Indoor Fingerprinting Positioning Based on the Least Anticipation Loss

the fingerprinting positioning is commonly used for indoor location estimation, since wireless signal outage, severe multipath propagation and interference when people walking can often lead to decrease ranging base positioning accuracy in indoor environments. Aiming at the situation that most of existing algorithms is not high in positioning accuracy and system robustness, this paper puts forward an optimal fingerprinting positioning algorithm based on the least risk Bayesian method. With the introduction of positioning loss, the algorithm makes each positioning result the least anticipation loss to minimize the positioning error. Theoretical analysis shows that the least anticipation loss positioning is optimal in positioning precision compared to the optimal positioning error probability methods. Experimental results demonstrate the better performance of proposed algorithm in precision and evaluation.