融合多维姿态自适应感知的无人机目标定位

UAV target positioning integrating multi-dimensional attitude adaptive sensing

针对无人机光电侦察平台目标定位中各姿态系统误差难以辨识和消除的问题,研究了融合多维姿态自适应感知算法的目标定位系统。本文依据目标定位原理建立了全流程的目标位置解算模型,利用光电侦察平台的锁定跟踪特性对合作目标点进行多次测量,分析多维姿态的系统误差偏移对定位结果的差异表征,基于深度神经网络设计多任务学习的时序特征提取模型对各系统误差进行自适应估计和反向补偿。实验结果表明无人机在4000 m飞行高度下,该系统能有效消除77%的系统误差,目标定位的误差由103 m下降至19 m,定位精度提升81%,可实现高精度的无人机目标定位。

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 4000 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.