Bachelor's Thesis

Statistical Modeling for Cooperative Positioning with RSSi data

Christoffer Löffler (M.Sc.)

01/2020 – 06/2020


While classical RF positioning systems usually rely on a set of immobile infrastructure tags with fixed positions, recently, cooperative systems have been proposed. In addition to providing distance-related information between mobile agents and infrastructure tags, in cooperative systems, communication and ranging between agents is possible [1]. This allows for a very high amount of positioning information available even in systems with small or no fixed positioning infrastructure available. The rapid development and deployment of edge devices, e.g., wearables and other sensor nodes, for various applications like fitness tracking and industrial surveillance and the inclusion of sidelink communication and meshed networks in modern communication standards provide a solid technological background.

Cooperative Positioning has been implemented using Belief Propagation Networks [2] or Adaptive Kalman Filters [3][4]. These algorithms use statistical estimates of the physical states of cooperating agents to weigh the reliability of the individual signals. UWB ranges and RSSi data have been used, usually in combination with stabilizing IMU positioning.

In this thesis, an existing cooperative positioning system based on RSSi data and IMU data based on an adaptive Extended Kalman Filter is to be enhanced by providing improved statistical models for signal and/or belief propagation. The proposed enhancements are to be evaluated using simulation and/or collected data.



  1. Wymeersch, Henk et al. “Cooperative Localization in Wireless Networks.” Proceedings of the IEEE (2009): Vol. 97, pp. 427-450
  2. Velde, Samuel & Arora, Gundeep & Vallozzi, Luigi & Rogier, Hendrik & Steendam, Heidi.. “Cooperative hybrid localization using Gaussian processes and belief propagation.” IEEE ICCW, pp.785-790, London, UK, 2015.
  3. Jamali-Rad, Hadi & Waterschoot, Toon & Leus, Geert. “Cooperative localization using efficient Kalman filtering for mobile wireless sensor networks”. European Signal Processing Conference, Barcelona, Spain, 2011
  4. Zhang, & Cao,”Cooperative Localization Approach for Multi-Robot Systems Based on State Estimation Error Compensation”. Sensors 2019, 19, 3842.