基于多基站IMMKF-DOA辅助车辆协同定位算法研究

doi:10.3901/JME.2023.04.125

机械工程学报 ›› 2023, Vol. 59 ›› Issue (4): 125-134.doi: 10.3901/JME.2023.04.125

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基于多基站IMMKF-DOA辅助车辆协同定位算法研究

王法安^1,2, 殷国栋², 庄伟超², 刘帅鹏², 梁晋豪², 卢彦博²

1. 昆明理工大学现代农业工程学院昆明 650500;
2. 东南大学机械工程学院南京 211189

收稿日期:2022-03-26 修回日期:2022-07-01 出版日期:2023-02-20 发布日期:2023-04-24
通讯作者: 殷国栋(通信作者),男,1976年出生,博士,教授,博士研究生导师。主要研究方向为先进电动汽车、车辆动力学与控制、智能无人汽车和车辆主动安全控制。E-mail:ygd@seu.edu.cn
作者简介:王法安,男,1990年出生,博士,硕士研究生导师。主要研究方向为智能网联汽车的多车协同定位、多源信息融合。E-maill:faanwang@seu.edu.cn
基金资助:
国家自然科学基金(U1664258，51975118，51905095)和国家重点研发计划(2016YFB100906，2022YFD2002004)资助项目。

IMMKF-DOA Auxiliary Vehicle Cooperative Localization Algorithm Based on Multi-base Station

WANG Faan^1,2, YIN Guodong², ZHUANG Weichao², LIU Shuaipeng², LIANG Jinhao², LU Yanbo²

1. Faculty of Modern Agricultural Engineering, Kunming University of Science and Technology, Kunming 650500;
2. School of Mechanical Engineering, Southeast University, Nanjing 211189

Received:2022-03-26 Revised:2022-07-01 Online:2023-02-20 Published:2023-04-24

摘要/Abstract

摘要： 针对传统卡尔曼滤波算法在进行车辆实时运动过程中难以精准定位问题，提出一种基于运动状态自适应的交互多模型卡尔曼滤波(Interacting multiple model Kalman filter，IMMKF)与多基站到达方向(Direction-of-arrival，DOA)相融合进行车辆位置实时估计算法。基于无偏估计器对测量噪声协方差进行实时更新并将其嵌入标准卡尔曼滤波算法中实现自适应交互多模型卡尔曼滤波。针对车辆不同运动状态及动态行驶环境对车辆定位估计精度的影响，构建自适应交互多模型卡尔曼滤波器与多基站信息融合算法进行车辆位置实时估计，考虑不同车速与不同基站数等行驶工况下车辆定位精度的变化趋势，实现车辆实时位置的准确估计。利用PreScan-Simulink联合仿真平台进行虚拟仿真验证和实车试验验证。结果表明，基于交互多模型卡尔曼滤波与到达方向角的融合算法相对标准的卡尔曼滤波估计精度高，较好地改善了传统单一模型的卡尔曼滤波算法在进行车辆实时运动状态估计过程中精准定位问题，实车试验验证了提出算法对车辆定位精度较传统卡尔曼滤波算法的精度提高了一个数量级，实现了更精确的车辆位置估计。

关键词: 交互多模型卡尔曼滤波, 到达方向, 融合算法, 多输入多输出

Abstract: The traditional Kalman filter algorithm is difficult to accurately localization the vehicle in the process of real-time motion of the vehicle. Therefore, a motion state adaptive interactive multiple model Kalman filter(IMMKF) and multiple base station direction of arrival(DOA) algorithm is proposed to estimation the real-time position of vehicle. Based on the unbiased estimator, the measurement noise covariance is updated in real time and embedded in the standard Kalman filter algorithm to realize the adaptive IMMKF. In view of the impact of different vehicle motion states and dynamic driving environments on the accuracy of vehicle positioning estimation, an adaptive IMMKF and multi-base station information fusion algorithm are constructed to estimate the vehicle position in real time. The proposed IMMKF-DOA fusion algorithm considering the change trend of vehicle positioning accuracy under different speeds of vehicle and different number of base stations, and achieves accurate estimation of vehicle real-time position. Using PreScan-Simulink union simulation platform for virtual simulation verification and real vehicle test verification. The results show that the fusion algorithm based on the IMMKF and the DOA has a higher estimation accuracy than the standard Kalman filter, which better improves the accuracy of the traditional single-model Kalman filter algorithm in the process of real-time vehicle motion state estimation. For the positioning problem, the actual vehicle test verified that the proposed algorithm has improved the accuracy of the vehicle positioning by an order of magnitude compared with the accuracy of the traditional Kalman filter algorithm and achieved more accurate vehicle position estimation.

Key words: interacting multiple model kalman filter (IMMKF), direction of arrival (DOA), fusion algorithm, multiple input multiple output (MIMO)

中图分类号:

U471

王法安, 殷国栋, 庄伟超, 刘帅鹏, 梁晋豪, 卢彦博. 基于多基站IMMKF-DOA辅助车辆协同定位算法研究[J]. 机械工程学报, 2023, 59(4): 125-134.

WANG Faan, YIN Guodong, ZHUANG Weichao, LIU Shuaipeng, LIANG Jinhao, LU Yanbo. IMMKF-DOA Auxiliary Vehicle Cooperative Localization Algorithm Based on Multi-base Station[J]. Journal of Mechanical Engineering, 2023, 59(4): 125-134.

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基于多基站IMMKF-DOA辅助车辆协同定位算法研究

IMMKF-DOA Auxiliary Vehicle Cooperative Localization Algorithm Based on Multi-base Station

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