Mohamed Sadek Kemouche

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In this paper, we present a recursive estimation algorithm for nonlinear non-Gaussian tracking systems based on an adaptive Gaussian mixture technique. This estimation cannot be efficiently performed for nonlinear non-Gaussian systems because of the complex representation of the state density. To alleviate this complexity, approximation techniques based on(More)
In this paper, we propose a Gaussian mixture (GM)-probability hypothesis density (PHD) filter based algorithm for multiple objects tracking. To reduce the number of used Gaussians, we introduced a clustering procedure and observation Gaussians estimation, to avoid the exponential growth of mixture components when the number of measurement highly increases.(More)
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