Due to the micro-scale size and low power consumption of Microelectromechanical systems (MEMS), this technology is now being utilized in a variety of fields. This leading-edge resource focuses on the application of MEMS inertial sensors to navigation systems. The book explains how to minimize cost by adding and removing inertial sensors. Moreover, this practical reference presents various integration strategies with examples from real field tests. From an introduction to MEMS navigation related applications...to special topics on Alignment for MEMS-Based Navigation...to discussions on the Extended Kalman Filter, this comprehensive book covers a wide range of critical topics in this fast-growing area.
Author(s): Priyanka Aggarwal, Zainab Syed, Aboelmagd Noureldin, Naser El-Sheimy
Series: GNSS Technology and Applications
Publisher: Artech House Publishers
Year: 2010
Language: English
Pages: 213
MEMS-Based Integrated Navigation......Page 2
Contents......Page 6
Preface......Page 12
1.1 Introduction
......Page 16
1.2.2 Personnel Tracking and Navigation......Page 18
1.2.4 Event Data Recorder......Page 19
1.2.6 Patient Monitoring......Page 20
1.2.9 Land Vehicle Navigation......Page 21
1.3.1 Aiding Sources in Coordinate Domain......Page 22
1.3.2 Aiding Sources in Velocity Domain......Page 25
1.3.3 Aiding Sources in Attitude Domain
......Page 26
References......Page 27
2.1 Introduction
......Page 30
2.2 Accelerometers......Page 31
2.2.1 Working Principle for MEMS Accelerometers......Page 32
2.2.2 Classifications of Accelerometers......Page 34
2.3.1 Principle of MEMS Gyroscopes......Page 36
2.3.2 Classification of MEMS Gyroscopes......Page 37
2.4 MEMS Inertial Sensors for the Most Economical Land Navigation......Page 39
2.5 Method to Compute Minimum Sensors......Page 41
2.6.1 Drift Errors Without NHC......Page 44
2.6.2 Drift Errors with NHC......Page 46
References......Page 47
3.1 Introduction......Page 50
3.2 Systematic Errors......Page 51
3.2.1 Bias......Page 52
3.2.2 Input Sensitivity or Scale Factor......Page 53
3.2.3 Nonorthogonality/ Misalignment Errors......Page 54
3.2.4 Run-to-Run (Repeatability) Bias/Scale Factor......Page 56
3.2.5 In Run (Stability) Bias/Scale Factor......Page 57
3.3 Calibration of Systematic Sensor Errors......Page 58
3.3.1 6-Position Static Test......Page 59
3.3.2 Angular Rate Test......Page 60
3.3.3 Thermal Calibration Test......Page 61
3.4.1 Examples of Random Processes......Page 68
3.5 Stochastic Modeling......Page 72
3.5.2 Allan Variance Methodology......Page 73
3.6 Sensors Measurement Models......Page 75
3.6.2 Gyroscope Measurement Model......Page 76
References......Page 77
4.1 Introduction......Page 78
4.2 Considerations for MEMS Sensor Navigation......Page 80
4.3 Portable Navigation System......Page 81
4.4.1 Economically Desirable Configuration......Page 83
4.4.2 Complete Six DOF IMU—Economically Less Desirable......Page 89
4.5.1 Static Alignment for MEMS Sensors......Page 92
4.5.2 Static Alignment Example......Page 93
4.6 Velocity Matching Alignment......Page 94
4.7 Transfer Alignment......Page 95
References......Page 96
5 Navigation Equations......Page 98
5.1.1 e-Frame to i-Frame......Page 99
5.1.2 ENU l-Frame to e-Frame......Page 100
5.1.3 NED l-Frame to e-Frame......Page 102
5.1.4 b-Frame to ENU l-Frame......Page 103
5.1.5 b-Frame to NED l-Frame......Page 104
5.2.1 ENU Realization......Page 105
5.2.2 NED Realization......Page 110
5.3.1 Classical Method......Page 111
References......Page 112
6.1 Introduction......Page 114
6.1.2 Tightly Coupled Mode of Integration......Page 116
6.2 Introduction to Kalman Filter......Page 117
6.2.1 Dynamic Model......Page 118
6.3.1 The Prediction Stage......Page 120
6.4 Introduction to Extended Kalman Filter......Page 121
6.4.1 Linearization......Page 122
6.4.2 EKF Limitations......Page 125
References......Page 127
7.1 Introduction......Page 130
7.2 Types of ANNs......Page 132
7.2.1 Multilayer Perception Neural Network (MLPNN)......Page 133
7.2.2 Radial Basis Function Neural Network (RBFNN)......Page 135
7.2.3 Adaptive Neuro Fuzzy Inference System (ANFIS)......Page 139
7.3 Whole Navigation States Architecture......Page 141
7.3.1 Example of Position Update Architecture
......Page 142
7.4 Navigation Error States Architecture......Page 143
7.4.1 Architecture for INS/GPS Integration......Page 145
7.4.2 System Implementation......Page 147
7.4.3 Combined P – dP and V – dV Architecture for INS/GPS......Page 148
7.4.4 ANN/KF Augmented Module for INS/GPS Integration......Page 150
References......Page 152
8.1 Introduction......Page 154
8.3 Importance Sampling Method......Page 159
8.4 Resampling Methods......Page 161
8.4.2 Systematic Resampling (SR)......Page 163
8.5 Basic Particle Filters......Page 164
8.6.1 Extended Particle Filter (EPF) and Unscented Particle Filter (UPF)......Page 165
8.6.3 Likelihood Particle Filter (LPF)......Page 171
8.6.5 Gaussian Particle Filter (GPF) and Gaussian Sum Particle Filter......Page 172
8.7 Hybrid Extended Particle Filter (HEPF)......Page 173
8.7.1 Zero Velocity Condition Detection Algorithm......Page 174
8.7.2 Algorithm of the Hybrid Extended Particle Filter......Page 175
8.7.3 HEPF Results......Page 177
8.7.4 Partial Sensor Configuration......Page 182
References......Page 184
A.1 System Model for Loosely Coupled Approach......Page 188
A.1.1 Attitude Errors......Page 189
A.1.2 Velocity Linearization......Page 190
A.1.4 Sensor Errors......Page 192
A.3 System Model for the Tightly Coupled Approach......Page 193
A.4 The Update Stage......Page 198
References......Page 199
About the Authors......Page 202
Index......Page 204
