Information

Partner: CSEM, Switzerland
Advanced technology: Localization Solver
Contact: Martin Sénéclauze
Email: martin.seneclauze@csem.ch
Tel: +41 32 720 53 40

Localization Solver

A GPS Free Generic Radio Localization Solver.

WHAT IS A GPS FREE LOCALIZATION SOLVER?

The aim of the positioning solver is to allow localization of any communicating device in a reliable manner whether indoor or outdoor. Based on the information collected directly from the radio, the solver is capable of parsing, filtering and analysing the transmission quality to integrate a reliable position.

Tailored around a particle filtering technique, information collected from the radio environment is transformed into a series of possible points, called particles. For each of these particles, a probability of being the searched position is calculated. The probabilities of all particles are processed through an iterative process until convergence.

CSEM GPS FREE LOCALIZATION ALLOWS:

  • Localization of any objects communicating wirelessly provided adequate infrastructure.
  • Integration with any type of radio capable of generating RSSI, SNR, ToF, AoA, TDoA, DTDoA (LoRa® / LTE-M / NB-IoT / WiFi / BT or customised hardware)
  • Tracking devices without modifying the infrastructure nor the hardware allowing low power localisation
  • Easy port to handheld device or cloud servers (AWS, Microsoft Azure, …)
  • Early data fusion to integrate obvious data rejection (Map Matching …)

APPLICATIONS

  • Logistics, Security
  • Pets / Objects / People Tracking / Finding
  • Occupancy detection
  • Drone navigation and obstacle avoidance
  • Planification

What’s new?

  • By using random based initial distribution, the caveats of the generally used geometrical calculation is greatly reduced (less impact of local minima)
  • Solver was intensively tested on LoRa® network and RSSI with results 10 to 50% better than evaluated competition

WHAT’s NEXT

CSEM will continue to develop its GPS Free Localization Solver to improve its level of precision. The next path to be evaluated is the usage of Machine Learning to discriminate bad measurements and limit the impact of multipath.

Depending on the need of our partners, we are introducing probability based constraints to early adapt to other types of information like proximity, room, path …

FOR MORE INFORMATION

Contact: Martin Sénéclauze
Email: martin.seneclauze@csem.ch
Tel: +41 32 720 53 40