In wireless communication environment, the time-varying channel and angular spreads caused by multipath fading and the mobility of Mobile Stations (MS) degrade the performance of the conventional Direction-Of-Arrival (DOA) tracking algorithms. On the other hand, although the DOA estimation methods based on the Maximum Likelihood (ML) principle have higher resolution than the beamforming and the subspace based methods, prohibitively heavy computation limits their practical applications. This letter first proposes a new suboptimal DOA estimation algorithm that combines the advantages of the lower complexity of subspace algorithm and the high accuracy of ML based algo- rithms, and then proposes a Kalman filtering based tracking algorithm to model the dynamic property of directional changes for mobile terminals in such a way that the association between the estimates made at different time points is maintained. At each stage during tracking process, the current suboptimal estimates of DOA are treated as measurements, predicted and updated via a Kalman state equation, hence adaptive tracking of moving MS can be carried out without the need to perform unduly heavy computations. Computer simulation results show that this proposed algorithm has better per- formance of DOA estimation and tracking of MS than the conventional ML or subspace based algo- rithms in terms of accuracy and robustness. In wireless communication environment, the time-varying channel and angular spreads caused by multipath fading and the mobility of Mobile Stations (MS) degrade the performance of the conventional Direction-Of-Arrival (DOA) tracking algorithms. On the other hand, although the DOA estimation methods based on the Maximum Likelihood (ML) principle have higher resolution than the beamforming and subspace based methods, prohibitively heavy computation limits their practical applications. This letter first proposes a new suboptimal DOA estimation algorithm that combines the advantages of the lower complexity of subspace algorithm and the high accuracy of ML based algo- rithms, and then viiao a Kalman filtering based tracking algorithm to model the dynamic property of directional changes for mobile terminals in such a way that the association between the estimates made at different time points is At each stage during tracking process, the current suboptimal estimates of DOA are treated as measurements, predicted and updated via a Kalman state equation, hence adaptive tracking of moving MS can be carried out without the need to perform unduly heavy computations. Computer simulation results show that this proposed algorithm has better per-formance of DOA estimation and tracking of MS than the conventional ML or subspace based algo- rithms in terms of accuracy and robustness.