应用统计线性回归的系统误差最大似然配准

doi:10.19665/j.issn1001-2400.2021.04.010

西安电子科技大学学报 ›› 2021, Vol. 48 ›› Issue (4): 73-82.doi: 10.19665/j.issn1001-2400.2021.04.010

• 信息与通信工程&电子科学与技术 • 上一篇下一篇

应用统计线性回归的系统误差最大似然配准

李佳炜¹(),江晶²(),吴卫华¹(),郑玉军³()

1.空军预警学院预警情报系,湖北武汉 430019
2.空军预警学院空天预警系,湖北武汉 430019
3.中国人民解放军94710部队,江苏无锡 214000

收稿日期:2020-05-18 出版日期:2021-08-30 发布日期:2021-08-31
作者简介:李佳炜(1992—),男,空军预警学院博士研究生,E-mail: jiaweili1992@163.com|江晶(1964—),男,教授,博士,E-mail: jiangj36@sina.com|吴卫华(1987—),男,副教授,博士,E-mail: weihuawu1987@163.com|郑玉军(1988—),男,工程师,博士,E-mail: junleida@163.com
基金资助:
国家自然科学基金(61601510)

Maximum likelihood registration for systemic error based on statistical linear regression

LI Jiawei¹(),JIANG Jing²(),WU Weihua¹(),ZHENG Yujun³()

1. Early Warning Intelligence Department,Air Force Early Warning Academy,Wuhan 430019,China
2. Aerospace Early Warning Department,Air Force Early Warning Academy,Wuhan 430019,China
3. Unit 94710 of the PLA,Wuxi 214000,China

Received:2020-05-18 Online:2021-08-30 Published:2021-08-31

摘要/Abstract

摘要：

针对协同多传感器系统探测目标过程中存在着非随机系统误差的问题,提出一种基于统计线性回归的最大似然配准算法。首先通过联合最大化目标状态和系统误差的似然函数,建立多传感器系统的配准方程;然后利用一组不完全相同的回归点处理非线性量测转换的线性化问题,通过统计线性回归,构建目标状态关于去偏量测的回归方程,并得到投影后目标状态的前二阶统计特性;最后利用似然最大化迭代求解配准方程,实现对系统误差和目标状态的联合估计。仿真结果表明,基于统计线性回归的最大似然配准算法能在各观测维度上实现多个传感器的配准,且相比经典最大似然配准算法具有更高的配准精度和相当的计算复杂度。

关键词: 协同多传感器系统, 系统误差, 统计线性回归, 最大似然配准

Abstract:

Generally,there are non-random systemic errors in target detection with the cooperative multi-sensor system.In order to solve this problem,a maximum likelihood registration algorithm based on statistical linear regression (SLR-MLR) is presented.The registration equation for the multi-sensor system is established first by jointly maximizing the likelihood function of the target state and systemic error,on the basis of which the proposed algorithm utilizes a set of diverse regression points to handle the linearization problem of the nonlinear measurement transformation.The regression equation for the target state with respect to unbiased measurement is constructed through statistical linear regression,and then the first two statistical properties of the projected state can be obtained.Moreover,the algorithm uses the likelihood maximization iteration to seek the solution of the registration equation,thus achieving the joint estimation for the systemic error and target state.Simulation results show that the SLR-MLR can achieve the registration of multiple sensors in each observation dimension,and has a higher accuracy and near computational complexity compared with the classical MLR.

Key words: cooperative multi-sensor system, systemic error, statistical linear regression, maximum likelihood registration

中图分类号:

TP274

李佳炜,江晶,吴卫华,郑玉军. 应用统计线性回归的系统误差最大似然配准[J]. 西安电子科技大学学报, 2021, 48(4): 73-82.

LI Jiawei,JIANG Jing,WU Weihua,ZHENG Yujun. Maximum likelihood registration for systemic error based on statistical linear regression[J]. Journal of Xidian University, 2021, 48(4): 73-82.

