UNLOC: Optimal Unfolding Localization from Noisy Distance Data

Ordinal UNLOC: Target Localization With Noisy and Incomplete Distance Measures

“…For each target node j (i.e., sensor m + j, where 1 ≤ j ≤ n), the estimated anchor-to-target distances DY X together with the locations of the anchors {y i } can be used to infer the location of the target x j . We achieve this by formulating an unfolding optimization, with cost function [45]…”

“…. , δ m ] , and the solution is termed UNLOC [45]. Applying UNLOC to each column of the estimated distance matrix DY X leads to the estimated location of all the targets.…”

“…This can be solved in several ways, including those discussed in [21], a global optimization routine [46], or generalized to include weights [45].…”

“…In this section, we discuss the computational complexity of Ordinal UNLOC with reference to traditional localization methods. In terms of computational complexity, the final step of Ordinal UNLOC is a traditional multilateral algorithm such as the one discussed in [45]. Ordinal UNLOC is, therefore, more complex than traditional ToA/RSS-based systems.…”

“…First, we run Ordinal UNLOC on the RSSI values directly. This is compared with two other scenarios, where the distance is estimated from the RSSI values first using (20), and then, UNLOC [45] is applied in two ways. In the first scenario, a single estimate of the path loss exponent, G, is available for the entire environment, as is typically the case [32], [37].…”