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GM-PHD filter implementation in python (Gaussian mixture probability hypothesis density filter)

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GMPHD-py

This repository contains a Python implementation of the Gaussian Mixture - Probability Hypothesis Density (GM-PHD) filter described in [1] (forked from the Dan Stowell repository) and its application to underwater robotic mapping [2].

References

[1] B. N. Vo and W. K. Ma. The Gaussian Mixture Probability Hypothesis Density Filter IEEE Transactions on Signal Processing, 2006.

[2] T. Fabbri, F. Di Corato, D. Fenucci, D. Meucci and A. Caiti, Multiple target tracking in seabed surveys using the GM-PHD filter OCEANS 2015 - MTS/IEEE Washington, Washington, DC, 2015

[3] Stowell and M. D. Plumbley, Multi-target pitch tracking of vibrato sources in noise using the GM-PHD filter In Proceedings of Proceedings of the 5th International Workshop on Machine Learning and Music, July 2012.

Dependencies

GM-PHD Filter dependencies [3]:

  • Numpy
  • Scipy

Dependencies for the application of underwater robotic mapping [2]:

  • MOOS
  • MOOS-IVP
  • Python-moos

Notes

There are some differences from the GM-PHD algorithm described in Vo & Ma's paper:

  • I have not implemented "spawning" of new targets from old ones, since I don't need it. It would be straightforward to add it - see the original paper.

  • Weights are adjusted at the end of pruning, so that pruning doesn't affect the total weight allocation.

  • I provide an alternative approach to state-extraction (an alternative to Table 3 in the original paper) which makes use of the integral to decide how many states to extract.

MOOSApp

License

(C) 2016 Tommaso Fabbri - University of Pisa - Automation and Robotics Laboratory

This code represents free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program. If not, see http://www.gnu.org/licenses/.

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