Particle flow for sequential Monte Carlo implementation of probability hypothesis density

Target tracking is a challenging task and generally no analytical solution is available, especially for the multi-target tracking systems. To address this problem, probability hypothesis density (PHD) filter is used by propagating the PHD instead of the full multi-target posterior. Recently, the particle flow filter based on the log homotopy provides a new way for state estimation. In this paper, we propose a novel sequential Monte Carlo (SMC) implementation for the PHD filter assisted by the particle flow (PF), which is called PF-SMC-PHD filter. Experimental results show that our proposed filter has higher accuracy than the SMC-PHD filter and is computationally cheaper than the Gaussian mixture PHD (GM-PHD) filter.

[1]  Sergei Vassilvitskii,et al.  k-means++: the advantages of careful seeding , 2007, SODA '07.

[2]  Fred Daum,et al.  Zero curvature particle flow for nonlinear filters , 2012, Defense, Security, and Sensing.

[3]  Yaakov Bar-Shalom,et al.  Sonar tracking of multiple targets using joint probabilistic data association , 1983 .

[4]  Martin Ulmke,et al.  Non-linear and non-Gaussian state estimation using log-homotopy based particle flow filters , 2014, 2014 Sensor Data Fusion: Trends, Solutions, Applications (SDF).

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

[6]  Fred Daum,et al.  Exact particle flow for nonlinear filters , 2010, Defense + Commercial Sensing.

[7]  Jun S. Liu,et al.  Sequential Imputations and Bayesian Missing Data Problems , 1994 .

[8]  Juan M. Corchado,et al.  A particle dyeing approach for track continuity for the SMC-PHD filter , 2014, 17th International Conference on Information Fusion (FUSION).

[9]  Frederick E. Daum,et al.  Particle flow for nonlinear filters , 2011, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[10]  Ba-Ngu Vo,et al.  Improved SMC implementation of the PHD filter , 2010, 2010 13th International Conference on Information Fusion.

[11]  Sumeetpal S. Singh,et al.  Sequential monte carlo implementation of the phd filter for multi-target tracking , 2003, Sixth International Conference of Information Fusion, 2003. Proceedings of the.

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

[13]  Fred Daum,et al.  Particle flow for nonlinear filters with log-homotopy , 2008, SPIE Defense + Commercial Sensing.

[14]  Juan M. Corchado,et al.  Algorithm design for parallel implementation of the SMC-PHD filter , 2016, Signal Process..

[15]  Josef Kittler,et al.  Mean-Shift and Sparse Sampling-Based SMC-PHD Filtering for Audio Informed Visual Speaker Tracking , 2016, IEEE Transactions on Multimedia.

[16]  Josef Kittler,et al.  Audio Assisted Robust Visual Tracking With Adaptive Particle Filtering , 2015, IEEE Transactions on Multimedia.

[17]  Lingling Zhao,et al.  Particle flow for particle filtering , 2016, 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[18]  Fred Daum,et al.  Renormalization group flow and other ideas inspired by physics for nonlinear filters, Bayesian decisions, and transport , 2014, Defense + Security Symposium.

[19]  Junjie Wang,et al.  Gaussian particle flow implementation of PHD filter , 2016, Defense + Security.

[20]  Fred Daum,et al.  Small curvature particle flow for nonlinear filters , 2012, Defense + Commercial Sensing.

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