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Significant resources have been spent in collecting and storing large and heterogeneous radar datasets during expensive Arctic and Antarctic fieldwork. The vast majority of data available is unlabeled, and the labeling process is both time-consuming and expensive. One possible alternative to the labeling process is the use of synthetically generated data with artificial intelligence. Instead of labeling real images, we can generate synthetic data based on arbitrary labels. In this way, training data can be quickly augmented with additional images. In this research, we evaluated the performance of synthetically generated radar images based on modified cycle-consistent adversarial networks. We conducted several experiments to test the quality of the generated radar imagery. We also tested the quality of a state-of-the-art contour detection algorithm on synthetic data and different combinations of real and synthetic data. Our experiments show that synthetic radar images generated by generative adversarial network (GAN) can be used in combination with real images for data augmentation and training of deep neural networks. However, the synthetic images generated by GANs cannot be used solely for training a neural network (training on synthetic and testing on real) as they cannot simulate all of the radar characteristics such as noise or Doppler effects. To the best of our knowledge, this is the first work in creating radar sounder imagery based on generative adversarial network.
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This content will become publicly available on May 3, 2026
High-Quality Synthetic Data Generation for Omnidirectional Human Activity Recognition
The impact of aspect angle on Doppler effect hinders the capability of a monostatic radar to achieve human activity recognition (HAR) from all aspect angles, i.e., omnidirectional. To alleviate the “angle sensitivity”, sufficient and high-quality training data from multiple aspect angles is mandated. However, it would be time-consuming for the monostatic radar to collect the training data from all aspect angles. To address this issue, this paper proposes a high-quality synthetic data generation algorithm based on high-dimensional model representation (HDMR) for omnidirectional HAR. The aim is to augment a high-quality dataset with collected samples at the radar line-of-sight direction and few samples from other aspect angles. The quality of synthetic samples is evaluated by dynamic time wrapping distance (DTWD) between the synthetic and real samples. Subsequently, the synthetic samples are utilized to train a classifier based on ResNet50 to achieve omnidirectional HAR. Experimental results demonstrate that the averaged HAR accuracy of the proposed algorithm exceeds 91% at different aspect angles. The quality of the synthetic samples generated by the proposed algorithm outperforms two commonly-used algorithms in the literature.
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- Award ID(s):
- 2233536
- PAR ID:
- 10616048
- Publisher / Repository:
- IEEE
- Date Published:
- ISBN:
- 979-8-3315-3956-6
- Page Range / eLocation ID:
- 1 to 6
- Format(s):
- Medium: X
- Location:
- Atlanta, GA, USA
- Sponsoring Org:
- National Science Foundation
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