Complexity reduced multipath mitigation in GNSS with the GRANADA bit-true software receiver

The positioning performance of global navigation satellite systems (GNSSs) mass market receivers severely degrades when the received satellite signals are subject to multipath propagation. Therefore, the estimation of several unknown channel amplitudes and taps in a multipath environment is an important approach to mitigate the multipath effects. In professional receivers, viable multipath mitigation approaches are the maximum likelihood (ML) estimator, the expectation maximization (EM) approach and the space alternating generalized expectation maximization (SAGE) algorithm. However, all methods require a high computational complexity when used in spread spectrum systems due to long spreading sequences. Therefore, one contribution of this paper is that we apply subspace methods to decrease the computational complexity before executing the above iterative estimation algorithms. Further, we assess respective performance of the algorithms and the future Galileo navigation system by using the Galileo receiver analysis and design application (GRANADA) simulator. The complexity reduction algorithms are specifically adjusted to the El Galileo binary offset carrier (BOC) signal, which superimposes data and pilot signals. Moreover, we adapt the complexity reduction so that it can handle any sampling frequency that is not necessarily an integer multiple of the chip rate.