Geodetic Network Optimization Algorithm for Anchor Selection in Harsh Environments
Agnes Koller, Maximilian Schuh, Carlo Alberto Boano, Kay Römer, Klaus Witrisal, Graz University of Technology
Date/Time: Thursday, Sep. 22, 10:40 a.m.
Highly accurate, robust, and reliable position determination is a topic attaining broad interest by the research community, since there are many scenarios and applications where current satellite-based positioning systems are not available or suitable, e.g. assisted living, robot navigation, and asset tracking. Improving performance and resource-efficiency for reliable and highly accurate indoor radio positioning in challenging environments is a problem of great interest. This applies especially if the number of anchors is scaled up in a large-scale deployment, and if harsh radio channel conditions apply, e.g., dense multipath propagation. However, little is known about determining the minimum anchor configuration to achieve targeted performance metrics. Robust and accurate indoor positioning can be provided by ultra-wideband (UWB) time-of-arrival measurements of the line-of-sight (LOS) component. To enable scalable real-time localization systems (RTLS) with an increasing amount of devices, reducing the number of nessessary rangings is of high importance. In this paper, we evaluate criterion-based performance metrics in harsh environments by applying a geodetic network optimization algorithm. We introduce fundamental performance metrics inside our UWB-network configuration through a radio environment map (REM). A REM is formulated, based on a signal-to-interference-plus-noise ratio (SINR), to quantify for each anchor the expected measurement accuracy and reliability throughout the environment. These SINR REMs are used as an input for the proposed algorithm. The results show that radio environment maps can maximize the localization precision, while minimizing the number of distance measurements needed, which is highly beneficial for the scalability of location-aware indoor-positioning systems. The algorithm is evaluated by experimental measurements.
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