Volume II-1
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., II-1, 31-35, 2014
https://doi.org/10.5194/isprsannals-II-1-31-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., II-1, 31-35, 2014
https://doi.org/10.5194/isprsannals-II-1-31-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.

  07 Nov 2014

07 Nov 2014

Indoor Ultra-Wide Band Network Adjustment using Maximum Likelihood Estimation

Z. Koppanyi1,2 and C. K. Toth1 Z. Koppanyi and C. K. Toth
  • 1Dept. of Civil, Environmental and Geodetic Engineering, The Ohio State University, 470 Hitchcock Hall, 2070 Neil Ave., Columbus, OH 43210, USA
  • 2Dept. of Photogrammetry and Geoinformatics, Budapest University of Technology and Economics, 3 Müegyetem rkp., K building first floor 31. Budapest, H-1111, Hungary

Keywords: ultra-wide band, maximum likelihood estimation, network adjustment, error assessment, indoor positioning

Abstract. This study is the part of our ongoing research on using ultra-wide band (UWB) technology for navigation at the Ohio State University. Our tests have indicated that the UWB two-way time-of-flight ranges under indoor circumstances follow a Gaussian mixture distribution that may be caused by the incompleteness of the functional model. In this case, to adjust the UWB network from the observed ranges, the maximum likelihood estimation (MLE) may provide a better solution for the node coordinates than the widely-used least squares approach. The prerequisite of the maximum likelihood method is to know the probability density functions. The 30 Hz sampling rate of the UWB sensors enables to estimate these functions between each node from the samples in static positioning mode. In order to prove the MLE hypothesis, an UWB network has been established in a multi-path density environment for test data acquisition. The least squares and maximum likelihood coordinate solutions are determined and compared, and the results indicate that better accuracy can be achieved with maximum likelihood estimation.