Joint angle and delay estimation for GNSS multipath signals based on multiple sparse Bayesian Learning

Abstract

Multipath signals formed by signal reflection coming from objects in the vicinity of Global Navigation Satellite System (GNSS) receivers result in a degradation of the tracking performance and an increase in the positioning error. By estimating the parameters of both line-of-sight signal and the multipath signals, superior multipath mitigation, spoofing suppression, and localization can be attained. We propose using the multiple sparse Bayesian learning method together with the joint angle and delay estimation technique in GNSS multipath environment to fully exploit the sparsity present in both the spatial and the temporal domains. We also extend the techniques to the estimation of fractional Doppler frequency besides the angle and delay. To counteract the intrinsic drawbacks of sparse representations, two different algorithms based on on-grid and off-grid estimators are proposed to either reduce the complexity or enhance the resolution such that the proposed multipath mitigation approach can be adapted to various GNSS practical situations. Subsequently, a third algorithm with improved resolution is obtained by applying the Space Alternating Generalized Expectation–Maximization algorithm to refine the MSBL-based joint angle and delay estimates. Simulation results indicate that the three proposed algorithms can effectively resolve the GNSS multipath signals and have better performance than existing methods even in severe situations, like the cases of signals with low carrier-to-noise-power-density ratio and spatially and temporally correlated multipath.