1. Detection of Magnetic Anomaly in Heading Estimation for Smartphone-Based Indoor Localization Marzieh Jalal Abadi University of New South Wales , Sydney, Australia
Thanks for attending the presentation

2. Introduction
Pedestrian dead reckoning(PDR): Independent Positioning System
It estimate current position with respect to the previous position by monitoring the person’s movement via sensors
Magnetometer sensor
Perturbation

3. Motivation The proposed hypothesis : Collaborative approach
Need to detect the erroneous heading estimates
Exclude them before fusion

4. Nature of Heading Error (using Magnetometer)
Very high heading estimation error(180°)
Different heading error at the same location but different direction
Heading error is high at certain pocket of space
8.1m × 6.3m Room
264 points in a grid : 45cm
Samsung Galaxy SIII
Heading error for HT =280° and HT =100°

5. Discuss the pattern and results
Data Analysis
Data Collection
Data Display
Verifying Data
Measurement
Model
Fit
Discuss the pattern and results
Express Data
Generate Data
Characterizing Source of Error in data
Multivariable analysis
Statistical tests: Dependency of multi pedestrian’s heading
Factor analysis
Conjoint analysis
Correlation
Parametric and non-parametric
Normal Kernel distribution
Compare
Fit Parameters

6. Data Analysis
The magnetometer measurements (heading estimates) can not be modeled with a parametric distribution
Perturbation is highly localized and we need a prior knowledge of the magnetic field distribution in each corridor (for different directions).

7. Perturbation Detector
Data Collection: (mx ,my ,mz )
Data Processing
1. (mx , my , mz , Ferr , Herr , Ierr)
2. Time window : 0.5 sec ~ 1 step 3. Binary class : C0 ,C1
4. |hest-hT|<threshold(γ)
Training
Feature Selection:
Correlation-Based Feature Selection
Classifier : Logistic Regression, 10cv

8. Perturbation Detector
“Mean” is the selected feature for all parameters
Logistic regression can classify local perturbation within 15° error threshold : accuracy 98% in average
To model the perturbation in a path, we need a prior knowledge of its magnetic field distribution
We can not build a general model that holds for all environments
The model of one corridor is not applicable for other corridors
One limitation of our method is its dependence on prior model for perturbation detector: Find a general model for perturbation in all environments.

9. Thank you