Kalman-Gain Aided Particle PHD Filter for Multitarget Tracking

We propose an efficient sequential Monte Carlo probability hypothesis density (PHD) filter which employs the Kalman-gain approach during weight update to correct predicted particle states by minimizing the mean square error between the estimated measurement and the actual measurement received at a given time in order to arrive at a more accurate posterior. This technique identifies and selects those particles belonging to a particular target from a given PHD for state correction during weight computation. Besides the improved tracking accuracy, fewer particles are required in the proposed approach. Simulation results confirm the improved tracking performance when evaluated with different measures.

[1]  S. Kay.  Fundamentals of statistical signal processing: estimation theory , 1993 .

[2]  R.P.S. Mahler,et al.  "Statistics 101" for multisensor, multitarget data fusion , 2004, IEEE Aerospace and Electronic Systems Magazine.

[3]  Ba-Ngu Vo,et al.  On performance evaluation of multi-object filters , 2008, 2008 11th International Conference on Information Fusion.

[4]  Murat Efe,et al.  A novel auxiliary particle PHD filter , 2012, 2012 15th International Conference on Information Fusion.

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

[6]  Yohan Petetin,et al.  A mixed GM/SMC implementation of the probability hypothesis density filter , 2012, 2012 11th International Conference on Information Science, Signal Processing and their Applications (ISSPA).

[7]  Ba-Ngu Vo,et al.  Labeled Random Finite Sets and the Bayes Multi-Target Tracking Filter , 2013, IEEE Transactions on Signal Processing.

[8]  Ronald P. S. Mahler,et al.  “Statistics 102” for Multisource-Multitarget Detection and Tracking , 2013, IEEE Journal of Selected Topics in Signal Processing.

[9]  J. Webster,et al.  Wiley Encyclopedia of Electrical and Electronics Engineering , 2010 .

[10]  Kuk-Jin Yoon,et al.  Gaussian mixture importance sampling function for unscented SMC-PHD filter , 2013, Signal Process..

[11]  Thia Kirubarajan,et al.  PHD filter based track-before-detect for MIMO radars , 2012, Signal Process..

[12]  Frank Dellaert,et al.  MCMC-based particle filtering for tracking a variable number of interacting targets , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  Ba-Ngu Vo,et al.  Labeled Random Finite Sets and Multi-Object Conjugate Priors , 2013, IEEE Transactions on Signal Processing.

[14]  Thia Kirubarajan,et al.  Estimation with Applications to Tracking and Navigation: Theory, Algorithms and Software , 2001 .

[15]  Simon J. Godsill,et al.  On sequential Monte Carlo sampling methods for Bayesian filtering , 2000, Stat. Comput..

[16]  M. Melzi,et al.  Joint multiple target tracking and classification using the Unscented Kalman Particle PHD filter , 2011, 2011 IEEE 9th International New Circuits and systems conference.

[17]  Jian Huang,et al.  Improved Particle Implementation of the Probability Hypothesis Density Filter in Resampling , 2012, 2012 IEEE 12th International Conference on Computer and Information Technology.

[18]  Branko Ristic,et al.  Beyond the Kalman Filter: Particle Filters for Tracking Applications , 2004 .

[19]  Sumeetpal S. Singh,et al.  Random finite sets and sequential Monte Carlo methods in multi-target tracking , 2003, 2003 Proceedings of the International Conference on Radar (IEEE Cat. No.03EX695).

[20]  Simon J. Julier,et al.  Optimised proposals for improved propagation of multi-modal distributions in particle filters , 2013, Proceedings of the 16th International Conference on Information Fusion.

[21]  Bo Ma,et al.  Kalman Particle PHD Filter for Multi-target Visual Tracking , 2011, IScIDE.

[22]  Jianjun Yin,et al.  The Gaussian Particle multi-target multi-Bernoulli filter , 2010, 2010 2nd International Conference on Advanced Computer Control.

[23]  Ba-Ngu Vo,et al.  The Cardinality Balanced Multi-Target Multi-Bernoulli Filter and Its Implementations , 2009, IEEE Transactions on Signal Processing.

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

[25]  Weihua Wu,et al.  Fast Gaussian Mixture Probability Hypothesis Density Filter , 2014 .

[26]  Y. Bar-Shalom,et al.  Multitarget Tracking , 2015 .

[27]  R. Mahler.  PHD filters of higher order in target number , 2007 .

[28]  Niclas Bergman,et al.  Recursive Bayesian Estimation : Navigation and Tracking Applications , 1999 .

[29]  Ba-Ngu Vo,et al.  Generalized Labeled Multi-Bernoulli Approximation of Multi-Object Densities , 2014, IEEE Transactions on Signal Processing.

[30]  S. Godsill,et al.  Auxiliary Particle Implementation of the Probability Hypothesis Density Filter , 2007, 2007 5th International Symposium on Image and Signal Processing and Analysis.

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

[32]  Hongbing Ji,et al.  A new Gaussian mixture particle CPHD filter for multitarget tracking , 2010, 2010 International Symposium on Intelligent Signal Processing and Communication Systems.

[33]  Yakov Bar-Shalom,et al.  Multitarget-Multisensor Tracking: Principles and Techniques , 1995 .

[34]  Ba-Ngu Vo,et al.  Bayesian Multi-Target Tracking With Merged Measurements Using Labelled Random Finite Sets , 2015, IEEE Transactions on Signal Processing.

[35]  Ba-Ngu Vo,et al.  Gaussian Particle Implementations of Probability Hypothesis Density Filters , 2007, 2007 IEEE Aerospace Conference.

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

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

[38]  Xuemin Shen,et al.  An Efficient Data-Driven Particle PHD Filter for Multitarget Tracking , 2013, IEEE Transactions on Industrial Informatics.

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