A selection of \(P=N\) leads to the standard case of SO-CFAR while at the other extreme \(P=1\) implies an unrefined estimate of the background noise or clutter. The performance of this detector will be contingent on the choice of P. The smallest of these two averages is selected \(\gamma = \min \ (\gamma _1, \gamma _2)\) as the estimate to be applied by ( 1). Currently, a mainstream method to solve the issue of insufficienttraining samples is to use deep learning generative adversarial network (GAN) to enhance the data of radar signal feature images 2932. This detector may readily be replaced by other type of detectors preferably, as long as the detector can be tuned to yield a larger or fewer number of correct and incorrect detections. lenging issue in the fieldof radar waveform recognition is the recognition with small samples 28. In this text, we limit ourselves to the modified version of the classical SO-CFAR detector as it can be used to demonstrate the applicability on both noise-only and clutter based scenarios. Targets need to be differentiated with high probability of detection ( \(P_\) performance of the system and the network is only taught to identify false detections with respect to a given detector. Today Process.Discriminating targets from background noise and interference is a fundamental task of all radar systems. 25(5), 620–624 (2018)ĭeb, T., Anjan, K., Mukherjee, A.: Singular value decomposition applied to associative memory of hopfield neural network. Miao, J., Cheng, G., Cai, Y.: Approximate joint singular value decomposition algorithm based on givens-like rotation. Guo, Q., Nan, P., Zhang, X., Zhao, Y., Wan, J.: Recognition of radar emitter signals based on SVD and AF main ridge slice. 7.10 Simultaneous vascular network segmentation and classification using a multi-tasks semi-supervised neural network 7.11 Self Supervised Neural Network. Radford, A., Metz, L.: Unsupervised representative learning with Deep Convolutional Generative Adversarial Network. arXiv preprint arXiv:1606.03498 (2016)Īrjovsky, M.: Towards Principled Methods for Training Generative Adversarial Network. Salimans, T., Goodfellow, I.: Improved Techniques for Training GANs. Goodfellow, I.J.: Generative adversarial nets. 148, 9–19 (2018)Ĭzarnecki, K., Fourer, D., Auger, F.: A fast time-frequency multi-window analysis using a tuning directional kernel. Moghadasian, S.S., Fatemi-Behbahani, E.: A structure for representation of localized time-frequency distributions in presence of noise. Zhang, Y.: A survey on generative adversarial networks. 25(3), 447–451 (2018)īai, H., Zhao, Y., Hu, D.: Radar emitter identification based on image feature of Choi-Williams time-frequency distribution. Create and train a Convolution Neural Network (CNN) to classify SAR targets. The task of G is to learn an effective mapping that simulates the real data distribution and generates new samples. Synthesize and label radar waveforms to train deep learning networks. It consists of two components: a generator and a discriminator. Radar 3 Giovanni Floreale, Piero Baraldi, Enrico Zio and Olga Fink Exploiting. 33(4), 9–14 (2010)ĭjurovic, I.: QML-RANSAC instantaneous frequency estimator for overlapping multicomponent signals in the time-frequency plane. GAN is a generative model, which generates new samples according to the real data distribution through adversarial training. Chen, W., Tao, J.: Summary of new system radar and their countermeasure techniques.
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