In order to improve the accuracy of laser imaging guidance and realize the effective recognition under occlusion conditions,a laser image matching algorithm based on improved Hausdorff distance and particle swarm optimization is proposed.Firstly,the edge features of the reference image and the real-time image were extracted.Then,because the original Hausdorff distance is susceptible to the noise,isolated point and occlusion,an adaptive partial mean Hausdorff distance is proposed as the similarity measure function.Finally,the particle swarm optimization is improved to complete the search matching.On the one hand,the chaotic inertia weight is proposed to improve the searching ability,on the other hand,the chaos local search is used to avoid the premature convergence.Experimental results show that this algorithm has a high matching success rates,and has good real-time.