A Low-Cost and Efficient Indoor Fusion Localization Method

Abstract

Accurate indoor location information has considerable social and economic value in applications, such as pedestrian heatmapping and indoor navigation. Ultrasonic-based approaches have received significant attention mainly since they have advantages in terms of positioning with temporal correlation. However, it is a great challenge to gain accurate indoor localization due to complex indoor environments such as non-uniform indoor facilities. To address this problem, we propose a fusion localization method in the indoor environment that integrates the localization information of inertial sensors and acoustic signals. Meanwhile, the threshold scheme is used to eliminate outliers during the positioning process. In this paper, the estimated location is fused by the adaptive distance weight for the time difference of arrival (TDOA) estimation and improved pedestrian dead reckoning (PDR) estimation. Three experimental scenes have been developed. The experimental results demonstrate that the proposed method has higher localization accuracy in determining the pedestrian location than the state-of-the-art methods. It resolves the problem of outliers in indoor acoustic signal localization and cumulative errors in inertial sensors. The proposed method achieves better performance in the trade-off between localization accuracy and low cost.

Deep Learning-Based Geomagnetic Navigation Method Integrated with Dead Reckoning

“…Tong et al [51] focused on integrating a PDR and a UWB-based localization algorithm, which can achieve high accuracy in harsh environments. Yan et al [52] proposed a cost-effective and high-efficiency localization algorithm that combines inertial sensors and acoustic signals, which has higher localization accuracy than the state-of-theart methods. In ref.…”

“…Based on the real-time and accuracy requirements of localization, we resample geomagnetic sequences in every step. The step length is estimated based on the work in [52], which is related to ours and can predict pedestrian states more accurately.…”

“…The nonlinear model uses the correlation between the step length and acceleration data to estimate the step length, which is more in line with the actual pedestrian state. An improved nonlinear step length estimation method, which is our previous work [52], can provide a more accurate pedestrian step length, as shown below:…”

“…2023, 15, x FOR PEER REVIEW 13 of 26 more in line with the actual pedestrian state. An improved nonlinear step length estimation method, which is our previous work [52], can provide a more accurate pedestrian step length, as shown below:…”

“…Therefore, it is necessary to transfer the coordinate system for heading direction estimation. We adopt the quaternion method, which converts the target from the Step length estimation at different steps with the constant [61], linear [62], Weinberg [64] and Yan+2022 [52] methods: (a) trial path 1; (b) trial path 2; (c) trial path 3.…”