Extension-deformation approach to extended object tracking

This paper presents a new approach based on extension deformation for extended object tracking (EOT). In this approach, the extension of an object is assumed to be deformed from a reference extension by moving some control points in the latter to those in the former. That is, the properties of an extension can be fully captured by the control points, given the reference extension. Thus, modeling and estimation of the extension can be reduced to those of the control points. In this way, not only various extended objects can be described conveniently, but also the extension-evolution model can be simplified. Moreover, the measurement model derived from the extended object model is linear, which largely simplifies estimation. In addition, an online adaptation method is proposed to incorporate online information into the reference extension, so the proposed approach can describe and estimate the extension accurately. Simulation results of scenarios for maneuvering EOT are given to illustrate the effectiveness of the proposed approach.

[1]  J.W. Koch,et al.  Bayesian approach to extended object and cluster tracking using random matrices , 2008, IEEE Transactions on Aerospace and Electronic Systems.

[2]  Uwe D. Hanebeck,et al.  Tracking connected objects using interacting shape models , 2014, 17th International Conference on Information Fusion (FUSION).

[3]  X. Rong Li,et al.  Tracking of extended object or target group using random matrix: new model and approach , 2016, IEEE Transactions on Aerospace and Electronic Systems.

[4]  X. Rong Li,et al.  Modeling of extended objects based on support functions and extended Gaussian images for target tracking , 2014, IEEE Transactions on Aerospace and Electronic Systems.

[5]  David Levin,et al.  The approximation power of moving least-squares , 1998, Math. Comput..

[6]  D. Clark,et al.  Group Target Tracking with the Gaussian Mixture Probability Hypothesis Density Filter , 2007, 2007 3rd International Conference on Intelligent Sensors, Sensor Networks and Information.

[7]  D. Salmond,et al.  Spatial distribution model for tracking extended objects , 2005 .

[8]  Scott Schaefer,et al.  Image deformation using moving least squares , 2006, ACM Trans. Graph..

[9]  Yaakov Bar-Shalom,et al.  Estimation and Tracking: Principles, Techniques, and Software , 1993 .

[10]  Dietrich Fränken,et al.  Tracking of Extended Objects and Group Targets Using Random Matrices , 2008, IEEE Transactions on Signal Processing.

[11]  X. R. Li,et al.  Extended target tracking using star-convex model with nonlinear inequality constraints , 2012, Proceedings of the 31st Chinese Control Conference.

[12]  LI X.RONG,et al.  Survey of maneuvering target tracking. Part I. Dynamic models , 2003 .

[13]  X. Rong Li,et al.  Tracking of Maneuvering Non-Ellipsoidal Extended Object or Target Group Using Random Matrix , 2014, IEEE Transactions on Signal Processing.

[14]  Uwe D. Hanebeck,et al.  Shape tracking of extended objects and group targets with star-convex RHMs , 2011, 14th International Conference on Information Fusion.

[15]  Mohammad A. Husain Estimation and filtering for digital signals , 1989 .

[16]  Ronald P. S. Mahler,et al.  PHD filters for nonstandard targets, I: Extended targets , 2009, 2009 12th International Conference on Information Fusion.