Semi-Unsupervised Mitigation of Human Body Shadowing for Indoor UWB pedestrian tracking.
2023 13th International Conference on Indoor Positioning and Indoor Navigation (IPIN)(2023)
摘要
in Ultra Wideband (UWB), large ranging errors occur under Non-Line-of-Sight (NLoS) conditions, which significantly degrades positioning accuracy. Human body shadowing (HBS) is a specific case of NLoS, which is a prominent error source for on-body UWB positioning. This work presents a tracking algorithm based on a Particle Filter (PF), designed to mitigate HBS-induced positioning errors by using an orientation-adaptive measurement model, consisting of a bank of Gaussian Mixture Models. The relative orientation is derived from Inertial Measurement Unit (IMU) data, and predicted positions from the tracking algorithm itself. We propose a second tracking algorithm in order to train the adaptive measurement model in a semi-unsupervised way, eliminating the need for accurate ground truth. The proposed algorithm outperforms a state of the art algorithm by an average of 11% (unsupervised) to 39% (supervised) in an experimental evaluation.
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关键词
Gaussian Mixture Model,human body shadowing,indoor positioning,IMU,NLoS,unsupervised learning,UWB
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