Context-Aware Adaptive Multipath Compensation Based on Channel Pattern Recognition

What is it about?

The possibility of identifying the type of multipath environment and receiver motion (e.g. pedestrian, vehicular) through the use of pattern recognition approaches based on multipath parameters is investigated. This allows the receiver to adjust its tracking strategy and optimally tune its tracking parameters to mitigate code multipath effects. A Support Vector Machine (SVM) classification method with a modified Gaussian kernel is applied in this method. A set of temporal and spectral features is extracted from the correlation samples of the received signals in different environments to train the classifier. The latter is then used in the structure of stochastic gradient-based adaptive multipath compensation and tracking techniques to tune the signal tracking parameters based on the environment and receiver motion. Simulation tests using Galileo E1B/C signals are performed to assess the validity of the proposed environment identification approaches and to evaluate the impact of the proposed context-based receiver parameter tuning techniques on tracking performance in multipath environments.

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Why is it important?

One important issue in mitigating the effect of multipath signals based on advanced methods is that different types of multipath environments require different mitigation and tracking strategies, or at least different tuning parameters. In this paper, a pattern recognition algorithm is used to detect the type of multipath environment and the type of receiver motion. This is to provide additional capability for a GNSS receiver to adjust its tracking strategy and tune its parameters accordingly.