A synthetic noise generator M. Hueller LTPDA meeting, AEI Hannover 27/04/2007.

Slides:



Advertisements
Similar presentations
Interharmonics IEEE T&D Show New Orleans Panel Session PN13 Non-periodic Currents: Causes,
Advertisements

Status of Channel Models IEEE WG Session #7 March 15-19, 2004 Qiang Guo Editor, Channel Modeling Correspondence Group C /30.
S i m u l i n k Prof. Muhammad Saeed Mathematical Modeling and Simulation UsingMATLAB 1.
Beyond The Standard Quantum Limit B. W. Barr Institute for Gravitational Research University of Glasgow.
DCSP-13 Jianfeng Feng
President UniversityErwin SitompulModern Control 7/1 Dr.-Ing. Erwin Sitompul President University Lecture 7 Modern Control
Objectives Regression analysis Sensor signal processing.
Data points are spread over the space according to two of their component values Using real data sets to simulate evolution within complex environments.
Root Locus Diagrams Professor Walter W. Olson
Lectures 12&13: Persistent Excitation for Off-line and On-line Parameter Estimation Dr Martin Brown Room: E1k Telephone:
SYMIAN: Analysis and Performance Improvement of the IT Incident Management Process Group 5 Presented by Xiao ZHENG Jiaming GUO 09/10/2012.
Insert Date HereSlide 1 Using Derivative and Integral Information in the Statistical Analysis of Computer Models Gemma Stephenson March 2007.
VAR Models Gloria González-Rivera University of California, Riverside
Bottoms Up Factoring. Start with the X-box 3-9 Product Sum
X-box Factoring. X- Box 3-9 Product Sum Factor the x-box way Example: Factor 3x 2 -13x (3)(-10)= x 2x 3x 2 x-5 3x +2.
Computer Vision Lecture 7: The Fourier Transform
Hierarchical Control Notes – Blending Style Follows quad example Note: coil drivers and ESD are LASTI style; seismic noise is outdated 1G v3.
Chapter 4 Continuous Time Signals Time Response Continuous Time Signals Time Response.
MDOF SYSTEMS WITH DAMPING General case Saeed Ziaei Rad.
Lecture 15 Orthogonal Functions Fourier Series. LGA mean daily temperature time series is there a global warming signal?
Use of Kalman filters in time and frequency analysis John Davis 1st May 2011.
ELEC 303 – Random Signals Lecture 20 – Random processes
Data mining and statistical learning - lecture 6
Environmental Data Analysis with MatLab Lecture 24: Confidence Limits of Spectra; Bootstraps.
Communication Systems Simulation - III Harri Saarnisaari Part of Simulations and Tools for Telecommunication Course.
Course AE4-T40 Lecture 5: Control Apllication
Linear and generalised linear models Purpose of linear models Least-squares solution for linear models Analysis of diagnostics Exponential family and generalised.
Low Noise Amplifier. DSB/SC-AM Modulation (Review)
Lecture 24 Introduction to state variable modeling Overall idea Example Simulating system response using MATLAB Related educational modules: –Section 2.6.1,
Adaptive Signal Processing
Principles of the Global Positioning System Lecture 13 Prof. Thomas Herring Room A;
Out response, Poles, and Zeros
Mark Allie comp.dsp Signal to Noise and Numeric Range issues for Direct Form I & II IIR Filters on Modern Analog Devices and TI Digital Signal Processors.
Introduction to Adaptive Digital Filters Algorithms
Zorica Stanimirović Faculty of Mathematics, University of Belgrade
Chapter 2: First Steps in MuPAD MATLAB for Scientist and Engineers Using Symbolic Toolbox.
 Embedded Digital Signal Processing (DSP) systems  Specification with floating-point data types  Implementation in fixed-point architectures  Precision.
Progress in identification of damping: Energy-based method with incomplete and noisy data Marco Prandina University of Liverpool.
Resonance Enhancement of the intensity of a particular frequency component(s) with respect to the intensity of the other components that occurs when its.
ECE 5525 Osama Saraireh Fall 2005 Dr. Veton Kepuska
MatLAB Transfer Functions, Bode Plots, and Complex Numbers.
0 - 1 © 2007 Texas Instruments Inc, Content developed in partnership with Tel-Aviv University From MATLAB ® and Simulink ® to Real Time with TI DSPs Spectrum.
Some thoughts on error handling for FTIR retrievals Prepared by Stephen Wood and Brian Connor, NIWA with input and ideas from others...
EEL 6586: AUTOMATIC SPEECH PROCESSING Speech Features Lecture Mark D. Skowronski Computational Neuro-Engineering Lab University of Florida February 27,
, Free vibration Eigenvalue equation EIGENVALUE EQUATION
Linear Filters. denote a bivariate time series with zero mean. Let.
Spectrum Sensing In Cognitive Radio Networks
Matlab Tutorial for State Space Analysis and System Identification
Nonlinear State Estimation
Simulated Inductance Experiment 25.
September 28, 2000 Improved Simultaneous Data Reconciliation, Bias Detection and Identification Using Mixed Integer Optimization Methods Presented by:
Geology 6600/7600 Signal Analysis 15 Oct 2015 © A.R. Lowry 2015 Last time(s): PSE The Kirby approach to wavelet transformation (the fan wavelet) preserves.
The Chinese University of Hong Kong
EEE Chapter 6 Random Processes and LTI Huseyin Bilgekul EEE 461 Communication Systems II Department of Electrical and Electronic Engineering Eastern.
State Space Models The state space model represents a physical system as n first order differential equations. This form is better suited for computer.
UTN synthetic noise generator M. Hueller LTPDA meeting, Barcelona 26/06/2007.
ECE 576 – Power System Dynamics and Stability Prof. Tom Overbye University of Illinois at Urbana-Champaign 1 Lecture 23: Small Signal.
Electronic Devices Ninth Edition Floyd Chapter 15.
(5) Notes on the Least Squares Estimate
Unscented Kalman Filter for a coal run-of-mine bin
Department of Civil and Environmental Engineering
لجنة الهندسة الكهربائية
Unfolding Problem: A Machine Learning Approach
OptiSystem-MATLAB data interchange model and features
Lect5 A framework for digital filter design
Linear Filters.
An Examination of the ARX as a Residuals
Principles of the Global Positioning System Lecture 13
Chapter 4. Time Response I may not have gone where I intended to go, but I think I have ended up where I needed to be. Pusan National University Intelligent.
FEMAS Development - Progress
Presentation transcript:

