Abstract
For the problems that Gamma Gaussian Inverse Wishart Cardinalized Probability Hypothesis Density (GGIW-CPHD) filter cannot accurately estimate the extended target shape and has a bad tracking performance under the condition of low SNR, a new generalized labeled multi-Bernoulli algorithm based on Gaussian process regression is proposed. The algorithm adopts the star convex to model the extended target, and realizes the online learning of the Gaussian process by constructing the state space model to complete the estimation of the extended target shape. At the same time, in the low SNR environment, the target motion state is tracked by the good tracking performance of the generalized label Bernoulli filter. Simulation results show that for any target with unknown shape, the proposed algorithm can well offer its extended shape and in the low SNR environment it can greatly improve the accuracy and stability of target tracking.
Highlights
In an extended target tracking problem, target measurements collected by the sensor are no longer one point for one target, but a set of points
In 2012, Granstrom applied the random matrix to the extended target probability hypothesis density(PHD) filter and proposed the Gaussian Inverse Wishart PHD (GIWPHD)filter[2], he applied it to the Cardinalized probability hypothesis density (CPHD) filter and got Gamma Gaussian inverse Wishart CPHD (GGIWCPHD)filter[3]
A new generalized labeled multi-Bernoulli algorithm based on Gaussian process regression is proposed to solve the problem that GGIW-CPHD filter cannot accurately estimate the extended target shape and has a bad tracking performance under the condition of low SNR
Summary
In an extended target tracking problem, target measurements collected by the sensor are no longer one point for one target, but a set of points. In 2008, Baum proposed an extended target tracking method based on random hypersurfaces[4] He modelled the targets as star-convex[5] and realized the tracking of irregular shape targets. In 2015, Gaussian process regression of machine learning is proposed to estimate extended target shape[6], avoiding complex calculation problems of the above algorithm due to excessive parameters, and achieving the accurate estimation of the target of any unknown shape. A new generalized labeled multi-Bernoulli algorithm based on Gaussian process regression is proposed to solve the problem that GGIW-CPHD filter cannot accurately estimate the extended target shape and has a bad tracking performance under the condition of low SNR. The algorithm improves the accuracy and stability of target tracking in low SNR environment and realizes the accurate estimation of the unknown extended target shape using the Gaussian process regression
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