RESEARCH ON THE NLOS LOCALIZATION TECHNIQUE IN WIRELESS SENSOR NETWORK
RESEARCH ON THE NLOS LOCALIZATION TECHNIQUE IN WIRELESS SENSOR NETWORK
Nan Hu
Shenyang Jianzhu University
Yu Ying
Shenyang Jianzhu University
Tianbo Xu
Shenyang Jianzhu University
Copyright © 2021 by Cayley Nielson Press, Inc.
ISBN: 978-1-7348822-6-1
Cayley Nielson Press Scholarly Monograph Series Book Code No.: 211-10-2
US$186.50
Preface
Due to the availability of such low energy cost sensors, micro processor and radio frequency circuitry for information transmission, there is a wide and rapid diffusion of wireless sensor network (WSN). Since the sensor node is equipped with sensing, processing, and communication modules, the WSN can be used in smart environment for some practical purposes. And WSN is the basic infrastructure of many smart environmental monitoring applications.
Localization is one of the most important subjects. The WSN based localization strategy has the features of good flexibility, convenient maintenance, and low-cost updates. So WSNs are increasingly being used. If line of sight (LOS) propagation existed between unknown node and all beacon nodes, high location accuracy can be achieved. However, in certain environments, especially in indoor areas, the direct path from the unknown node to a beacon is blocked by obstacles, the signal measurements include an error due to the excess path traveled because of the reflection or diffusion of signal, which is termed as the NLOS error. The NLOS error results in the large location estimation error. Therefore the research on the localization in the NLOS environment remains a challenge topic and has highly practical meaning. The contents of the book are organized into six chapters.
In chapter 1, the mean shift-based mobile localization method in mixed LOS/NLOS environments for wireless sensor network is introduced. In chapter 2 is about the essential ones of mean shift-based multi-source localization method in wireless binary sensor network. In chapter 3 is about the essential ones of the mobile localization method. In chapter 4, the essential ones of studying on a mobile localization strategy for wireless sensor network in NLOS conditions. The chapter 5 is about a robust localization algorithm based on NLOS identification for wireless sensor network. The chapter 6 is about an indoor localization algorithm based on IMM in mixed LOS/NLOS environments. The last chapter is about the indoor robust localization algorithm based on data association technique.
I wanted to thank the authors of the book chapters, especially Rui Men, Hongwei Li, Wei Gong, Rui Zhang, Meiju Liu, Xiaoxi Tian, Jialin Sun, Ying Zhang, Jing Hou, because they offered to the technology of the wireless sensor networks applied to many areas of the latest progress and the existing challenges. This work was supported by the Education Department of Liaoning Province Science and Technology Research Projects lnqn202014.
Nan Hu
Shenyang Jianzhu University
Shenyang, Liaoning, China
July 10, 2021
Contents
PREFACE 1
CHAPTER 1 MEAN SHIFT-BASED MOBILE LOCALIZATION METHOD IN MIXED LOS/NLOS ENVIRONMENTS FOR WIRELESS SENSOR NETWORK
1.1 INTRODUCTION 6
1.2 RELATED WORK 8
1.3 BACKGROUND 10
1.3.1 System Model 11
1.3.2 Mean shift Method 13
1.4 PROPOSED NLOS LOLALIZATION METHOD 14
1.4.1 Kalman Predication 16
1.4.2 NLOS Detection 17
