Simultaneous Model and Parameter Estimation for Joint Communication and Positioning

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.