In the area of ladar target recognition, orientation estimation is always needed to align the point cloud with models for recognition. The relationship between the target′s orientation and the point cloud projection distribution is analyzed, a feature called projection density entropy (PDE) is extracted to measure this distribution. A novel method for target orientation estimation is proposed according to the relationship between the target′s orientation and PDE. The point cloud is rotated around one coordinate axis, then the PDE of the rotated point cloud is calculated, and the rotation angle with the least PDE is taken as the orientation angle. Point clouds of five kinds of ground armored targets under different viewpoints are simulated, and the performances of PDE, rectangle fitting and principle component analysis (PCA) methods under the situations of self-occlusion and occlusion are analyzed. The strategy of parameter selection is discussed, and a fast realization method of PDE is proposed. The experimental results show that the estimation performance and robustness of PDE method outperform rectangle fitting and PCA methods greatly in the cases of self-occlusion and occlusion. PDE is very suitable for application under severe occlusion.