Autonomous landing has become a core technology of unmanned aerial vehicle ( UAV) guidance, navigation, and control system in recent years. This paper discusses the vision-based relative position and attitude estimation between fixed-wing UAV and runway, which is a key issue in autonomous landing. Images taken by a airborne camera was used and a runway detection method based on long-line feature and gradient projection is proposed, which solved the problem that the traditional Hough transform requires much calculation time and easily detects end points by mistake. Under the premise of the known width and length of the runway, position and attitude estimation algorithm used the image processing results and adopted an estimation algorithm based on orthogonal iteration. The method took the objective space error as error function and effectively improved the accuracy of linear algorithm through iteration. The experimental results verified the effectiveness of the proposed algorithms.