|Table of Contents|

Fast square root CKF for automotive millimeter-waveradar target tracking

《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

Issue:
2016年01期
Page:
56-
Research Field:
Publishing date:

Info

Title:
Fast square root CKF for automotive millimeter-waveradar target tracking
Author(s):
Liu HuajunLai Shaofa
School of Computer Science and Engineering,Nanjing University of Science and Technology,Nanjing 210094,China
Keywords:
millimeter-wave radar target tracking square root cubature Kalman filter
PACS:
TN958
DOI:
-
Abstract:
QR decomposition of error covariance matrix of the square root cubature Kalman Filter(CKF)is directly used for filter’s prediction and updating,which can avoid breaking down when the covariance matrix is non-positive-definite.However,the prediction and updating process based on sigma-point propagation leads to obviously high computational burden.In this paper,a fast square root CKF method for automotive millimeter-wave radar target tracking is proposed.In this method,the Kalman equations are used to predict the state means and covariance during the prediction process,while square root CKF equations are used to compute the Kalman gain and update the state means and covariance during the updating process.Many experimental results show,either on efficiency or precision,our proposed method is superior to the similar square root unscented Kalman filter(UKF)and square root CKF algorithms.

References:

[1] Darms M.Data fusion of environment-perception sensors for ADAS[M].[S.L.]:Handbook of Driver Assistance Systems,2015:1-13.
[2]Fleming W.Forty-year review of automotive electronics:A unique source of historical information on automotive electronics[J].IEEE Vehicular Technology Magazine,2015,10(3):89-90.
[3]Kim Daebong,Hong Sunmog.Multiple-target tracking and track management for an FMCW radar networks[J].EURASIP Journal on Advances in Signal Processing,2013(1):1-9.
[4]Han Seulki,Ra Wonsang,Whang Ickho,et al.Linear recursive automotive target tracking filter for advanced collision warning systems[J].Appl Math Inf,2014,8(3):1145-1151.
[5]Gustafsson F,Hendeby G.Some relations between extended and unscented Kalman filters[J].IEEE Transactions on Signal Processing,2012,60(2):545-555.
[6]Arasaratnam I,Simon H.Cubature Kalman filters[J].IEEE Transactions on Automatic Control,2009,54(6):1254-1269.
[7]张文,孙瑞胜.EKF与UKF的性能比较及应用[J].南京理工大学学报,2015,39(5):614-618.

Zhang Wen,Sun Ruisheng.Research on performance comparison of EKF and UKF and their application[J].Journal of Nanjing University of Science and Technology,2015,39(5):614-618.
[8]Zhen Ding,Bhashyam B.Comparison of the unscented and cubature Kalman filters for radar tracking applications[C]//IET International Conference on Radar Systems(Radar 2012).Glasgow,UK:[s.n.],2012:1-5.
[9]郝燕玲,杨峻巍,陈亮,等.平方根容积卡尔曼滤波器[J].弹箭与制导学报,2012:32(2):169-172.
Hao Yanling,Yang Junwei,Chen Liang,et al.Square root cubature Kalman filter[J].Journal of Projectiles,Rockets,Missiles and Guidance,2012:32(2):169-172.
[10]穆静,蔡远利.平方根容积卡尔曼滤波算法及其应用[J].兵工自动化,2011,30(6):11-13.
Mu Jing,Cai Yuanli.Square root cubature Kalman filter algorithm and application[J].Ordnance Industry Automation,2011,30(6):11-13.
[11]肖雷,刘高峰,魏建仁.几种动目标运动模型的跟踪性能对比[J].火力与指挥控制,2005,32(5):106-109.
Xiao Lei,Liu Gaofeng,Wei Jianren.The tracking performance contrast of some kind of maneuvering target motion model[J].Fire Control and Command Control,2005,32(5):106-109.
[12]Yousefi S,Chang Xiaowen,Champagne B.Mobile localization in non-line-of-sight using constrained square-root unscented Kalman filter[J].IEEE Transactions on Vehicular Technology,2015,64(5):2071-2083.

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Last Update: 2016-02-29