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A. G. Huizing, and M. Geurts, “Gesture recognition with a low power FMCW radar and a deep convolutional neural P. Molchanov, K. Egiazarian, J. Astola, R. I. A. Harmanny, and J. J. M. de Wit, “Classification of small UAVs and birds
from Deep Learning for Classification of Mini-UAVs Using Micro-Doppler Spectrograms in Cognitive Radar
by Jinghua
integration time in the Doppler filtering had a higher impact on the classification performance than the type of the deep neural network (CNN versus LSTM-RNN) in the classifier. The use of simulated spectrograms to classify measured spectrograms requires a higher fidelity modeling of the micro-Doppler signals than the modeling approach based on principal scatterers used in this paper.
When a cognitive radar measures a micro-Doppler spectrogram from a target class that is not represented in the training set, an anomaly detection algorithm could provide a trigger to the cognitive radar scheduler to collect micro-Doppler spectrograms from this unknown target. The performance of two deep learning techniques, Softmax and GANomaly, for the detection of spectrograms from unknown target classes has been compared. Both techniques had a similar performance with respect to the probability of detecting unknown classes with the GANomaly technique showing a larger performance variation due to the difficulty in training adversarial networks. Different GAN architectures for anomaly detection that are easier to train, such as a Wasserstein GAN may provide a better performance [22].
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A denoising adversarial auto-encoder has been trained to remove noise from spectrograms. An improvement of 20 dB in the SNR of a measured spectrogram has been observed. If this denoising technique does not alter the micro-Doppler characteristics significantly, a considerable increase in the range at which a cognitive radar can recognize mini-UAVs could be achieved.
To mitigate the effects of class imbalances in the number of training examples on the performance of a classifier, an InfoGAN can be used to generate additional spectrograms of underrepresented classes. A quantitative study concerning the benefits of this approach will be conducted in the future research.
The overall conclusion of this paper is that deep learning techniques have a high potential for improving the classification of mini-UAVs using micro-Doppler spectrograms in cognitive radar. However, the selection and tuning of the appropriate neural network architectures for different processing tasks, such as classification and denoising is tedious and time consuming. A better understanding of the theory behind deep neural networks, potentially enabled by the link with compressive sensing and sparse signal representations, would not only accelerate the development process, but also improve the acceptance of these advanced techniques in military radar applications.
ACKNOWLEDGMENT
TNO has received funds for this study from the Netherlands Ministry of Defense, under Grant V1512 and Grant V1908. The study has been conducted within the scope of the Dutch Radar Center of Expertise (D-RACE), a strategic alliance between Thales Nederland B.V. and TNO.
REFERENCES
[1] S. Haykin, Y. Xue, and P. Setoodeh, “Cognitive radar: Step toward bridging the gap between neuroscience and engineering,” Proc. IEEE, vol. 100, no. 11, pp. 3102–3130, Nov. 2012. [2] J. R. Guerci, Cognitive Radar: The Knowledge-Aided Fully Adaptive Approach, Norwood, MA, USA: Artech House, 2010. [3] M. S. Greco, F. Gini, P. Stinco, and K. Bell, “Cognitive radars: On the road to reality: Progress thus far and possibilities for the future,” IEEE Signal Process. Mag., vol. 35, no. 4, pp. 112–125, Jul. 2018. [4] Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, no. 7553, pp. 436–444, 2015. [5] D. A. E. Morgan, “Deep convolutional neural networks for ATR from SAR imagery,” Proc. SPIE, vol. 9475, 2015, Art. no. 94750F. [6] Y. Kim and T. Moon, “Human detection and activity classification based on micro-Doppler signatures using deep convolutional neural networks,” IEEE Geosci. Remote Sens. Lett., vol. 13, no. 1, pp. 8–12, Jan. 2016. [7] M. S. Seyfio glu, S. Z. G € urb € uz, A. M. € Ozbayo glu, and M. Y € uksel, “Deep learning of micro-Doppler features for aided and unaided gait recognition,” in Proc. IEEE Radar Conf., 2017, pp. 1125–1130. [8] R. P. Trommel, R. I. A. Harmanny, L. Cifola, and J. N. Driessen, “Multi-target human gait classification using deep convolutional neural networks on microDoppler spectrograms,” in Proc. Eur. Radar Conf., 2016, pp. 81–84. [9] B. Dekker, S. Jacobs, A. S. Kossen, M. C. Kruithof, A. G. Huizing, and M. Geurts, “Gesture recognition with a low power FMCW radar and a deep convolutional neural network,” in Proc. Eur. Radar Conf., 2017, pp. 163–166. [10] P. Molchanov, K. Egiazarian, J. Astola, R. I. A. Harmanny, and J. J. M. de Wit, “Classification of small UAVs and birds by micro-Doppler signatures,” in Proc. Eur. Radar Conf., 2013, pp. 172–175. [11] R. Harmanny, J. J. M. de Wit, and G. Premel-Cabic, “Radar micro-Doppler mini-UAV classification using spectrograms and cepstrograms,” Int. J. Microw. Wireless Technol., vol. 7, no. Special Issue 3/4, pp. 469–477, 2015. [12] V. C. Chen, F. Li, S.-S. Ho, and H. Wechsler, “MicroDoppler effect in radar: Phenomenon, model, and simulation study,” IEEE Trans. Aerosp. Electron. Syst., vol. 42, no. 1, pp. 2–21, Jan. 2006. [13] P. J. Speirs, A. Schr € oder, M. Renker, P. Wellig, and A. Murk, “Comparisons between simulated and measured X-band signatures of quad-, hexa- and octocopters,” in Proc. 15th Eur. Radar Conf., 2018, pp. 325–328.
