A statistics-based least squares (SLS) method for non-line-of-sight error of indoor localization

The main challenge of indoor wireless positioning is the large positive bias of non-line-of-sight (NLOS) ranging, which directly degrades the localization accuracy. It is necessary for a practical positioning algorithm to learn the specific of indoor ranging from statistics. Thus, we analyze the ranging characteristics based on real-world indoor experiments with both static and mobile cases, which finds that bounding-box algorithm is robust to the NLOS bias. Then, we propose a twostep statistics-based least squares (SLS) method consisting of a NLOS bias elimination and a linear least squares (LLS) process. SLS first removes the NLOS bias by an intermediate estimation obtained from bounding-box algorithm, then uses a weighted LLS estimator to handle the remained ranging error. The difference between SLS and other NLOS mitigation approaches is that SLS aims to remove the bias away from the NLOS range while the others try to less emphasize or discard the NLOS range. SLS is compared with three NLOS mitigation algorithms on a sensor test-bed in a typical hallway. Results demonstrate that an effective NLOS mitigation of SLS.