Geometry-Aware Distributed Kalman Filtering for Affine Formation Control under Observation Losses

Conference Paper (2023)
Author(s)

Z. Li (TU Delft - Signal Processing Systems)

Raj Rajan (TU Delft - Signal Processing Systems)

Research Group
Signal Processing Systems
Copyright
© 2023 Z. Li, R.T. Rajan
DOI related publication
https://doi.org/10.23919/FUSION52260.2023.10224101
More Info
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Publication Year
2023
Language
English
Copyright
© 2023 Z. Li, R.T. Rajan
Research Group
Signal Processing Systems
ISBN (electronic)
9798890344854
Reuse Rights

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Abstract

Affine formation control of multiagent systems has recently received increasing attention in various applications. The distributed control of these agents, under single integrator dynamics, relies on the observations of relative positions of the neighboring agents, which when unavailable is detrimental to the mission. In this paper, we propose an adaptive fusion estimator of the relative positions under intermittent and consecutive observation loss settings. A relative affine localization (RAL) solution is developed by exploiting the geometry of affine formation, which is then embedded into a distributed relative Kalman filtering (RKF) framework, leading to the geometry-aware relative Kalman filter (GA-RKF). We show through simulations that the GA-RKF exhibits enhanced robustness to both intermittent and consecutive observation losses, as compared to RAL and existing state-of-art methods.

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