A Gaussian mixture extended-target multi-Bernoulli(GM-ET-MBer)filter is proposed to address the complex data association. The filter can simultaneously estimate the state and the number of extended targets without data association between observations and extended targets States and observations of extended targets are modeled as a Bernoulli random finite set and a Poisson random finite set
respectively. An updated state of extended targets is derived by combining the predicted states
and then
the state of extended targets is recursively estimated in linear Gaussian models. Compared with the Gaussian mixture extended-target probability hypothesis density(GM-ET-PHD)filter
the GM-ET-MBer filter can effectively improve estimation accuracy to the number of extended targets. Simulation results show that estimation of the proposed filter to the number of targets is unbiased and the standard deviations of estimations are 0.267 3 and 0.395 3
respectively
for both the GM-ET-MBer filter and GM-ET-PHD filter.
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references
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