Research on Optimization of Target Positioning Error Based on Unmanned Aerial Vehicle Platform

Achieving precise target localization for UAVs is a complex problem that is often discussed. In order to achieve precise spatial localization of targets by UAVs and to solve the problems of premature convergence and easy to fall into local optimum in the original dung beetle algorithm, an error handling method based on the coordinate transformation of an airborne measurement system and the dung beetle optimization with crisscross and 3 Sigma Rule optimization (CCDBO) is proposed. Firstly, the total standard deviation is calculated by integrating the carrier position, the attitude angle, the pod azimuth, the pitch angle, and the given alignment error of the pod’s orientation. Subsequently, the Taylor series expansion method is adopted to linearize the approximated coordinate transformation process and simplify the error propagation model. Finally, in order to further improve the positioning accuracy, a target position correction strategy with the improved dung beetle optimization algorithm is introduced. The simulation and flight experiment results show that this method can significantly reduce the target positioning error of UAVs and improve the positioning accuracy by 20.42% on average compared with that of the original dung beetle algorithm, which provides strong support for the high-precision target observation and identification of UAVs in complex environments.