Kernel Particle Filter (KPF) is an effective method for target tracking of a dynamic system with small noises, in which the selection of the kernel bandwidth is a critical step of Kernel Density Estimation(KDE) in KPF.In this paper, a Variable Bandwidth Kernel Particle Filter (VBKPF) based on covariance matrix is proposed.Firstly, the covariance matrix of particle sets is used to compute the coarse bandwidth and the coarse posterior Probability Density Functions (PDFs).Then, each particle can acquire its own accurate bandwidth by adjusting the global kernel bandwidth to improve the precision of the KDE.Finally, to get a more effective particle allocation, the variable bandwidth KDE in the VBKPF is used to approximate the PDFs by moving particles toward the posterior, which gives a closed-form expression of the true distribution.Experimental results show that the proposed VBKPF performs better than the standard particle filter(PF), Unscented Particle Filter(UPF) and the Kernel Particle Filter(KPF) both in efficiency(20% particle number of PF)and estimation precision for optoelectronic target tracking systems.