多传感器空间配准的病态性及其稳健估计

doi:10.3969/j.issn.1000-1093.2017.10.012

摘要/Abstract

摘要： 为提高多传感器空间配准的精度和稳定性，以最小二乘空间配准算法为基础，分析多传感器空间配准病态性产生的原因，指出系统误差估计Fisher信息矩阵的病态性是影响空间配准质量的主要因素。运用条件指标对病态性程度进行评价，并根据信息矩阵的奇异值呈阶梯型分布特点，提出一种混合的奇异值修正稳健估计算法。该算法通过只对较小的条件指标进行修正，实现对系统误差观测方程中不确定成分的有效抑制和确定成分的保留，能较好解决多传感器空间配准系统误差实时估计问题；通过两种不同场景下的系统误差估计结果证明，该算法的使用可显著地提高系统误差参数估计的精度和稳定性。

关键词: 信息处理技术, 空间配准, 病态性, 稳健估计, 奇异值分解, 多传感器

Abstract: In order to improve the accuracy and stability of multi-sensor spatial registration, the causes of the ill condition of the multi sensor spatial registration are analyzed based on LS estimation for multi-sensor spatial registration. The analysis result shows that the ill-condition of the Fisher information matrix is the key factor which influences the quality of system error parameter estimation. The conditional index of information matrix is used to measure the ill-condition of spatial registration. A new algorithm of compound modified singular value decomposition (CSVD) for robust estimation is proposed according to the characteristics of trapezia distribution of the information matrix singular values. The algorithm can effectively suppress uncertainty components and retain deterministic components in the observation equation by modifing only minor conditional index, and can effectively solve the multi sensor real time estimation of spatial registration. Two different scenarios of the estimatied results show that CSVD method can be used to significantly improve the estimation accuracy and stability of system error parameters. Key

Key words: informationprocessingtechnology, spatialregistration, ill-condition, robustestimation, singularvaluedecomposition, multi-sensor

中图分类号:

TN953⁺.6

潘江怀，乔慧. 多传感器空间配准的病态性及其稳健估计[J]. 兵工学报, 2017, 38(10): 1965-1973.

PAN Jiang-huai, QIAO Hui. Ill-condition Analysis and Robust Estimation for Multi-sensor Spatial Registration[J]. Acta Armamentarii, 2017, 38(10): 1965-1973.

参考文献

［1］ Bo Y, Chen Z, Yin M, et al. Improved different dimen-sional sensors combined space registration algorithm［J］. Mathematical Problems in Engineering, 2015(1):1-9.
［2］程志刚. 多雷达组网系统空间配准方法研究［J］. 舰船科学技术, 2015, 37(12):131-134.
CHENG Zhi-gang. Research on radar space registration method for multi-radar network system［J］. Ship Science and Technology, 2015, 37(12):131-134.(in Chinese)
［3］胡勤振, 苏洪涛, 刘子威. 配准误差下的多基地雷达目标检测算法［J］. 电子与信息学报, 2017,39(1):87-94.
HU Qin-zhen, SU Hong-tao, LIU Zi-wei. Target detection algorithm for multistatic radar with registration errors［J］. Journal of Electronics & Information Technology, 2017,39(1):87-94.(in Chinese)
［4］史江林, 周学平, 刘长海,等. 基于EM算法的联合数据关联与空间配准［J］. 现代雷达, 2016, 38(1):51-55.
SHI Jiang-lin, ZHOU Xue-ping, LIU Chang-hai, et al. Joint data association and spatial registration based on EM［J］. Modern Radar, 2016, 38(1):51-55.(in Chinese)
［5］方峰, 蔡远利. 机动目标多传感器组网空间配准方法［J］. 固体火箭技术, 2016, 39(4):574-579.
FANG Feng, CAI Yuan-li. Multi-sensor space registration for maneuvering target tracking ［J］. Journal of Solid Rocket Technology, 2016, 39(4):574-579. (in Chinese)
［6］ Okello N N, Chall S L.A joint radar registration and track-to-track fusion for distributed trackers［J］. IEEE Transactions on Aerospace and Electronic Systems, 2004, 40(3):808-823.
［7］ Herman S M, Poore A B.Nonlinear least-squares estimation for radar and navigation biases［J］. Proceedings of SPIE, Signal and Data Processing of Small Targets, 2006, 6236:1701-1717.
［8］ Okello N N, Ristic B. Maximum likelihood registration for multiple dissimilar radars［J］. IEEE Transactions on Aerospace and Electronic Systems, 2003, 39(3):1235-1240.
［9］潘江怀, 李洪梅, 何佳洲.三维传感器组网偏差估计方法［J］.火力与指挥控制, 2008, 4(4):104-106.

