# BRSR-OpGAN: Blind Radar Signal Restoration using Operational Generative Adversarial Network

arXiv: https://arxiv.org/abs/2407.13949 · HTML: https://mlsanity.com/paper/2407.13949/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 38 references that are in the map.
- First posted on arXiv 18 Jul 2024.

## Builds on (most cited first, 10 of 10)

- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Self-Organized Operational Neural Networks with Generative Neurons](https://mlsanity.com/paper/2004.11778/) (2020)
- [Operational Neural Networks](https://mlsanity.com/paper/1902.11106/) (2019)
- [FastONN -- Python based open-source GPU implementation for Operational Neural Networks](https://mlsanity.com/paper/2006.02267/) (2020)
- [Exploiting Heterogeneity in Operational Neural Networks by Synaptic Plasticity](https://mlsanity.com/paper/2009.08934/) (2020)
- [Robust Peak Detection for Holter ECGs by Self-Organized Operational Neural Networks](https://mlsanity.com/paper/2110.02381/) (2021)
- [Blind ECG Restoration by Operational Cycle-GANs](https://mlsanity.com/paper/2202.00589/) (2022)
- [Global ECG Classification by Self-Operational Neural Networks with Feature Injection](https://mlsanity.com/paper/2204.03768/) (2022)
- [Robust R-Peak Detection in Low-Quality Holter ECGs using 1D Convolutional Neural Network](https://mlsanity.com/paper/2101.01666/) (2020)
- [Blind Restoration of Real-World Audio by 1D Operational GANs](https://mlsanity.com/paper/2212.14618/) (2022)

## Abstract (arXiv)

Objective: Many studies on radar signal restoration in the literature focus on isolated restoration problems, such as denoising over a certain type of noise, while ignoring other types of artifacts. Additionally, these approaches usually assume a noisy environment with a limited set of fixed signal-to-noise ratio (SNR) levels. However, real-world radar signals are often corrupted by a blend of artifacts, including but not limited to unwanted echo, sensor noise, intentional jamming, and interference, each of which can vary in type, severity, and duration. This study introduces Blind Radar Signal Restoration using an Operational Generative Adversarial Network (BRSR-OpGAN), which uses a dual domain loss in the temporal and spectral domains. This approach is designed to improve the quality of radar signals, regardless of the diversity and intensity of the corruption. Methods: The BRSR-OpGAN utilizes 1D Operational GANs, which use a generative neuron model specifically optimized for blind restoration of corrupted radar signals. This approach leverages GANs' flexibility to adapt dynamically to a wide range of artifact characteristics. Results: The proposed approach has been extensively evaluated using a well-established baseline and a newly curated extended dataset called the Blind Radar Signal Restoration (BRSR) dataset. This dataset was designed to simulate real-world conditions and includes a variety of artifacts, each varying in severity. The evaluation shows an average SNR improvement over 15.1 dB and 14.3 dB for the baseline and BRSR datasets, respectively. Finally, even on resource-constrained platforms, the proposed approach can be applied in real-time.