Gaussian process Poisson multi-Bernoulli mixture filtering and its variational optimization
In response to the problem of low tracking accuracy for multi-object tracking in existing algorithms,a Gaus-sian process Poisson multi-Bernoulli mixture(GP-PMBM)filtering algorithm and its variational optimization are proposed.Firstly,an augmented state space model is established based on the principles of Gaussian processes.Subsequently,to ad-dress the issue of decreased filtering accuracy in GP-PMBM caused by the use of nonlinear filtering techniques,variable Bayesian optimization is utilized to update the results,achieving optimized updates of the target states and enhancing the estimation accuracy of the filter.Simulation results demonstrate that the proposed algorithm has higher tracking accura-cy compared to existing filtering algorithms and exhibits more stable tracking performance in scenarios with only partial measurements.
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