吉林大学学报(工学版) ›› 2014, Vol. 44 ›› Issue (6): 1537-1544.doi: 10.13229/j.cnki.jdxbgxb201406001

• •    下一篇

车载毫米波雷达对前方目标的运动状态估计

高振海1, 王竣1, 佟静1, 李红建2, 郭章勇1, 娄方明1   

  1. 1.吉林大学 汽车仿真与控制国家重点实验室,长春 130022;
    2.中国第一汽车集团公司 技术中心,长春 130011
  • 收稿日期:2013-09-10 出版日期:2014-11-01 发布日期:2014-11-01
  • 通讯作者: 佟静(1977-),女,工程师.研究方向:汽车行驶性能测试.E-mail:tongjing@jlu.edu.cn
  • 作者简介:高振海(197-),男,教授,博士生导师.研究方向:智能辅助驾驶.E-mail:
  • 基金资助:
    “973”国家重点基础研究发展计划项目-前期研究专项(2012CB723802); 长江学者和创新团队发展计划项目(IRT1017)

Target motion state estimation for vehicle-borne millimeter-wave radar

GAO Zhen-hai1, WANG Jun1, TONG Jing1, LI Hong-jian2, GUO Zhang-yong1, LOU Fang-ming1   

  1. 1.State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130022, China;
    2.China FAW Group Corporation R&D Center, Changchun 130011, China
  • Received:2013-09-10 Online:2014-11-01 Published:2014-11-01

摘要: 基于对汽车前方目标运动特点和车载雷达信息检测机理的分析,在大地坐标系、本车的车辆运动坐标系和车载雷达运动坐标系的相对运动关系基础上,考虑了地面车辆运动以地表平面上二维运动为主、机动性小、跟踪坐标系运动的特点,建立了基于车载雷达运动坐标系的前方目标的运动状态模型。并考虑到系统过程噪声及雷达等车载传感器观测噪声的统计特性难以事先确定的问题,采用自适应卡尔曼滤波算法实现了前方目标的侧纵向速度和侧纵向位置等运动状态的完备准确实时估计。最终通过真实道路交通环境下装备毫米波雷达和高精度汽车状态测试系统的实车对比试验,对算法的可行性和估计精度进行了试验验证,试验结果显示:估计结果具有良好的精度,且长时间跟踪过程中滤波收敛稳定。

关键词: 车辆工程, 车载毫米波雷达, 前方目标运动模型, 状态估计, 自适应卡尔曼滤波

Abstract: With the analysis of preceding object motion feature and vehicle-borne radar measuring principle, a novel target motion model is established in the vehicle-borne radar coordinate system. This target motion model considers the relative motion of the intertial coordinate, the vehicle coordinate and the radar coordinate system. Also other specialness of ground vehicle were taken into account in the model, such as that the motion of ground vehicle is a 2D motion because of the limitation of ground surface, the vehicle mobility is small and the tracking coordinate system is moving. The whole motion states of the preceding target, including the longitudinal and lateral velocities, the longitudinal and lateral positions were estimated by the algorithm of adaptive Kalman filter, because it was difficult to determine the statistics of the system process noise and measure noise. Finally, road experiments, in which the host car was equipped with millimeter-wave radar and the preceding car was equipped with high precision automotive testing equipments, were carried out to verify the feasibility and performance of the estimation method. The results prove that the method can provide fine estimation accuracy, better filter convergence and stability.

