Aiming at the problem of particle impoverishment caused by the resampling process in standard particle filter, we propose a firefly optimized particle filter algorithm based on the spring mechanism. Firstly, adopting the attraction and movement mechanism of the firefly algorithm, we design the motion controlled strategy where the optimal particle is used to guide the particles moving towards the high likelihood region. Secondly, the real-time distribution of particles is evaluated, and the optimization intensity of particles is adaptive controlled by the proportion of particles in the high likelihood region. Finally, the density of the particles around the optimal particle is detected, and the elastic mechanism of spring is introduced, and the positions of particles are adjusted according to the particle density around the optimal particle, which can make the distribution of particles more reasonable and enhance the precision of the particle filter. The experimental results show that the filtering accuracy of the improved algorithm is 12% to 25% higher than that of the standard particle filter when the number of particles is small, and the operation time of the improved algorithm is about 20% to 30% less than that of the standard particle filter under the same filtering accuracy requirements, and the improved algorithm has the better comprehensive performance.