东南大学学报（英文版）2017，Vol.33Issue(2)：177-181,5.DOI:10.3969/j.issn.1003-7985.2017.02.009
一种用于车辆定位的交互式多模型两级卡尔曼滤波
An interacting multiple model-based two-stage Kalman filter for vehicle positioning
摘要
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.关键词
交互式多模型/两级滤波/不确定噪声/车辆定位Key words
interacting multiple model(IMM)/two-stage filter/uncertain noise/vehicle positioning分类
交通工程引用本文复制引用
徐启敏,李旭,李斌,宋向辉..一种用于车辆定位的交互式多模型两级卡尔曼滤波[J].东南大学学报（英文版）,2017,33(2):177-181,5.基金项目
The National Natural Science Foundation of China(No.61273236),the Scientific Research Foundation of Graduate School of Southeast University(No.YBJJ1637),China Scholarship Council. （No.61273236）