Key words: vehicle engineering, vehicle-borne millimeter-wave radar, target motion model, state estimation, adaptive Kalman filter

中图分类号: 

  • U461.91
[1] 王建强,王海鹏,张磊,等.基于电控液压制动装置的车辆主动报警/避撞系统[J]. 吉林大学学报:工学版,2012,42(4):816-822. Wang Jian-qiang, Wang Hai-peng, Zhang Lei,et al. Vehicle collision warning and avoidance system based on electronic hydraulic brake device[J]. Journal of Jilin University (Engineering and Technology Edition), 2012,42(4):816-822.
[2] 金立生, Van Arem Bart,杨双宾,等.高速公路汽车辅助驾驶安全换道模型[J].吉林大学学报:工学版,2009,39(3):582-586. Jin Li-sheng, Van Arem Bart, Yang Shuang-bin, et al. Safety lane change model of vehicle assistant driving on highway[J]. Journal of Jilin University(Engineering and Technology Edition), 2009,39(3):582-586.
[3] 李向瑜,高振海,袁昌碧,等. 汽车巡航控制系统的环境评估[J]. 吉林大学学报:工学版,2008,38(增刊1):28-31. Li Xiang-yu, Gao Zhen-hai, Yuan Chang-bi, et al. Environment evaluation of vehicle cruise control[J]. Journal of Jilin University(Engineering and Technology Edition), 2008, 38(Sup.1):28-31.
[4] Sarholz F, Mehnert J, Klappstein J, et al. Evaluation of different approaches for road course estimation using imaging radar[C]∥International Conference on Intelligent Robots and Systems, San Francisco CA, USA,2011: 4587-4592.
[5] Uhler W, Scherl M, Lichtenberg B. Driving course prediction using distance sensor data[C]∥SAE Paper, 1999-01-1234.
[6] Schiffmann J K, Widmann G R. Model-based scene tracking using radar sensors for intelligent automotive vehicle systems[C]∥IEEE Conference on Intelligent Transportation Systems, Boston MA,USA,1997: 421-426.
[7] 赵又群. 汽车动力学中若干关键状态和参数估计研究的现状与发展[J]. 中国机械工程, 2010, 21(10): 1250-1253. Zhao You-qun. Present state and perspectives of estimation research for several key states and parameters in vehicle dynamics[J]. China Mechanic Engineering, 2010, 21(10): 1250-1253.
[8] 高越,高振海,李向瑜. 基于自适应Kalman滤波的汽车横摆角速度软测量算法[J]. 江苏大学学报:自然科学版,2005,26(1):24-27. Gao Yue, Gao Zhen-hai, Li Xiang-yu. Soft measurement method for vehicle yaw rate based on adaptive Kalman filter[J]. Journal of Jiangsu University (National Science Edition), 2005, 26(1):24-27.
[9] Hac A,Simpson M D. Estimation of vehicle side slip angle and yaw rate[C]∥SAE Paper,2000-01-0696.
[10] Jiang Fang-jun,Gao Zhi-qiang.An adaptive nonlinear filter approach to the vehicle velocity estimation for ABS[C]∥IEEE Conference on Control Applications, Anchorage, USA, 2000:490-495.
[11] Farrelly J, Wellstead P. Estimation of vehicle lateral velocity[J].IEEE Conference on Control Applications,Dearborn MI, USA, 1996:552-557.
