非理想關聯(lián)下多傳感器系統(tǒng)誤差的穩(wěn)健估計

田威; 黃高明

doi:10.11999/JEIT170579

非理想關聯(lián)下多傳感器系統(tǒng)誤差的穩(wěn)健估計

doi: 10.11999/JEIT170579

田威¹,
黃高明²

2.
(海軍工程大學電子工程學院武漢 430033)

基金項目:

中國博士后科學基金第61批面上項目(2017M613370)

計量
- 文章訪問數(shù): 1128
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- PDF下載量: 188
- 被引次數(shù): 0
出版歷程
- 收稿日期: 2017-06-14
- 修回日期: 2017-11-17
- 刊出日期: 2018-03-19

Robust Multisensor Bias Estimation Under Nonideal Association

TIAN Wei¹,
HUANG Gaoming²

2.
(School of Electronic Engineering, Naval Engineering University, Wuhan 430033, China)

Funds:

The 61st Genernal Program Supportting Fund of China Postdoctoral Science Foundation (2017M613370)

摘要

摘要: 在數(shù)據(jù)融合系統(tǒng)中，傳感器自身系統(tǒng)誤差造成其上報融合中心的目標位置狀態(tài)出現(xiàn)系統(tǒng)性偏差，若得不到有效估計與補償，融合系統(tǒng)難以實現(xiàn)預期的性能優(yōu)勢。然而，基于目標關聯(lián)配對關系而構造的超定方程組是系統(tǒng)誤差估計的出發(fā)點。復雜環(huán)境下，受隨機噪聲、系統(tǒng)誤差、虛警、漏報等因素的干擾，數(shù)據(jù)關聯(lián)模塊的輸出結果常常包含錯誤關聯(lián)。針對非理想關聯(lián)下多傳感器系統(tǒng)誤差的穩(wěn)健估計問題，該文提出基于最小截平方的系統(tǒng)誤差穩(wěn)健估計方法，并進一步提出剔除異常方程的重加權最小二乘方法。與最小二乘及最小中值平方相比，所提方法在保證估計器穩(wěn)健性能的前提下，降低了估計結果對隨機噪聲的敏感程度。仿真實驗驗證了所提方法的有效性。
- 多傳感器數(shù)據(jù)融合 /
- 系統(tǒng)誤差估計 /
- 非理想關聯(lián) /
- 最小截平方
Abstract: In the data fusion system, sensor biases lead to systematic deviation of the position states of targets reported to the fusion center. If sensor biases could not be estimated and compensated correctly, the fusion system will fail to achieve the expected performance superiority. However, the starting point of sensor bias estimation is the overdetermined equations construted on the biasis of data association. In the complicated environment, with the presence of interference factors such as random errors, sensor biases, false alarms and missed detections, the data association module outputs some misassociations inevitably. In view of the multisensor bias estimation problem under nonideal association, the robust estimation approach based on the least trimmed squares is proposed. Furthermore, the reweighted least squares apporach through eliminating abnormal equations is presented. Compared with the least squares and the least median of squares, the proposed approaches can not only ensure the robust performance on bias estimation, but also are less sensitive to random errors. Simulation results verify the effectiveness of the proposed methods.
- Multisensor data fusion /
- System bias estimation /
- Nonideal association /
- Least Trimmed Squares (LTS)

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參考文獻(28)

田威. 復雜環(huán)境下多傳感器航跡關聯(lián)與抗差處理[D]. [博士論文], 清華大學, 2014: 1-37.

CHANDRASEKARAN B, GANGADHAR S, and CONRAD J M. A survey of multisensor fusion techniques, architectures and methodologies[C]. IEEE SoutheastCon., Charlotte, NC, USA, 2017: 1-8. doi: 10.1109/SECON.2017.7925311.

TIAN Wei. Multisensor track-to-track association and bias removal in complex environments[D]. [Ph.D. dissertation], Tsinghua University, 2014: 1-37.

TAGHAVI E, THARMARASA R, KIRUBARAJAN T, et al. A practical bias estimation algorithm for multisensor- multitarget tracking[J]. IEEE Transactions on Aerospace and Electronic Systems, 2016, 52(1): 2-19. doi: 10.1109/TAES. 2015.140574.

COWLEY B C and SHAFAI B. Registration in multi-sensor data fusion and tracking[C]. Proceedings of American Control Conference, San Francisco, CA, 1993: 875-879.

ZHOU Yifeng, LEUNG H, and YIP P C. An exact maximum likelihood registration algorithm for data fusion[J]. IEEE Transactions on Signal Processing, 1997, 45(6): 1560-1573. doi: 10.1109/78.599998.

ZHENG Ziwei and ZHU Yisheng. New least squares registration algorithm for data fusion[J]. IEEE Transactions on Aerospace and Electronic Systems, 2004, 40(4): 1410-1416. doi: 10.1109/TAES.2004.1386893.

OKELLO N and RISTIO 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.

FORTUNATI S, FARINA A, GINI F, 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): 1075-1085. doi: 10.1109/TSP.2010. 2097258.

