This book is also a valuable resource for both radar engineers seeking to learn more about deep learning, as well as computer scientists who are seeking to explore novel applications of machine learning. Further chapters focus on specific radar applications, which relate to DNN design for micro-Doppler analysis, SAR-based automatic target recognition, radar remote sensing, and emerging fields, such as data fusion and image reconstruction.Д®dited by an acknowledged expert, and with contributions from an international team of authors, this book provides a solid introduction to the fundamentals of radar and machine learning, and then goes on to explore a range of technologies, applications and challenges in this developing field. Subsequently, the book summarizes radar-specific issues relating to the different domain representations in which radar data may be presented to DNNs and synthetic data generation for training dataset augmentation. Radar echoes returned from targets are usually submerged in noise, clutter or jamming. Abstract Synthetic aperture radar (SAR) automatic target recognition (ATR) technology is one of the research hotspots in the field of image cognitive learning. Radar target detection (RTD) is one of the most significant techniques in radar systems, which has been widely. Novel deep learning approaches are achieving state-of-the-art accuracy in the area. The book begins with three introductory chapters on radar systems and phenomenology, machine learning principles, and optimization for training common deep neural network (DNN) architectures. A method of radar target detection based on convolutional neural network Abstract. At present, the research of deep neural network in the field of target recognition mostly focuses on the modulation pattern recognition of signal, but there are. Deep Neural Network Design for Radar Applications 1785618520, 9781785618529. This book provides an introduction to the unique aspects of machine learning for radar signal processing that any scientist or engineer seeking to apply these technologies ought to be aware of. Novel deep learning approaches are achieving state-of-the-art accuracy in the area of radar target recognition, enabling applications beyond the scope of human-level performance. The book provides an introduction to the key aspects of machine learning that any radar engineer seeking to apply deep learning to radar signal processing ought to be aware of. FMCW radar could detect objects range, speed and Angle-of-Arrival, advantages are robust to bad weather, good range resolution, and good speed resolution. Novel deep learning models are achieving state-of-the-art accuracy in the area of radar target recognition, sometimes exceeding human-level performance. Efficient convolutional neural network for FMCW radar based hand gesture recognition.

Neural network radar