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.
Highlights
Interest in joint communication and positioning is steadily increasing [1]
This paper aims at obtaining soft information for the parameter estimates in order to improve the positioning accuracy before sensor fusion is applied
6 Conclusions In this paper, a channel parameter estimator based on the maximum-likelihood approach is proposed for joint communication and positioning
Summary
Synergetic effects like improved resource allocation and new applications like locationbased services or a precise location determination of emergency calls are attractive features of joint communication and positioning. In [13], Bates and Watts describe nonlinear least-squares estimation from a geometric point of view and introduce measures of nonlinearity. These measures indicate the applicability of a linearization and its effects on inference. The nonlinear least-squares problem is reviewed: A set of parameters θ = [θ1, θ2, . Where hl (θ) is a nonlinear function of the parameters θ and εl is additive zero mean measurement noise with variance σε.
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