this paper
Mao, Y., Wang, J. (2022). Incremental Learning Recognition Method for Radar Emitter Based on Minimum Sample Distance. In: Proceedings of 2022 10th China Conference on Command and Control. C2 2022. Lecture Notes in Electrical Engineering, vol 949. Springer, Singapore. https://doi.org/10.1007/978-981-19-6052-9_22
Download citation
DOI: https://doi.org/10.1007/978-981-19-6052-9_22
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-6051-2
Online ISBN: 978-981-19-6052-9
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)