Aerial-Ground Person Re-identification (AGPReID) aims to retrieve target individuals across UAV and ground cameras, focusing on the perspective variations due to altitude changes. The unique overhead perspective of UAVs presents challenges in achieving accurate semantic alignment for person re-identification (ReID). We propose a novel method named Perspective-Driven Prototype Alignment (PDPA) to address this issue. First, we design two learnable prompts for each identity to obtain view representations from different perspectives. Second, we propose a View-Guided Progressive Alignment (VGPA) module for cross-perspective refinement of text descriptions, exploring the intermediate feature space between different perspectives by combining the image and text features using a memory bank. To reduce the gap between image feature space and intermediate feature space, we propose an image-to-prototype cross-entropy loss to train the image encoder. Extensive experiments show that our method achieves SOTA performance on the CARGO and AG-ReID_v1 datasets.