Global track extraction for probability hypothesis density filter

The probability hypothesis density (PHD), a well-known scheme for multi-target tracking in clutters, can obtain peaks of possible tracks, and its cluster-indexed method is widely accepted in further track extraction. However, the track extraction may face high risk in the case that the targets are so approached that it is hardly to discern their measurements. The concept of the distance between track sets in two adjacent times is defined and a consistency measure metric between any two peaks in two adjacent times is further proposed based on “global information”, containing spatial information (topology feature) among tracks, along with the temporal information of each track. Then, a global track extraction method is proposed based on the consistency belief and four decision rules. Via the simulation comparison with the cluster-indexed method, the proposed method can avoid the track break and mistake association.