Deep Neural Network Design For Radar Applications

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. 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.

This exciting new resource covers various emerging applications of short range radars, including people counting and tracking, gesture sensing, human activity recognition, air-drawing, material classification, object classification, vital sensing by extracting features such as range-Doppler Images (RDI), range-cross range images, Doppler Spectrogram or directly feeding raw ADC data to the classifiers. The book also presents how deep learning architectures are replacing conventional radar signal processing pipelines enabling new applications and results. It describes how deep convolutional neural networks (DCNN), long-short term memory (LSTM), feedforward networks, regularization, optimization algorithms, connectionist This exciting new resource presents emerging applications of artificial intelligence and deep learning in short-range radar. The book covers applications ranging from industrial, consumer space to emerging automotive applications. The book presents several human-machine interface (HMI) applications, such as gesture recognition and sensing, human activity classification, air-writing, material classification, vital sensing, people sensing, people counting, people localization and in-cabin automotive occupancy and smart trunk opening. The underpinnings of deep learning are explored, outlining the history of neural networks and the optimization algorithms to train them. Modern deep convolutional neural network (DCNN), popular DCNN architectures for computer vision and their features are also introduced. The book presents other deep learning architectures, such as long-short term memory (LSTM), auto-encoders, variational auto-encoders (VAE), and generative adversarial networks (GAN). The application of human activity recognition as well as the application of air-writing using a network of short-range radars are outlined. This book demonstrates and highlights how deep learning is enabling several advanced industrial, consumer and in-cabin applications of short-range radars, which weren't otherwise possible. It illustrates various advanced applications, their respective challenges, and how they are been addressed using different deep learning architectures and algorithms.

Introduces multiple state-of-the-art deep learning architectures for mmwave radar in a variety of advanced applications Methods and Techniques in Deep Learning: Advancements in mmWave Radar Solutions provides a timely and authoritative overview of the use of artificial intelligence (AI)-based processing for various mmwave radar applications. Focusing on practical deep learning techniques, this comprehensive volume explains the fundamentals of deep learning, reviews cutting-edge deep metric learning techniques, describes different typologies of reinforcement learning (RL) algorithms, highlights how domain adaptation (DA) can be used for improving the performance of machine learning (ML) algorithms, and more. Throughout the book, readers are exposed to product-ready deep learning solutions while learning skills that are relevant for building any industrial-grade, sensor-based deep learning solution. A team of authors with more than 70 filed patents and 100 published papers on AI and sensor processing illustrate how deep learning is enabling a range of advanced industrial, consumer, and automotive applications of mmwave radars. In-depth chapters cover topics including multi-modal deep learning approaches, the elemental blocks required to formulate Bayesian deep learning, how domain adaptation (DA) can be used for improving the performance of machine learning algorithms, and geometric deep learning are used for processing point clouds. In addition, the book: Discusses various advanced applications and how their respective challenges have been addressed using different deep learning architectures and algorithms Describes deep learning in the context of computer vision, natural language processing, sensor processing, and mmwave radar sensors Demonstrates how deep parametric learning reduces the number of trainable parameters and improves the data flow Presents several human-machine interface (HMI) applications such as gesture recognition, human activity classification, human localization and tracking in-cabin automotive occupancy sensing Methods and Techniques in Deep Learning: Advancements in mmWave Radar Solutions is an invaluable resource for industry professionals, researchers, and graduate students working in systems engineering, signal processing, sensors, data science and AI.

This book provides a breadth of innovative and impactful research in the field of telecommunications led by women investigators. Topics covered include satellite communications, cognitive radars, remote sensing sensor networks, quantum Internet, and cyberspace. These topics touch on many of the challenges facing the world today and these solutions by women researchers are valuable for their technical excellence and their non-traditional perspective. As an important part of the Women in Engineering and Science book series, the work highlights the contribution of women leaders in telecommunications, inspiring women and men, girls and boys to enter and apply themselves to secure our future in.

This volume constitutes the proceedings of the 10th International Work-Conference on IWBBIO 2023, held in Meloneras, Gran Canaria, Spain, during July 12-14, 2022. The total of 79 papers presented in the proceedings, was carefully reviewed and selected from 209 submissions. The papers cove the latest ideas and realizations in the foundations, theory, models, and applications for interdisciplinary and multidisciplinary research encompassing disciplines of computer science, mathematics, statistics, biology, bioinformatics, and biomedicine.

This book gives you a comprehensive overview of key optimization tools that can be used to design radar waveforms and adaptive signal processing strategies under practical constraints -- strategies such as power method-like iterations, coordinate descent, and majorization-minimization – that help you to meet the more and more stressing sensing system requirements. The book walks you through how radar waveform synthesis is obtained as the solution to a constrained optimization problem such as finite energy, unimodularity (or being constant-modulus), and finite or discrete-phase (potentially binary) alphabet, which are dictated by the practical limitations of the real systems. Several approaches in each of these broad frameworks are detailed and various applications of these optimization techniques are described. Focusing on a holistic approach rather than a problem-specific approach, the book shows you what you need to effectively formulate waveform design and understand the flexibility of the framework for adapting to your own specific needs. You’ll have full access to the tools and knowledge you need to design waveform with optimized correlation/cross-correlation properties for SISO/SIMO and MIMO radars, taking into account spectral constraints for cognitive rads, as well as coexistence with communications and mitigate possible Doppler and quantization errors, and more. The book also includes representative software codes that further help you generate the described solutions. With its unique style of covering mathematical results along with their applications from diverse areas, this is a much-needed, detailed handbook for industry researchers, scientists and designers including medical, marine, defense, and automotive companies. It is also an excellent resource for advanced courses on radar signal processing.

The text highlights the designing of efficient, wearable, and textile antennas for medical and wireless applications. It further discusses antenna design for the Internet of Things, biomedical, and 5G applications. The book presents machine learning and deep learning techniques for antenna design and analysis. It also covers radio frequency, micro-electromechanical systems, and nanoelectromechanical systems devices for smart antenna design. This book: Explores wearable reconfigurable antennas for wireless communication and provide the latest technique in term of its structure, defective ground plane, and fractal design Focuses on current and future technologies related to antenna design, and channel characterization for different communication links, and applications Discusses machine learning techniques for antenna design and analysis Demonstrates how nano patch antenna resonates at multiple frequencies by varying the chemical potential Covers the latest antenna technology for microwave sensors, and for fiber optical sensor communications It is primarily for senior undergraduate, graduate students, and academic researchers in the fields of electrical engineering, electronics and communications engineering.

This authoritative resource presents a comprehensive illustration of modern Artificial Intelligence / Machine Learning (AI/ML) technology for radio frequency (RF) data exploitation. It identifies technical challenges, benefits, and directions of deep learning (DL) based object classification using radar data, including synthetic aperture radar (SAR) and high range resolution (HRR) radar. The performance of AI/ML algorithms is provided from an overview of machine learning (ML) theory that includes history, background primer, and examples. Radar data issues of collection, application, and examples for SAR/HRR data and communication signals analysis are discussed. In addition, this book presents practical considerations of deploying such techniques, including performance evaluation, energy-efficient computing, and the future unresolved issues.