Human Dead Reckoning Using a Particle Filter and Map Constraints

Journal article

This paper presents an approach for tracking a person's position by integrating inertial measurement unit (IMU) sensor values, utilising a particle filter with known physical constraints, such as a map of the space. While such approaches are well established, the effect of constraint choice in the absence of observation measurements remains poorly understood. The effect of varying the tolerance of these constraints is investigated with data collected from a Movella DOT held by a human participant walking around a 100 m running track. In particular, the dimensions of the map are varied, along with the shape. Results showed (a) that it is viable to correct particle filter error without external sensor feedback, provided constraints are provided, with a mean error of 1.8 m, and (b) there is a minimum acceptable tolerance of map width around the edge of the true activity zone, in this case approximately 8 m, and that tightening the map boundary further than this can counterintuitively lead to reduced accuracy.

Authors: Russell, J; Bergmann, JHM

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: MDPI
• Journal: Sensors
• Volume: 26
• Issue: 11
• Pages: 3500
• Publication date: 2026-06-02
• DOI: 10.3390/s26113500
• EISSN: 1424-8220
• ISSN: 1424-8220
• Pmid: 42281016
• Language: English
• Keywords: Particle Filter; Dead Reckoning; Map Constraints; Inertial Measurement Units; Human Tracking