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Article

Unified Probabilistic and Similarity-Based Position Estimation from Radio Observations

1
Center for Artificial Intelligence (CAIRO), Technical University of Applied Sciences Wuerzburg-Schweinfurt (THWS), 97082 Wuerzburg, Germany
2
cronn GmbH, 53227 Bonn, Germany
*
Author to whom correspondence should be addressed.
Sensors 2025, 25(13), 4092; https://doi.org/10.3390/s25134092
Submission received: 17 March 2025 / Revised: 19 June 2025 / Accepted: 27 June 2025 / Published: 30 June 2025

Abstract

We propose a modeling approach for position estimation based on the observed radio propagation in an environment. The approach is purely similarity-based and therefore free of explicit physical assumptions. What distinguishes it from classical related methods are probabilistic position estimates. Instead of just providing a point estimate for a given signal sequence, our model returns the distribution of possible positions as continuous probability density function, which allows for appropriate integration into recursive state estimation systems. The estimation procedure starts by using a kernel to compare incoming data with reference recordings from known positions. Based on the obtained similarities, weights are assigned to the reference positions. An arbitrarily chosen density estimation method is then applied given this assignment. Thus, a continuous representation of the distribution of possible positions in the environment is provided. We apply the solution in a Particle Filter (PF) system for smartphone-based indoor localization. The approach is tested both with radio signal strength (RSS) measurements (Wi-Fi and Bluetooth Low Energy RSSI) and round-trip time (RTT) measurements, given by Wi-Fi Fine Timing Measurement. Compared to distance-based models, which are dedicated to the specific physical properties of each measurement type, our similarity-based model achieved overall higher accuracy at tracking pedestrians under realistic conditions. Since it does not explicitly consider the physics of radio propagation, the proposed model has also been shown to work flexibly with either RSS or RTT observations.
Keywords: indoor; localization; fingerprinting; similarity; density estimation; particle filter indoor; localization; fingerprinting; similarity; density estimation; particle filter

Share and Cite

MDPI and ACS Style

Werner, M.; Bullmann, M.; Fetzer, T.; Deinzer, F. Unified Probabilistic and Similarity-Based Position Estimation from Radio Observations. Sensors 2025, 25, 4092. https://doi.org/10.3390/s25134092

AMA Style

Werner M, Bullmann M, Fetzer T, Deinzer F. Unified Probabilistic and Similarity-Based Position Estimation from Radio Observations. Sensors. 2025; 25(13):4092. https://doi.org/10.3390/s25134092

Chicago/Turabian Style

Werner, Max, Markus Bullmann, Toni Fetzer, and Frank Deinzer. 2025. "Unified Probabilistic and Similarity-Based Position Estimation from Radio Observations" Sensors 25, no. 13: 4092. https://doi.org/10.3390/s25134092

APA Style

Werner, M., Bullmann, M., Fetzer, T., & Deinzer, F. (2025). Unified Probabilistic and Similarity-Based Position Estimation from Radio Observations. Sensors, 25(13), 4092. https://doi.org/10.3390/s25134092

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