基于鲁棒自适应SCKF的智能汽车目标状态跟踪研究

doi:10.3901/JME.2021.20.181

机械工程学报 ›› 2021, Vol. 57 ›› Issue (20): 181-193.doi: 10.3901/JME.2021.20.181

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基于鲁棒自适应SCKF的智能汽车目标状态跟踪研究

张志达^1,2, 郑玲^1,2, 李以农^1,2, 吴行^1,2, 余颖弘^1,2

1. 重庆大学机械与运载工程学院重庆 400044;
2. 重庆大学机械传动国家重点实验室重庆 400044

收稿日期:2020-05-08 修回日期:2021-04-23 出版日期:2021-10-20 发布日期:2021-12-15
通讯作者: 郑玲(通信作者),女,1963年出生,博士,教授,博士研究生导师。主要研究方向为智能汽车的环境感知、决策与动力学控制。E-mail:zling@cqu.edu.cn
作者简介:张志达,男,1990年出生,博士研究生。主要研究方向为智能汽车状态估计与循迹控制。E-mail:zhangzhida_edu@163.com;李以农,男,1961年出生,博士,教授,博士研究生导师。主要研究方向为车辆系统动力学与控制、振动噪声控制。E-mail:ynli@cqu.edu.cn
基金资助:
国家自然科学基金（51875061）、重庆市研究生科研创新（CYB19063）和重庆市技术创新与应用发展专项（cstc2019jscx-zdztzxX0032）资助项目。

Research on Intelligent Vehicle Target State Tracking Based on Robust Adaptive SCKF

ZHANG Zhida^1,2, ZHENG Ling^1,2, LI Yinong^1,2, WU Hang^1,2, YU Yinghong^1,2

1. College of Mechanical and Vehicle Engineering, Chongqing University, Chongqing 400044;
2. State Key Lab of Mechanical Transmissions, Chongqing University, Chongqing 400044

Received:2020-05-08 Revised:2021-04-23 Online:2021-10-20 Published:2021-12-15

摘要/Abstract

摘要： 准确的自车和前车状态估计是智能汽车有效决策和控制的前提，而以往的研究通常不考虑噪声统计特性不确定的问题，导致某些情况下车辆状态估计的误差很大。为此，提出一种鲁棒自适应平方根容积卡尔曼滤波（Robust adaptive square-root cubature Kalman filter，RASCKF）算法，以降低噪声统计不确定性对估计精度的影响。首先，采用最大后验概率准则估计了过程噪声协方差和测量噪声协方差的统计值，以提高噪声稳定时状态估计的精确性。然后，基于标准化测量新息序列设计了故障检测规则，利用实时测量新息对噪声协方差进行校正处理，保证状态估计算法的鲁棒性。最后，在不同的噪声干扰工况下对RASCKF算法进行了仿真验证。结果表明，RASCKF算法在估计精度和稳定性上明显优于标准SCKF算法，有效地解决了智能汽车目标状态跟踪过程中噪声统计特性不确定的问题。

关键词: 智能汽车, 目标状态跟踪, 平方根容积卡尔曼滤波, 鲁棒自适应

Abstract: Accurate estimation of the states of vehicle and front vehicle is the premise of the effective decision-making and control of the intelligent vehicle. However, previous studies usually do not consider the uncertainty of noise statistical characteristics, which leads to a large error of vehicle state estimation in some cases. Therefore, a robust adaptive square-root cubature Kalman filter (RASCKF) algorithm is proposed to reduce the influence of noise statistical uncertainty on estimation accuracy. Firstly, the statistical values of process noise covariance and measurement noise covariance are estimated by the maximum a posterior (MAP) criterion to improve the accuracy of state estimation when the noise is stable. Secondly, the fault detection rules are designed based on the standardized measurement innovation sequence, and the real-time measurement innovation is used to correct the noise covariances for ensure the robustness of the state estimation algorithm. Finally, the RASCKF algorithm is verified by simulation under different noise interference conditions. The results show that RASCKF algorithm is superior to standard SCKF algorithm in estimation accuracy and stability, which effectively solves the problem of uncertain noise statistical characteristics in the process of intelligent vehicle target state tracking.

Key words: intelligent vehicle, target state tracking, square-root cubature Kalman filter, robust adaptive

中图分类号:

U461

张志达, 郑玲, 李以农, 吴行, 余颖弘. 基于鲁棒自适应SCKF的智能汽车目标状态跟踪研究[J]. 机械工程学报, 2021, 57(20): 181-193.

ZHANG Zhida, ZHENG Ling, LI Yinong, WU Hang, YU Yinghong. Research on Intelligent Vehicle Target State Tracking Based on Robust Adaptive SCKF[J]. Journal of Mechanical Engineering, 2021, 57(20): 181-193.

