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基于新息的自適應(yīng)增量Kalman濾波器

孫小君 周晗 閆廣明

孫小君, 周晗, 閆廣明. 基于新息的自適應(yīng)增量Kalman濾波器[J]. 電子與信息學(xué)報(bào), 2020, 42(9): 2223-2230. doi: 10.11999/JEIT190493
引用本文: 孫小君, 周晗, 閆廣明. 基于新息的自適應(yīng)增量Kalman濾波器[J]. 電子與信息學(xué)報(bào), 2020, 42(9): 2223-2230. doi: 10.11999/JEIT190493
Xiaojun SUN, Han ZHOU, Guangming YAN. Adaptive Incremental Kalman Filter Based on Innovation[J]. Journal of Electronics & Information Technology, 2020, 42(9): 2223-2230. doi: 10.11999/JEIT190493
Citation: Xiaojun SUN, Han ZHOU, Guangming YAN. Adaptive Incremental Kalman Filter Based on Innovation[J]. Journal of Electronics & Information Technology, 2020, 42(9): 2223-2230. doi: 10.11999/JEIT190493

基于新息的自適應(yīng)增量Kalman濾波器

doi: 10.11999/JEIT190493
基金項(xiàng)目: 國(guó)家自然科學(xué)基金(61104209),黑龍江大學(xué)杰出青年科學(xué)基金(JCL201103),黑龍江大學(xué)電子工程重點(diǎn)實(shí)驗(yàn)室基金(DZZD2010-5),黑龍江大學(xué)青年科學(xué)基金(QL201212)
詳細(xì)信息
    作者簡(jiǎn)介:

    孫小君:女,1980年生,副教授,研究方向?yàn)槎鄠鞲衅餍畔⑷诤?、狀態(tài)估計(jì)、信號(hào)處理

    周晗:男,1996年生,碩士生,研究方向?yàn)槎鄠鞲衅餍畔⑷诤?、系統(tǒng)辨識(shí)

    閆廣明:男,1979年生,講師,研究方向?yàn)槎鄠鞲衅餍畔⑷诤?、狀態(tài)估計(jì)

    通訊作者:

    孫小君 sxj@hlju.edu.cn

  • 中圖分類號(hào): TN713; TP18

Adaptive Incremental Kalman Filter Based on Innovation

Funds: The National Natural Science Foundation of China (61104209), The Outstanding Youth Science Foundation of Heilongjiang University (JCL201103), The Key Laboratory of Electronics Engineering, College of Heilongjiang Province (DZZD2010-5), The Youth Science Foundation of Heilongjiang University (QL201212)
  • 摘要: 在一定環(huán)境條件下,當(dāng)系統(tǒng)的量測(cè)方程沒(méi)有進(jìn)行驗(yàn)證或校準(zhǔn)時(shí),使用該量測(cè)方程往往會(huì)產(chǎn)生未知的系統(tǒng)誤差,從而導(dǎo)致較大的濾波誤差。增量方程的引入可以有效解決欠觀測(cè)系統(tǒng)的狀態(tài)估計(jì)問(wèn)題。該文考慮帶未知噪聲統(tǒng)計(jì)的線性離散增量系統(tǒng),首先提出一種基于新息的噪聲統(tǒng)計(jì)估計(jì)算法??梢缘玫较到y(tǒng)噪聲統(tǒng)計(jì)的無(wú)偏估計(jì)。進(jìn)而,提出一種新的增量系統(tǒng)自適應(yīng)Kalman濾波算法。相比已有的自適應(yīng)增量濾波算法,該文所提算法得到的狀態(tài)估計(jì)精度更高。兩個(gè)仿真實(shí)例證明了其有效性和可行性。
  • 圖  1  未知噪聲統(tǒng)計(jì)的真值和估計(jì)值比較

