In order to accurately estimate the target number of Probability Hypothesis Density (PHD) filtersa cardinality compensation method based on Information-weighted Consensus Filter (ICF) and data clustering is proposedand ICF is used for information fusion.In a dense clutter environmentwhen the noise and clutter in the measurement are hightracking loss will occurand the performance of target number estimation will be degraded.For this reasona cardinality compensation process is added to the PHD filterbased on an information fusion stepthe estimated cardinality obtained from the PHD filter and the measured cardinality obtained through data clustering are used to obtain the final target number estimation.In order to verify the performance of the proposed methodthe simulation is carried out and it is demonstrated that the tracking performance of multiple targets is improved.