基于多视观测优化的无人机运动目标测速方法研究  

Moving Target Velocity Measurement Method Based on Multi-view Observation Optimization of UAV Image

作　　者：吴雨欣 张志龙[1,2] 刘奥旭 邹江威[1] 李楚为 Wu Yuxin;Zhang Zhilong;Liu Aoxu;Zou Jiangwei;LI Chuwei(College of Electronic Science and Technology,National University of Defense Technology,Changsha 410003,China;National Laboratory on Automatic Target Recognition,National University of Defense Technology,Changsha 410003,China)

机构地区：[1]国防科技大学电子科学学院,湖南长沙410003 [2]国防科技大学自动目标识别全国实验室,湖南长沙410003

出　　处：《系统仿真学报》2025年第1期40-53,共14页Journal of System Simulation

基　　金：国家重点实验室基金(2035250202)。

摘　　要：测量运动目标的位置和速度是无人机视频分析的重要需求。提出一种基于图像多视观测最小二乘优化的运动目标定位测速算法:利用机载光电系统获取的视频及相应的位姿参数建立多个观测时刻的视线模型,通过坐标变换统一到WGS-84坐标系,基于最小二乘算法估计运动目标的位置和速度。该算法无需无人机与目标之间的激光测距信息,也不需要地形高程信息,是一种隐蔽性强的无源定位和测速算法。为了考察该算法的精度和应用条件,在实验部分模拟了3种无人机测速场景,考虑了实际测量过程中的多种误差源,利用蒙特卡罗模拟法进行了仿真实验。结果表明:该算法能够快速准确地估计目标的位置和速度,在典型应用场景中定位精度达到1.5 m、测速精度达到0.2 m/s,可以满足情报分析的准确性和可靠性要求。Measuring the position and velocity of moving targets is an important requirement for drone video analysis.In this paper,a moving target localization and velocity estimation algorithm based on least square optimization of UAV multi-view observation images is proposed:the video and corresponding pose parameters obtained by the airborne optoelectronic system are used to establish a line-of-sight model at multiple observation times,it is unified to the WGS-84 coordinate system by coordinate transformation,the position and velocity of the moving target are estimated based on the least squares algorithm.This algorithm does not require laser ranging information between the UAV and the target,nor does it require terrain elevation information,making it a highly covert passive positioning and velocity measurement algorithm.In order to investigate the accuracy and application conditions of this algorithm,this article simulates three scenarios of UAV speed measurement in the experimental part,considers various error sources in the actual measurement process,and conducts simulation experiments using Monte Carlo simulation method.The results sh ow that the algorithm can quickly and accurately estimate the position and velocity of the target,with a positioning accuracy of 1.5 m and a velocity measurement accuracy of 0.2 m/s in typical application scenarios,which meets the accuracy and reliability requirements of intelligence analysis.

关 键 词：侦察无人机 无源定位 测速 多视观测 蒙特卡罗法 

分 类 号：TP391.9[自动化与计算机技术—计算机应用技术]