Particle filters are a commonly used technique for sensor fusion in indoor localization use-cases. Multiple strategies exist, that control when the particle filter is updated and with which data. We take a look at three commonly used update strategies. The first runs a full particle filter update for every incoming measurement, the second uses a fixed-interval update rate and the third is triggered by events, such as detected steps. All of these strategies have different advantages and disadvantages. The first strategy, for example, has the problem that steps are recognized only after they have been completed - which makes for a constant temporal discrepancy between the proposal distribution and the measurements evaluated on top. Due to the configured delay, the fixed-interval strategy, in comparison, can pre-date incoming step events to mitigate this discrepancy. In this paper we present PIPF as a novel approach to combine advantages of the fixed-interval update strategy with advantages of the strategy that runs a full update per measurement. This works by using the transition to calculate a trajectory, which is then followed during the evaluation. The proposal distribution in the form of particles is interpolated on this trajectory for every incoming measurement, which removes the temporal discrepancy between both. To evaluate PIPF's performance and characteristics at different configurations, we compare it to a conventional fixed-interval particle filter on a real-world indoor positioning scenario.
|Title of host publication||2022 IEEE 12th International Conference on Indoor Positioning and Indoor Navigation (IPIN)|
|Publication status||Published - 26.10.2022|
Research Areas and Centers
- Centers: Center for Artificial Intelligence Luebeck (ZKIL)