Abstract: The degrees of freedom in the motion state of aerial targets are higher, and the target motion state is more difficult to obtain. Existing methods focus on estimating the relative motion trajectory in two-dimensional space (azimuth and pitch), ignoring the interference of the reconnaissance platform's own attitude changes on the target's motion trajectory estimation, making direct application to airborne IR platform applications difficult. To address this problem, this study proposes a three-dimensional (3D) motion estimation method for airborne targets under an airborne IR platform to measure the target's motion status in all directions in the coordinate system of the northwest sky. To improve the accuracy of the target position estimation, this method introduces the target distance and the attitude of the detection platform to enhance the anti-interference performance of the IR target motion state estimation. Our method first uses a target-tracking module based on the TLD and a Kalman filter utilizing a detection-based tracking strategy. The Kalman filter is employed to alleviate the effects of target centroid jitter on the target position estimation accuracy. Second, a long-short strategic distance prediction module is proposed to supplement the target distance information not obtained by the laser rangefinder. Finally, the motion status of the target in each direction in the northwest-sky coordinate system is obtained using the aerial target motion estimation module based on prior information. Under the condition that the 3D motion information of the aerial target is known, the 2D spatial information of the target in the current reconnaissance system can be solved in reverse using this method. Experimental results show that the error in the target distance prediction result of this method is less than 50 m, and the velocity error of the northwest-sky coordinate system is less than 25 m/s. When the attitude angle of the detection system is changed, the target-tracking stability of this method is better than that of the Kalman filter.