Abstract: ‍ ‍Airborne radars are often faced with significant intensity and wide scope of clutter. Space-time adaptive processing is one of the key technologies of the modern airborne radar system， which can effectively suppress strong clutter. However， when the radial velocity of the target is close to zero， the target and clutter have similar space-time characteristics， and it is difficult to distinguish the target from the clutter in the two-dimensional space-time domain. To improve the detection probability of the target signal in the main lobe clutter， space-time-polarization adaptive processing introduces the polarization domain information on the basis of space-time domain. By distinguishing the target and clutter signal in the polarization domain， it can realize the suppression of the main lobe clutter to a certain extent， thus enhancing the airborne radar’s detection capability of the slow-moving target. In the process of space-time-polarization adaptive processing， the selection of the polarization domain constraint vector will significantly affect the performance. The space-time-polarization adaptive processing method proposed in this paper is based on the estimation of the polarization parameter of the main lobe clutter. First， Doppler-domain space-time dimensionality reduction processing is performed on the echo signal， and the polarization covariance matrix of the main lobe clutter is estimated. Next， the constraint vectors in the polarization domain are determined with the criterion of optimizing the performance of clutter suppression. Then， the main lobe clutter is suppressed by the space-time-polarization adaptive processing. This method requires lower computational complexity and avoids the difficulty of estimating the target polarization parameters in the main lobe clutter. Simulation results show that， compared with the conventional space-time adaptive and space-time polarization adaptive processing techniques based on the minimum variance unbiased estimation （MVU） method， the method proposed in this paper outputs lower residual clutter power and higher signal-to-clutter-plus-noise ratio （SCNR）， which significantly improves the detection performance of the airborne radar for slow-moving targets.