Abstract: Quasi-Monte-Carlo particle filter (QMC-PF) can not meet the requirement of target tracking because of the high computational complexity. A novel Quasi-Monte-Carlo particle filter (NQMC-PF) algorithm for maneuvering radar target tracking is proposed. The algorithm applies QMC algorithm to generating the low-discrepancy offsprings of the the particles with heavy weight to replace the particles with low weight, which guarantees the quality and diversity of samples. Generalized regression neural network (GRNN) is used to calculate the weights of the offsprings, which improves the precision and the speed of the filter. The simulation results show that the calculation precision of the algorithm is higher than standard QMC-PF, and it possesses the advantages of short computation time and real-time standard. It can be applied to the radar target tracking.