
Deep Learning Applications of Short-Range Radars
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Content
- Intro
- Deep Learning Applications of Short-Range Radars
- Contents
- Preface
- 1 Introduction to Radar Signal Processing
- 1.1 Types of Radar
- 1.1.1 CW Radar
- 1.1.2 Modulated CW Radar
- 1.1.3 Impulse UWB Radar
- 1.1.4 Other Short Range Radars
- 1.2 Waveform Design and Ambiguity Function
- 1.3 System Concept
- 1.4 Target Model
- 1.5 3D Data-cube Processing
- 1.5.1 1D Processing
- 1.5.2 2D Range Doppler Images
- 1.5.3 Range Cross-Range Images
- 1.6 Detection Strategy and Clustering
- 1.6.1 Detection Algorithm
- 1.6.2 Clustering
- 1.7 Parameter Estimation and Cramer-Rao Bound
- 1.8 Tracking
- 1.8.1 Track Management
- 1.8.2 Track Filtering
- 1.9 Applications of Short-Range Radar
- 1.10 Problems
- References
- 2 Introduction to Deep Learning
- 2.1 Perceptron
- 2.2 Multilayer Perceptron
- 2.2.1 Training
- 2.2.2 Activation Functions
- 2.2.3 Optimizers
- 2.2.4 Types of Models
- 2.3 Convolutional Neural Networks
- 2.3.1 Convolution Layer
- 2.3.2 Popular Architectures
- 2.3.3 Transfer Learning
- 2.4 LSTM
- 2.5 Autoencoders
- 2.6 Variational Autoencoder
- 2.7 Generative Adversarial Network
- 2.8 Robust Deep Learning
- 2.9 Problems
- References
- 3 Gesture Sensing and Recognition
- 3.1 Introduction
- 3.1.1 RelatedWork
- 3.2 Gesture Sensing/Detection
- 3.3 Micro-Gestures
- 3.3.1 System Parameters
- 3.3.2 Micro-Gesture Set
- 3.4 2D All CNN-LSTM
- 3.4.1 Architecture and Learning
- 3.4.2 System Evaluation
- 3.5 3D CNN and Pseudo-3D CNN
- 3.5.1 3D CNN Architecture and Learning
- 3.5.2 Pseudo-3D CNN Architecture and Learning
- 3.6 Meta-Learning
- 3.6.1 Architecture and Learning
- 3.6.2 System Evaluation
- 3.7 Macro-Gestures
- 3.7.1 System Parameters
- 3.7.2 Macro-Gesture Set
- 3.8 Unguided Attention 2D DCNN-LSTM
- 3.8.1 Architecture and Learning
- 3.8.2 System Evaluation
- 3.9 FutureWork and Direction
- 3.10 Problems
- References
- 4 Human Activity Recognition and Elderly-Fall Detection
- 4.1 Introduction
- 4.1.1 RelatedWork
- 4.2 Preprocessing for Feature Image
- 4.2.1 Fast-Time FFT
- 4.2.2 Coherent Pulse Integration
- 4.2.3 MTI Filtering
- 4.2.4 Adaptive Detection Thresholding
- 4.2.5 Euclidean Clustering
- 4.2.6 Kalman Filter
- 4.3 Input Feature Images
- 4.3.1 Range Spectrogram
- 4.3.2 Doppler Spectrogram
- 4.3.3 Video of RDI
- 4.4 Human Activity Data Set
- 4.5 DCNN Activity Classification
- 4.5.1 Architecture and Learning
- 4.5.2 Results and Discussion
- 4.6 Bayesian Classification
- 4.6.1 Integrated Classifier and Tracker
- 4.6.2 Results and Discussion
- 4.7 Fall-Motion Recognition
- 4.7.1 Architecture and Learning
- 4.7.2 Deformable CNN
- 4.7.3 Loss Function
- 4.7.4 Results and Discussion
- 4.8 FutureWork and Directions
- 4.9 Problems
- References
- 5 Air-Writing
- 5.1 Introduction
- 5.2 Radar Network Placement
- 5.3 Preprocessing
