A survey of PHD filter and CPHD filter implementations

The probability hypothesis density (PHD) filter has attracted increasing interest since the author first introduced it in 2000. Potentially practical computational implementations of this filter have been devised, based on sequential Monte Carlo or on Gaussian mixture techniques. Research groups in at least a dozen different nations are investigating the PHD filter and its generalization, the CPHD filter, for use in various applications. Some of this work suggests that these filters may, under certain circumstances, outperform conventional multitarget filters such as MHT and JPDA. This paper summarizes these research efforts and their findings.

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[19]  Syed Ahmed Pasha,et al.  A Gaussian Mixture PHD Filter for Jump Markov System Models , 2009, IEEE Transactions on Aerospace and Electronic Systems.

[20]  Ba-Ngu Vo,et al.  Closed Form PHD Filtering for Linear Jump Markov Models , 2006, 2006 9th International Conference on Information Fusion.

[21]  K. Punithakumar,et al.  Multiple-model probability hypothesis density filter for tracking maneuvering targets , 2004, IEEE Transactions on Aerospace and Electronic Systems.

[22]  A. El-Fallah,et al.  Unified Bayesian situation assessment sensor management , 2005, SPIE Defense + Commercial Sensing.

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[29]  S. Singh,et al.  Novel data association schemes for the probability hypothesis density filter , 2007, IEEE Transactions on Aerospace and Electronic Systems.

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[31]  Ba-Ngu Vo,et al.  Convergence Analysis of the Gaussian , 2007 .

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

[33]  Ronald Mahler,et al.  Detecting, tracking, and classifying group targets: a unified approach , 2001, SPIE Defense + Commercial Sensing.

[34]  A.D. Lanterman,et al.  A probability hypothesis density-based multitarget tracker using multiple bistatic range and velocity measurements , 2004, Thirty-Sixth Southeastern Symposium on System Theory, 2004. Proceedings of the.

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

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

[37]  Ba-Ngu Vo,et al.  Tracking an unknown time-varying number of speakers using TDOA measurements: a random finite set approach , 2006, IEEE Transactions on Signal Processing.

[38]  Ronald P. S. Mahler,et al.  Multitarget filtering using a multitarget first-order moment statistic , 2001, SPIE Defense + Commercial Sensing.

[39]  A. Cantoni,et al.  The Cardinalized Probability Hypothesis Density Filter for Linear Gaussian Multi-Target Models , 2006, Annual Conference on Information Sciences and Systems.

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

[41]  Matti Vihola,et al.  Random set particle filter for bearings-only multitarget tracking , 2005, SPIE Defense + Commercial Sensing.

[42]  Ronald P. S. Mahler,et al.  Extended first-order Bayes filter for force aggregation , 2002, SPIE Defense + Commercial Sensing.

[43]  Hedvig Kjellström,et al.  An information fusion demonstrator for tactical intelligence processing in network-based defense , 2007, Inf. Fusion.

[44]  A. El-Fallah,et al.  Advancements in situation assessment sensor management , 2006, SPIE Defense + Commercial Sensing.

[45]  Peter Willett,et al.  Gaussian mixture cardinalized PHD filter for ground moving target tracking , 2007, 2007 10th International Conference on Information Fusion.

[46]  Ronald Mahler,et al.  Multitarget Moments and their Application to Multitarget Tracking , 2001 .

[47]  Abhijit Sinha,et al.  A distributed implementation of a sequential Monte Carlo probability hypothesis density filter for sensor networks , 2006, SPIE Defense + Commercial Sensing.

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

[49]  Ashraf A. Kassim,et al.  Tracking a Variable Number of Human Groups in Video Using Probability Hypothesis Density , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

[50]  Y. Bar-Shalom,et al.  Probability hypothesis density filter for multitarget multisensor tracking , 2005, 2005 7th International Conference on Information Fusion.

[51]  Yaakov Bar-Shalom,et al.  A physical-space approach for the probability hypothesis density and cardinalized probability hypothesis density filters , 2006, SPIE Defense + Commercial Sensing.

[52]  Ba-Ngu Vo,et al.  Analytic Implementations of the Cardinalized Probability Hypothesis Density Filter , 2007, IEEE Transactions on Signal Processing.

[53]  Hedvig Kjellström,et al.  Tracking Random Sets of Vehicles in Terrain , 2003, 2003 Conference on Computer Vision and Pattern Recognition Workshop.

[54]  Ronald Mahler,et al.  MULTITARGET SENSOR MANAGEMENT OF DISPERSED MOBILE SENSORS , 2004 .

[55]  Ronald P. S. Mahler,et al.  Particle-systems implementation of the PHD multitarget-tracking filter , 2003, SPIE Defense + Commercial Sensing.

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

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

[58]  Ronald P. S. Mahler,et al.  Multisensor-multitarget sensor management with target preference , 2004, SPIE Defense + Commercial Sensing.