Ordinal UNLOC: Target Localization with Noisy and Incomplete Distance Measures

TL;DR

Ordinal UNLOC is a novel localization framework that accurately estimates target positions using only ordinal signal strength data, effectively handling noisy and incomplete measurements in complex indoor environments.

Contribution

It introduces a new computational approach that does not rely on reliable distance measures, combining rank aggregation, function learning, and unfolding optimization for robust localization.

Findings

Achieves accurate localization with noisy, incomplete data in simulations.

Validated effectiveness through hardware experiments in indoor settings.

Handles unknown parameters in transmission models successfully.

Abstract

A main challenge in target localization arises from the lack of reliable distance measures. This issue is especially pronounced in indoor settings due to the presence of walls, floors, furniture, and other dynamically changing conditions such as the movement of people and goods, varying temperature, and airflows. Here, we develop a new computational framework to estimate the location of a target without the need for reliable distance measures. The method, which we term Ordinal UNLOC, uses only ordinal data obtained from comparing the signal strength from anchor pairs at known locations to the target. Our estimation technique utilizes rank aggregation, function learning as well as proximity-based unfolding optimization. As a result, it yields accurate target localization for common transmission models with unknown parameters and noisy observations that are reminiscent of practical settings. Our results are validated by both numerical simulations and hardware experiments.