Biased Constrain Hybrid Kalman Filter for Wireless Indoor Localization

Many exist localization algorithms are unbiased estimators. However, the estimation performance presents biased feature in the real location systems. On the other hand, many biased location estimators show advantages that unbiased estimators can not achieve, e.g., robust to the noise, more accurate estimation and low complexity. In this paper, we propose a biased localization estimator and a hybrid Kalman filtering algorithm. The proposed algorithm is robust to the complicated environment with high accuracy. Both theoretical analysis and experimental evaluation indicate that the proposed algorithm outperform the unbiased optimal estimation methods.