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The algorithm is termed Isolated Kalman Filtering (IsoKF) and exploits the sparsity in the output coupling by applying approximations that mitigate the need for non-involved estimates. We prove that the approximations made during the isolated coupling of estimates are based on an implicit maximum determinant completion of the incomplete a priori covariance matrix. The steady state behavior is studied on eleven different observation graphs and a buffering scheme to support delayed (i.e.\u00a0out-of-order) measurements is proposed. We discussed handling of delayed measurements in both, an optimal or a suboptimal way. The credibility of the isolated estimates are evaluated on a linear and nonlinear toy example in Monte Carlo simulations.  The presented paradigm is made available online to the community within a generic C++ estimation framework supporting both, modular sensor fusion and collaborative state estimation.<\/jats:p>","DOI":"10.1007\/s10514-025-10191-x","type":"journal-article","created":{"date-parts":[[2025,2,13]],"date-time":"2025-02-13T07:46:51Z","timestamp":1739432811000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Isolated Kalman filtering: theory and decoupled estimator design"],"prefix":"10.1007","volume":"49","author":[{"given":"Roland","family":"Jung","sequence":"first","affiliation":[]},{"given":"Lukas","family":"Luft","sequence":"additional","affiliation":[]},{"given":"Stephan","family":"Weiss","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,2,13]]},"reference":[{"key":"10191_CR1","doi-asserted-by":"crossref","unstructured":"Allak, E., Barrau, A., Jung, R., Steinbrener, J. and Weiss, S. 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