In this study, cognitive radar (CR) applications including radar waveform parameters and track update interval selection are investigated in order to balance the time resource cost and increase the accuracy performance of multiple target tracking systems. For the target tracking part, the unscented Kalman filter (UKF) is applied together with the joint probabilistic data association (JPDA) and the interacting multiple models (IMM) algorithm, which is used to realize more than one target motion model. The waveform parameters and track update interval are adaptively updated by using the outputs of the radar data processing block including target tracking and classification algorithms. The waveform parameters to be updated, the product of the pulse width and the number of integrated pulses, and the track update interval are selected. In the optimization function, the limit values of the parameter selections are decided by using target class information which is supplied by a random forest classifier. Along with the proposed cost function, track continuity and time resource allocation are tested and system performance is demonstrated depending on the target characteristics. In the simulations part, multiple target scenarios that include targets with different maneuvers and radar cross sections (RCS) have been examined and it is shown that the proposed cost function can be applied in multiple target tracking scenarios.