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Sparse Spatial and Temporal Estimation for Multipath Mitigation in GNSS

The multipath signals will degrade the tracking performance and increase the positioning errors of the Global Navigation Satellite System (GNSS). Superior multipath mitigation can be obtained by jointly estimating the angles of arrival and delays of both the line of sight signal and the multipath signals. In to do so, this paper proposes the use of the multiple Bayesian learning (MSBL) method together with the joint angle and delay estimation technique in GNSS multipath scenarios. Moreover, to further enhance the resolution, off-grid estimation is adopted to delay while on-grid estimation is kept for angle to reduce the complexity. Simulation results are presented to evaluate the performance of the proposed joint on-grid angle and off-grid delay estimation based on MSBL algorithm under several multipath scenarios and it is shown to outperform existing methods even in the most difficult cases of spatially correlated multipath signals and low carrier-to-noise ratio.