Particle PHD filter multiple target tracking in sonar image

Two contrasting approaches for tracking multiple targets in multi-beam forward-looking sonar images are considered. The first approach is based on assigning a Kalman filter to each target and managing the measurements with gating and a measurement-to-track data association technique. The second approach uses the recently developed particle implementation of the multiple-target probability hypothesis density (PHD) filter and a target state estimate-to-track data association technique. The two approaches are implemented and compared on both simulated sonar and real forward-looking sonar data obtained from an autonomous underwater vehicle (AUV) and demonstrate that the PHD filter with data association compares well with traditional approaches for multiple target tracking

[1]  Lin Lin.  Parameter estimation and data association for multitarget tracking , 2004 .

[2]  Ronald P. S. Mahler,et al.  Multitarget miss distance via optimal assignment , 2004, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[3]  Yvan R. Petillot,et al.  Detection and Tracking of Multiple Metallic Objects in Millimetre-Wave Images , 2007, International Journal of Computer Vision.

[4]  D. Clark,et al.  Multi-target state estimation and track continuity for the particle PHD filter , 2007, IEEE Transactions on Aerospace and Electronic Systems.

[5]  D.E. Clark,et al.  Data Association for the PHD Filter , 2005, 2005 International Conference on Intelligent Sensors, Sensor Networks and Information Processing.

[6]  Ba-Ngu Vo,et al.  Probability hypothesis density filter versus multiple hypothesis tracking , 2004, SPIE Defense + Commercial Sensing.

[7]  D. Clark,et al.  PHD filter multi-target tracking in 3D sonar , 2005, Europe Oceans 2005.

[8]  David M. Mount,et al.  A local search approximation algorithm for k-means clustering , 2002, SCG '02.

[9]  I. Tena Ruiz,et al.  Tracking and classification of multiple objects in multibeam sector scan sonar image sequences , 1998, Proceedings of 1998 International Symposium on Underwater Technology.

[10]  Yvan Petillot,et al.  Multiple Target Tracking and Data Association in Sonar Images , 2006 .

[11]  G. Pulford.  Taxonomy of multiple target tracking methods , 2005 .

[12]  David M. Lane,et al.  Feature extraction and data association for AUV concurrent mapping and localisation , 2001, Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation (Cat. No.01CH37164).

[13]  A. Doucet,et al.  Sequential Monte Carlo methods for multitarget filtering with random finite sets , 2005, IEEE Transactions on Aerospace and Electronic Systems.

[14]  B. Vo,et al.  A closed-form solution for the probability hypothesis density filter , 2005, 2005 7th International Conference on Information Fusion.

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

[16]  Daniel E. Clark,et al.  Bayesian multiple target tracking in forward scan sonar images using the PHD filter , 2005 .

[17]  Yvan Petillot,et al.  Underwater vehicle obstacle avoidance and path planning using a multi-beam forward looking sonar , 2001 .

[18]  Yvan Petillot,et al.  Tracking objects in underwater multibeam sonar images , 1999 .

[19]  Y. Bar-Shalom.  Tracking and data association , 1988 .