Joint communication and positioning based on soft channel parameter estimation

Abstract

A joint communication and positioning system based on maximum-likelihood channel parameter estimation is proposed. The parameters of the physical channel, needed for positioning, and the channel coefficients of the equivalent discrete-time channel model, needed for communication, are estimated jointly using a priori information about pulse shaping and receive filtering. The paper focusses on the positioning part of the system. It is investigated how soft information for the parameter estimates can be obtained. On the basis of confidence regions, two methods for obtaining soft information are proposed. The accuracy of these approximative methods depends on the nonlinearity of the parameter estimation problem, which is analyzed by so-called curvature measures. The performance of the two methods is investigated by means of Monte Carlo simulations. The results are compared with the Cramer-Rao lower bound. It is shown that soft information aids the positioning. Negative effects caused by multipath propagation can be mitigated significantly even without oversampling.

Simultaneous Model and Parameter Estimation for Joint Communication and Positioning

“…Instead of performing model selection and parameter estimation together, [6], [33] propose model simplification to yield an acceptable ranging accuracy to computational cost tradeoff in multipath scenarios. Such approaches have the drawback that they result in a positioning accuracy limited by a modeling error, which can be very high depending on the applied scenario.…”

“…• Focusing the model order detection to the positioningrelevant parameters, the delays. The results [6] (page 74 and page 123) indicate that simply assuming the correct model order is not the optimal choice for positioning. We formulate a for TOA error performance beneficial automatism to estimate the model order optimally.…”

“…In [48](page 16) we provided proof for the fact that the cost functions (20) and (19) are equivalent and the delay estimates are the sameθ…”

“…Underestimating the model order is beneficial in these cases. For positioning or delay estimation, excluding paths potentially yields a lower delay estimation error [6]. Following this line of thought, the probability of correct detection is not a sufficient performance measure to assess the model order selection performance.…”