参考文献

[1] KIM M H,SON J. On-road assessment of in-vehicle driving workload for older drivers:design guidelines for intelligent vehicles[J]. International Journal of Automo- tive Technology,2011,12(2):265-272.
[2] SHLADOVER S E. Connected and automated vehicle systems:Introduction and overview[J]. Journal of Intelli-gent Transportation Systems,2018,22(3):190-200.
[3] 谢有浩,魏振亚,赵林峰,等. 基于μ综合方法的智能车辆人机共驾的鲁棒横向控制[J]. 机械工程学报,2020,56(4):104-114. XIE Youhao,WEI Zhenya,ZHAO Linfeng,et al. Robust lateral control of intelligent vehicle in the human- machine sharing based on μ-synthesis[J]. Journal of Mechanical Engineering,2020,56(4):104-114.
[4] SCHUBERT R,RICHTER E,WANIELIK G. Compari- son and evaluation of advanced motion models for vehicle tracking[C]//Information Fusion,200811th Internati- onal Conference on. IEEE,2008:730-735.
[5] 任玥,郑玲,张巍,等. 基于模型预测控制的智能车辆主动避撞控制研究[J]. 汽车工程,2019,41(4):404- 410. REN Yue,ZHENG Ling,ZHANG Wei,et al. A study on active collision avoidance control of autonomous vehicles based on model predictive control[J]. Automot- ive Engineering,2019,41(4):404-410.
[6] REN D B,ZHANG J Y,ZHANG J M,et al. Trajectory planning and yaw rate tracking control for lane changing of intelligent vehicle on curved road[J]. Science China (Technological Sciences),2011,54(3):630-642.
[7] DAVINS V J,PLESTAN F,MOUSSAOUI S,et al. Design and optimization of nonlinear observers for road curvature and state estimation in automated vehicles[J]. IEEE Transactions on Intelligent Transportation Systems,2017,18(12),3315-3327.
[8] ANANDALLI M,BALIGAR V P. A novel approach in real-time vehicle detection and tracking using Raspberry Pi[J]. Alexandria Engineering Journal,2018,57(3):1597-1607.
[9] 张志勇,张淑芝,黄彩霞,等. 基于自适应扩展卡尔曼滤波的分布式驱动电动汽车状态估计[J]. 机械工程学报,2019,55(6):156-165. ZHANG Zhiyong,ZHANG Shuzhi,HUANG Caixia,et al. State estimation of distributed drive electric vehicle based on adaptive extended Kalman filter[J]. Journal of Mechanical Engineering,2019,55(6):156-165.
[10] 汪,殷国栋,耿可可,等. 基于NACKF的分布式驱动电动汽车轮胎侧向力与质心侧偏角估计[J]. 机械工程学报,2019,55(22):103-112. WANG Yan,YIN Guodong,GENG Keke,et al. Tire tateral forces and sideslip angle estimation for distri- buted drive electric vehicle using noise adaptive cubature Kalman filter[J]. Journal of Mechanical Engineering,2019,55(22):103-112.
[11] SCHUBERT R,WANIELIK G. A unified bayesian approach for object and situation assessment[J]. IEEE Intelligent Transportation Systems Magazine,2011,3(2):6-19.
[12] REINA G,MESSINA A. Vehicle dynamics estimation via augmented extended Kalman filtering[J]. Measure- ment,2019,133:383-395.
[13] HEIDFELD H,MARTIN S,KASPER R. UKF-based state and tire slip estimation for a 4WD electric vehicle[J]. Vehicle System Dynamics,2019,57(11):1-18.
[14] 宋义彤,舒红宇,陈仙宝,等. 分布式电动汽车状态与参数无迹卡尔曼滤波估计[J]. 机械工程学报,2020,56(16):204-213. SONG Yitong,SHU Hongyu,CHEN Xianbao,et al. State and parameters estimation for distributed drive electric vehicle based on unscented Kalman filter[J]. Journal of Mechanical Engineering,2020,56(16):204- 213.
[15] ASKELAND S A,EKMAN T. Tracking with a high- resolution 2D spectral estimation based automotive radar[J]. IEEE Transactions on Intelligent Transportation Systems,2015,16(5):2418-2423.
[16] XIE G,GAO H,QIAN L,et al. Vehicle trajectory prediction by integrating physics- and maneuver-based approaches using interactive multiple models[J]. IEEE Transactions on Industrial Electronics,2018,65(7):5999-6008.
[17] SCHUBERT R,ADAM C,OBST M,et al. Empirical evaluation of vehicular models for ego motion estimation[C]//2011 IEEE Intelligent Vehicles Symposium (IV). IEEE,2011:534-539.
[18] KATRINIOK A,ABEL D. Adaptive EKF-based vehicle state estimation with online assessment of local observability[J]. IEEE Transactions on Control Systems Technology,2016,24(4):1368-1381.
