This paper proposes a novel convex optimization framework for designing robust Kalman filters. It addresses the importance of robustness margins in quantifying filter performance under uncertainties. The methodology is validated through examples from aerospace engineering, showcasing the significance of process and sensor noise in filter design. The research contributes to optimizing sensor selection and placement for increased filter robustness, offering an efficient approach to error budgeting. By considering uncertainties in both process and sensor noise, this work advances the field of Kalman filtering by providing a joint formulation that enhances reliability and adaptability in dealing with system variations.
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by Himanshu Pra... at arxiv.org 03-06-2024
https://arxiv.org/pdf/2403.02996.pdfDeeper Inquiries