Superresolution Multipoint Ranging with Optimized Sampling via Orthogonally Designed Golomb Rulers

TL;DR

This paper introduces a novel multipoint ranging algorithm that combines superresolution techniques with optimized orthogonal Golomb ruler sampling, enhancing accuracy and efficiency in wireless localization systems.

Contribution

It proposes a new multipoint ranging method using superresolution and orthogonal Golomb rulers, including a genetic algorithm for designing these rulers, and provides theoretical and simulation validation.

Findings

The proposed algorithm improves multipoint ranging accuracy.

The genetic algorithm effectively designs orthogonal Golomb rulers.

CRLB analysis validates the optimality of the approach.

Abstract

We consider the problem of performing ranging measurements between a source and multiple receivers efficiently and accurately, as required by distance-based wireless localization systems. To this end, a new multipoint ranging algorithm is proposed, which is obtained by adapting superresolution techniques to the ranging problem, using for the sake of illustration the specific cases of ToA and PDoA, unified under the same mathematical framework. The algorithm handles multipoint ranging in an efficient manner by employing an orthogonalized non-uniform sampling scheme optimised via Golomb rulers. Since the approach requires the design of mutually orthogonal sets of Golomb rulers with equivalent properties -- a problem that founds no solution in current literature -- a new genetic algorithm to accomplish this task is presented, which is also found to outperform the best known alternative when used to generate a single ruler. Finally, a CRLB analysis of the overall optimised multipoint ranging solution is performed, which together with a comparison against simulation results validates the proposed techniques.