Due to adoption of the kernel density estimation (KDE)

kernel particle filter (KPF) is an effective method for target tracking in dynamic system with small noise. However

selection of the kernel bandwidth is a critical step in KDE. In this paper

a variable bandwidth kernel particle filter (VBKPF) based on covariance matrix is proposed. Firstly

covariance matrix of the 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 estimation. At last

to get a more effective allocation of particles

we use the variable bandwidth KDE in the VBKPF to approximate the PDFs by moving particles toward the posterior. This gives a closed-form expression of the true distribution. Experimental results show that the proposed VBKPF performs better than the standard particle filter

unscented particle filter and the kernel particle filter both in efficiency and estimation precision of the optoelectronic target tracking system.