LTPDA Toolbox™ |
contents |

This series of help pages consitute the second training session of LTPDA. The various data-packs used throughout the tutorials are available for download on the LTPDA web-site. This tutorial focusses primarily on data analysis activities associated with the LISA Pathfinder mission. It requires access to the LPF extension module for LTPDA (LPF_DA_Module). As such this tutorial is not intended for the general public.

- Topic 1 - LTPDA Review
- Topic 2 - Simulating LPF noise
- Topic 3 - Estimating residual acceleration
- Topic 4 - Simulating LPF with injected signals
- Topic 5 - Introduction to LPF System Identification

Throughout the course of this training session, we will perform a full analysis of some LPF data. The data will be generated using our LPF simulator which is built in to the LTPDA toolbox and associated extension module.

During each topic of the training session, the data will be manipulated using the tools introduced in that topic (and previous topics). The aim of the data analysis is to estimate some parameters of the LPF system and use those to estimate the residual differential acceleration of the two test-masses. In particular the steps will be:

- Topic 1
LTPDA Review.
- Introducing objects in LTPDA
- Saving and loading objects
- Review of spectral estimators
- Preparing data segments (splitting)
- Review of filtering and whitening in LTPDA
- LTPDA scripting best practices
- Introduction to LTPDA extension modules

- Topic 2
Simulating LPF noise in LTPDA.
- Introduction to state-space models in LTPDA
- Introduction to the LPF state-space models in LTPDA
- Building an LTP model
- Introduction to the various LPF noise models
- Building an LPF model
- Simulating LPF noise
- Changing system parameters
- Simulate LPF with matched stiffness

- Topic 3
Estimation of residual differential acceleration.
- Principles and theory
- Tools for estimating the residual differential acceleration in LTPDA
- Estimate equivalent accelerations from simulation data

- Topic 4
Simulating LPF with injected signals.
- LPF model inputs
- Building signals
- How to inject signals
- Simulate LTP with injected signals (no noise)
- Inject noise signals to LTP
- Estimate tranfser functions from simulated signals, compare with Bode estimates
- Simulate LPF with injected signals

- Topic 5
Introduction to system identification of LPF.
- Introduction to LTPDA's fitting tools (theory, implementation, usage)
- A simplified LPF system identification experiment
- Create simulated experiment data sets
- Build state-space models for system identification
- Calculate expected covariance of the parameters (FIM)
- Perform system identification to estimate desired parameters
- Results and Comparison
- Use parameter estimates to estimate residual differential acceleration

IFO/Temperature Example - signal subtraction | Topic 1 - LTPDA Review. |

©LTP Team