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While linear estimators are optimal when the model is linear and all random noise is Gaussian, they are very sensitive to outlying tracks. Non-linear vertex reconstruction algorithms offer a higher degree of robustness against such outliers. Two of the algorithms presented, the Adaptive filter and the Trimmed Kalman Filter are able to down-weight or discard these outlying tracks, while a third, the...
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