The Timevarying Ionosphere. measured by GPS

Slides:

Advertisements

Similar presentations

Modelling complexity in the upper atmosphere using GPS data Chris Budd, Cathryn Mitchell, Paul Spencer Bath Institute for Complex Systems, University of.

Advertisements

On an Improved Chaos Shift Keying Communication Scheme Timothy J. Wren & Tai C. Yang.

State Space Models. Let { x t :t T} and { y t :t T} denote two vector valued time series that satisfy the system of equations: y t = A t x t + v t (The.

Near real time assessment of the Space Weather effect on navigation based on the DGPS technique S.Lejeune, R.Warnant, A. Barré, M. Bavier Royal Observatory.

Principles of the Global Positioning System Lecture 12 Prof. Thomas Herring Room A;

Air Force Technical Applications Center 1 Subspace Based Three- Component Array Processing Gregory Wagner Nuclear Treaty Monitoring Geophysics Division.

Generalised Inverses Modal Analysis and Modal Testing S. Ziaei Rad.

Tensors and Component Analysis Musawir Ali. Tensor: Generalization of an n-dimensional array Vector: order-1 tensor Matrix: order-2 tensor Order-3 tensor.

Introduction to Subspace Tracking IEEE AES Dallas Chapter Meeting Richardson, TX Feb 28, 2006 Scott Baker L-3 Communications Greenville, TX

Using a DPS as a Coherent Scatter HF Radar Lindsay Magnus Lee-Anne McKinnell Hermanus Magnetic Observatory Hermanus, South Africa.

Page 1 Singular Value Decomposition applied on altimeter waveforms P. Thibaut, J.C.Poisson, A.Ollivier : CLS – Toulouse - France F.Boy, N.Picot : CNES.

Observation. Defining Behavior page 192 Topography Function Characteristics Duration Latency Frequency Amplitude.

Analyse de la cohérence en présence de lumière partiellement polarisée François Goudail Laboratoire Charles Fabry de l’Institut d’Optique, Palaiseau (France)

Linear Algebra Jean Walrand EECS – U.C. Berkeley.

Approaches to the infrasound signal denoising by using AR method N. Arai, T. Murayama, and M. Iwakuni (Research Dept., Japan Weather Association) 2008.

Offline and Real-time signal processing on fusion signals

Timed. Transects Statistics indicate that overall species Richness varies only as a function of method and that there is no difference between sites.

Ordinary least squares regression (OLS)

Exploring Microarray data Javier Cabrera. Outline 1.Exploratory Analysis Steps. 2.Microarray Data as Multivariate Data. 3.Dimension Reduction 4.Correlation.

Estimating High Dimensional Covariance Matrix and Volatility Index by making Use of Factor Models Celine Sun R/Finance 2013.

MIMO Multiple Input Multiple Output Communications © Omar Ahmad

Chapter 2 Dimensionality Reduction. Linear Methods

Algorithms for a large sparse nonlinear eigenvalue problem Yusaku Yamamoto Dept. of Computational Science & Engineering Nagoya University.

Dr. M. Sc. Gintcho Kostov, Bulgaria “GEO ZEMIA” Ltd. Study of the Behaviour of the Rover and Assessment of the Overall Quality of the Results from RTK.

UTSA Estimating Model Parameters from Ionospheric Reverse Engineering (EMPIRE) G. S. Bust and G. Crowley UTSA S. Datta-Barua ASTRA.

Least SquaresELE Adaptive Signal Processing 1 Method of Least Squares.

Method of Least Squares. Least Squares Method of Least Squares:  Deterministic approach The inputs u(1), u(2),..., u(N) are applied to the system The.

Claudinei Rodrigues de Aguiar Federal University of Technology - Parana Paulo de Oliveira Camargo São Paulo State University.

Experimental research in noise influence on estimation precision for polyharmonic model frequencies Natalia Visotska.

Statistical Arbitrage Ying Chen, Leonardo Bachega Yandong Guo, Xing Liu February, 2010.

Slicing and dicing the 153-year record of monthly sea level at San Francisco, California using singular spectrum analysis Larry Breaker Moss Landing Marine.

Modern Navigation Thomas Herring MW 11:00-12:30 Room

Fractal Dimension and Maximum Sunspot Number in Solar Cycle R.-S. Kim 1,2, Y. Yi 1, S.-W. Kim 2, K.-S. Cho 2, Y.-J. Moon 2 1 Chungnam national University.

VARIABILITY OF TOTAL ELECTRON CONTENT AT EUROPEAN LATITUDES A. Krankowski(1), L. W. Baran(1), W. Kosek (2), I. I. Shagimuratov(3), M. Kalarus (2) (1) Institute.

PARALLEL FREQUENCY RADAR VIA COMPRESSIVE SENSING

Principles of the Global Positioning System Lecture 12 Prof. Thomas Herring Room ;

Estimation of covariance matrix under informative sampling Julia Aru University of Tartu and Statistics Estonia Tartu, June 25-29, 2007.

Project-Final Presentation Blind Dereverberation Algorithm for Speech Signals Based on Multi-channel Linear Prediction Supervisor: Alexander Bertrand Authors:

Analysis of Traction System Time-Varying Signals using ESPRIT Subspace Spectrum Estimation Method Z. Leonowicz, T. Lobos

Electron density profile retrieval from RO data Xin’an Yue, Bill Schreiner  Abel inversion error of Ne  Data Assimilation test.

