The Gaussian mixture cardinalized probability hypothesis density fi lter (GM-CPHD) is a recursive Bayesian filter for track-before-detect multita rge t tracking algorithm in clutter, which propagates the first moment of the multi target posterior density, incorporating track initiation and termination without consideration of measurement-to-track association. Due to the fact that GM-C PHD filer has a great computational complexity: (nm3), where n is the nu mber of targets and m is the cardinality of measurement set, an adaptive gating alg orit hm is proposed . The algorithm reduces the measurement set by using a maximum likelihood adaptive gate, and only the measurements falling into the gate are us ed to update the PHD estimation. Simulation results show that the proposed algor ithm reduces the computational complexity obviously, and obtains a similar perf ormance.