Smartphone Sensor-Based Orientation Determination for Indoor-Navigation

Ettlinger, Andreas; Neuner, Hans; Burgess, T.

doi:10.1007/978-3-319-47289-8_3

Cited by 5 publications

(2 citation statements)

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“…This means that the estimated orientation mainly relies on the system equation. The reason for the high weight of the system equation is that the resulting trajectory should be smoothed [ 32 ]. The problem is that if the model assumptions made in the system equation do not capture the reality, the resulting deviations have high influence on the estimated orientation.…”

Section: Resultsmentioning

confidence: 99%

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Development of a Kalman Filter in the Gauss-Helmert Model for Reliability Analysis in Orientation Determination with Smartphone Sensors

Ettlinger

Neuner

Burgess³

2018

Sensors

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Abstract:The topic of indoor positioning and indoor navigation by using observations from smartphone sensors is very challenging as the determined trajectories can be subject to significant deviations compared to the route travelled in reality. Especially the calculation of the direction of movement is the critical part of pedestrian positioning approaches such as Pedestrian Dead Reckoning ("PDR"). Due to distinct systematic effects in filtered trajectories, it can be assumed that there are systematic deviations present in the observations from smartphone sensors. This article has two aims: one is to enable the estimation of partial redundancies for each observation as well as for observation groups. Partial redundancies are a measure for the reliability indicating how well systematic deviations can be detected in single observations used in PDR. The second aim is to analyze the behavior of partial redundancy by modifying the stochastic and functional model of the Kalman filter. The equations relating the observations to the orientation are condition equations, which do not exhibit the typical structure of the Gauss-Markov model ("GMM"), wherein the observations are linear and can be formulated as functions of the states. To calculate and analyze the partial redundancy of the observations from smartphone-sensors used in PDR, the system equation and the measurement equation of a Kalman filter as well as the redundancy matrix need to be derived in the Gauss-Helmert model ("GHM"). These derivations are introduced in this article and lead to a novel Kalman filter structure based on condition equations, enabling reliability assessment of each observation.

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Section: Resultsmentioning

confidence: 99%

“…The observations in the measurement equation are directly observed Euler angles, as also shown in [ 32 ]. Therefore, the design matrix A m,k of the Kalman filter equals the identity matrix.…”

Section: Orientation Determinationmentioning

confidence: 99%