automatically extract the relevant features from a set of radar measurements or radar simulations that are stored in the KB. The extracted features can then be used by the KAP to classify the target. Deep learning has become the preferred method for many pattern recognition applications, such as segmentation, speech recognition, and face recognition since the quantum leap in image recognition performance realized with the AlexNet convolutional neural network (CNN) in the ImageNet 2012 challenge [4]. Despite the major achievements of deep learning techniques, such as CNNs in the past few years, it is not obvious that the success of deep learning in the commercial domain can be replicated in the military domain. Large labeled datasets, which are the key to the success of most commercial applications of deep learning, are often not available in military applications. In addition, the cost of decision errors in the military domain is typically much higher than in the commercial applications. This makes it mandatory to achieve a robust performance with a low error rate. Furthermore, military ATR algorithms should not only be accurate, but their behavior should also be predictable to gain the trust of military commanders. One of the first applications of deep learning for ATR using synthetic aperture radar imagery was presented by Morgan [5]. Since then many papers on the application in radar of CNNs and other types of deep neural networks have been published, including the application of deep learning for target classification, and human gait and gesture recognition using micro-Doppler spectrograms [6]–[9]. This paper investigates the potential of deep learning techniques for the classification of mini-UAVs using microDoppler spectrograms in the context of cognitive radar. The paper also presents preliminary results on the use of deep learning in the preprocessing and training process of a classifier. This paper is organized as follows. “CLASSIFICATION OF MINI-UAVS” shows how deep neural networks can be applied for the classification of mini-UAVs using measured or simulated sets of micro-Doppler spectrograms that are stored in the long-term memory (or knowledge base) of a cognitive radar. “DETECTION OF UNKNOWN TARGET NOVEMBER 2019
CLASSES” describes how the spectrograms of target classes that are not represented in the training set, i.e., targets unknown by the cognitive radar, can be detected with deep neural networks. The detection of an unknown target can provide a trigger for the radar scheduler to collect micro-Doppler spectrograms of this target for inclusion in the training set, i.e., the knowledge base. “DENOISING OF SPECTROGRAMS” shows how a trained neural network can be used to denoise spectrograms. This denoising process may enable a cognitive radar to recognize targets at a longer range. “ADVERSARIAL TRAINING FOR SPECTROGRAM GENERATION” describes the generation of new spectrograms for training a classifier using generative adversarial networks. Finally, in “CONCLUSION,” conclusions are drawn with respect to the application of deep learning for classification of mini-UAVs with cognitive radar.
CLASSIFICATION OF MINI-UAVS The commercial availability of compact electronics and advanced open source software has led to a proliferation of mini-UAVs that can be used for many different purposes including criminal, terrorist, and military activities. Owing to their relatively slow speed and small size, they are hard to distinguish from natural targets, such as birds. Recently, radar signal processing techniques using features extracted from spectrograms and cepstrograms have been developed to discriminate birds and mini-UAVs [10], [11]. The recognition of different classes of miniUAVs is, however, a more difficult problem due to the overlapping features and rapidly changing characteristics of mini-UAVs. Therefore, an enhanced capability for radars is needed to recognize the type of mini-UAV for threat evaluation and assignment of countermeasures.
MINI-UAV MEASUREMENTS To investigate the potential of deep learning techniques for the recognition of mini-UAVs, radar measurements of five different types of mini-UAVs have been acquired
IEEE A&E SYSTEMS MAGAZINE
47
Authorized licensed use limited to: Khwaja Fareed University of Eng & IT. Downloaded on July 04,2020 at 08:11:03 UTC from IEEE Xplore. Restrictions apply.
