Group state estimation algorithm using Foliage Penetration GMTI radar detections

This paper describes an algorithm for integrating detections from Foliage Penetrating (FOPEN) Ground Moving Target Indicator (GMTI) radar, to recognize groups of dismounts moving through dense foliage, and to estimate the group states, including the group sizes and the directions of their movements. Difficulties of processing FOPEN GMTI radar detections are best characterized as low target-state-dependent detection probabilities and high non-uniform persistent false alarm densities. To overcome these difficulties, we use the Sum-of-Gaussian (SOG) or Gaussian-Mixture (GM) Cardinalized Probability Hypothesis Density (CPHD) method to detect and track individual dismounts, and then, apply a group dynamics recognition method to the CPHD outputs to recognize the formation and the behavior of the dismounts groups.

[1]  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.

[2]  Simon J. Godsill,et al.  Poisson models for extended target and group tracking , 2005, SPIE Optics + Photonics.

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

[4]  Christian Lundquist,et al.  Extended Target Tracking using a Gaussian-Mixture PHD Filter , 2017, IEEE Transactions on Aerospace and Electronic Systems.

[5]  Fredrik Gustafsson,et al.  Pedestrian group tracking using the GM-PHD filter , 2013, 21st European Signal Processing Conference (EUSIPCO 2013).

[6]  Daniel E. Clark,et al.  Extended object filtering using spatial independent cluster processes , 2010, 2010 13th International Conference on Information Fusion.

[7]  Christian Lundquist,et al.  A Gaussian mixture PHD filter for extended target tracking , 2010, 2010 13th International Conference on Information Fusion.

[8]  Christian Lundquist,et al.  An Extended Target CPHD Filter and a Gamma Gaussian Inverse Wishart Implementation , 2013, IEEE Journal of Selected Topics in Signal Processing.

[9]  Oliver E. Drummond,et al.  A bibliography of cluster (group) tracking , 2004, SPIE Defense + Commercial Sensing.

[10]  Stefano Coraluppi,et al.  All-Source Track and Identity Fusion , 2000 .

[11]  Ronald P. S. Mahler,et al.  Statistical Multisource-Multitarget Information Fusion , 2007 .

[12]  Ba-Ngu Vo,et al.  The Gaussian Mixture Probability Hypothesis Density Filter , 2006, IEEE Transactions on Signal Processing.

[13]  Jeffrey D. Scargle,et al.  An Introduction to the Theory of Point Processes, Vol. I: Elementary Theory and Methods , 2004, Technometrics.

[14]  Chee-Yee Chong,et al.  Tracking of Groups of Targets Using Generalized Janossy Measure Density Function , 2006, 2006 9th International Conference on Information Fusion.

[15]  Dietrich Fränken,et al.  Advances on tracking of extended objects and group targets using random matrices , 2009, 2009 12th International Conference on Information Fusion.

[16]  S.S. Blackman,et al.  Multiple hypothesis tracking for multiple target tracking , 2004, IEEE Aerospace and Electronic Systems Magazine.

[17]  R. Mahler Multitarget Bayes filtering via first-order multitarget moments , 2003 .

[18]  R. Mahler,et al.  PHD filters of higher order in target number , 2006, IEEE Transactions on Aerospace and Electronic Systems.

[19]  Uwe D. Hanebeck,et al.  Extended object and group tracking with Elliptic Random Hypersurface Models , 2010, 2010 13th International Conference on Information Fusion.

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

[21]  Peter Willett,et al.  GMTI Tracking via the Gaussian Mixture Cardinalized Probability Hypothesis Density Filter , 2010, IEEE Transactions on Aerospace and Electronic Systems.

[22]  Chee-Yee Chong,et al.  An alternative form of cardinalized PHD filter or I.I.D.-approximation filter , 2007, 2007 10th International Conference on Information Fusion.