[12] Liu C, Peng H. Road friction coefficient estimation for vehicle path prediction[J]. Vehicle System Dynamics, 1996, 25(Sup.1): 413-425.
[13] Ito M, Yoshioka K, Saji T. Estimation of road surface conditions using wheel speed behavior[J]. JSAE Review, 1995, 16(2): 221-222.
[14] 陈利斌, 佟明安. 机动目标跟踪的交互式多模型自适应滤波算法[J]. 火力与指挥控制, 2000, 25(4): 36-38. Chen Li-bin, Tong Ming-an. Interacting multiple model adaptive filtering algorithm for maneuvering tracking[J]. Fire Control & Command Control, 2000, 25(4): 36-38.
[15] 左东广, 韩崇昭, 卞树檀, 等. 闪烁噪声机动目标跟踪的模型集交互跟踪算法[J]. 系统仿真学报, 2004, 16(4): 767-771. Zuo Dong-guang, Han Chong-zhao, Bian Shu-tan, et al. Model sets interacting algorithm for maneuvering target tracking in the presence of glint noise[J]. Journal of System Simulation, 2004, 16(4): 767-771.
[16] Blackman S S. Multiple hypothesis tracking for multiple target tracking[J]. Aerospace and Electronic Systems Magazine,2004, 19(1):5-18.
[17] Pitre R R, Jilkov V P, Li X R. A comparative study of multiple-model algorithms for maneuvering target tracking[C]∥Proceedings of SPIE-The International Society for Optical Engineering, Orlando FL, USA, 2005:549-560.
[18] 石章松, 刘忠等. 目标跟踪与数据融合理论及方法[M]. 北京: 国防工业出版社, 2010.
[19] 张明友, 汪学刚. 雷达系统[M]. 3版. 北京: 电子工业出版社, 2011.
[1] 常成,宋传学,张雅歌,邵玉龙,周放. 双馈电机驱动电动汽车变频器容量最小化[J]. 吉林大学学报(工学版), 2018, 48(6): 1629-1635.
[2] 席利贺,张欣,孙传扬,王泽兴,姜涛. 增程式电动汽车自适应能量管理策略[J]. 吉林大学学报(工学版), 2018, 48(6): 1636-1644.
[3] 何仁,杨柳,胡东海. 冷藏运输车太阳能辅助供电制冷系统设计及分析[J]. 吉林大学学报(工学版), 2018, 48(6): 1645-1652.
[4] 那景新,慕文龙,范以撒,谭伟,杨佳宙. 车身钢-铝粘接接头湿热老化性能[J]. 吉林大学学报(工学版), 2018, 48(6): 1653-1660.
[5] 刘玉梅,刘丽,曹晓宁,熊明烨,庄娇娇. 转向架动态模拟试验台避撞模型的构建[J]. 吉林大学学报(工学版), 2018, 48(6): 1661-1668.
[6] 赵伟强, 高恪, 王文彬. 基于电液耦合转向系统的商用车防失稳控制[J]. 吉林大学学报(工学版), 2018, 48(5): 1305-1312.
[7] 宋大凤, 吴西涛, 曾小华, 杨南南, 李文远. 基于理论油耗模型的轻混重卡全生命周期成本分析[J]. 吉林大学学报(工学版), 2018, 48(5): 1313-1323.
[8] 朱剑峰, 张君媛, 陈潇凯, 洪光辉, 宋正超, 曹杰. 基于座椅拉拽安全性能的车身结构改进设计[J]. 吉林大学学报(工学版), 2018, 48(5): 1324-1330.
[9] 那景新, 浦磊鑫, 范以撒, 沈传亮. 湿热环境对Sikaflex-265铝合金粘接接头失效强度的影响[J]. 吉林大学学报(工学版), 2018, 48(5): 1331-1338.
[10] 王炎, 高青, 王国华, 张天时, 苑盟. 混流集成式电池组热管理温均特性增效仿真[J]. 吉林大学学报(工学版), 2018, 48(5): 1339-1348.
[11] 金立生, 谢宪毅, 高琳琳, 郭柏苍. 基于二次规划的分布式电动汽车稳定性控制[J]. 吉林大学学报(工学版), 2018, 48(5): 1349-1359.
[12] 隗海林, 包翠竹, 李洪雪, 李明达. 基于最小二乘支持向量机的怠速时间预测[J]. 吉林大学学报(工学版), 2018, 48(5): 1360-1365.
[13] 王德军, 魏薇郦, 鲍亚新. 考虑侧风干扰的电子稳定控制系统执行器故障诊断[J]. 吉林大学学报(工学版), 2018, 48(5): 1548-1555.
[14] 胡满江, 罗禹贡, 陈龙, 李克强. 基于纵向频响特性的整车质量估计[J]. 吉林大学学报(工学版), 2018, 48(4): 977-983.
[15] 刘国政, 史文库, 陈志勇. 考虑安装误差的准双曲面齿轮传动误差有限元分析[J]. 吉林大学学报(工学版), 2018, 48(4): 984-989.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!