Adjusting weight of intensity in a PHD filter based on sensor track ID

摘要

In one embodiment, a method for tracking multiple objects with a probabilistic hypothesis density filter is provided. The method includes comparing second track IDs corresponding to newly obtained measurements to one or more first track IDs corresponding to a T_k+1 predicted intensity having a predicted weight. If all of the one or more first track IDs match any of the second track IDs, the predicted weight is multiplied by a first value. If less than all of the one or more first track IDs match any of the second track IDs, the predicted weight is multiplied by a second value, wherein the second value is greater than the first value. The method then determines whether to prune the T_k+1 predicted intensity based on the predicted weight after multiplying with either the first value or the second value.