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Tracking speakers is an important application in smart environments. Acoustic tracking using microphone arrays is a challenging task due to two major reasons: On the one hand, multiple persons may speak simultaneously and thus the number of speakers varies over time; on the other hand, due to the nature of reverberated speech, the provided position hypotheses contain many gaps and clutter. In the proposed approach, the "glimpsing model" is realized by neurobiologically in spired calculation of robust but sparse position hypotheses in combination with a Gaussian mixture cardinalized probability hypothesis density filter. By iteratively applying the filter to the position hypotheses from multiple frequency bands, good results are achieved. Using a statistical speech model derived from recordings, a real-time capable implementation is used to track multiple speakers in a conference room with significant reverberation.