Position Tracking using Likelihood Modeling of Channel Features with Gaussian Processes

• URL: http://arxiv.org/abs/2203.13110v1
• Date: Thu, 24 Mar 2022 15:06:01 GMT
• Title: Position Tracking using Likelihood Modeling of Channel Features with Gaussian Processes
• Authors: Sebastian Kram, Christopher Kraus, Tobias Feigl, Maximilian Stahlke, J\"org Robert, Christopher Mutschler
• Abstract summary: Recent localization frameworks exploit spatial information of complex channel measurements to estimate accurate positions. We propose a novel framework that adapts well to sparse datasets with strong multipath propagation. Our framework combines the trained GPs with line-of-sight ranges and a dynamics model in a particle filter.
• Score: 2.3977391435533373
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent localization frameworks exploit spatial information of complex channel measurements (CMs) to estimate accurate positions even in multipath propagation scenarios. State-of-the art CM fingerprinting(FP)-based methods employ convolutional neural networks (CNN) to extract the spatial information. However, they need spatially dense data sets (associated with high acquisition and maintenance efforts) to work well -- which is rarely the case in practical applications. If such data is not available (or its quality is low), we cannot compensate the performance degradation of CNN-based FP as they do not provide statistical position estimates, which prevents a fusion with other sources of information on the observation level. We propose a novel localization framework that adapts well to sparse datasets that only contain CMs of specific areas within the environment with strong multipath propagation. Our framework compresses CMs into informative features to unravel spatial information. It then regresses Gaussian processes (GPs) for each of them, which imply statistical observation models based on distance-dependent covariance kernels. Our framework combines the trained GPs with line-of-sight ranges and a dynamics model in a particle filter. Our measurements show that our approach outperforms state-of-the-art CNN fingerprinting (0.52 m vs. 1.3 m MAE) on spatially sparse data collected in a realistic industrial indoor environment.

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