Ordinal UNLOC: Target Localization With Noisy and Incomplete Distance Measures

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