[1] Xu Qimin, Li Xu, Li Bin, Song Xianghui, et al. An interacting multiple model-based two-stage Kalman filterfor vehicle positioning [J]. Journal of Southeast University (English Edition), 2017, 33 (2): 177-181. [doi:10.3969/j.issn.1003-7985.2017.02.009]

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An interacting multiple model-based two-stage Kalman filterfor vehicle positioning()

一种用于车辆定位的交互式多模型两级卡尔曼滤波

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Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:

33

Issue:

2017 2

Page:

177-181

Research Field:

Traffic and Transportation Engineering

Publishing date:

2017-06-30

Info

Title:

An interacting multiple model-based two-stage Kalman filterfor vehicle positioning

一种用于车辆定位的交互式多模型两级卡尔曼滤波

Author(s):

Xu Qimin¹;  Li Xu¹;  Li Bin²;  Song Xianghui²

¹School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China
² Key Laboratory of Technology on Intelligent Transportation Systems, Research Institute of Highway of Ministry of Transport, Beijing 100088, China

徐启敏¹;  李旭¹;  李斌²;  宋向辉²

¹东南大学仪器科学与工程学院, 南京210096; ²交通运输部公路科学研究院智能交通技术交通行业重点实验室, 北京100088

Keywords:

interacting multiple model(IMM);  two-stage filter;  uncertain noise;  vehicle positioning

交互式多模型;  两级滤波;  不确定噪声;  车辆定位

PACS:

U491

DOI:

10.3969/j.issn.1003-7985.2017.02.009

Abstract:

To address the problem that a general augmented state Kalman filter or a two-stage Kalman filter cannot achieve satisfactory positioning performance when facing uncertain noise of the micro-electro-mechanical system(MEMS)inertial sensors, a novel interacting multiple model-based two-stage Kalman filter(IMM-TSKF)is proposed to adapt to the uncertain inertial sensor noise. Three bias filters are developed based on different noise characteristics to cover a wide range of noise levels. Then, an accurate estimation of biases is calculated by the interacting multiple model algorithm to correct the bias-free filter. Thus, the vehicle positioning system can achieve good performance when suffering from uncertain inertial sensor noise. The experimental results indicate that the average position error of the proposed IMM-TSKF is 25% lower than that of the general TSKF.

为了解决一般状态增强型卡尔曼滤波和两级卡尔曼滤波在微机械惯性传感器不确定噪声的影响下难以获得良好定位性能的问题, 提出一种新型的基于交互式多模型的两级卡尔曼滤波方法来适应微机械惯性传感器的不确定噪声.根据不同的噪声特性, 建立3个偏差滤波器来覆盖大范围的噪声水平.交互式多模型算法根据3个偏差滤波器可以准确估计出惯性传感器的偏差值, 并用来修正无偏差滤波器.因此, 应用所提出的滤波方法后, 车辆定位系统在不确定噪声的影响下也能获得较好的性能.实验结果显示所提出的交互式多模型两级卡尔曼滤波的平均定位误差比一般两级卡尔曼滤波方法低25%.

References:

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Memo

Memo:

Biographies: Xu Qimin(1989—), male, graduate; Li Xu(corresponding author), male, doctor, associate professor, lixu.mail@163.com.
Foundation items: The National Natural Science Foundation of China(No.61273236), the Scientific Research Foundation of Graduate School of Southeast University(No.YBJJ1637), China Scholarship Council.
Citation: Xu Qimin, Li Xu, Li Bin, et al. An interacting multiple model-based two-stage Kalman filter for vehicle positioning[J].Journal of Southeast University(English Edition), 2017, 33(2):177-181.DOI:10.3969/j.issn.1003-7985.2017.02.009.

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