Gaussian Processes for Positioning Using Radio Signal Strength Measurements.
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| Superior document: | Linköping Studies in Science and Technology. Dissertations Series ; v.1968 |
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| Place / Publishing House: | Linköping : : Linkopings Universitet,, 2019. {copy}2019. |
| Year of Publication: | 2019 |
| Edition: | 1st ed. |
| Language: | English |
| Series: | Linköping Studies in Science and Technology. Dissertations Series
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| Online Access: | |
| Physical Description: | 1 online resource (74 pages) |
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Table of Contents:
- Intro
- Abstract
- Populärvetenskaplig sammanfattning
- Acknowledgments
- Contents
- Notation
- 1 Introduction
- 1.1 Application Examples
- 1.2 Main Contributions
- 1.3 Thesis outline
- 1.3.1 Outline of Part I
- 1.3.2 Outline of Part II
- I Background
- 2 Estimation
- 2.1 Static Estimators
- 2.1.1 Best Linear Unbiased Estimator
- 2.1.2 Maximum Likelihood Estimator
- 2.1.3 Least Squares
- 2.2 Dynamic Estimators
- 2.3 Sequential Monte Carlo Methods for State Inference
- 2.3.1 Particle Filter
- 2.3.2 Particle Smoother
- 3 Modeling
- 3.1 Linear Regression for Parametric Modeling
- 3.2 Gaussian Process for Non-parametric Modeling
- 4 The Cramér-Rao Bound
- 4.1 Cramér-Rao Bound for Static Estimator
- 4.2 Cramér-Rao Bound for Dynamic Estimator
- 5 Concluding Remarks
- 5.1 Summary of Contribution
- 5.2 Some Insights into Future Work
- Bibliography
- II Publications.