Weighted Least-Squares by Bounding-Box (B-WLS) for NLOS Mitigation of Indoor Localization

The major problem of indoor localization is the imprecise ranging, which directly degrades the localization accuracy. The ordinary least-squares (OLS) estimator is able to handle unbiased and homoscedastic ranging errors, but incapable for the bias and heteroscedasticity as characterized by real-world ranging of indoor scenarios, especially the non-line-of-sight (NLOS) error. A potential improvement of LS is to weight each element related to the corresponding ranging error, known as the weighted LS (WLS). However, current weighting metrics are either impractical to get or still involve the NLOS error. We propose to weight each element in a linear LS (LLS) estimator by the difference between the measured ranges and Bounding-box results, named B-WLS. Compared with five LS type algorithms in simulations and a mobile target experiments, results demonstrate that B-WLS efficiently enables the LLS estimation to suppress to the NLOS error.