基于改進(jìn)神經(jīng)網(wǎng)絡(luò)增強(qiáng)自適應(yīng)UKF的組合導(dǎo)航系統(tǒng)

陳光武; 程鑒皓; 楊菊花; 劉昊; 張琳婧

doi:10.11999/JEIT181171

基于改進(jìn)神經(jīng)網(wǎng)絡(luò)增強(qiáng)自適應(yīng)UKF的組合導(dǎo)航系統(tǒng)

doi: 10.11999/JEIT181171

陳光武^{1, 2},
程鑒皓^{1, 2, ,},
楊菊花³,
劉昊^{1, 2},
張琳婧^{1, 2}

1.
蘭州交通大學(xué)自動(dòng)控制研究所 ??蘭州 ??730070
2.
甘肅省高原交通信息工程及控制重點(diǎn)實(shí)驗(yàn)室蘭州 730070
3.
蘭州交通大學(xué)交通運(yùn)輸學(xué)院 ??蘭州 ??730070

基金項(xiàng)目: 國(guó)家自然科學(xué)基金(61863024)，甘肅省基礎(chǔ)研究創(chuàng)新群體計(jì)劃(1606RJIA327)，甘肅省高等學(xué)?？蒲许?xiàng)目(2018C-11)，甘肅省自然科學(xué)基金(18JR3RA107)，甘肅省科技計(jì)劃資助(18CX3ZA004)

詳細(xì)信息

作者簡(jiǎn)介:
陳光武：男，1976年生，博士后，研究方向?yàn)閼T性導(dǎo)航和組合導(dǎo)航

程鑒皓：男，1995年生，碩士生，研究方向?yàn)閼T性導(dǎo)航和組合導(dǎo)航

楊菊花：女，1978年生，博士，研究方向?yàn)榻煌ㄟ\(yùn)輸工程

劉昊：男，1995年生，碩士生，研究方向?yàn)閼T性導(dǎo)航和組合導(dǎo)航

張琳婧：女，1994年生，碩士生，研究方向?yàn)閼T性導(dǎo)航和組合導(dǎo)航

通訊作者:
程鑒皓　cjhwww2005@163.com

中圖分類號(hào): TN967.2
計(jì)量
- 文章訪問(wèn)數(shù): 2837
- HTML全文瀏覽量: 780
- PDF下載量: 85
- 被引次數(shù): 0
出版歷程
- 收稿日期: 2018-12-19
- 修回日期: 2019-04-22
- 網(wǎng)絡(luò)出版日期: 2019-05-22
- 刊出日期: 2019-07-01

Improved Neural Network Enhanced Navigation System of Adaptive Unsented Kalman Filter

Guangwu CHEN^{1, 2},
Jianhao CHENG^{1, 2
, ,},
Juhua YANG³,
Hao LIU^{1, 2},
Linjing ZHANG^{1, 2}

1.
Automatic Control Institute, Lanzhou Jiaotong University, Lanzhou 730070, China
2.
Gansu Provincial Key Laboratory of Traffic Information Engineering and Control, Lanzhou 730070, China
3.
School of Traffic and Transportation, Lanzhou Jiaotong University, Lanzhou 730070, China

Funds: The National Natural Science Foundation of China (61863024), The Gansu Province Basic Research Innovation Group Program (1606RJIA327), The Gansu Province Higher Education Research Project (2018C-11), The Gansu Province Natural Science Foundation (18JR3RA107), The Gansu Province Science and Technology Plan Funding (18CX3ZA004)

摘要

摘要: 基于微機(jī)電系統(tǒng)(MEMS)的慣性器件和全球定位系統(tǒng)(GPS)的組合導(dǎo)航系統(tǒng)在衛(wèi)星信號(hào)失鎖時(shí)存在誤差發(fā)散的問(wèn)題，該文提出一種基于人工蜂群算法(ABC)改進(jìn)的徑向基函數(shù)(RBF)神經(jīng)網(wǎng)絡(luò)增強(qiáng)改進(jìn)的自適應(yīng)無(wú)跡卡爾曼濾波算法(AUKF)。在GPS信號(hào)失鎖的情況下利用訓(xùn)練好的神經(jīng)網(wǎng)絡(luò)輸出預(yù)測(cè)信息來(lái)對(duì)捷聯(lián)慣導(dǎo)系統(tǒng)進(jìn)行誤差校正。最后通過(guò)車載半實(shí)物仿真實(shí)驗(yàn)驗(yàn)證該方法的性能。實(shí)驗(yàn)結(jié)果表明該方法在失鎖情況下對(duì)于捷聯(lián)慣導(dǎo)系統(tǒng)的誤差發(fā)散有較為明顯的抑制效果。
- 組合導(dǎo)航 /
- 徑向基神經(jīng)網(wǎng)絡(luò) /
- 無(wú)跡卡爾曼濾波 /
- GPS故障
Abstract: In order to solve the problem of speed and position error divergence in the integrated navigation system based on MicroElectro Mechanical Systems (MEMS) inertial device and GPS system combined positioning, an improved Adaptive Unsecnted Kalman Filter (AUKF) enhanced by the Radial Basis Function(RBF) neural network based on Artificial Bee Colony(ABC) algorithm is proposed. When the GPS signal is out of lock, the trained network outputs predictied information to perform error correction on the Strapdown Inertial Navigation System(SINS). Finally, the performance of the method is verified by vehicle-mounted semi-physical simulation experiments. The experimental results show that the proposed method has a significant inhibitory effect on the error divergence of the strapdown inertial navigation system in the case of loss of lock.
- Intergrated navigation /
- Radial basis neural network /
- Unscented Kalman Filter(UKF) /
- GPS break down

