# Enhanced Radar Imaging Using a Complex-valued Convolutional Neural Network

arXiv: https://arxiv.org/abs/1712.10096 · HTML: https://mlsanity.com/paper/1712.10096/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Dec 2017.

## Built on by (most cited first, 18 of 18)

- [A Survey of Complex-Valued Neural Networks](https://mlsanity.com/paper/2101.12249/) (2021)
- [Complex-valued Convolutional Neural Networks for Enhanced Radar Signal Denoising and Interference Mitigation](https://mlsanity.com/paper/2105.00929/) (2021)
- [A Vision Transformer Approach for Efficient Near-Field Irregular SAR Super-Resolution](https://mlsanity.com/paper/2305.02074/) (2023)
- [Coherent, super resolved radar beamforming using self-supervised learning](https://mlsanity.com/paper/2106.13085/) (2021)
- [Scene-aware Learning Network for Radar Object Detection](https://mlsanity.com/paper/2107.01469/) (2021)
- [Improved Static Hand Gesture Classification on Deep Convolutional Neural Networks using Novel Sterile Training Technique](https://mlsanity.com/paper/2305.02039/) (2023)
- [BCNN: Binary Complex Neural Network](https://mlsanity.com/paper/2104.10044/) (2021)
- [Efficient CNN-based Super Resolution Algorithms for mmWave Mobile Radar Imaging](https://mlsanity.com/paper/2305.02092/) (2023)
- [Deep Learning-Based Multiband Signal Fusion for 3-D SAR Super-Resolution](https://mlsanity.com/paper/2305.02017/) (2023)
- [Complex-Valued Neural Networks for Data-Driven Signal Processing and Signal Understanding](https://mlsanity.com/paper/2309.07948/) (2023)
- [Steinmetz Neural Networks for Complex-Valued Data](https://mlsanity.com/paper/2409.10075/) (2024)
- [Efficient Physics-Based Learned Reconstruction Methods for Real-Time 3D Near-Field MIMO Radar Imaging](https://mlsanity.com/paper/2312.16959/) (2023)
- [RotaTouille: Rotation Equivariant Deep Learning for Contours](https://mlsanity.com/paper/2508.16359/) (2025)
- [Neural Networks with Complex-Valued Weights Have No Spurious Local Minima](https://mlsanity.com/paper/2103.07287/) (2021)
- [Super-Resolution Radar Imaging with Sparse Arrays Using a Deep Neural Network Trained with Enhanced Virtual Data](https://mlsanity.com/paper/2306.09839/) (2023)
- [Emerging Approaches for THz Array Imaging: A Tutorial Review and Software Tool](https://mlsanity.com/paper/2309.08844/) (2023)
- [Phase-Aware Deep Learning with Complex-Valued CNNs for Audio Signal Applications](https://mlsanity.com/paper/2510.09926/) (2025)
- [Exploring Polarimetric Properties Preservation during Reconstruction of PolSAR images using Complex-valued Convolutional Neural Networks](https://mlsanity.com/paper/2602.07094/) (2026)

## Abstract (arXiv)

Convolutional neural networks (CNN) have been successfully employed to tackle several remote sensing tasks such as image classification and show better performance than previous techniques. For the radar imaging community, a natural question is: Can CNN be introduced to radar imaging and enhance its performance? The presented letter gives an affirmative answer to this question. We firstly propose a processing framework by which a complex-valued CNN (CV-CNN) is used to enhance radar imaging. Then we introduce two modifications to the CV-CNN to adapt it to radar imaging tasks. Subsequently, the method to generate training data is shown and some implementation details are presented. Finally, simulations and experiments are carried out, and both results show the superiority of the proposed method on imaging quality and computational efficiency.