Data Augmentation for Imbalanced HRRP Recognition Using Deep Convolutional Generative Adversarial Network

Abstract

In radar high-resolution range profile (HRRP) recognition, the recognition accuracy will decline when the training samples in some classes (majority classes) greatly outnumbers other classes (minority classes). To alleviate the above imbalanced problem, an HRRP data augmentation framework is proposed. A onedimensional (1-D) deep convolutional generative adversarial network (DCGAN) is developed to generate artificial HRRPs. The fidelity of the generated HRRPs is evaluated subjectively in the raw data domain and quantitatively by the similarity in the feature domain. The experimental results show that the generated data are similar to the true HRRPs and demonstrate that the proposed framework outperforms the state-of-the-art oversampling methods when handling the imbalanced problem. INDEX TERMS High resolution range profile (HRRP), imbalanced problem, data augmentation, 1-D deep convolutional generative adversarial network (DCGAN)

Deep-Learning for Radar: A Survey

“…Commonly used sensitivity removal approaches include time-shift compensation, energy normalization, and average processing [54]- [56]. The DNN structures used for radar HRRP target recognition include the deep belief network [54], [55], recurrent attentional network [57], [58], concatenated neural network, CNNs [62]- [64], stacked auto-encoder (SAE) [65], and convolutional LSTM [66], [67].…”

“…Some researchers used measured HRRP data for performance evaluation. For example, the HRRP data from Yak-42 (large jet), Cessna Citation S/II (small jet), and An-26 (twin-engine turboprop) were used in [54]- [58]; the HRRP data from Airbus A319, A320, A321, and Boeing B738 were used in [59]; the HRRP data from seven types of ship of different sizes (length from 89.3 m to 182.8 m) were used in [60]; the HRRP data from various types of ground vehicles were used in [62], [66], [67]. Since most researchers only have access to a limited mount of HRRP measurement data associated with a handful of vehicles, many of them resort to simulated HRRP data generated by software based on the specific CAD models of vehicles for research purposes.…”

“…Another feasible alternative is data augmentation with generative adversarial network (GAN). Specifically, in [62], GAN was adopted to address the problem of unbalanced training samples, i.e. the labeled training samples for some classes (majority classes) significantly outnumber the other classes (minority classes).…”

Multi-View HRRP Generation With Aspect-Directed Attention GAN

“…Recently proposed models also show that the class (or label) information is helpful to improve the quality of generation [43,44]. There are few works on HRRP generation as well [45,46]. In [45], DCGAN is used for imbalanced HRRP recognition and the quality is comprehensively evaluated on data-, feature-and recognition-level.…”

“…There are few works on HRRP generation as well [45,46]. In [45], DCGAN is used for imbalanced HRRP recognition and the quality is comprehensively evaluated on data-, feature-and recognition-level. Shi proposed to use a pre-trained GAN and transferred features for generation from only one known sample [46].…”