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Time-Delay estimation via CPD-GEVD applied to tensor-based GNSS arrays with errors

: De Lima, D.V.; Da Costa, J.P.C.L.; Antreich, F.; Miranda, R.K.; Del Galdo, G.


Institute of Electrical and Electronics Engineers -IEEE-:
7th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2018 : 10-13 December 2017 in Curaçao, Netherlands Antilles
Piscataway, NJ: IEEE, 2018
ISBN: 978-1-5386-1251-4
ISBN: 978-1-5386-1250-7
ISBN: 978-1-5386-1252-1
International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP) <7, 2018, Curaçao>
Conference Paper
Fraunhofer IIS ()

Safety-critical applications (SCA), such as autonomous driving, and liability critical applications (LCA), such as fisheries management, require a robust positioning system in demanding signal environments with coherent multipath while ensuring reasonably low complexity. In this context, antenna array-based Global Navigation Satellite Systems (GNSS) receivers with array signal processing schemes allow the spatial separation of line-of-sight (LOS) from multipath components. In real-world scenarios array imperfections alter the expected array response, resulting in parameter estimation and filtering errors. In this paper, we propose an approach to time-delay estimation for a tensor-based GNSS receiver that mitigates the effect of multipath components while also being robust against array imperfections. This approach is based on the Canonical Polyadic Decomposition by a Generalized Eigenvalue Decomposition (GPD-GEVD) to recover the signal for each impinging component. Our scheme outperforms both the Higher-Order Singular Value Decomposition (HOSVD) eigenfilter and Direction of Arrival and Khatri-Rao factorization (DoA/KRF) approaches, which are state-of-the-art tensor-based schemes for time-delay estimation, particularly when array imperfections are present.