14 June 1996 General data fusion for estimates with unknown cross covariances
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In this paper we present a new theoretic framework for combining sensor measurements, state estimates, or any similar type of quantity given only their means and covariances. The key feature of the new framework is that it permits the optimal fusion of estimates that are correlated to an unknown degree. This framework yields a new filtering paradigm that avoids all of the restrictive independence assumptions required by the standard Kalman filter, though at the cost of reduced rates of convergence for cases in which independence can be established.
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Jeffrey K. Uhlmann, "General data fusion for estimates with unknown cross covariances", Proc. SPIE 2755, Signal Processing, Sensor Fusion, and Target Recognition V, (14 June 1996); doi: 10.1117/12.243195; https://doi.org/10.1117/12.243195

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