FORTUNATI S, GINI F, GRECO M, et al. Least squares estimation and hybrid Cramr-Rao lower bound for absolute sensor registration[C]. Tyrrhenian Workshop on Advances in Radar and Remote Sensing (TyWRRS), Naples, Italy, 2012: 30-35. doi: 10.1109/TyWRRS.2012.6381098.

FORTUNATI S, GINI F, FARINA A, et al. On the application of the expectation- maximisation algorithm to the relative sensor registration problem [J]. IET Radar, Sonar Navigation, 2013, 7(2): 191-203. doi: 10.1049/iet-rsn.2012. 0050.

LIN Xiangdong, BAR-SHALOM Y, and KIRUBARAJAN T. Multisensor multitarget bias estimation for general asynchronous sensors[J]. IEEE Transactions on Aerospace and Electronic Systems, 2005, 41(3): 899-921. doi: 10.1109/ TAES.2005.1541438.

PU Wenqiang, LIU Yafeng, YAN Junkun, et al. A two-stage optimization approach to the asynchronous multi-sensor registration problem[C]. International Conference on Acoustics, Speech and Signal Processing (ICASSP), New Orleans, LA, USA, 2017: 3271-3275. doi: 10.1109/ICASSP. 2017.7952761.

GENG Hang, LIANG Yan, LIU Yurong, et al. Bias estimation for asynchronous multi-rate multi-sensor fusion with unknown inputs[J]. Information Fusion, 2018, 39: 139-153. doi: 10.1016/j.inffus.2017.03.002.

TIAN Wei, WANG Yue, DU Xiongjie, et al. Reference pattern-based track-to-track association with biased data[J]. IEEE Transactions on Aerospace and Electronic Systems, 2016, 52(1): 501-512. doi: 10.1109/TAES.2015.140433.

TIAN Wei, WANG Yue, SHAN Xiuming, et al. Track-to- track association for biased data based on the reference topology feature[J]. IEEE Signal Processing Letters, 2014, 21(4): 449-453. doi: 10.1109/LSP.2014.2305305.

TIAN Wei, WANG Yue, SHAN Xiuming, et al. Analytic performance prediction of track-to-track association with biased data in multi-sensor multi-target tracking scenarios[J]. Sensors, 2013, 13(9): 12244-12265. doi: 10.3390/S130912244.

田威, 王鉞, 山秀明, 等. 基于一致關聯(lián)數(shù)最大化的航跡關聯(lián)算法[J]. 航空學報, 2014, 35(11): 3115-3122. doi: 10.7527/ s1000-6893.

TIAN Wei, WANG Yue, SHAN Xiuming, et al. Track-to- track association based on maximizing the consistent association number[J]. Acta Aeronautica et Astronautica Sinica, 2014, 35(11): 3115-3122. doi: 10.7527/s1000-6893.

田威, 王鉞, 山秀明, 等. 穩(wěn)健的聯(lián)合航跡關聯(lián)與系統(tǒng)誤差估計[J]. 清華大學學報(自然科學版), 2013, 53(7): 946-950. doi: 10.16511/j.cnki.qhdxxb.2013.07.009.

TIAN Wei, WANG Yue, SHAN Xiuming, et al. Robust method for joint track association and sensor bias estimation [J]. Journal of Tsinghua University (Science and Technology), 2013, 53(7): 946-950. doi: 10.16511/j.cnki.qhdxxb.2013.07. 009.

田威, 王鉞, 山秀明, 等. 基于系統(tǒng)誤差估計殘差的錯誤關聯(lián)檢測方法[J]. 系統(tǒng)工程與電子技術, 2013, 35(10): 2062-2068. doi: 10.3969/j.issn.1001-506X.2013.10.08.

TIAN Wei, WANG Yue, SHAN Xiuming, et al. Misassociation detection method based on the residual errors of system bias estimation[J]. Systems Engineering and Electronics, 2013, 35(10): 2062-2068. doi: 10.3969/j.issn. 1001-506X.2013.10.08.

LIN Xiangdong, KIRUBARAJAN T, and BAR-SHALOM Y. Multisensor bias estimation using local tracks without a priori association[C]. Proceedings of SPIE, Bellingham, WA, 2003, 5204: 334-345. doi: 10.1117/12.503715.

ROUSSEEUW P J, LEROY AM, and WILEY J. Robust Regression and Outlier Detection[M]. New York: Wiley Online Library, 1987: 1-18.

WENG Yang, NEGI R, LIU Qixing, et al. Robust state- estimation procedure using a Least Trimmed Squares pre- processor[C]. 2011 IEEE PES Innovative Smart Grid Technologies (ISGT), Anaheim, CA, USA, 2011: 1-6. doi: 10.1109/ISGT.2011.5759135.

TIAN Xin and BAR-SHALOM Y. Sliding window test vs. single time test for track-to-track association[C]. IEEE 11th International Conference on Information Fusion, Cologne, Germany, 2008: 1-8.

KAPLAN L M, BAR-SHALOM Y, and BLAIR W D. Assignment costs for multiple sensor track-to-track association[J]. IEEE Transactions on Aerospace and Electronic Systems, 2008, 44(2): 655-677. doi: 10.1109/TAES. 2008.4560213.

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