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• W4376612348 endingPage "4744" @default.
• W4376612348 startingPage "4744" @default.
• W4376612348 abstract "Indoor positioning enables mobile machines to perform tasks (semi-)automatically, such as following an operator. However, the usefulness and safety of these applications depends on the reliability of the estimated operator localization. Thus, quantifying the accuracy of positioning at runtime is critical for the application in real-world industrial contexts. In this paper, we present a method that produces an estimate of the current positioning error for each user stride. To accomplish this, we construct a virtual stride vector from Ultra-Wideband (UWB) position measurements. The virtual vectors are then compared to stride vectors from a foot-mounted Inertial Measurement Unit (IMU). Using these independent measurements, we estimate the current reliability of the UWB measurements. Positioning errors are mitigated through loosely coupled filtering of both vector types. We evaluate our method in three environments, showing that it improves positioning accuracy, especially in challenging conditions with obstructed line of sight and sparse UWB infrastructure. Additionally, we demonstrate the mitigation of simulated spoofing attacks on UWB positioning. Our findings indicate that positioning quality can be judged at runtime by comparing user strides reconstructed from UWB and IMU measurements. Our method is independent of situation- or environment-specific parameter tuning, and as such represents a promising approach for detecting both known and unknown positioning error states." @default.
• W4376612348 created "2023-05-17" @default.
• W4376612348 creator A5063619304 @default.
• W4376612348 creator A5073000737 @default.
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• W4376612348 date "2023-05-14" @default.
• W4376612348 modified "2023-10-14" @default.
• W4376612348 title "Pedestrian Localization with Stride-Wise Error Estimation and Compensation by Fusion of UWB and IMU Data" @default.
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• W4376612348 doi "https://doi.org/10.3390/s23104744" @default.
• W4376612348 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37430659" @default.
• W4376612348 hasPublicationYear "2023" @default.
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