In this paper, we propose a robust and efficient particle filtering framework for indoor localization in wireless sensor network (WSN) which searches effective anchors and constructs particle filter dynamically. Within this framework, three algorithms are integrated into the dynamic particle filter: anchor selection algorithm, location constraint resampling and SIR particle filter. The proposed scheme searches the maximum number of anchors with line of sight (LOS) to the target to guarantee the effective measurement. Then, we construct a dynamic particle filter with the chosen anchors and develop a novel resampling scheme which generates the particles within the indoor location constraints. The proposed scheme is proved to be robust and computational efficient. Simulation results show that our scheme is accurate with low computation cost, which is promising for real-time implementation.
['Yubin Zhao', 'Yuan Yang', 'Marcel Kyas']
Dynamic searching particle filtering scheme for indoor localization in wireless sensor network