Aiming to address the challenge of identifying and eliminating systematic errors in various atti-tudes during target positioning of UAV optoelectronic reconnaissance platforms,a study was conducted on a target positioning system that integrates multi-dimensional attitude adaptive sensing algorithms.This pa-per presented a comprehensive target position calculation model that was based on the principles of target positioning.It utilized the optoelectronic reconnaissance platform's locking and tracking capabilities to per-form multiple measurements of the cooperative target point.Furthermore,it analyzed the differentiated representations of the multi-dimensional attitude systematic error offset from the positioning results.Based on the principles of deep neural networks,the proposed model aimed to perform adaptive estimation and re-verse compensation of systematic errors in a multi-task temporal feature extraction framework.Results of experiments indicate that when UAV operates at an altitude of 4 000 m,the system is able to mitigate 77%of systematic errors.It successfully reduces the target positioning error from 103 m to 19 m,result-ing in an 81%increase in overall positioning accuracy.Consequently,this system enables high-precision positioning of UAV targets.
万方数据
维普
