{"ID":2873264,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.06317","arxiv_id":"2509.06317","title":"$\\mathcal{H}_\\infty$ Optimal Navigation in the Cislunar Space with LFT Models","abstract":"Navigation in the cislunar domain presents significant challenges due to chaotic and unmodeled dynamics, as well as state-dependent sensor errors. This paper develops a robust estimation framework based on Linear Fractional Transformation (LFT) models, and state estimation in $\\mathcal{H}_\\infty$ and $μ$ synthesis framework to address these challenges. The cislunar dynamics are embedded into an LFT form that captures nonlinearities in the gravitational model and state-dependent sensor errors as structured uncertainty. A nonlinear estimator is then synthesized in the $\\mathcal{H}_\\infty$ sense to ensure robust performance guarantees in the presence of the stated uncertainties. Simulation results demonstrate the effectiveness of the estimator for navigation in a surveillance constellation.","short_abstract":"Navigation in the cislunar domain presents significant challenges due to chaotic and unmodeled dynamics, as well as state-dependent sensor errors. This paper develops a robust estimation framework based on Linear Fractional Transformation (LFT) models, and state estimation in $\\mathcal{H}_\\infty$ and $μ$ synthesis fram...","url_abs":"https://arxiv.org/abs/2509.06317","url_pdf":"https://arxiv.org/pdf/2509.06317v1","authors":"[\"Tanay Kumar\",\"Raktim Bhattacharya\"]","published":"2025-09-08T03:47:28Z","proceeding":"math.OC","tasks":"[\"math.OC\"]","methods":"[]","has_code":false}