基于新息的自適應(yīng)增量Kalman濾波器

孫小君; 周晗; 閆廣明

doi:10.11999/JEIT190493

基于新息的自適應(yīng)增量Kalman濾波器

doi: 10.11999/JEIT190493

孫小君^{1, 2, ,},
周晗¹,
閆廣明^{1, 2}

1.
黑龍江大學(xué)電子工程學(xué)院哈爾濱 150080
2.
黑龍江省信息融合估計(jì)與檢測(cè)重點(diǎn)實(shí)驗(yàn)室哈爾濱 150080

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
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- 文章訪問(wèn)數(shù): 2732
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- PDF下載量: 145
- 被引次數(shù): 0
出版歷程
- 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

Adaptive Incremental Kalman Filter Based on Innovation

Xiaojun SUN^{1, 2
, ,},
Han ZHOU¹,
Guangming YAN^{1, 2}

1.
Electrical Engineering Institute, Heilongjiang University, Harbin 150080, China
2.
Key Laboratory of Information Fusion Estimation and Detection, Heilongjiang Province, Harbin 150080, China

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

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。
- 自適應(yīng)Kalman濾波 /
- 增量濾波器 /
- 欠觀測(cè)系統(tǒng) /
- 增量系統(tǒng) /
- 濾波精度
Abstract: Under certain environmental conditions, the unknown system errors often occur and yield to larger filtering errors when the unverified or uncalibrated measurement equation is used. Incremental equation can be introduced, which can effectively solve the problem of state estimation for the systems under poor observation condition. In this paper, the linear discrete incremental system with unknown noise statistics is considered. Firstly, a noise statistics estimation algorithm is proposed based on innovation. The unbiased estimation of system noise statistics can be obtained. Furthermore, a new incremental system adaptive Kalman filtering algorithm is proposed. Compared with the existing adaptive incremental filtering algorithm, the state estimation accuracy of the proposed algorithm is higher. Two simulation examples prove its effectiveness and feasibility.
- Adaptive Kalman filtering /
- Incremental filters /
- Systems under poor observation condition /
- Incremental systems /
- Filtering accuracy

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圖 1 未知噪聲統(tǒng)計(jì)的真值和估計(jì)值比較

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圖 2 基于兩種不同算法的噪聲統(tǒng)計(jì)估計(jì)誤差比較

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圖 3 狀態(tài)真值和兩種自適應(yīng)增量濾波器比較

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圖 4 兩種自適應(yīng)增量濾波誤差比較

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圖 5 未知噪聲統(tǒng)計(jì)的真值和估計(jì)值比較

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圖 6 基于兩種不同算法的噪聲統(tǒng)計(jì)估計(jì)誤差比較

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圖 7 狀態(tài)真值和兩種自適應(yīng)增量濾波器比較

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圖 8 兩種自適應(yīng)增量濾波誤差比較

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參考文獻(xiàn)(20)

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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