Deep Learning Techniques and Applications of MIMO Radar Theory
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "AI Remote Sensing".
Deadline for manuscript submissions: 31 July 2025 | Viewed by 548
Special Issue Editor
Interests: radar signal processing; object detection; image segmentation
Special Issue Information
Dear Colleagues,
With the rapid development of deep learning technology, traditional MIMO (multiple input multiple output) radar systems are undergoing technological innovation. MIMO radar systems have been widely used in target detection, imaging and tracking due to their high resolution and high precision detection capability. Deep learning methods, particularly convolutional neural networks (CNNS), recurrent neural networks (RNNs), and generative adversarial networks (Gans), have been shown to have significant advantages in signal processing, target recognition, and parameter estimation.
The purpose of this Special Issue is to explore how deep learning techniques can be effectively integrated into MIMO radar systems to improve their performance and expand their applications. We hope to compile a series of recent research results to demonstrate innovative applications of deep learning in MIMO radar, address the limitations of existing technologies, and suggest new research directions.
The themes of this Special Issue include, but are not limited to, the following:
- MIMO radar signal processing methods;
- MIMO radar target detection and classification;
- Using deep learning to improve the anti-jamming ability of MIMO radar systems;
- Target tracking and parameter estimation based on deep learning;
- Real-time processing and optimization of MIMO radar systems;
- Deep learning-based MIMO Radar Theory;
- MIMO radar data fusion and multi-sensor collaboration;
- Joint optimization of MIMO radar waveform design and deep learning;
- The application of generative models and LLMs in MIMO radar signal simulation;
- MIMO radar image reconstruction and super resolution.
Through this Special Issue, we hope to provide cutting-edge technical ideas and methods for researchers and engineers in the field of MIMO radar and promote the wider application of deep learning technology in radar systems.
Reviews and research articles are both suitable.
Dr. Chen Zhao
Guest Editor
Manuscript Submission Information
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Keywords
- deep learning
- MIMO radar
- signal processing
- target detection and classification
- target tracking
- parameter estimation
- data fusion
- multi-sensor collaboration
- generative model and LLM
- image reconstruction
- super resolution
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