Cognitive Indoor Positioning and Tracking using Multipath Channel Information

TL;DR

This paper introduces a robust indoor positioning system that leverages multipath channel information to improve accuracy and adapt to complex environments, inspired by visual attention mechanisms.

Contribution

It proposes a novel approach that filters relevant multipath information and models measurement uncertainty for improved indoor positioning accuracy.

Findings

Enhanced positioning accuracy in multipath environments

Effective separation of relevant and irrelevant signals

Adaptive behavior improves robustness in indoor settings

Abstract

This paper presents a robust and accurate positioning system that adapts its behavior to the surrounding environment, mimicking the capability of the visual brain to filtering out clutter and focusing attention on activity and relevant information. Especially in indoor environments, which are characterized by harsh multipath propagation, robust positioning is still hard to achieve under the constraint of reasonable infrastructural needs. In such environments it is essential to separate relevant from irrelevant information and attain an appropriate uncertainty model for measurements that are used for positioning.