Improbability filtering for rejecting false positives

We describe an approach, called improbability filtering, to rejecting false-positive observations from degrading 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 extended 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 ball which varies from free rolling under friction, to roiling up 45° inclined walls at the boundary, to being manipulated in unpredictable ways by a mechanical apparatus on each robot. The sensing apparatus, a camera and color blob tracking algorithms, suffers from the usual noise, latency, intermittency, as well as from false-positives caused by the misidentification of an observed object with a nonnegligible likelihood