1.4.3 Mean Shift-based Data Association 18
1.4.4 Kalman Update 19
1.4.5 ML-based Location 19
1.5 PERFORMANCE EVALUATION 20
1.6 CONCLUSION 26
CHAPTER 2 MEAN SHIFT-BASED MULTI-SOURCE LOCALIZATION METHOD IN WIRELESS BINARY SENSOR NETWORK
2.1 INTRODUCTION 27
2.2 BACKGROUND 32
2.2.1 Energy Attenuation Model 32
2.2.2 Neyman-pearson model 35
2.2.3 Mean shift method 39
2.3 PROPOSED METHOD 40
2.4 PERFORMANCE EVALUATION 46
2.5 CONCLUSION 50
CHAPTER 3 A MOBILE LOCALIZATION METHOD BASED ON A ROBUST EXTEND KALMAN FILTER AND IMPROVED M-ESTIMATION IN INTERNET OF THINGS
3.1 INTRODUCTION 52
3.2 RELATED WORK 55
3.2.1 Related Work 55
3.2.2 The Architecture of the Proposed Algorithm 57
3.3 PROPOSED METHOD 58
3.3.1 System Model 58
3.3.2 Improved Extend Kalman Filter 60
3.3.3 Improved Nearest Neighbor Variable Estimation Localization Algorithm 63
3.4 SIMULATION RESULTS 68
3.5 CONCLUSIONS 78
CHAPTER 4 A MOBILE LOCALIZATION STRATEGY FOR WIRELESS SENSOR NETWORK IN NLOS CONDITIONS
4.1 INTRODUCTION 80
4.2. RELATED WORKS 82
4.3 SYSTEM MODEL 85
4.4. PROPOSED ALGORITHM 87
4.4.1 General Concept 87
4.4.2 Kalman Prediction 88
4.4.3 NLOS Identification and Correction Method 89
4.4.4 Kalman Update 92
4.4.5 Localization Method 92
4.5. SIMULATION RESULTS 94
4.5.1 NLOS Error Obeys the Gaussian Distribution 96
4.5.2 NLOS Error Obeys the Uniform Distribution 100
4.6. CONCLUSION 101
CHAPTER 5 A ROBUST LOCALIZATION ALGORITHM BASED ON NLOS IDENTIFICATION FOR WIRELESS SENSOR NETWORK
5.1 INTRODUCTION 103
5.2 RELATED WORKS 106
5.3 PROBLEM STATEMENT 110
5.3.1 Signal Model 110
5.3.2 A Brief Introduction to IMM 112
5.4 PROPOSED METHOD 113
5.4.1 General Concept 113
5.4.2 Interaction 115
5.4.3 Model Matching 116
5.4.4 REKF Algorithm 121
5.4.5 NLOS KEF With LOS Reconstruction Algorithm 126
5.4.6 Model Probability Update 128
5.4.7 Combination 128
5.5 SIMULATION AND EXPERIMENTAL RESULTS 129
5.5.1. Simulation Environment and Parameter Settings 129
5.5.2 Simulation Results 131
5.5.3 Experimental Environment and Parameter Settings 140
5.5.4 Experimental Results 143
5.6. CONCLUSION 144
CHAPTER 6 AN INDOOR LOCALIZATION ALGORITHM BASED ON IMM IN MIXED LOS/NLOS ENVIRONMENTS
6.1 INTRODUCTION 146
6.2 SYSTEM MODEL 148
6.3 PROPOSED METHOD 150
6.3.1 NLOS Judgment Based on Residual Calculation 151
6.3.2 Kalman Filtering 152
6.3.3 Interactive Multi-Model Algorithm 154
6.3.4 Unscented Kalman Filtering 156
6.3.5 Parameter Correction 159
6.3.6 Location Estimation 160
6.4 SIMULATION RESULTS 162
6.5 CONCLUSIONS 165
CHAPTER 7 AN INDOOR ROBUST LOCALIZATION ALGORITHM BASED ON DATA ASSOCIATION TECHNIQUE
7.1 INTRODUCTION 167
7.2 RELATED WORKS 169
7.3 SIGNAL MODEL 173
7.4 PROPOSED ALGORITHM 175
7.4.1 General Concept 175
7.4.2 Grouping and Hypothesis Testing 177
7.4.3 Association Probability Updating 180
7.4.4 NLOS Tracking 183
7.5 EXPERIMENT SIMULATION 186
7.5.1 The NLOS errors obeys Gaussian Distribution 187
7.5.2 The NLOS errors obeys Uniform Distribution 192
7.6 CONCLUSIONS 200
REFERENCES 201
Readership
This book should be useful for students, scientists, engineers and professionals working in the areas of optoelectronic packaging, photonic devices, semiconductor technology, materials science, polymer science, electrical and electronics engineering. This book could be used for one semester course on adhesives for photonics packaging designed for both undergraduate and graduate engineering students.
