Native Matlab MHT based on MeMBer-Poisson factorization

Version 1.0.0.0 (23.6 KB) by EFB
A simple implementation of Multiple Hypothesis Tracking

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Updated Sun, 11 Mar 2018 18:57:44 +0000

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The zip-file contains m-files for an implementation of Multiple Hypothesis Tracking in accordance with the random finite set formulation of the multitarget Bayes filter. The main reference underlying the method is
Brekke, E. & Chitre, M.: "The multiple hypothesis tracker derived from finite set statistics", Proc. Fusion 2017.

which again is based on

Williams, J.: "Marginal multi-Bernoulli filters: RFS derivation of MHT, JIPDA, and association-based MeMBer", IEEE Transactions on Aerospace and Electronic Systems, 2015, vol. 51, pp. 1664-1687.

The program (executed by running script_mbmp_mht01.m) is developed with the purpose of testing some of the bounds developed in

Devijver, P. A.: "On a New Class of Bounds on Bayes Risk in Multihypothesis Pattern Recognition", IEEE Transactions on Computers, 1974, Vol. 23, pp. 70-80.

This is a very simple MHT implementation that is fully hypothesis-oriented and which only uses pruning operations (and no clustering/merging) to mitigate complexity. It has been tested for scenarios involving up to 4 targets and 10 time steps. The main benefit of the implementation is that it is 100% Matlab and therefore does not require any compilation. At the core of the program is an implementation of Murty's method which originally was written by Umut Orguner.

Cite As

EFB (2023). Native Matlab MHT based on MeMBer-Poisson factorization (https://www.mathworks.com/matlabcentral/fileexchange/66450-native-matlab-mht-based-on-member-poisson-factorization), MATLAB Central File Exchange. Retrieved .

MATLAB Release Compatibility
Created with R2014b
Compatible with any release
Platform Compatibility
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Version Published Release Notes
1.0.0.0