NLOS Mitigation Using Sparsity Feature And Iterative Methods

TL;DR

This paper introduces SRNI, an iterative sparse recovery algorithm that effectively mitigates NLOS errors in mobile station localization by treating NLOS measurements as sparse unknowns, improving accuracy and speed.

Contribution

The paper proposes SRNI, a novel sparse recovery algorithm using IMAT to handle NLOS errors as unknown sparse variables in localization systems.

Findings

SRNI outperforms conventional algorithms in accuracy.

SRNI is computationally efficient for large base station setups.

SRNI maintains high accuracy with fewer base stations.

Abstract

Well-known methods are employed to localize mobile station (MS) using line of sight (LOS) measurements. These methods may result in large error if they are fed with non LOS (NLOS) measurements. Our proposed algorithm, referred to as Sparse Recovery of NLOS using IMAT (SRNI), considers NLOS as unknown variables and solves the resultant underdetermined system emphasizing on its sparsity feature based on IMAT methods. Simulations are conducted to investigate the performance of SRNI in comparison of other conventional algorithms. Results demonstrate that SRNI is fast enough to deal with large combination of BSs and also accurate in lower number of BSs