In order to improve the tracking accuracy of infrared small target under the interference of background clutter and imaging noisean infrared small target tracking method based on structural information modeling and discriminant sparsity is proposed. Firstlythe small target signal is sparse decomposed in the generalized Gaussian target super complete dictionary to extract the spatial structure information of the small target from the infrared image which is corrupted by noise and clutter. Thenthe particle filter algorithm is improvedand the transfer constrained particle filter tracking algorithm is designed to improve the sampling probability of particles. Finallyin the framework of transfer constrained particle filterthe sparse coefficients of candidate targets are calculated based on discriminant sparse representation and L1 norm minimization framework to achieve small target tracking. Experimental results based on various infrared sequences show that the proposed method can track small target stably under the interference of clutter and noiseand its center erroroverlap rate and average video playback frame rate are 3 pixel0.7 and 0.632fpsrespectivelywhich are better than other comparison methods and have strong robustness.