[19] STRANO S,TERZO M. Constrained nonlinear filter for vehicle sideslip angle estimation with no a priori knowledge of tyre characteristics[J]. Control Enginee- ring Practice,2017,71:10-17.
[20] GUSTAFSSON F,HENDEBY G. Some relations between extended and unscented Kalman filters[J]. Signal Processing,IEEE Transactions on,2012,60(2):545-555.
[21] CHANG L,HU B,LI A,et al. Transformed unscented Kalman filter[J]. IEEE Transactions on Automatic Control,2013,58(1):252-257.
[22] ARASARATNAM I,HAYKIN S. Cubature Kalman filters[J]. IEEE Transactions on Automatic Control,2009,54(6):1254-1269.
[23] AFSHARI H H,GADSDEN S A,HABIBI S. Gaussian filters for parameter and state estimation:a general review of theory and recent trends[J]. Signal processing,2017,135(6):218-238.
[24] BISHT S S,SINGH M P. An adaptive unscented Kalman filter for tracking sudden stiffness changes[J]. Mecha- nical Systems & Signal Processing,2014,49:181-195.
[25] 张志达,郑玲,吴行,等. 基于鲁棒自适应UKF的分布式电动汽车状态估计[J]. 中国科学:技术科学,2020,50(11):1461-1473. ZHANG Zhida,ZHENG Ling,WU Hang,et al. State estimation of distributed electric vehicle based on robust adaptive UKF[J]. Science China (Technological Scie- nces),2020,50(11):1461-1473.
[26] KIANI M,POURTAKDOUST S H. Adaptive square- root cubature-quadrature Kalman particle filter for satellite attitude determination using vector obser- vations[J]. Acta Astronautica,2014,105(1):109-116.
[27] SHEN C,ZHANG Y,GUO X T,et al. Seamless GPS/inertial navigation system based on self-learning square-root cubature Kalman filter[J] IEEE Transactions on Industrial Electronics,2021,68(1):499-508.
[28] LIU X,QU H,ZHAO J,et al. Maximum correntropy square-root cubature Kalman filter with application to SINS/GPS integrated systems[J]. ISA Transactions,2018,80(9):195-202.
[29] 赵望宇,李必军,单云霄,等. 融合毫米波雷达与单目视觉的前车检测与跟踪[J]. 武汉大学学报,2019,44(12):1832-1840. ZHAO Wangyu,LI Bijun,SHAN Yunxiao,et al. Vehicle detection and tracking based on fusion of millimeter wave radar and monocular vision[J]. Journal of Wuhan University,2019,44(12):1832-1840.
[30] ZHAO L,WANG J,YU T,et al. Design of adaptive robust square-root cubature Kalman filter with noise statistic estimator[J]. Applied Mathematics and Computation,2015,256:352-367.
[31] REZA M A,YASHAR S,SILVIO S,et al. Adaptive square- root unscented Kalman filter:An experimental study of hydraulic actuator state estimation[J]. Mechanical Systems & Signal Processing,2019,132:670-691.
[32] GAO Z,MU D,GAO S,et al. Adaptive unscented Kalman filter based on maximum posterior and random weighting[J]. Aerospace Science & Technology,2017,71:12-24.
[33] GAO S,HU G,ZHONG Y. Windowing and random weighting-based adaptive unscented Kalman filter[J]. International Journal of Adaptive Control & Signal Processing,2015,29(2):201-223.
[34] SOKEN H E,HAJIYEV C. Pico satellite attitude estimation via robust unscented Kalman filter in the presence of measurement faults[J]. ISA Transactions,2010,49(3):249-256.
[35] HAJIYEV C,SOKEN H E. Robust adaptive unscented Kalman filter for attitude estimation of pico satellites[J]. International Journal of Adaptive Control and Signal Processing,2014,28(2):107-120.
[36] RAHIMI A,KUMAR K D,ALIGHANBARI H. Fault estimation of satellite reaction wheels using covariance based adaptive unscented Kalman filter[J]. Acta astronautica,2017,134(5):159-169.
[37] MENF Y,GAO S,ZHONG Y,et al. Covariance matching based adaptive unscented Kalman filter for direct filtering in INS/GNSS integration[J]. Acta Astronautica,2016,120:171-181.

基于鲁棒自适应SCKF的智能汽车目标状态跟踪研究

Research on Intelligent Vehicle Target State Tracking Based on Robust Adaptive SCKF

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