PAN Jiang-huai, LI Hong-mei, HE Jia-zhou.Bias estimation in 3D sensor networks［J］. Fire Control and Command Control, 2008, 4(4): 104-106.(in Chinese)

［10］宋强, 崔亚奇, 何友.反馈式多目标多传感器系统误差融合估计技术［J］. 宇航学报, 2011, 32(1):115-122.
SONG Qiang, CUI Ya-qi, HE You.Multi-target multi-sensor systematic error fusion estimation technique with feedback［J］. Journal of Astronautics, 2011, 32(1):115-122 .(in Chinese)
［11］王成飞, 王航宇, 石章松, 等. 基于高精度导航设备的海上多传感器配准算法［J］.控制理论与应用, 2011, 28(4):497-503.
WANC Cheng-fei, WANG Hang-yu, SHI Zhang-song, et al. Maritime sensor registration algorithm based on high-precision navigation equipment［J］. Control Theory and Applications, 2011, 28(4):497-503.(in Chinese)
［12］崔亚奇, 熊伟, 何友.基于ＭＬＲ的机动平台传感器误差配准算法［J］.航空学报, 2012, 33(1):118-128.
CUI Ya-qi, XIONG Wei, HE You.Mobile platform sensor registration algorithm based on MLR［J］. Acta Aeronautica et Astronautica Sinica, 2012, 33(1):118-128.(in Chinese)
［13］连峰, 韩崇昭, 彭一峰, 等.基于广义似然比的自适应在线配准算法［J］. 控制与决策, 2009, 24(1):23-28.
LIAN Feng, HAN Chong-zhao, PENG Yi-feng, et al.Adaptive on-line registration algorithm based on GLR［J］. Control and Decision, 2009, 24(1):23-28.(in Chinese)
［14］吴卫华, 江晶. WGS-84坐标系下多空基无源传感器最大似然配准［J］. 系统工程与电子技术, 2015, 37(2): 304-309.
WU Wei-hua, JIANG Jing. Maximun likelihood registration for passive sensors of multiple airborne platforms in WGS-84［J］. Systems Engineering and Electronics, 2015, 37(2):304-309.(in Chinese)
［15］ Huang D, Leung H.A pseudo-measurement approach to simultaneous registration and track fusion［J］. IEEE Transactions on Aerospace and Electronic System, 2012, 48(3):2315-2331.
［16］ Steafano F, Alfonso F, Fulvio G, et al.Least squares estimation and Cramer-Rao type lower bounds for relative sensor registration process［J］. IEEE Transactions on Signal Processing, 2011, 59(3): 1075-1087.
［17］ Boer Y，Driessen J N. Multi-target particle filter track before detect application［J］. IEEE Proceedings Radar, Sonar and Navigation, 2004, 151(6):351-357.
［18］ Ong H T.Sensor registration using airlanes maximum likelihood solution［J］. Proceedingd of SPIE, Signal and Data Processing of Small Targets, 2003,5204:390-401.
［19］ Watson G A, Rice T R.Sensor alignment and compensation for composite tracking［J］. Proceedings of SPIE, Signal and Data Processing of Small Targets, 2002, 4728: 354-367.

第38卷第10期2017 年10月兵工学报ACTA
ARMAMENTARIIVol.38No.10Oct. 2017

[1]	夏琳琳, 张晶晶, 初妍, 张道畅, 宋梓维, 崔家硕, 刘瑞敏. 融合天空偏振光的视觉SLAM研究进展与展望[J]. 兵工学报, 2023, 44(6): 1588-1601.
[2]	黄文宽, 钱林方, 尹强, 刘太素. 基于迁移学习的供药装置故障诊断方法[J]. 兵工学报, 2023, 44(10): 2964-2974.
[3]	李松洲，周荻，杜润乐，刘佳琪，方艺忠. 多传感器多目标被动跟踪的“鬼点”形成条件[J]. 兵工学报, 2022, 43(12): 3103-3112.
[4]	陆瑶敏, 龚建伟, 王博洋, 关海杰. 基于多重示范的智能车辆运动基元表征与序列生成[J]. 兵工学报, 2021, 42(4): 851-861.
[5]	李金朋，范红波，张英堂，李志宁，尹刚. 斜磁化条件下多磁源目标的边界识别方法[J]. 兵工学报, 2020, 41(10): 2033-2044.
[6]	李金朋，张英堂，范红波，李志宁，张光. 强剩磁条件下磁性目标三维正则化聚焦反演方法[J]. 兵工学报, 2018, 39(11): 2202-2210.
[7]	曾溢良，蓝金辉，邹金霖. 滑动置信度约束的红外弱小目标跟踪算法研究[J]. 兵工学报, 2017, 38(9): 1771-1778.
[8]	伍明，张国良，李琳琳，付光远，李承剑. 基于动态和静态环境对象观测一致性约束的移动机器人多传感器标定优化方法[J]. 兵工学报, 2017, 38(8): 1630-1641.
[9]	李关防，崔杰，袁富宇. 基于线谱瞬时频率估计的被动声纳目标运动分析[J]. 兵工学报, 2017, 38(7): 1395-1401.
[10]	朱甦，薄煜明，何亮. 基于冗余字典的多特征压缩感知目标跟踪算法[J]. 兵工学报, 2017, 38(6): 1140-1146.
[11]	王啸, 韩太林, 张永立, 刘轩, 王义君, 宫玉琳. 多通道瞬态信号自适应变频算法[J]. 兵工学报, 2017, 38(6): 1161-1167.
[12]	张石，张百海，王飞帆，关子霄. 基于跳数量化的无线传感器网络节点定位算法[J]. 兵工学报, 2017, 38(5): 932-939.
[13]	夏平, 任强, 吴涛, 雷帮军. 融合多尺度统计信息模糊C均值聚类与Markov随机场的小波域声纳图像分割[J]. 兵工学报, 2017, 38(5): 940-948.
[14]	曹俊，郑翠娥，孙大军，张殿伦. 基于凸优化的水下载体定位研究[J]. 兵工学报, 2017, 38(3): 520-526.
[15]	向颉，茅永兴，郭才发. 船载雷达测速数据的船速修正新方法[J]. 兵工学报, 2017, 38(11): 2268-2273.