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ICRA
2002
IEEE

Improbability Filtering for Rejecting False Positives

9 years 4 months ago
Improbability Filtering for Rejecting False Positives
—In this paper we describe a novel approach, called improbability filtering, to rejecting false-positive observations from degrading the tracking performance of an Extended Kalman-Bucy filter. False-positives, incorrect observations reported with a high confidence, are a form of non-Gaussian white noise and therefore degrade the tracking performance of an Extended Kalman-Bucy Filter. Improbability filtering removes false-positives by rejecting low likelihood observations as determined by the model estimates. It offers a computationally fast and robust method for removing this form of white noise without the need for a more advanced filter. We describe an application of the improbability filter approach to Extendend Kalman-Bucy filters for tracking ten robots and a ball moving at speeds approaching 5 m.s-1 both accurately and reliably in real-time based on the observations of a single color camera. The environment is highly dynamic and non-linear, as exemplified by the motion of the b...
Brett Browning, Michael H. Bowling, Manuela M. Vel
Added 15 Jul 2010
Updated 15 Jul 2010
Type Conference
Year 2002
Where ICRA
Authors Brett Browning, Michael H. Bowling, Manuela M. Veloso
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