Indoor localization system based on received signal strength (RSS) often operate under non-line-of-sight (NLOS) conditions that can cause ranging errors. To identify non-line-of-sight status and line-of-sight (LOS) status and improve the accuracy of indoor localization, a D-S evidence theory based NLOS identification algorithm was proposed. The algorithm is divided into two parts. In the first part, respectively extract a variety of RSS features in NLOS and LOS status and choose the appropriate filter for data processing; in the second part, RSS features fused by D-S evidence theory are used to identify NLOS and LOS status. In an actual test environment, the algorithm was applied to fingerprinting localization, then analyzed the advantages of fusing RSS features and the influence of RSS features selection on experimental results. Finally, compared with several commonly used NLOS and LOS identification algorithm, it shows the proposed algorithm can deal with the indoor localization environment with obstacles with a high localization accuracy and good stability.
['Shang Li', 'Zhaopeng Meng', 'Chung-Ming Own']
The indoor NLOS identification on dempster-shafer evidence theory