For camera calibration based on direct linear transformation (DLT), the camera’s intrinsic and extrinsic parameters are simultaneously calibrated, which may cause coupling errors in the parameters and affect the calibration parameter accuracy. In this paper, we propose an improved direct linear transformation (IDLT) algorithm for calibration parameter decoupling. This algorithm uses a linear relationship of calibration parameter errors and obtains calibration parameters by moving a three-dimensional template. Simulation experiments were conducted to compare the calibration accuracy of DLT and IDLT algorithms with image noise and distortion. The results show that the IDLT algorithm calibration parameters achieve higher accuracy because the algorithm removes the coupling errors.

Cooperative UAV missions often require multiple vehicles to reach a target area at nearly the same time. For small-turbofan UAVs flying along recorded routes, this timing task is affected by path-length differences, spatially varying wind, and phase-circle segments kept for waiting or coordination. Under these conditions, using only a fixed cruise setting often leaves limited room for arrival-time adjustment. This paper develops a wind-aware synchronized-arrival optimization method for small-turbofan UAVs based on segmented RPM scheduling and phase-circle retention. The method combines local wind projection along the path, an empirical RPM-to-airspeed model, and discrete flight-time integration, and it evaluates three timing-adjustment modes: fixed-alpha segmented RPM optimization, fixed-RPM phase-circle retention optimization, and their joint optimization. The method is tested on the recorded trajectories of UAV 13 and UAV 14, using a reconstructed wind field obtained from multi-UAV flight data. In the wind-aware constant-RPM baseline, both UAVs keep the full phase-circle segments and fly at 6500 rpm, which gives a predicted synchronization error of $1.192$ s. Segmented RPM optimization reduces the error to $0.013$ s. Phase-circle retention optimization reduces it to $0.007$ s and shortens the total path length from 37,752.3 m to 26,103.4 m. The joint optimization gives the best result, with a synchronization error of $6.58\times {10}^{-4}$ s and a total path length of 26,094.4 m. A no-wind ablation study also shows that ignoring wind leads to a total flight-time bias of about 29–31 s. These results indicate that phase-circle retention is effective for coarse flight-time adjustment through path shortening, whereas segmented RPM scheduling is better suited to fine timing correction. Using the two together improves synchronized-arrival prediction and adjustment for recorded UAV paths in wind.