Abstract
Location-based services for smartphones are becoming more and more popular. The core of location-based services is how to estimate a user’s location. An INS/floor-plan indoor localization system, using the Firefly Particle Filter (FPF), is proposed to estimate a user’s location. INS includes an attitude angle module, a step length module and a step counting module. In the step length module, we propose a hybrid step length model. The proposed step length algorithm reasonably calculates a user’s step length. Because of sensor deviation, non-orthogonality and the user’s jitter, the main bottleneck for INS is that the error grows over time. To reduce the cumulative error, we design cascade filters including the Kalman Filter (KF) and FPF. To a certain extent, KF reduces velocity error and heading drift. On the other hand, the firefly algorithm is used to solve the particle impoverishment problem. Considering that a user may not cross an obstacle, the proposed particle filter is proposed to improve positioning performance. Results show that the average positioning error in walking experiments is 2.14 m.
Highlights
Location services using smartphones have attracted more and more attention in recent years
Inspired by the above work, we propose Firefly Particle Filter (FPF) for an INS/floor-plan indoor localization system
In a teaching building experiment, we showed that FPF combined with a floor plan can effectively constrain the heading drift of INS
Summary
Location services using smartphones have attracted more and more attention in recent years. Unlike mature outdoor satellite positioning schemes, indoor positioning techniques, using smartphones, cannot provide real-time, long-term meter-level positioning accuracy. The position of a user is estimated by calculating the similarity between the measured signals and the fingerprint in the database. Taking into account that the changes in the indoor environment have a great impact on fingerprint positioning, researchers need to update the fingerprint database within a certain period of time. Another bottleneck is that fingerprint matching costs excessive computation. (2) FA is used to modify the crossing-wall particles, and the improved particle filter (FPF) is presented to improve the indoor positioning accuracy.
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