Simultaneous Model and Parameter Estimation for Joint Communication and Positioning

Rebecca Adam, Peter Höher

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Abstract

Joint communication and positioning based on a unified signal structure yields synergy
and can complement and assist system designs enabling higher coverage and quality of service for both
communication and positioning. For the time of arrival (TOA) based joint communication and positioning
the TOA estimation accuracy is crucial. It is known to translate directly into position estimation accuracy.
In the presence of multipath propagation, the estimation accuracy of signal arrival times in return strongly
depends on the actual as well as the estimated number of the physical path parameters, the model order. In
this work, we assess the performance and the mutual impact of simultaneous model order and parameter
estimation for channel-estimation-based joint communication and positioning. Besides introducing a
terrestrial channel-estimation-based unified joint communication and positioning system framework, we
discuss and numerically compare different methods to sequentially or jointly estimate the parameters and
the model order. We show that a TOA error-minimizing model order estimation is preferable over estimating
the correct model order. Furthermore, we compare the performance with a proposed focused order-related
lower bound. This bound determines the optimal model order for a chosen estimator. It depends on the
actual and hypothetical model order and it replaces the here unsuitable Cramer-Rao lower bound. Secondly,
the comparison shows that employing the parameter and model order-dependent inverse Fisher information
matrix yields a close-to-optimal approach. We numerically show for a realistic channel scenario with many
multipath parameters that the method is still accurate.
Original languageEnglish
JournalIEEE Access
Volume9
Pages (from-to)2934-2949
ISSN2169-3536
DOIs
Publication statusPublished - 2021

Keywords

  • Parameter estimation
  • model selection
  • information theoretic criteria
  • information complexity criterion
  • Fisher information

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