Most conventional tracking gate techniques only use the targets’ kinematic measurement information, which typically results in great uncertainties of measurement-to-track association for multi-target tracking in clutter. Considering that the target class information can be derived from attribute sensors, the tracking gate technique for joint target state-class probability density is proposed. Firstly, a joint probability density description of the target state and target class is given, by which the method for constructing the class-conditioned gates is developed. In order to comply with nonlinearity in practical application, evaluating of the gate threshold adopted an algorithm based on simulation. Scenario 1 shows that if the target predictive measurement density is skewed distribution, the simulation-based threshold-evaluating algorithm can achieve optimal gate volume; and scenario 2 presents a target tracking process for ground formation. Compared with the data association methods using traditional tracking gates, the class-conditioned gate technique significantly improves the probabilities of the measurement-to-track association.