A Distributed Kalman-like Observer with Dynamic Inversion-Based Correction for Multi-Agent Estimation
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| Title: | A Distributed Kalman-like Observer with Dynamic Inversion-Based Correction for Multi-Agent Estimation |
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| Authors: | De Carli, Nicola, Dimarogonas, Dimos V. |
| Source: | IEEE Control Systems Letters. 9:523-528 |
| Subject Terms: | cooperative localization, Multi-agent systems, observer design |
| Description: | We present a novel distributed Kalman-like observer for cooperative state estimation in multi-agent systems. Our approach builds on a class of existing Kalmanlike observers that replace the process covariance matrix with a forgetting factor. We show that this replacement enables the propagation of the information matrix dynamics in a fully distributed manner, while preserving key stability properties. We compute the observers correction term by solving a linear equation dynamically in a distributed manner, circumventing the need for direct centralized matrix inversion. Unlike existing methods that partially discard cross-information to allow distributed computations, our approach preserves inter-agent coupling. Rigorous stability guarantees are provided, and numerical simulations in a cooperative localization scenario demonstrate the effectiveness of the approach in estimating agent states. |
| File Description: | electronic |
| Access URL: | https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-364455 https://doi.org/10.1109/LCSYS.2025.3576671 |
| Database: | SwePub |
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Nájsť tento článok vo Web of Science | Abstract: | We present a novel distributed Kalman-like observer for cooperative state estimation in multi-agent systems. Our approach builds on a class of existing Kalmanlike observers that replace the process covariance matrix with a forgetting factor. We show that this replacement enables the propagation of the information matrix dynamics in a fully distributed manner, while preserving key stability properties. We compute the observers correction term by solving a linear equation dynamically in a distributed manner, circumventing the need for direct centralized matrix inversion. Unlike existing methods that partially discard cross-information to allow distributed computations, our approach preserves inter-agent coupling. Rigorous stability guarantees are provided, and numerical simulations in a cooperative localization scenario demonstrate the effectiveness of the approach in estimating agent states. |
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| ISSN: | 24751456 |
| DOI: | 10.1109/LCSYS.2025.3576671 |