Target Recognition and Tracking for Millimeter Wave Radar in Intelligent Transportation

Description

This book starts with the introduction of the radar working system from a mathematical point of view. It shows the composition of transmitted signal and echo signal, and describes the principle of speed measurement and distance measurement for different radar systems. The book focuses on millimeter-wave radar technologies related to echo signal denoising, target identification, clustering, and target tracking and develops a workable plan for the information fusion idea between millimeter-wave radar and machine vision. This book breaks down the systematic processing flow of millimeter-wave radar echo signals one by one from the standpoint of intelligent transportation, concentrating on the introduction of principles and reinforced by a plethora of experimental instances. So that readers from all relevant professions may comprehend millimeter-wave radar's involvement throughout the complete system process. Preface Contents 1 Introduction 1.1 Research Background and Significance 1.2 Research Status 1.2.1 Research Status of Traffic Surveillance Radar 1.2.2 Research Status of Radar Data Processing 1.3 Radar Speed Measurement System 1.4 Research Status of Video Object Detection 1.5 Summary 2 Traffic Radar System 2.1 CW System 2.2 LFMCW System 2.3 FSK System 2.4 CW-FMCW Composite System 2.5 Summary 3 Microwave Velocity Radar Signal Processing Algorithm 3.1 Denoising Algorithm 3.1.1 EMD-Based Denoising Algorithm 3.1.2 Self-Related Testing 3.2 Speed Radar Angle Adaptive Algorithm 3.2.1 Train Speed Radar System 3.2.2 Amendment Algorithm Based on Radar Corners Based on Sample Statistical Characteristics 3.3 Summary 4 Target Recognition and Tracking of Microwave Velocity Radar 4.1 Algorithm Optimization of Single Target Radar 4.1.1 Optimization of Velocity and Distance Measurement Algorithm Based on Kalman Filter 4.1.2 DSP Algorithm Improvement Based on CW-FMCW Hybrid System 4.1.3 Simulation and Analysis 4.2 FSK Radar Speed Measurement Algorithm 4.2.1 FSK Radar Speed Measurement Principle 4.2.2 FSK Radar Target Recognition Algorithm Implementation and Simulation 4.3 Research and Implementation of Multi-target Detection and Tracking Algorithm 4.3.1 Multi-target Detection and Tracking Algorithm 4.3.2 Multi-target Tracking Trigger Process 4.3.3 Optimization of Target Detection Based on Constant False Alarm Detection 4.3.4 Test and Analysis 4.4 Summary 5 Clustering Algorithms 5.1 Classical Clustering Algorithms 5.1.1 DBSCAN Clustering Algorithm 5.1.2 FCM Clustering Algorithm 5.1.3 K-Means Clustering Algorithm 5.2 Spindle-based Density Peaks Fuzzy Clustering Algorithm 5.2.1 Initial Clustering Algorithm Based on Density Peak 5.2.2 Fuzzy Clustering Algorithm Based on Spindle Update 5.2.3 Experimental Design 5.3 Summary 6 Data Association Algorithms 6.1 Improved Particle Filtering Algorithm 6.1.1 Track Association 6.1.2 Moving Target Tracking 6.1.3 Experimental Comparison and Drive Test Results 6.2 Improved Kalman Filter Algorithm 6.2.1 Bayesian Robust Kalman Filter Based on Posterior Noise Statistics (KFPNS) 6.2.2 Experimental Comparison 6.3 Data Association Algorithms 6.3.1 Nearest Neighbor Data Association 6.3.2 Joint Probabilistic Data Association 6.3.3 K-Nearest Neighbor Joint Probabilistic Data Association Algorithm 6.3.4 Experimental Design 6.4 Convex Variational Inference for Multi-hypothesis Fractional Belief Propagation Based Data Association in Multiple Target Tracking Systems 6.4.1 Multiple-Hypothesis Tracking 6.4.2 Multi-hypothesis Fractional Belief Propagation 6.4.3 Experimental Design 6.5 Design of Multi-target Tracking System 6.5.1 Requirements Analysis 6.5.2 Overall Design 6.5.3 Application of Multi-target Tracking System 6.6 Summary 7 Information Fusion and Target Recognition Based on Millimeter Wave Radar and Machine Vision 7.1 Target Detection Based on Deep Learning 7.1.1 Target Detection 7.1.2 Principle of Deep Learning Target Detection 7.1.3 Test and Simulation 7.2 Information Fusion Based on Millimetre Wave Radar and Machine Vision 7.2.1 Data Fusion of mmWave Radar and Cameras 7.2.2 Machine Learning-Based Vehicle Recognition 7.2.3 Experimental Verification and Analysis 7.3 A Target Detection Method Based on the Fusion Algorithm of Radar and Camera 7.4 Summary