Lecture Note 1 – Linear Algebra Shuaiqiang Wang Department of CS & IS University of Jyväskylä

3 “Products” of Principle Component Analysis

Chapter 13 Discrete Image Transforms

Global and Regional Total Electron Content Anthony Mannucci, Xing Meng, Panagiotis Vergados, Attila Komjathy JPL/Caltech Collaborators: Sarah E. McDonald,

PRINCIPAL COMPONENT ANALYSIS(PCA) EOFs and Principle Components; Selection Rules LECTURE 8 Supplementary Readings: Wilks, chapters 9.

Slicing and dicing the 153-year record of monthly

ASEN 5070: Statistical Orbit Determination I Fall 2014

Exploring Microarray data

PMU Emulator for Power System Dynamics Simulators

Neural data-analysis Workshop

First validation of Level 2 CAT-2 products: FAC/IBI/TEC

Last update on June 15, 2010 Doug Young Suh

Scale: Kilometers.

Lessons Learned in Developing the USU Kalman GAIM J. J. Sojka, R. W

Feature Space Based Watermarking in Multi-Images

A Digital Watermarking Scheme Based on Singular Value Decomposition

A Digital Watermarking Scheme Based on Singular Value Decomposition

Results and Discussions Data Used and Methodology

Singular Value Decomposition SVD

Matrix Algebra and Random Vectors

SVD, PCA, AND THE NFL By: Andrew Zachary.

Outline Singular Value Decomposition Example of PCA: Eigenfaces.

METHODS OF TRANSFORMING NON-POSITIVE DEFINITE CORRELATION MATRICES

Principles of the Global Positioning System Lecture 11

Principal Component Analysis

Scale: Kilometers.

Apex scanning strategy & map-making

Motivation Time Series Analysis Spectra and Results Conclusions

HG contribution to the GRC and more

Outline Numerical Stability Singular Value Decomposition

Presentation transcript:

The Timevarying Ionosphere. measured by GPS The Timevarying Ionosphere measured by GPS Presented by: Jakob Jakobsen

Outline Timeserie of absolute TEC from 1999-2007 General Timevarying characteristics SVD analysis Representation of the eigenvalues and relation to timevarying parameters 2004 as an example Generated models Power spectrum Conclusion NNF: High Precision Navigation and Positioning | 10. June 2008 | page 2

Timeserie Dual frequency data with 30 second sampling rate Three stations 130 - 207 km. separation 56°N latitude Timeseries (1999-2007) calculated using a Kalman Filter estimating Absolute TEC Latitude variation Longitude variation NNF: High Precision Navigation and Positioning | 10. June 2008 | page 3

Observation model SV1 SV2 SV3 Icenter IP12 SV4 IP22 IP11 IP21 Rx2 Rx1 400 km NNF: High Precision Navigation and Positioning | 10. June 2008 | page 4

Kalman filter 1: 2: 3: 4: 5: Given and : NNF: High Precision Navigation and Positioning | 10. June 2008 | page 5

General Ionospheric Timevarying Parameters Daily Yearly 11 year period Follow the length of day Highest during equinoxes NNF: High Precision Navigation and Positioning | 10. June 2008 | page 6

Singular Value Decomposition (SVD) For each year one matrix 96 = * * 365 U1 U2 U3 S1 S2 S3 V1 V2 V3 X = U S VT U and V are orthogonal matrices, and S is a diagonal matrix consisting of singular values. NNF: High Precision Navigation and Positioning | 10. June 2008 | page 7

First eigenvalue 2004 U1 S V1 NNF: High Precision Navigation and Positioning | 10. June 2008 | page 8

NNF: High Precision Navigation and Positioning | 10. June 2008 | page 9

Second eigenvalue 2004 U2 S V2 NNF: High Precision Navigation and Positioning | 10. June 2008 | page 10

NNF: High Precision Navigation and Positioning | 10 NNF: High Precision Navigation and Positioning | 10. June 2008 | page 11

Third eigenvalue 2004 U3 S V3 NNF: High Precision Navigation and Positioning | 10. June 2008 | page 12

NNF: High Precision Navigation and Positioning | 10 NNF: High Precision Navigation and Positioning | 10. June 2008 | page 13

Correlation with Sunspot Number Coefficients 0.93 – 0.97 NNF: High Precision Navigation and Positioning | 10. June 2008 | page 14

Generated Ionospheric model of absolute TEC, 2004 NNF: High Precision Navigation and Positioning | 10. June 2008 | page 15

Models generated NNF: High Precision Navigation and Positioning | 10. June 2008 | page 16

Power spectrum NNF: High Precision Navigation and Positioning | 10. June 2008 | page 17

Conclusions The first three eigenvalues represent the main characteristics of the time varying ionosphere for a year The eigenvalues for each year show a clear correlation with the sunspot number with a correlation coefficients from 0.93 – 0.97 The power spectrum for time series show a clear daily and yearly signal, with amplitude of 7.4 and 2.8 TECU respectively. NNF: High Precision Navigation and Positioning | 10. June 2008 | page 18

Jakob Jakobsen jj@space.dtu.dk http://www.heisesgade.dk NNF: High Precision Navigation and Positioning | 10. June 2008 | page 19