HTML全文

圖 1 組合導(dǎo)航系統(tǒng)結(jié)構(gòu)圖

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圖 2 改進(jìn)神經(jīng)網(wǎng)絡(luò)訓(xùn)練流程

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圖 3 仿真軌跡速度誤差對(duì)比

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圖 4 仿真軌跡位置誤差對(duì)比

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圖 5 仿真軌跡天向速度、位置誤差對(duì)比

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圖 6 車載軌跡與實(shí)驗(yàn)設(shè)備示意圖

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圖 7 失鎖時(shí)間15 s的誤差對(duì)比圖

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圖 8 失鎖時(shí)間20 s的誤差對(duì)比圖

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表 1 傳感器誤差參數(shù)

性能指標(biāo)	陀螺儀			加速度計(jì)			更新頻率
性能指標(biāo)	分辨率	零偏	隨機(jī)游走	分辨率	零偏	隨機(jī)游走	更新頻率
參數(shù)	0.007°/s	0.007°/s	2.4°/(s·$\sqrt {{\rm{Hz}}} $)	0.3 mg	0.2 mg	0.2 mg/$\sqrt {{\rm{Hz}}} $	100 Hz

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表 2 仿真軌跡誤差

算法	東向速度(m/s)		北向速度(m/s)		東向位置(m)		北向位置(m)
算法	均值	標(biāo)準(zhǔn)差	均值	標(biāo)準(zhǔn)差	均值	標(biāo)準(zhǔn)差	均值	標(biāo)準(zhǔn)差
UKF	0.0020	0.0176	–0.0051	0.0151	–0.0372	0.5383	–0.3385	0.5731
AUKF	0.0014	0.0133	–0.0045	0.0115	–0.0231	0.333	–0.1554	0.3662
改進(jìn)的AUKF	0.0012	0.0062	–0.0016	0.0063	–0.017	0.1516	–0.0134	0.1901

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表 3 失鎖15 s誤差對(duì)比

算法	東向速度 (m/s)		北向速度 (m/s)		東向位置 (m)		北向位置 (m)
算法	最大誤差	標(biāo)準(zhǔn)差	最大誤差	標(biāo)準(zhǔn)差	最大誤差	標(biāo)準(zhǔn)差	最大誤差	標(biāo)準(zhǔn)差
SINS	6.8848	3.4896	17.1512	8.3593	48.1258	15.5383	113.5873	36.453
RBF/UKF	2.0323	0.6450	5.8072	2.5089	11.6579	1.0626	42.2483	19.0634
RBF/AUKF	1.1013	0.3399	4.0176	1.9044	6.2061	1.1394	30.6046	13.1792
ABC_RBF/AUKF	0.4931	0.1887	1.1604	0.5895	2.1414	0.7315	5.7511	2.2276

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表 4 誤差收斂幅度(%)

算法	東向速度		北向速度		東向位置		北向位置
算法	最大誤差	標(biāo)準(zhǔn)差	最大誤差	標(biāo)準(zhǔn)差	最大誤差	標(biāo)準(zhǔn)差	最大誤差	標(biāo)準(zhǔn)差
RBF/UKF	70.48	81.52	66.14	69.99	75.78	93.16	62.81	50.47
RBF/AUKF	84.00	90.26	76.58	77.22	87.10	92.67	73.06	63.85
ABC_RBF/AUKF	92.84	94.59	93.24	92.95	95.50	95.29	94.94	93.90

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表 5 失鎖20 s誤差對(duì)比結(jié)果

算法	東向速度 (m/s)		北向速度 (m/s)		東向位置 (m)		北向位置 (m)
算法	最大誤差	標(biāo)準(zhǔn)差	最大誤差	標(biāo)準(zhǔn)差	最大誤差	標(biāo)準(zhǔn)差	最大誤差	標(biāo)準(zhǔn)差
SINS	3.8304	1.9431	42.3022	21.2832	31.6512	9.5199	397.7599	131.2909
RBF/UKF	1.4031	0.6460	2.1983	0.6543	13.4591	6.9092	10.5738	6.545
RBF/AUKF	0.7504	0.4599	1.4436	0.5315	10.2079	5.3060	4.9074	2.8413
ABC_RBF/AUKF	0.4424	0.1527	1.4165	0.4434	4.6339	2.1145	4.3115	1.5682

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表 6 誤差收斂幅度對(duì)比(%)

算法	東向速度		北向速度		東向位置		北向位置
算法	最大誤差	標(biāo)準(zhǔn)差	最大誤差	標(biāo)準(zhǔn)差	最大誤差	標(biāo)準(zhǔn)差	最大誤差	標(biāo)準(zhǔn)差
RBF/UKF	63.37	66.75	94.8	96.93	57.48	27.42	97.34	95.01
RBF/AUKF	80.41	76.33	96.59	97.52	67.75	44.26	98.77	97.84
ABC_RBF/AUKF	88.45	92.14	96.65	97.92	85.36	77.79	98.92	98.81

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