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Improving MIMO radar's performance through receivers' positioning
Chitgarha, M. M
Cataloging brief
Improving MIMO radar's performance through receivers' positioning
Author :
Chitgarha, M. M
Publisher :
Institution of Engineering and Technology
Pub. Year :
2017
Subjects :
Antennas MIMO systems Radar Radar antennas Radar signal processing Radar systems ...
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Table of Contents
(8)
List of Figures
(11)
List of Tables
(14)
1 Introduction
(15)
1.1 Introduction
(15)
1.1.1 Motivation
(19)
1.1.2 Contributions
(20)
1.1.3 Thesis Structure
(21)
2 Related Works
(23)
2.1 Inertial Navigation
(23)
2.2 Visual Navigation
(23)
2.2.1 Map-based
(24)
2.2.2 Map-less
(25)
2.3 GPS
(25)
2.4 Aided Navigation
(26)
2.4.1 Visual Aided Navigation
(26)
2.4.2 GPS Aided Navigation
(27)
2.5 Noise Reduction Filters
(28)
2.5.1 Alpha-Beta-Gama Filters
(29)
2.6 Bias Estimation
(29)
3 Theoretical Backgrounds
(31)
3.1 Visual Gyroscope
(31)
3.1.1 Modeling of the Imaging Camera
(31)
3.1.2 Calibration Matrix
(32)
3.1.3 Projection Matrix and its Application in Positioning
(32)
3.1.4 Basic Steps to Estimate Robot Rotation Using Key Points Inside the Image
(33)
3.1.5 Detect Key Points in Pictures
(33)
3.1.6 Extracting Feature Vector
(34)
3.1.7 Matching Feature Vectors
(35)
3.1.8 Estimated Rotation Between Images with Matching Points
(35)
3.1.9 RANSAC Algorithm
(37)
3.1.10 The Methods Presented to Estimate Robot Rotation from Images
(37)
3.1.11 Eight-Point Method
(38)
3.1.12 Five-Point Method
(38)
3.2 Attitude Determination and Navigation Equations
(39)
3.2.1 Coordinate Frames and Transformations
(40)
3.2.2 Transformation from Body to Navigation Frame
(42)
3.2.3 Navigation Equation
(44)
3.3 MEMS Gyroscope
(47)
3.3.1 MEMS Inertial Sensors Errors
(48)
3.3.2 Calibration Methods
(49)
3.3.3 Inertial Sensors Noise
(50)
3.3.4 Gyroscope Model
(50)
3.4 Kalman Filter
(54)
3.4.1 The Discrete Kalman Filter Algorithm
(55)
3.4.2 Extended Kalman Filter
(55)
3.4.3 Direct and Indirect Kalman Filter
(56)
3.5 Fading-Memory Filter
(59)
3.6 Alpha Beta Filter
(59)
3.6.1 Fading-Memory-Filter Structure and Properties
(60)
4 Proposed Method
(61)
4.1 Process Model and Measurement
(61)
4.2 Arun's Algorithm
(64)
4.2.1 Decoupling Rotation and Translation for 3D points
(64)
4.2.2 Rotation only Estimation from 2D Correspondences
(65)
4.3 Gyroscope Raw Data Denoising
(66)
4.4 Dataset
(67)
4.4.1 Sensor Setup
(67)
5 Experimental Results
(69)
5.1 Bias Cancellation Using Sensor Fusion
(69)
5.1.1 Results without Bias Cancellation
(69)
5.1.2 Results of Sensor Fusion for Bias Cancellation
(69)
5.1.3 The Effect of the Number of Features on The Precision of Bias Estimation:
(80)
5.2 Filter Evaluation
(80)
5.2.1 Filter Evaluation for Different Gains
(83)
5.2.2 Choosing the Best Gain for the Filter
(86)
5.2.3 Filter with Optimal Gain
(89)
5.2.4 IIR Filter Evaluation
(89)
5.2.5 Applying Filter to Other Datasets
(92)
6 Conclusion and Future Work
(99)
6.1 Conclusion
(99)
6.1.1 Future Work
(101)
Bibliography
(102)
