Crowdsourcing-Based Indoor Localization Accuracy

This study delves into the accuracy of crowdsourcing-based indoor localization, exploring methods such as RF fingerprinting and modeling over calibration. It examines related infrastructure-based localization systems and steps like normalized auto-correlation-based step counting. The research also covers tracking with augmented particle filters and utilizing existing measurement databases for improved WiFi localization techniques in indoor environments.

• Crowdsourcing
• Indoor Localization
• RF Fingerprinting
• Modeling
• Augmented Particle Filter

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Uploaded on Feb 16, 2025 | 0 Views

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Presentation Transcript

1. LOCALIZATION ACCURACY OF CROWDSOURCING BASED INDOOR LOCALIZATION CHENG WU 5110309335 Reference Zee: Zero-Effort Crowdsourcing for Indoor Localization

2. RELATED WORK Infrastructure-Based Localization Systems RF infrared RFID sniffers acoustic visual transmitters

3. RF FINGERPRINTING BASED LOCALIZATION accelerometer inertial sensors compass RF Fingerprinting WiFi or cellular signals gyroscope

4. Modeling instead of Calibration THE WHOLE STRUCTURE PICTURE

5. Modeling Steps 1.Placement Independent Motion Estimator (PIME). 2.Augmented Particle Filter(APF). 3.Creating the WiFi Database 4.WiFi-based initialization in APF. 5.Refinement of the WiFi database

6. COUNTING STEPS Typical mobile phone placement scenarios men{shirt pockets or rear pant pockets} women{in handbags and sometimes in pant pockets}

7. Normalized Auto-correlation based Step Counting (NASC).

8. PERFORMANCE OF STEP COUNTING

9. Estimatimg heading offset range Magnetic Offset: Heading Offset: Placement Offset:

10. Tracking using augmented particle filter(APF ) stride length estimation

11. Put it all together : crowdsourcing Using existing measurement database for subsequent crowdsourcing. We can determine where in the floor a certain WiFi measurement was taken and generate location-annotated WiFi measurements of the form (location, WiFi RSS). This database of measurements can then be used to locate new users using existing WiFi localization techniques.

12. Performance of WiFi localization using crowdsourcing ERROR DISTRIBUTIONS

13. Q&A THANK YOU FOR LISTENING