# Enhanced Radar Imaging Using a Complex-valued Convolutional Neural Network Source: [Enhanced Radar Imaging Using a Complex-valued Convolutional Neural Network]() Collection index: [llms.txt]() Data updated: 2025-10-27T07:12:29.913Z Authors: Jingkun Gao, Bin Deng, Yuliang Qin, Hongqiang Wang, Xiang Li Published: 2017-12-29T02:14:25.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()