RSS-based Cooperative Localization and Orientation Estimation Exploiting Directive Antenna Patterns

TL;DR

This paper introduces a cooperative localization method using RSS measurements and directive antenna patterns, jointly estimating position and orientation to enhance indoor positioning accuracy with large-scale sensor networks.

Contribution

It presents a novel factor-graph-based algorithm that models antenna directivity and jointly estimates agent positions and orientations, validated through extensive simulations and measurements.

Findings

Significant improvement in positioning accuracy by considering antenna directivity.

Effective joint estimation of position and orientation in large-scale sensor networks.

Validation with over 900 agents demonstrates robustness and scalability.

Abstract

In this paper, we propose a factor-graph-based cooperative positioning algorithm that uses RSS radio measurements and accounts for the directivity of the antennas. This is achieved by modeling the directivity with a parametric antenna pattern and jointly estimating position and orientation of the agents. We propose two different approaches whereas the first one uses a continuous representation of the orientation state and the second one a discrete representation. We validate our proposed methods with simulations and measurements in a static sensor network with more than 900 agents in an indoor environment and show that the positioning accuracy can be improved significantly by considering the influence of orientations.