图/表 7

图1

图2

图3

图4

图5

图6

图7

参考文献 25

[1]	KHALEGHI B, KHAMIS A, KARRAY F, et al. Multisensor Data Fusion:A Review of The State-Of-The-Art[J]. Information Fusion, 2013, 14(1):28-44. doi: 10.1016/j.inffus.2011.08.001
[2]	HE S M, SHIN H S, XU S Y, et al. Distributed Estimation Over A Low-Cost Sensor Network:A Review of State-Of-The-Art[J]. Information Fusion, 2020, 54:21-43. doi: 10.1016/j.inffus.2019.06.026
[3]	BAR-SHALOM Y, LI X R. Multitarget-Multisensor Tracking:Principles and Techniques[M]. Storrs:YBS, 1995:87-93.
[4]	GUO J J, HAN C Z, LI L F. Data Fusion and Bias Registration Based on Sensor Selection for Large-Scale Sensor Networks[C]//Proceedings of the 2017 29th Chinese Control and Decision Conference.Piscataway:IEEE, 2017:7286-7291.
[5]	TOKRA A, HOCAOGLU A K. Sensor Bias Estimation for Track-To-Track Association[J]. IEEE Signal Processing Letters, 2019, 26(10):1426-1430. doi: 10.1109/LSP.97
[6]	ZHANG T, LI H, YANG L, et al. Multi-Radar Bias Estimation without A Priori Association[J]. IEEE Access, 2018, 6:44616-44625. doi: 10.1109/Access.6287639
[7]	ZHU H Y, WANG C. Joint Track-To-Track Association and Sensor Registration at The Track Level[J]. Digital Signal Processing, 2015, 41:48-59. doi: 10.1016/j.dsp.2015.03.012
[8]	ZHU H, LEUNG H, YUEN K V. A Joint Data Association,Registration,and Fusion Approach for Distributed Tracking[J]. Information Sciences, 2015, 324:186-196. doi: 10.1016/j.ins.2015.06.042
[9]	CLEMONS T M, CHANG K C. Effect of Sensor Bias on Space-Based Bearing-Only Tracker[C]//Proceedings of the SPIE,the International Society for Optical Engineering.Orlando:SPIE, 2008: 696809.
[10]	CLEMONS T M, CHANG K C. Bias Correction Using Background Stars for Space-Based IR Tracking[C]//Proceedings of the 2009 12th International Conference on Information Fusion.Piscataway:IEEE, 2009:2028-2035.
[11]	CLEMONS T M, CHANG K C. Sensor Calibration Using In-Situ Celestial Observations to Estimate Bias in Space-Based Missile Tracking[J]. IEEE Transactions on Aerospace and Electronic Systems, 2012, 48(2):1403-1427. doi: 10.1109/TAES.2012.6178070
[12]	薛永宏, 安玮, 张涛, 等. 高轨红外扫描相机视轴指向实时校正算法[J]. 光学学报, 2013, 33(6):321-326.
	XUE Yonghong, AN Wei, ZHANG Tao, et al. Real-Time Line of Sight Calibration Algorithm for High Earth Orbit Infrared Scanning Sensor[J]. Acta Optica Sinica, 2013, 33(6):321-326.
[13]	陈军, 安玮, 邓新蒲, 等. 天基光学传感器视线指向确定与校正[J]. 光学学报, 2016, 36(7):304-310.
	CHEN Jun, AN Wei, DENG Xinpu, et al. Determination and Calibration of Space Based Optical Sensor Line of Sight Attitude[J]. Acta Optica Sinica, 2016, 36(7):304-310.
[14]	FORTUNATI S, FARINA A, GINI F, et al. Least Squares Estimation and CramÉR-Rao Type Lower Bounds for Relative Sensor Registration Process[J]. IEEE Transactions on Signal Processing, 2011, 59(3):1075-1087. doi: 10.1109/TSP.2010.2097258
[15]	OKELLO N, RISTIC B. Maximum Likelihood Registration for Multiple Dissimilar Sensors[J]. IEEE Transactions on Aerospace and Electronic Systems, 2003, 39(3):1074-1083. doi: 10.1109/TAES.2003.1238759
[16]	RISTIC B, OKELLO N. Sensor Registration in ECEF Coordinates Using the MLR Algorithm[C]//Proceedings of the 6th International Conference of Information Fusion.Piscataway:IEEE, 2003:135-142.
[17]	CUI Y Q, XIONG W, HE Y, et al. Mobile Sensor Registration in ECEF Coordinates Using the MLR Algorithm[C]//Proceedings of the 2011 IEEE CIE International Conference on Radar.Piscataway:IEEE, 2011:1784-1787.
[18]	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 Yaqi, XIONG Wei, HE You. Mobile Platform Sensor Registration Algorithm Based on MLR[J]. Acta Aeronautica et Astronautica Sinica, 2012, 33(1):118-128.
[19]	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 Weihua, JIANG Jin. Maximum Likelihood Registration for Passive Sensors of Multiple Airborne Platforms in WGS-84[J]. Systems Engineering and Electronics, 2015, 37(2):304-309.
[20]	李佳炜, 江晶, 刘重阳, 等. 多星载光学传感器系统误差极大似然配准算法[J]. 系统工程与电子技术, 2020, 42(1):1-9.
	LI Jiawei, JIANG Jin, LIU Chongyang, et al. Maximum Likelihood Registration for Systemic Error of Multiple Spaceborne Optical Sensors[J]. Systems Engineering and Electronics, 2020, 42(1):1-9.
[21]	LEFEBVRE T, BRUYNINCKX H, SCHULLER J D. Comment on “A New Method for the Nonlinear Transformation of Means and Covariances in Filters and Estimators”[J]. IEEE Transactions on Automatic Control, 2002, 47(8):1406-1408. doi: 10.1109/TAC.2002.800742
[22]	VERCAUTEREN T, WANG X D. Decentralized Sigma-Point Information Filters for Target Tracking in Collaborative Sensor Networks[J]. IEEE Transactions on Signal Processing, 2005, 53(8):2997-3009. doi: 10.1109/TSP.2005.851106
[23]	JULIER S J, UHLMANN J K. Unscented Filtering and Nonlinear Estimation[J]. Proceedings of the IEEE, 2004, 92(3):401-422. doi: 10.1109/JPROC.2003.823141
[24]	LIN X D, BAR-SHALOM Y, KIRUBARAJAN T. Exact Multisensor Dynamic Bias Estimation with Local Tracks[J]. IEEE Transactions on Aerospace and Electronic Systems, 2004, 40(2):576-590. doi: 10.1109/TAES.2004.1310006
[25]	HUANG D, LEUNG H, BOSSE E. A Pseudo-Measurement Approach to Simultaneous Registration and Track Fusion[J]. IEEE Transactions on Aerospace and Electronic Systems, 2012, 48(3):2315-2331. doi: 10.1109/TAES.2012.6237594

应用统计线性回归的系统误差最大似然配准

Maximum likelihood registration for systemic error based on statistical linear regression

RichHTML

PDF (PC)

赞

可视化

摘要/Abstract

引用本文

使用本文

图/表 7

参考文献 25

相关文章 1

Metrics

本文评价

推荐阅读 10