Pedestrian Indoor Localization Method Based on Integrated Particle Filter

Abstract

To solve the problem of time-consuming, laborious, and low accuracy in magnetic map construction, a trajectory guided 2-D magnetic map construction method is proposed in this article. The absolute displacement calculated by the inertial navigation system is mapped to the real trajectory to construct the magnetic map. The precision of the constructed magnetic map can reach 0.1141 m, and the mapping efficiency is greater than 1800 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text{m}^{\mathbf {2}}$ </tex-math></inline-formula> /h. Moreover, the cost of sensor is only <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\$ $ </tex-math></inline-formula> 15.4. To deal with the instability problem that the traditional particle filter (PF) in pedestrian dead reckoning (PDR) suffers from, this article proposes an integrated PF (IPF) algorithm. The algorithm uses two assumptions: 1) all noises obey Gaussian distribution and 2) the initial position and the results obtained from IPF are reliable enough. The particle distribution in the case of infinite particles is simulated, and similar particles are integrated. The algorithm breaks through the limit of particle number of PF and greatly reduces the randomness of PF algorithm. The real-world experiments show that the proposed positioning method can achieve an average positioning error of 0.32 m.