Improved Moving Horizon Estimation for Ultra-Wideband Localization on Small Drones

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Abstract

Moving Horizon Estimation (MHE) offers multiple advantages over Kalman Filters when it comes to the localization of drones. However, due to the high computational cost, they can not be used on Micro Air Vehicles (MAVs) with limited computational power. We have previously shown, that with a few assumptions and simplifications, MHE can be made more efficient while retaining good localization performance. In this paper, we present two additional improvements: the introduction of dynamic step sizes to the gradient descent algorithm, which leads to a significant increase in robustness, and the use of switching variables for outlier rejection, which further reduces the computational load. Both improvements are implemented and assessed in simulation and experiments. Using dynamic step sizes makes it possible to reliably use the estimator on board of a real drone, and the use of Newton’s method specifically opens the option to add different types of measurements. The new outlier rejection method on the other hand is shown to reduce the computational load significantly while having no big impact on accuracy.
Original languageEnglish
Title of host publication14th annual international micro air vehicle conference and competition
EditorsD. Moormann
Pages86-92
Publication statusPublished - 2023
Event14th anual International Micro Air Vehicle Conference and Competition - Aachen , Germany
Duration: 11 Sept 202315 Sept 2023
Conference number: 14
https://2023.imavs.org/ (14th anual International Micro Air Vehicle Conference and Competition)

Conference

Conference14th anual International Micro Air Vehicle Conference and Competition
Abbreviated titleIMAV 2023
Country/TerritoryGermany
CityAachen
Period11/09/2315/09/23
Internet address

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