Deep Learning Apps of Short Range Radar

Description

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