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Convexity Analysis of Optimization Framework of Attitude Determination from Vector Observations

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Abstract

In the past several years, there have been several representative attitude determination methods developed using derivative-based optimization algorithms. Optimization techniques e.g. gradient-descent algorithm (GDA), Gauss-Newton algorithm (GNA), Levenberg-Marquadt algorithm (LMA) suffer from local optimum in real engineering practices. A brief discussion on the convexity of this problem is presented recently [1] stating that the problem is neither convex nor concave. In this paper, we give analytic proofs on this problem. The results reveal that the target loss function is convex in the common practice of quaternion normalization, which leads to non-existence of local optimum.
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Dates and versions

hal-02432733 , version 1 (08-01-2020)

Identifiers

  • HAL Id : hal-02432733 , version 1

Cite

Jin Wu, Zebo Zhou, Min Song, Hassen Fourati, Ming Liu. Convexity Analysis of Optimization Framework of Attitude Determination from Vector Observations. CoDIT 2019 - 6th International Conference on Control, Decision and Information Technologies, Apr 2019, Paris, France. ⟨hal-02432733⟩
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