- 5.3.1 Coherent Pulse Integration
- 5.3.2 Moving Target Indication Filtering
- 5.3.3 Target Detection and Selection
- 5.3.4 Localization with Trilateration
- 5.3.5 Trajectory Smoothening Filters
- 5.4 Setup and Characters
- 5.4.1 Character Set
- 5.4.2 System Parameters
- 5.4.3 Setup and Data Acquistion
- 5.5 LSTM
- 5.5.1 Architecture
- 5.5.2 Loss Function: CTC
- 5.5.3 Design Considerations
- 5.5.4 Performance Evaluation
- 5.6 Deep Convolutional Neural Networks
- 5.6.1 Architecture
- 5.6.2 Weight Initialization
- 5.6.3 Learning Schedule
- 5.6.4 Data Augmentation
- 5.6.5 Performance Evaluation
- 5.7 1D CNN-LSTM
- 5.7.1 Architecture
- 5.7.2 Performance Evaluation
- 5.8 FutureWork and Directions
- 5.9 Problems
- References
- 6 Material Classification
- 6.1 Introduction
- 6.1.1 RelatedWork
- 6.2 Features: Range Angle Images
- 6.3 Deep Convolutional Neural Networks
- 6.3.1 Architecture and Learning
- 6.3.2 Design Considerations
- 6.3.3 Results and Discussion
- 6.4 Siamese Network
- 6.4.1 Architecture and Learning
- 6.4.2 Design Considerations
- 6.4.3 Results and Discussion
- 6.5 FutureWork and Directions
- 6.6 Problems
- References
- 7 Vital Sensing and Classification
- 7.1 Introduction
- 7.2 Vital Signal Fundamentals
- 7.2.1 Preprocessing Steps
- 7.3 Heart Rate Estimation through a Deep-Learning Approach
- 7.3.1 GAN-Based Data Augmentation
- 7.3.2 Results and Discussions
- 7.4 Adaptive Signal Processing with a Tracking Approach
- 7.4.1 Algorithm
- 7.4.2 Results and Discussion
- 7.5 IQ Signal Evaluation using Deep Learning
- 7.5.1 Deep Learning Architecture
- 7.5.2 Results and Discussion
- 7.6 FutureWork and Direction
- 7.7 Problems
- References
- 8 People Sensing, Counting,and Localization
- 8.1 Introduction
- 8.2 Presence Sensing: Signal Processing Approach
- 8.2.1 Challenges
- 8.2.2 Solution
- 8.3 Presence Sensing: Deep Learning Approach
- 8.3.1 Challenges
- 8.3.2 Solution
- 8.3.3 Results and Discussion
- 8.4 People Counting: Signal Processing Approach
- 8.4.1 Challenges
- 8.4.2 Solution
- 8.5 People Counting: Deep Learning Approach
- 8.5.1 Data Preparation and Processing
- 8.5.2 Solution: Framework and Learning
- 8.5.3 Results and Discussion
- 8.6 People Detection and Localization: Signal Processing Approach
- 8.7 People Detection and Localization: Deep Learning Approach
- 8.7.1 Challenges
- 8.7.2 Architecture and Learning
- 8.7.3 Results and Discussion
- 8.8 FutureWork and Direction
- 8.9 Problems
- References
- 9 Automotive In-Cabin Sensing
- 9.1 Introduction
- 9.2 Smart Trunk Opening
- 9.2.1 Challenges
- 9.2.2 Solution
- 9.2.3 Results and Discussion
- 9.3 Vehicle Occupancy Sensing
- 9.3.1 Challenges
- 9.3.2 Solution
- 9.4 Federated Learning
- 9.4.1 Challenges
- 9.4.2 Solution
- 9.5 FutureWork and Direction
- 9.6 Problems
- References
- About the Authors
- Index
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