A synthetic noise generator M. Hueller LTPDA meeting, AEI Hannover 27/04/2007

2 Purpose Simulate noise data with given continuous spectrum Choose between  input the model parameters (developing and modeling)  fit experimental data Use as a tool for system identification: data simulation

3 Input parameters: available features LP filters HP filters f -2 noise, by a LP filter with roll-off at very low frequency f -1 noise, by a cascade of LP filters with very low roll-off frequencies (not yet implemented) Mechanical resonances Mechanical forcing lines (not yet implemented)

4 The approach (1) x(t) is the output of a filter, with transfer function H(  ), with a white noise  (t) at input, with PSD=S 0 Assuming that the transfer function H(  ) has the form then the process x(t) can be seen as the process x(t) is equivalent to N p correlated processes

5 The approach (2) Once defined A powerful recursive formula One can calculate cross correlation of the innovation processes And for the starting values

6 Matlab implementation (1) Vector of starting values, with the given statistics Propagate through time evolution, adding contributions from innovation processes Innovations are evaluated starting from N p uncorrelated random variables, transformed according to: Eventually, add up the contribution from all correlated processes:

7 Matlab implementation (2) The base changing matrix A kj contains the eigenvectors of the cross-correlation matrix (diagonalization) Additionally, a phase factor must be applied to each eigenvector, to allow the sum of all the Np contribution to be real ↓ Force the first element of each eigenvector to be real Call from the command line (or other routines): [t_res2,x_res2] = syntetic_noise(1e6,10,'lp',1,'res',[1e-2 0.5],[ ],'notalk',‘nopl');

8 Numeric approach: some (precision?) problem associated with the calculation of the eigenvalues, impacting on the eigenvectors, being investigated Imaginary part of the output process x(t) is not zero This disagreement is associated with resonances (complex values in the cross correlation matrix) Disagreement increases with the number of resonances Compared with Mathematica evaluation, “zero” is bigger by a factor ~10 6 Workaround using Symbolic Math Toolbox? Coding not finished yet High-precision calculation in Mathematica passing the eigenvectors matrix to Matlab routine? This is also being considered

9 Some results LP, 1 Hz

10 Some results mHz, Q= points, evaluated in 60s

11 Some results mHz, Q= points, evaluated in 60s Normalization problem, under investigation !

12 Some results mHz, Q= points, evaluated in 60s

13 Some results mHz and 0.5 Hz, Q= 10 3 and points, evaluated in 63s Normalization problem, under investigation !

14 What comes next: Get the fitting features to work Pick the best solution for numerical precision Include into the AO architecture Use it as the tools for system identification …