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22 October 1993 Systematic error estimation in multisensor fusion systems
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For multisensor fusion systems it is a prerequisite to accurately estimate and correct all systematic errors. Adequate estimation methods only exist if all systematic errors are constant random variables, while in practice they may change with time. When the object states, the systematic errors and the observations vary according to a linear Gaussian system, then one large Kalman filter forms the optimal estimator for the combined state of all object states and all systematic errors. In general the numerical complexity of this Kalman filter prohibits practical application. In order to improve this situation we decouple the large Kalman filter into a number of separate filters: for each object one track maintenance Kalman filter, and for the estimation of all sensor related systematic errors one Kalman-like filter, which we call the Macro filter. The effectiveness of this approach is illustrated through simulations for a simple example.
© (1993) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Bas A. van Doorn and Henk A. P. Blom "Systematic error estimation in multisensor fusion systems", Proc. SPIE 1954, Signal and Data Processing of Small Targets 1993, (22 October 1993);

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