    圖  2  基于兩種不同算法的噪聲統(tǒng)計(jì)估計(jì)誤差比較

    圖  3  狀態(tài)真值和兩種自適應(yīng)增量濾波器比較

    圖  4  兩種自適應(yīng)增量濾波誤差比較

    圖  5  未知噪聲統(tǒng)計(jì)的真值和估計(jì)值比較

    圖  6  基于兩種不同算法的噪聲統(tǒng)計(jì)估計(jì)誤差比較

    圖  7  狀態(tài)真值和兩種自適應(yīng)增量濾波器比較

    圖  8  兩種自適應(yīng)增量濾波誤差比較

  • SAGE A P and HUSA G W. Adaptive filtering with unknown prior statistics[C]. Joint Automatic Control Conference, Boulder, American, 1969: 760–769.
    KALMAN R E. A new approach to linear filtering and prediction problems[J]. Journal of Basic Engineering, 1960, 82(1): 35–45. doi: 10.1115/1.3662552
    鄧自立. 自校正濾波理論及其應(yīng)用——現(xiàn)代時(shí)間序列分析方法[M]. 哈爾濱: 哈爾濱工業(yè)大學(xué)出版社, 2003: 6.1.
    何麗, 湯莉. 基于Kalman濾波的云數(shù)據(jù)中心能耗和性能優(yōu)化[J]. 計(jì)算機(jī)工程與科學(xué), 2018, 40(7): 1165–1172. doi: 10.3969/j.issn.1007-130X.2018.07.003

    HE Li and TANG Li. Energy and performance optimization based on Kalman filtering in the cloud data center[J]. Computer Engineering &Science, 2018, 40(7): 1165–1172. doi: 10.3969/j.issn.1007-130X.2018.07.003
    張宏偉, 謝維信. 平滑約束無(wú)跡卡爾曼濾波器[J]. 信號(hào)處理, 2019, 35(3): 466–471. doi: 10.16798/j.issn.1003-0530.2019.03.019

    ZHANG Hongwei and XIE Weixin. Smoothly constrained unscented Kalman filter[J]. Journal of Signal Processing, 2019, 35(3): 466–471. doi: 10.16798/j.issn.1003-0530.2019.03.019
    耿友林, 解成博, 尹川, 等. 基于卡爾曼濾波的接收信號(hào)強(qiáng)度指示差值定位算法[J]. 電子與信息學(xué)報(bào), 2019, 41(2): 455–461. doi: 10.11999/JEIT180268

    GENG Youlin, XIE Chengbo, YIN Chuan, et al. Received signal strength indication difference location algorithm based on Kalman filter[J]. Journal of Electronics &Information Technology, 2019, 41(2): 455–461. doi: 10.11999/JEIT180268
    汪玲, 朱棟強(qiáng), 馬凱莉, 等. 空間目標(biāo)卡爾曼濾波稀疏成像方法[J]. 電子與信息學(xué)報(bào), 2018, 40(4): 846–852. doi: 10.11999/JEIT170319

    WANG Ling, ZHU Dongqiang, MA Kaili, et al. Sparse imaging of space targets using Kalman filter[J]. Journal of Electronics &Information Technology, 2018, 40(4): 846–852. doi: 10.11999/JEIT170319
    SUN Xiaojun, GAO Yuan, DENG Zili, et al. Multi-model information fusion Kalman filtering and white noise deconvolution[J]. Information Fusion, 2010, 11: 163–173. doi: 10.1016/j.inffus.2009.06.004
    劉利生, 吳斌, 楊萍. 航天器精確定軌與自校準(zhǔn)技術(shù)[M]. 北京: 國(guó)防工業(yè)出版社, 2005: 9.2.

    LIU Lisheng, WU Bin, and YANG Ping. Orbit Precision Determination & Self-Calibration Technique of Spacecraft[M]. Beijing: National Defense Industry Press, 2005: 9.2.
    傅惠民, 婁泰山, 吳云章. 欠觀測(cè)條件下的擴(kuò)展增量Kalman濾波方法[J]. 航空動(dòng)力學(xué)報(bào), 2012, 27(4): 777–781. doi: 10.13224/j.cnki.jasp.2012.04.004

    FU Huimin, LOU Taishan, and WU Yunzhang. Extended incremental Kalman filter method under poor observation condition[J]. Journal of Aerospace Power, 2012, 27(4): 777–781. doi: 10.13224/j.cnki.jasp.2012.04.004
    傅惠民, 婁泰山, 吳云章. 增量粒子濾波方法[J]. 航空動(dòng)力學(xué)報(bào), 2013, 28(6): 1201–1207. doi: 10.13224/j.cnki.jasp.2013.06.005

