Synthetic Aperture Radar Target Recognition under Limited Training data. Theory and Methods

Synthetic Aperture Radar Target Recognition under Limited Training dаta: Theory and Methods by Chenwei Wang, Jifang Pei, Yulin Huang
English | 2026 | True PDF | 170 pages | 12.1 MB

This book reports the latest results in the study of synthetic aperture radar (SAR) automatic target recognition (ATR) and focuses on the theory and methods of limited-data SAR ATR, and experimental verification, and many other aspects.

This book contains two main parts: classical limited-data SAR ATR and limited-data SAR causal ATR. The first part consists of theoretical foundations, and limited-data SAR ATR based on data augmentation and model design. The second part focuses on the theoretical foundations of SAR causal ATR, and methods based on causal feature extraction and causal intervention. This book also includes the research results of realization technology and experimental verification.

In this context, Synthetic Aperture Radar Target Recognition Under Limited Training dаta: Theory and Methods offers a timely and rigorous response to these challenges. Instead of relying solely on conventional data augmentation or statistical regularization, Drs. Chenwei Wang, Jifang Pei, and Yulin Huang take a distinctive approach by integrating causal inference into the recognition framework.

This book is well-organized and technically rich, making it a valuable resource for graduate students, researchers, and engineers in radar systems, remote sensing, and Machine Learning. It bridges the gap between theoretical causal modeling and practical SAR applications, offering insights that extend beyond the specific algorithms. The authors have demonstrated profound insight and a coherent vision of the intersection of SAR ATR and Machine Learning.

Contents:

Download Synthetic Aperture Radar Target Recognition under Limited Training dаta: Theory and Methods