Extended object tracking using Monte Carlo methods

被引:74
作者
Angelova, Donka [1 ]
Mihaylova, Lyudmila [2 ]
机构
[1] Bulgarian Acad Sci, CLLP, Sofia 1113, Bulgaria
[2] Univ Lancaster, Dept Commun Syst, InfoLab 21, Lancaster LA1 4WA, England
基金
英国工程与自然科学研究理事会;
关键词
data augmentation; extended targets; mixture Kalman filtering; sequential Monte Carlo methods; PARTICLE METHODS; TARGET;
D O I
10.1109/TSP.2007.907851
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
080906 [电磁信息功能材料与结构]; 082806 [农业信息与电气工程];
摘要
This correspondence addresses the problem of tracking extended objects, such as ships or a convoy of vehicles moving in urban environment. Two Monte Carlo techniques for extended object tracking are proposed: an interacting multiple model data augmentation (IMM-DA) algorithm and a modified version of the mixture Kalman filter (MKF) of Chen and Liu [1], called the mixture Kalman filter modified (MKFm). The data augmentation (DA) technique with finite mixtures estimates the object extent parameters, whereas an interacting multiple model (IMM) filter estimates the kinematic states (position and speed) of the manoeuvring object. Next, the system model is formulated in a partially conditional dynamic linear (PCDL) form. This affords us to propose two latent indicator variables characterizing, respectively, the motion mode and object size. Then, an MKFm is developed with the PCDL model. The IMM-DA and the MKFm performance is compared with a combined IMM-particle filter (IMM-PF) algorithm with respect to accuracy and computational complexity. The most accurate parameter estimates are obtained by the DA algorithm, followed by the MKFm and PF.
引用
收藏
页码:825 / 832
页数:8
相关论文
共 16 条
[1]
Particle methods for change detection, system identification, and control [J].
Andrieu, C ;
Doucet, A ;
Singh, SS ;
Tadic, VB .
PROCEEDINGS OF THE IEEE, 2004, 92 (03) :423-438
[2]
Andrieu C, 2005, IEEE DECIS CONTR P, P332
[3]
Angelova D, 2006, LECT NOTES COMPUT SC, V3993, P624
[4]
Bar-Shalom Yaakov., 2001, ESTIMATION APPL TRAC
[5]
BOERS Y, 2006, P 9 INT C INF FUS IS
[6]
Mixture Kalman filters [J].
Chen, R ;
Liu, JS .
JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY, 2000, 62 :493-508
[7]
Tracking maneuvering and bending extended target in cluttered environment [J].
Dezert, J .
SIGNAL AND DATA PROCESSING OF SMALL TARGETS 1998, 1998, 3373 :283-294
[8]
DIEBOLT J, 1994, J ROY STAT SOC B MET, V56, P363
[9]
DOUCET A, 2001, SEQUENTIAL M CARLO M
[10]
Spatial distribution model for tracking extended objects [J].
Gilholm, K ;
Salmond, D .
IEE PROCEEDINGS-RADAR SONAR AND NAVIGATION, 2005, 152 (05) :364-371