    FU Huimin, LOU Taishan, and WU Yunzhang. Incremental particle filter method[J]. Journal of Aerospace Power, 2013, 28(6): 1201–1207. doi: 10.13224/j.cnki.jasp.2013.06.005
    傅惠民, 吳云章, 婁泰山. 欠觀測(cè)條件下的增量Kalman濾波方法[J]. 機(jī)械強(qiáng)度, 2012, 34(1): 43–47. doi: 10.16579/j.issn.1001.9669.2012.01.014

    FU Huimin, WU Yunzhang, and LOU Taishan. Incremental Kalman filter method under poor observation condition[J]. Journal of Mechanical Strength, 2012, 34(1): 43–47. doi: 10.16579/j.issn.1001.9669.2012.01.014
    SUN Xiaojun, YAN Guangming, and ZHANG Bo. A Kind of incremental Kalman smoother under poor observation condition[C]. The 36th Chinese Control Conference, Dalian, China, 2017: 2524–2527. doi: 10.23919/ChiCC.2017.8027740.
    SUN Xiaojun and YAN Guangming. Multi-sensor optimal weighted fusion incremental Kalman smoother[J]. Journal of Systems Engineering and Electronics, 2018, 29(2): 262–268. doi: 10.21629/JSEE.2018.02.06
    徐景碩, 秦永元, 彭蓉. 自適應(yīng)卡爾曼濾波器漸消因子選取方法研究[J]. 系統(tǒng)工程與電子技術(shù), 2004, 26(11): 1552–1554. doi: 10.3321/j.issn:1001-506X.2004.11.006

    XU Jingshuo, QIN Yongyuan, and PENG Rong. New method for selecting adaptive Kalman filter fading factor[J]. Systems Engineering and Electronics, 2004, 26(11): 1552–1554. doi: 10.3321/j.issn:1001-506X.2004.11.006
    魯平, 趙龍, 陳哲. 改進(jìn)的Sage-Husa自適應(yīng)濾波及其應(yīng)用[J]. 系統(tǒng)仿真學(xué)報(bào), 2007, 19(15): 3503–3505. doi: 10.3969/j.issn.1004-731X.2007.15.034

    LU Ping, ZHAO Long, and CHEN Zhe. Improved Sage-Husa adaptive filtering and its application[J]. Journal of System Simulation, 2007, 19(15): 3503–3505. doi: 10.3969/j.issn.1004-731X.2007.15.034
    傅惠民, 吳云章, 婁泰山. 自適應(yīng)增量Kalman濾波方法[J]. 航空動(dòng)力學(xué)報(bào), 2012, 27(6): 1125–1129.

    FU Huimin, WU Yunzhang, and LOU Taishan. Adaptive incremental Kalman filter method[J]. Journal of Aerospace Power, 2012, 27(6): 1125–1129.
    徐英蛟. 一種改進(jìn)自適應(yīng)增量Kalman濾波的傳遞對(duì)準(zhǔn)算法[J]. 指揮控制與仿真, 2018, 40(4): 33–37. doi: 10.3969/j.issn.1673-3819.2018.04.008

    XU Yingjiao. A improved adaptive incremental filtering algorithm of transfer alignment[J]. Command Control &Simulation, 2018, 40(4): 33–37. doi: 10.3969/j.issn.1673-3819.2018.04.008
    傅惠民, 吳云章, 婁泰山. 自適應(yīng)增量粒子濾波方法[J]. 航空動(dòng)力學(xué)報(bào), 2013, 28(8): 1764–1768.

    FU Huimin, WU Yunzhang, and LOU Taishan. Adaptive incremental particle filter method[J]. Journal of Aerospace Power, 2013, 28(8): 1764–1768.
    傅惠民, 吳瓊. 線性獨(dú)立增量過(guò)程分析方法[J]. 航空動(dòng)力學(xué)報(bào), 2010, 25(4): 930–935.

    FU Huimin and WU Qiong. Analysis method for linear process with independent increments[J]. Journal of Aerospace Power, 2010, 25(4): 930–935.
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  • 收稿日期:  2019-07-02
  • 修回日期:  2020-03-20
  • 網(wǎng)絡(luò)出版日期:  2020-08-06
  • 刊出日期:  2020-09-27

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