Evaluation of Low-Complexity Adaptive Full Direct-State Kalman Filter for Robust GNSS Tracking

Iñigo Cortés, Johannes Rossouw van der Merwe, Elena Simona Lohan, Jari Nurmi, Wolfgang Felber

Research output: Contribution to journalArticleScientificpeer-review

20 Downloads (Pure)

Abstract

This paper evaluates the implementation of a low-complexity adaptive full direct-stateKalman filter (DSKF) for robust tracking of global navigation satellite system (GNSS) signals. The full DSKF includes frequency locked loop (FLL), delay locked loop (DLL), and phase locked loop (PLL) tracking schemes. The DSKF implementation in real-time applications requires a high computational cost. Additionally, the DSKF performance decays in time-varying scenarios where the statistical distribution of the measurements changes due to noise, signal dynamics, multi-path, and non-line-of-sight effects. This study derives the full lookup table (LUT)-DSKF: a simplified full DSKF considering the steady-state convergence of the Kalman gain. Moreover, an extended version of the loop-bandwidth control algorithm (LBCA) is presented to adapt the response time of the full LUT-DSKF. This adaptive tracking technique aims to increase the synchronization robustness in time-varying scenarios. The proposed tracking architecture is implemented in an GNSS hardware receiver with an open software interface. Different configurations of the adaptive full LUT-DSKF are evaluated in simulated scenarios with different dynamics and noise cases for each implementation. The results confirm that the LBCA used in the FLL-assisted-PLL (FAP) is essential to maintain a position, velocity, and time (PVT) fix in high dynamics.
Original languageEnglish
Article number3658
JournalSensors
Volume23
Issue number7
DOIs
Publication statusPublished - Apr 2023
Publication typeA1 Journal article-refereed

Publication forum classification

  • Publication forum level 1

Fingerprint

Dive into the research topics of 'Evaluation of Low-Complexity Adaptive Full Direct-State Kalman Filter for Robust GNSS Tracking'. Together they form a unique fingerprint.

Cite this