Sens. Lett., vol. 13, no. 1, pp. 8–12, Jan. 2016. M. S. Seyfioglu, S. Z. Gurbuz, A. M. Ozbayoglu, and

Figure 5. ROC curve showing the true positive rate versus the false positive rate for detecting an unknown Align T-REX 550 mini-UAV with the Soft-Max and GANomaly methods.

The objective function for training the GANomaly networks consists of three loss functions. The adversarial loss function L adv computes the L 2 distance between features computed by the function ffrom an intermediate layer of the discriminator network for the real and generated spectrograms. This feature matching approach reduces the instability of GANs during training [18]. The context loss function L con penalizes the auto-encoder for reconstruction errors by measuring the L distance between the real and generated spectrograms. Finally, the encoder loss L enc measures the L 1 distance between the latent representations of the input and reconstructed spectrograms.

Experiments with the Soft-max and GANomaly methods for the detection of unknown target classes have been conducted using the dataset with mini-UAV spectrograms, as described in “CLASSIFICATION OF MINI-UAVS.” Each of the mini-UAVs has been used alternatively as an unknown target class when training the neural networks on the microDoppler spectrograms from the remaining ‘“known” miniUAVs. Figure 5 shows an example of an ROC curve for a test where the T-REX 550 helicopter was used as an unknown target class. The GANomaly method performs better at low and high false positive rates, whereas the Soft-max method works better at intermediate false positive rates.

Figure 6 shows the area-under-curve (AUC) for the detection of unknown target classes with the Soft-max and GANomaly methods. Each experiment is performed three times to capture the performance variations due to the random initialization of the neural networks and due to the random selection of the data samples in the test set and training set.

The above results are obtained using the measured spectrograms of test case E. All spectrograms are truncated after the first 64 time samples resulting in square input data of 64
64 pixels. This input size enabled the use of network configuration and training parameters described in the paper by Akcay et al. [17] that are proven to be successful for the GANomaly method. For Soft-max, the

Figure 6. AUC results for the detection of unknown target classes with Softmax and GANomaly. The error bars represent variations due to the use of random seeds for the initialization of the neural networks.

network and hyperparameters are as described in “CLASSIFICATION OF MINI-UAVS.” The models for GANomaly and Soft-max are trained for, respectively, 15 and 50 epochs.

Figure 6 shows that both the Soft-max and the GANomaly achieve AUC ranging from 0.5 to 0.8. Note that comparing the performance of deep learning methods is often tricky because the result can highly depend on the effort that has been put in optimizing the networks, training parameters, and data preprocessing steps. Especially adversarial training methods, such as GANomaly, are notorious for instability issues. In the experiments, it was observed that the final performance of the GANomaly method was not yet fully satisfactory, and perhaps better performances can be achieved with other GAN configurations for the detection of unknown target classes.

DENOISING OF SPECTROGRAMS

The micro-Doppler spectrograms collected during the measurement campaign described in “CLASSIFICATION OF MINI-UAVS” have a relatively high signal-to-noise ratio (SNR) due to the short range between the radar and the mini-UAVs. However, the SNR will be lower at longer ranges, and therefore, the performance of a target classifier will degrade. To investigate if deep learning can be used to improve the SNR of the spectrograms in the preprocessing before classification, and hence, increase the range at which a cognitive radar can recognize a mini-UAV, an experiment was conducted with a denoising adversarial auto-encoder. Adversarial trained auto-encoders can be used to restore input data, such as images that are corrupted by noise [19]. The architecture of the denoising adversarial auto-encoder is like the architecture of the