To estimate quickly the relative pose of a space target based on point cloud， a Binary Rotational Projection Histogram (BRoPH) feature descriptor was proposed. Firstly， a Local Reference Frame (LRF) for the feature point was established; Then， the density and depth images were generated under different views by rotationally projecting the local surface of feature point， Finally， the multi-scale binary string of the feature point was produced based on the images. For implementing the pose estimation of space target in real time， a Hamming distance threshold based feature matching strategy was proposed further to exclude false matching pairs to accelerate coarse pose estimation procedure. The comparison experiments were performed with SHOT descriptor and FPFH descriptor. The results demonstrate that BRoPH achieves an accurate pose estimation with only about 1/80 average memory cost of SHOT and FPFH descriptors. The average attitude error of BRoPH is under 0.1 °， and its average translation error is less than 1/180R. Besides， the Hamming distance threshold based feature matching strategy speeds up the subsequent RANSAC by 7 times， and the overall pose estimation frequency exceeds 7 Hz， which is 3 to 6.8 times faster than those of SHOT and FPFH descriptors respectively. The proposed feature descriptor is compact and efficient， and the pose estimation method is accurate and robust for the requirements of space target pose estimation.