Application of Super Resolution Correlation to Multipath Mitigation in an L5 Channel
Norman Krasner, Paul McBurney, OneNav
Date/Time: Wednesday, Sep. 21, 4:00 p.m.
Whereas multipath mitigation with Machine Learning relies on off-line training with an exhaustive number of labelled observations, current super resolution correlation methods, which include MUSIC, operate on-line by testing and choosing from a high number of candidate signal hypotheses. A new method of MUSIC is presented that reduces numerical complexity and is applied to processing L5 correlation vectors (corrVecs) to reduce multipath by identifying the earliest path. The rank of this estimator is examined in static and dynamic conditions in various signal environments. Higher rank allows more signal paths to be identified. This method is also complementary with various L5 signal tracking methods such as open and closed loop tracking.
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