Evaluation of Position-related Information in Multipath Components for Indoor Positioning

TL;DR

This paper introduces a unified framework using Fisher information to quantify position-related information from multipath components in indoor environments, enhancing the understanding of localization capabilities and system design.

Contribution

It presents a novel analytical framework for evaluating multipath-based indoor positioning accuracy using Fisher information and CRLB, considering various transmission scenarios.

Findings

Framework effectively characterizes localization capabilities.

Quantifies influence of system parameters on accuracy.

Shows robustness of multipath information for indoor positioning.

Abstract

Location awareness is a key factor for a wealth of wireless indoor applications. Its provision requires the careful fusion of diverse information sources. For agents that use radio signals for localization, this information may either come from signal transmissions with respect to fixed anchors, from cooperative transmissions inbetween agents, or from radar-like monostatic transmissions. Using a-priori knowledge of a floor plan of the environment, specular multipath components can be exploited, based on a geometric-stochastic channel model. In this paper, a unified framework is presented for the quantification of this type of position-related information, using the concept of equivalent Fisher information. We derive analytical results for the Cram\'er-Rao lower bound of multipath-assisted positioning, considering bistatic transmissions between agents and fixed anchors, monostatic transmissions from agents, cooperative measurements inbetween agents, and combinations thereof, including the effect of clock offsets. Awareness of this information enables highly accurate and robust indoor positioning. Computational results show the applicability of the framework for the characterization of the localization capabilities of a given environment, quantifying the influence of different system setups, signal parameters, and the impact of path overlap.