Terrain-relative navigation (TRN) encompasses a wide variety of algorithms that perform localization with respect to the terrain below a flying vehicle. In traditional approaches, measurements of the terrain are matched to a map carried onboard. This work presents a TRN filter with a position measurement inspired by neural activity associated with positioning in nature. The filter is shown to produce accurate position measurements that outperform popular optimization and template matching methods given poor prior knowledge of the position. The proposed method is also better-suited to distributed implementation than optimization-based methods.


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    Title :

    Terrain-Relative Navigation With Neuro-Inspired Elevation Encoding


    Contributors:


    Publication date :

    2024-06-01


    Size :

    3349050 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



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