Ordinal UNLOC: Target Localization with Noisy and Incomplete Distance Measures
This addresses the problem of indoor target localization for applications like tracking or navigation by providing a method that works with noisy and incomplete data, though it appears incremental as it builds on existing ordinal and optimization techniques.
The paper tackles the challenge of target localization with unreliable distance measures, especially in indoor environments, by developing Ordinal UNLOC, a framework that uses only ordinal signal strength comparisons to estimate target location, achieving accurate results validated through simulations and hardware experiments.
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.