Terrain-Relative Navigation with Neuro-Inspired Elevation Encoding

Michaelson, Kristen; Wang, Felix; Zanetti, Renato

doi:10.1109/taes.2024.3362760

Terrain-Relative Navigation with Neuro-Inspired Elevation Encoding

Journal Article · Thu Feb 15 23:00:00 EST 2024 · IEEE Transactions on Aerospace and Electronics Systems

DOI:https://doi.org/10.1109/taes.2024.3362760· OSTI ID:2345288

Michaelson, Kristen ^[1]; Wang, Felix ^[2]; Zanetti, Renato ^[1]

Univ. of Texas, Austin, TX (United States)
Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)

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 terrain-relative navigation filter with a position measurement inspired by neural activity associated with positioning in nature. Here, 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.

View Accepted Manuscript (DOE)

Research Organization:: Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)

Sponsoring Organization:: USDOE Laboratory Directed Research and Development (LDRD) Program

Grant/Contract Number:: NA0003525

OSTI ID:: 2345288

Report Number(s):: SAND--2024-05437J

Journal Information:: IEEE Transactions on Aerospace and Electronics Systems, Journal Name: IEEE Transactions on Aerospace and Electronics Systems Journal Issue: 3 Vol. 60; ISSN 0018-9251

Publisher:: IEEECopyright Statement

Country of Publication:: United States

Language:: English

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