Positioning Based on Noise-Limited Censored Path Loss Data
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review
Details
| Original language | English |
|---|---|
| Title of host publication | 2020 International Conference on Localization and GNSS, ICL-GNSS 2020 - Proceedings |
| Editors | Jari Nurmi, Elena-Simona Lohan, Joaquin Torres-Sospedra, Heidi Kuusniemi, Aleksandr Ometov |
| Publisher | IEEE |
| Number of pages | 6 |
| ISBN (Electronic) | 9781728164557 |
| DOIs | |
| Publication status | Published - Jun 2020 |
| Publication type | A4 Article in a conference publication |
| Event | International Conference on Localization and GNSS - Duration: 2 Jun 2020 → 4 Jun 2020 |
Publication series
| Name | 2020 International Conference on Localization and GNSS, ICL-GNSS 2020 - Proceedings |
|---|
Conference
| Conference | International Conference on Localization and GNSS |
|---|---|
| Period | 2/06/20 → 4/06/20 |
Abstract
Positioning is considered one of the most important features and enabler of various novel industry verticals in future radio systems. Since path loss or received signal strength-based measurements are widely available and accessible in the majority of wireless standards, path loss-based positioning has an important role among other positioning technologies. Conventionally path loss-based positioning has two phases; i) fitting a path loss model to training data, if such training data is available, and ii) determining link distance estimates based on the path loss model and calculating the position estimate. However, in both phases, the maximum measurable path loss is limited by measurement noise. Such immeasurable samples are called censored path loss data and such noisy data is commonly neglected in both the model fitting and in the positioning phase. In the case of censored path loss, the loss is known to be above a known threshold level and that information can be used in model fitting as well as in the positioning phase. In this paper, we examine and propose how to use censored path loss data in path loss model-based positioning and demonstrate with simulations the potential of the proposed approach for considerable improvements (over 30%) in positioning accuracy.
ASJC Scopus subject areas
Keywords
- censored data, localization, maximum-likelihood estimation, path loss, path loss model, positioning, probabilistic modeling., shadow fading, wireless networks