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Python Efficient Hypothesis Management (PyEHM)

PyPI Documentation Status CircleCI License DOI

PyEHM is a Python package that includes open-source implementations of the Efficient Hypothesis Management (EHM) Algorithms described in [1], [2] and covered by the patent [3].

[1] Maskell, S., Briers, M. and Wright, R., 2004, August. Fast mutual exclusion. In Signal and Data Processing of Small Targets 2004 (Vol. 5428, pp. 526-536). International Society for Optics and Photonics

[2] Horridge, P. and Maskell, S., 2006, July. Real-time tracking of hundreds of targets with efficient exact JPDAF implementation. In 2006 9th International Conference on Information Fusion (pp. 1-8). IEEE

[3] Maskell, S., 2003, July. Signal Processing with Reduced Combinatorial Complexity. Patent Reference:0315349.1

Documentation

Please see the PyEHM documentation for installation instructions, API references and usage examples.

License

PyEHM is licenced under Eclipse Public License 2.0. See License for more details.

This software is the property of QinetiQ Limited and any requests for use of the software for commercial use or other use outside of the Eclipse Public Licence should be made to QinetiQ Limited.

The current QinetiQ contact is Richard Lane (rlane1 [at] qinetiq [dot] com).

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pyehm's Issues

EHM for multi sensor fusion

Hi, I have a multi sensor fusion problem. It's not exactly clear from the docs/code - is it possible to use this library in case when there can be multiple measurements associated to one track? If it's not how hard would it be to adapt this implementation to such case?

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