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Consensus-based robust least-squares filter for multi-sensor systems
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DOI：10.1002/acs.3385.png)
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Abstract
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In this article, a consensus-based robust regularized least-squares filter is designed for multi-sensor systems with norm-bounded uncertainties. In this approach, a min-max optimization problem is presented based on a consensus protocol on estimates. The advantage of a consensus filter is that each node estimates its local states, in addition to reaching an agreement on estimates made by all sensors on the network. By introducing appropriate conversions the proposed optimization problem is converted to a robust regularized least-squares problem. Solving this problem results in the structure of the proposed filter. Then, the recursive formulation of the filter is obtained in measurement form and information form. Finally, in order to investigate the efficacy, good proficiency, and robustness of the proposed consensus-based robust least-squares filter, it has been applied to an uncertain multi-sensor system with 100 nodes and its results have been compared with existing consensus filters.
Keywords:
consensus on estimates
distributed estimation
multi-sensor systems
regularized least-squares problem
robust filtering
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