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Pré-Publication, Document De Travail Année : 2023

Integration of Distance Measurements between Agents with Unknown Correlation

Résumé

Cooperative localization is a promising solution to improve the accuracy and overcome the shortcomings of GNSS. Cooperation is often achieved by measuring the distance between users. To optimally integrate a distance measurement between two users into a navigation filter, the correlation between their position estimates must be known. Unfortunately, in large scale networks the agents cannot compute their correlations with other agents and must use consistent filters. A consistent filter provides an upper bound on the covariance of the estimator taking into account all the possible correlations. In this paper, a consistent linear filter for integrating a distance measurement is derived using Split Covariance Intersection. Its analysis shows that a distance measurement between two agents can only benefit one of them, i.e., only one of the two can use it to improve its estimator. Furthermore, in some cases, none can. A necessary condition for an agent to benefit from the measurement is given for a general class of objective functions. When the objective function is the trace or the determinant, necessary and sufficient conditions are given.
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Dates et versions

hal-04039619 , version 1 (21-03-2023)
hal-04039619 , version 2 (09-06-2023)

Identifiants

  • HAL Id : hal-04039619 , version 1

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Colin Cros, Pierre-Olivier Amblard, Christophe Prieur, Jean-François da Rocha. Integration of Distance Measurements between Agents with Unknown Correlation. 2023. ⟨hal-04039619v1⟩
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