An indoor probabilistic localization method using prior information
R Bera, NJ Kirsch, TS Fu - 2013 IEEE 78th Vehicular …, 2013 - ieeexplore.ieee.org
R Bera, NJ Kirsch, TS Fu
2013 IEEE 78th Vehicular Technology Conference (VTC Fall), 2013•ieeexplore.ieee.orgIn this paper, we propose a new probabilistic method for determining the position of an
unknown node in an indoor environment. Our analysis shows that using a small subset of
sensors reduces the error in comparison to larger sets. The best subset of sensors is
determined by matching the power received by all of the sensors and comparing it to prior
measurements. We present experimental measurements made that show the efficacy of this
approach and compare this method to previously published techniques. Our analysis shows …
unknown node in an indoor environment. Our analysis shows that using a small subset of
sensors reduces the error in comparison to larger sets. The best subset of sensors is
determined by matching the power received by all of the sensors and comparing it to prior
measurements. We present experimental measurements made that show the efficacy of this
approach and compare this method to previously published techniques. Our analysis shows …
In this paper, we propose a new probabilistic method for determining the position of an unknown node in an indoor environment. Our analysis shows that using a small subset of sensors reduces the error in comparison to larger sets. The best subset of sensors is determined by matching the power received by all of the sensors and comparing it to prior measurements. We present experimental measurements made that show the efficacy of this approach and compare this method to previously published techniques. Our analysis shows that the new method, Prior Measurement Comparison (PMC), yields greater estimation accuracy resulting in lower error.
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