Dear editor, The unmanned aerial vehicle (UAV) is a popular aerial instrument in both military and civil fields [1]. Radar is an efficient tool for aerial target surveillance and target parameter estimation [2] and has been widely used for air control. However, in recent years, it has become a challenge for radar to monitor this kind of small and lowaltitude flying target [3]. Therefore, it is necessary to investigate the detection and tracking techniques of UAVs.

For UAV detection and tracking, the key problem is clutter suppression. Because a UAV is easily influenced by ground clutter, false alarms and missed detections often occur. Moreover, the trajectory of a UAV is easily interrupted. The conventional clutter suppression methods include moving target indication (MTI) and moving target detection (MTD) which can suppress only the mainlobe of the clutter and do not offer good performance for small targets. In contrast, the CLEAN algorithm can suppress the sidelobe of clutter but suffers from a large computation burden [4]. The time-frequency analysis methods, such as wavelet transform, short-time Fourier transform (STFT)[5], and empirical mode decomposition (EMD)[6], have also been introduced to separate the effective target from the clutter. However, detection and tracking of small UAVs under cluttered environments remain as challenges.