The estimation error of the least square method in traditional Distance Vector-Hop (DV-Hop) algorithm is too large and the Particle Swarm Optimization (PSO) algorithm easily traps into local optimum. In order to overcome the problems, a fusion algorithm of improved particle swarm algorithm and DV-Hop algorithm was presented. First of all, PSO algorithm was improved from aspects of particle velocity, inertia weight, learning strategy and variation, which enhanced the ability of algorithm to jump out of local optimum and increased the search speed of the algorithm in later iterative stage. The node localization result was optimized by using the improved PSO algorithm in the third stage of the DV-Hop algorithm. The simulation results show that compared with the traditional DV-Hop algorithm,the improved DV-Hop based on chaotic PSO algorithm, and the DV-Hop algorithm based on improved PSO, the proposed algorithm has high positioning accuracy, good stability.