This paper offers a solution to challenge navigation in the indoor environment by making use of the existing infrastructure. Estimating pedestrian trajectory using pedestrian dead reckoning (PDR) and WiFi is a very popular technique. However, cumulative errors and mismatching are major problems in PDR and WiFi fingerprint matching, respectively. PDR and pedestrian heading are used as the state transition equation, and the step length and WiFi matching results are used as observation equations. A federated particle filter (FPF) based on the principle of information sharing is proposed to fusion PDR and WiFi, which improves pedestrian navigation accuracy. The experimental results show that the average positioning accuracy is 0.94 m and 1.5 m, respectively.

WiFi fingerprint-based positioning is a method for indoor localization with the advent of widespread deployment of WiFi and the Internet of Things. However, single WiFi fingerprint positioning has the problems of mismatch, unstable signal strength and limited accuracy. Aiming to address these issues, this paper proposes the fusion algorithm combining WiFi and pedestrian dead reckoning (PDR). Firstly, the particle swarm optimization (PSO) model is utilized to optimize the weighted k-nearest neighbors (WKNN) in the WiFi part. Additionally, the artemisinin optimization (AO) algorithm is used to optimize the particle filter (PF) to improve the fusion effect of the WiFi and PDR. Finally, to thoroughly validate the localization performance of the proposed algorithm, we designed experiments involving two scenarios with four smartphone gestures: calling, dangling, handheld, and pocketed. The experimental results unequivocally indicate that the positioning error of AO-PSO-PF algorithm is lower than that of other algorithms including PDR, WiFi, PF, APF, and FPF. The average positioning errors for the two experiments are 0.95 m and 1.42 m, respectively.