In order to overcome the uncertainty of target motion in non-orbit target tracking problems, a practical filtering algorithm, which compensated the variance of process noises, was proposed in this work. Based on the principle of strong tracking filter, the prediction covariance in Kalman filtering equations was modified real-time by monitoring innovation sequence in the proposed algorithm. In this way, the unknown variance of process noises was compensated effectively. Due to innovation-monitoring procedure, the proposed algorithm was adaptive to the uncertainty of target motion model. Simulation results of a rotary target tracking problem show the validity of the proposed algorithm.