高斯混合模型下的空间失稳目标分层运动估计方法  

Hierarchical Motion Estimation of Spatially Destabilized Targets under Gaussian Mixture Models

作　　者：周志强 孙日明[1] 郭成龙 朱宜龙 Zhou Zhiqiang;Sun Riming;Guo Chenglong;Zhu Yilong(School of Science,Dalian Jiaotong University,Dalian 116028,Liangning,China)

机构地区：[1]大连交通大学理学院,辽宁大连116028

出　　处：《光学学报》2024年第24期205-215,共11页Acta Optica Sinica

基　　金：国家自然科学基金(11801056)。

摘　　要：为提高线性测量系统下非合作空间目标在帧内运动差异和噪声干扰等复杂情况下的运动估计精度,提出一种基于最大期望高斯混合模型(EMGMM)的分层次非合作空间失稳目标运动估计方法。根据点云序列所具有的时间连续性,引入高斯混合模型,建立两个EM层次,使用按列基准映射对齐点云序列,定量迭代第一EM层次获得粗估计结果。进一步将无噪声点视为潜在变量,通过双曲正切降噪权重构造虚拟点替代原始测量点迭代第二EM层次,从而获取高精度运动参数。实验结果表明,所提方法可以有效抑制帧内运动差异和噪声干扰对运动估计的影响,较传统方法在不同噪声标准差下有着更高的精度和更强的鲁棒性。Objective Highprecision attitude measurement and motion estimation of noncooperative targets in space are critical for various onorbit service missions,including tracking,docking,rendezvous,and debris removal.Compared with other noncontact methods,linearray LiDAR offers advantages such as high imaging resolution and a large field of view,making it an ideal tool for precise space target measurement.However,due to the imaging mechanism of linearray systems,which only capture one line information per scan,the dynamic imaging of moving targets results in intraframe motion discrepancies caused by the relative motion between the target and the measurement system.Furthermore,environmental factors like lighting introduce noise,degrading the quality of point cloud data and complicating highprecision motion estimation for spatially noncooperative targets.To address these challenges,we propose a hierarchical motion estimation method for spatially destabilized targets based on the expectationmaximization Gaussian mixture model(EMGMM).This method is highprecision,stable,and robust,and it effectively overcomes the degradation of motion estimation accuracy caused by intraframe motion discrepancies and measurement noise under a linear measurement system.Methods In this paper,we apply the EMGMM framework to estimate the motion of spatially destabilized targets using point cloud data collected by a linear measurement system.A Gaussian mixture model(GMM)is introduced,establishing two layers of the expectationmaximization(EM)algorithm.In the first layer,the GMM’s center of mass is aligned to approximate the noiseless points by treating these noiseless points as hidden variables.The time continuity of the point cloud sequence is leveraged to correct the intraframe motion discrepancies using a columnwise benchmark mapping method,which aligns the point cloud data across frames.By continuously refining the motion parameters,the first EM layer provides a coarse estimation.The second EM layer refines this by constructing noise reduction

关 键 词：空间失稳目标 运动估计 高斯混合模型 线性测量 

分 类 号：TN249[电子电信—物理电子学]