Grid-Based Bayesian Filtering Methods for Pedestrian Dead Reckoning Indoor Positioning Using Smartphones

Abstract

Indoor positioning systems for smartphones are often based on Pedestrian Dead Reckoning, which computes the current position from the previously estimated location. Noisy sensor measurements, inaccurate step length estimations, faulty direction detections, and a demand on the real-time calculation introduce the error which is suppressed using a map model and a Bayesian filtering. The main focus of this paper is on grid-based implementations of Bayes filters as an alternative to commonly used Kalman and particle filters. Our previous work regarding grid-based filters is elaborated and enriched with convolution mask calculations. More advanced implementations, the centroid grid filter, and the advanced point-mass filter are introduced. These implementations are analyzed and compared using different configurations on the same raw sensor recordings. The evaluation is performed on three sets of experiments: a custom simple path in faculty building in Slovakia, and on datasets from IPIN competitions from a shopping mall in France, 2018 and a research institute in Italy, 2019. Evaluation results suggests that proposed methods are qualified alternatives to the particle filter. Advantages, drawbacks and proper configurations of these filters are discussed in this paper.

Off-Line Evaluation of Indoor Positioning Systems in Different Scenarios: The Experiences From IPIN 2020 Competition

“…a) System Description: The proposed positioning system [33] consists of multiple components merged together using the Bayesian filtering. A floor transition is detected using barometer measurements.…”

“…The centroid grid filter was applied on IPIN 2018 [1] and IPIN 2019 [2] competitions. In [33], the method was compared with other grid-based approaches and the recommended configuration was discussed. Multiple parameter settings were explored based on the previous observations leading to the selection of the final competition submission.…”

Adaptive Inertial Sensor-Based Step Length Estimation Model

“…Various machine learning techniques and statistical estimation methods are often used to achieve higher accuracy, yet they depend upon comprehensive training. For instance, artificial neural networks [ 37 ] and Bayesian filters, such as particle filters [ 38 , 39 , 40 ], grid-based approaches [ 41 ], and Kalman filters [ 42 , 43 ], have already been employed. Integrating acceleration in the walking direction with respect to time [ 11 , 44 , 45 ] presents another means of estimating step length.…”

Inertial Sensor-Based Step Length Estimation Model by Means of Principal Component Analysis

“…Proposed techniques for step length estimation vary in terms of the implementing means of deriving step length. For example, machine learning techniques are often utilized in the derivation process in combination with various statistical estimation methods to improve the estimation accuracy, i.e., Bayesian filters such as the Kalman filter [ 23 , 24 ], the particle filter [ 25 , 26 , 27 ], and grid-based approaches [ 28 ]. Similarly, artificial neural networks [ 29 ] are also known to be employed.…”