Joint channel and parameter estimation for combined communication and navigation using particle swarm optimization

In this paper, a new method for joint channel and parameter estimation in the framework of combined communication and navigation is investigated. The basic idea is to estimate the parameters needed for positioning from the channel impulse response: In the estimator not only the channel coefficients of the equivalent discrete-time channel model are estimated, but also the parameters of the physical channel including the propagation delay of the line-of-sight path. A priori information about the pulse shaping filter and the receive filter are used. The estimator is based on the maximum-likelihood principle, which leads to a nonlinear minimization problem. The corresponding metric is minimized by particle swarm optimization, which is a simple global optimization algorithm that does not use any gradient information. The performance of the estimator is evaluated by means of Monte Carlo simulations. The results are compared to the Cramer-Rao lower bound and it is shown that the estimator is asymptotically optimal and efficient. The mean squared error of the channel estimates is decreased compared to the mean squared error of standard least squares channel estimates.