A positioning algorithm based on improved robust extended Kalman filter with NLOS identification and mitigation

Abstract With the development of information age and maturity Internet Things technology, wireless sensor network has been widely applied in indoor localization. However, non-line-of-sight (NLOS) propagation complicated environment inherent noise will introduce errors measurements, which seriously lead to inaccurate positioning. In this paper, a novel localization scheme based on mean reconstruction method is proposed, reconstructs distance measurements from all beacon nodes by taking average twice weaken adverse effects NLOS. At same time, re-estimated when difference not too large. Next, robust extended Kalman filter (REKF) used process reconstructed obtain positioning results. To make results more accurate, hypothesis test as NLOS identification classify position estimates generated combinations least-squares. Then, residual weighting (RWGH) utilized combine that fall into validation region. last, we merge RWGH REKF. The simulation experimental show proposed algorithm high accuracy strong robustness.