Aiming at the problem of low accuracy of state estimation of the particle filter (PF) algorithm with unknown noise, the intelligent optimization costreference particle filter (IOCRPF) algorithm is investigated. The intelligent optimization resampling strategy is designed based on the characteristics of the costreference particle filter (CRPF) algorithm. The probability mass function is used to evaluate the credibility of particles, and the crossover and mutation operations are used to guide the particles to move to the less risky areas. Therefore, it can improve the impoverishment of samples resulted from samples update based on risk and cost. And the posterior distribution area is extended through the mutation of the riskier particles. The simulation results show that the IOCRPF algorithm has superior performance of particle optimization and improves the accuracy of state estimation under the condition of unknown noise statistical characteristics.