A method for tracking multiple objects with a probabilistic hypothesis density filter, the method comprising: generating a T k track intensity for an object, the T k track intensity including a weight, a state mean vector, and a state covariance matrix of statistics of a track of the object at time T k, wherein the T k track intensity has one or more first track identifiers, IDs, associated therewith, each track identifier, ID, associated with the T k track intensity corresponding to one or more measurements that contributed to the T k track intensity; generating a T k+1 predicted intensity for the object based on the T k track intensity, the T k+1 predicted intensity corresponding to time T k+1, wherein the T k+1 predicted intensity includes a predicted weight based on the weight of the T k track intensity; obtaining a plurality of measurements from a plurality of sensors, wherein the plurality of sensors provide a-plurality of second track IDs each respectively associated with the plurality of measurements, wherein the plurality of measurements correspond to time T k+1; comparing the plurality of second track IDs to each first track ID associated with each T k track intensity; if all of the one or more first track IDs match any of the plurality of second track IDs, multiplying the predicted weight by a first value; if less than all of the one or more first track IDs match any of the plurality of second track IDs, multiplying the predicted weight by a second value, wherein the second value is greater than the first value; determining 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; and if the T k+1 predicted intensity is not pruned, generating a T k+1 track intensity for the object at time T k+1 based on the T k+1 predicted intensity, the T k+1 track intensity having an updated weight based on the multiplying of the predicted weight. generating a T k track intensity for an object, the T k track intensity including a weight, a state mean vector, and a state covariance matrix of statistics of a track of the object at time T k, wherein the T k track intensity has one or more first track identifiers, IDs, associated therewith, each track identifier, ID, associated with the T k track intensity corresponding to one or more measurements that contributed to the T k track intensity; generating a T k+1 predicted intensity for the object based on the T k track intensity, the T k+1 predicted intensity corresponding to time T k+1, wherein the T k+1 predicted intensity includes a predicted weight based on the weight of the T k track intensity; obtaining a plurality of measurements from a plurality of sensors, wherein the plurality of sensors provide a-plurality of second track IDs each respectively associated with the plurality of measurements, wherein the plurality of measurements correspond to time T k+1; comparing the plurality of second track IDs to each first track ID associated with each T k track intensity; if all of the one or more first track IDs match any of the plurality of second track IDs, multiplying the predicted weight by a first value; if less than all of the one or more first track IDs match any of the plurality of second track IDs, multiplying the predicted weight by a second value, wherein the second value is greater than the first value; determining 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; and if the T k+1 predicted intensity is not pruned, generating a T k+1 track intensity for the object at time T k+1 based on the T k+1 predicted intensity, the T k+1 track intensity having an updated weight based on the multiplying of the predicted weight.