Algorithm for recursive Bayesian localization triggered by temporalseries measurement information

To improve the robustness of a position system and reduce the localization error, this paper proposes a fingerprint positioning method based on the recursive Bayesian. To solve the blindness and unreliability of the location fingerprint data in an offline phase, the fingerprint database based on the sample variance is developed to measure the confidence of sampling values and reduce the impact of environmental factors, improving the reliability for online localization. The proposed method provides the target position at the current moment by utilizing the Markov model that is established by the constraint relationship between moments in the source movement, which avoids the jump problem of the position estimation and poor robustness and improves the localization accuracy. Extensive experimental results demonstrate that the average localization error norm of the proposed algorithm is no more than 0.927m, indicating significantly lower errors than other traditional schemes (often by more than 30 percent).