For the nonlinear filtering problem of target tracking in aperiodic sparseness sampling environment

a novel auxiliary quadrature particle filter(AQPF) based on Gauss-Hermite quadrature and target characteristics is proposed.In the proposed algorithm

a set of quadrature point probability densities based on the Gauss-Hermite quadrature is proposed to approximate the important density function.At the same time

the proposed algorithm can incorporate target observation

time interval of the target observation and the target speed into the construction of important density function

which can effectively enhance the diversity of samples and improve the performance.Finally

the experimental results show that the performance of the proposed algorithm is better than these of the unscented Kalman filter(UKF)

quadrature Kalman filter(QKF)

particle filter(PF)

auxiliary particle filtering(APF)and Gaussian particle filter(GPF)

and can effectively estimate the target states.