A Carrier Estimation Method Based on MLE and KF for Weak GNSS Signals.

Area of Science:

• Signal Processing
• Satellite Navigation Systems
• Estimation Theory

Background:

• Maximum Likelihood Estimation (MLE) offers high performance in Global Navigation Satellite System (GNSS) receiver applications.
• Existing MLE methods for GNSS receivers rely on sampling data, leading to substantial computational demands.
• Weak GNSS signals present a challenge for conventional tracking loops due to noise and interference.

Purpose of the Study:

• To propose a novel, low-complexity MLE carrier tracking loop for GNSS receivers operating under weak signal conditions.
• To reduce the computational burden associated with traditional MLE-based GNSS tracking methods.
• To enhance the sensitivity, accuracy, and bit error rate performance of GNSS receivers in challenging environments.

Main Methods:

• Derivation of an MLE discriminator function from the cost function of signal parameters (amplitude, carrier phase, Doppler frequency).
• Iterative optimization of the cost function using the Levenberg-Marquardt (LM) method.
• Integration of an adaptive Kalman filter with the MLE discriminator for smoothed carrier phase and frequency estimation.
• Performance analysis using Cramér-Rao bound (CRB), dynamic characteristics, and Monte Carlo (MC) simulations.

Main Results:

• The proposed low-complexity MLE loop processes coherent integration results, avoiding the high computation burden of sampling data.
• Numerical analysis demonstrates favorable performance regarding Cramér-Rao bound (CRB) and dynamic characteristics.
• Simulations show improved sensitivity, accuracy, and bit error rate (BER) compared to conventional methods in both pedestrian and vehicle dynamics.
• An optimal combined loop architecture is proposed for robust performance across weak and strong signal conditions.

Conclusions:

• The developed low-complexity MLE carrier tracking loop effectively addresses the computational challenges of traditional methods for weak GNSS signals.
• The proposed approach offers significant improvements in tracking performance, particularly in dynamic scenarios.
• The integration of MLE with adaptive filtering provides a robust solution for enhanced GNSS receiver capabilities.