A Comprehensive Survey of Machine Learning Applied to Radar Signal Processing
arXiv:2009.13702
Abstract
Modern radar systems have high requirements in terms of accuracy, robustness and real-time capability when operating on increasingly complex electromagnetic environments. Traditional radar signal processing (RSP) methods have shown some limitations when meeting such requirements, particularly in matters of target classification. With the rapid development of machine learning (ML), especially deep learning, radar researchers have started integrating these new methods when solving RSP-related problems. This paper aims at helping researchers and practitioners to better understand the application of ML techniques to RSP-related problems by providing a comprehensive, structured and reasoned literature overview of ML-based RSP techniques. This work is amply introduced by providing general elements of ML-based RSP and by stating the motivations behind them. The main applications of ML-based RSP are then analysed and structured based on the application field. This paper then concludes with a series of open questions and proposed research directions, in order to indicate current gaps and potential future solutions and trends.
References in corpus (44)
- Very Deep Convolutional Networks for Large-Scale Image Recognition
- A Survey on Deep Learning in Medical Image Analysis
- YOLOv4: Optimal Speed and Accuracy of Object Detection
- MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
- Conditional Generative Adversarial Nets
- Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
- YOLOv3: An Incremental Improvement
- Deep learning in agriculture: A survey
- Deep Residual Learning for Image Recognition
- Generative Adversarial Networks: An Overview
- A Brief Survey of Deep Reinforcement Learning
- Stochastic Backpropagation and Approximate Inference in Deep Generative Models
- A Survey on Explainable Artificial Intelligence (XAI): Towards Medical XAI
- An Overview of Multi-Task Learning in Deep Neural Networks
- Understanding Neural Networks Through Deep Visualization
- Focal Loss for Dense Object Detection
- Recent Trends in Deep Learning Based Natural Language Processing
- InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets
- A Review on Deep Learning Techniques Applied to Semantic Segmentation
- A Survey of Model Compression and Acceleration for Deep Neural Networks
- Zero-Shot Learning Through Cross-Modal Transfer
- Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model
- Opening the Black Box of Deep Neural Networks via Information
- Deep Convolutional Inverse Graphics Network
- Light Gated Recurrent Units for Speech Recognition
- Towards Robust Interpretability with Self-Explaining Neural Networks
- Learning how to explain neural networks: PatternNet and PatternAttribution
- Transparency by Design: Closing the Gap Between Performance and Interpretability in Visual Reasoning
- The Neuro-Symbolic Concept Learner: Interpreting Scenes, Words, and Sentences From Natural Supervision
- Deep Learning applied to NLP
- Classification of Large-Scale High-Resolution SAR Images with Deep Transfer Learning
- Understanding and Improving Transformer From a Multi-Particle Dynamic System Point of View
- The Consciousness Prior
- SAR Target Recognition Using the Multi-aspect-aware Bidirectional LSTM Recurrent Neural Networks
- Explainable Neural Networks based on Additive Index Models
- Deep Learning Algorithms with Applications to Video Analytics for A Smart City: A Survey
- Guided patch-wise nonlocal SAR despeckling
- A New Ratio Image Based CNN Algorithm For SAR Despeckling
- An Explicitly Relational Neural Network Architecture
- Classification of Intra-Pulse Modulation of Radar Signals by Feature Fusion Based Convolutional Neural Networks
- Towards Interpretable R-CNN by Unfolding Latent Structures
- Modeling Attention Flow on Graphs
- Deep Learning in Robotics: A Review of Recent Research
- Causal Inference via Conditional Kolmogorov Complexity using MDL Binning