For the problem that the existing multiple target tracking filters can cause performance attenuation in clutter environment, an amplitude information generalized labeled multiBernoulli (AI-GLMB) filter is proposed. In general, the amplitude of clutter is lower than those from target returns. By introducing amplitude information to expand the target states, the amplitude likelihood function is derived. Then the new updating equation is deduced, and the sequential Monte Carlo implementation of the proposed method is given. Simulation results show that the AI-GLMB algorithm can adapt to the clutter environment effectively, and has higher tracking accuracy than the amplitude information assistant probability hypothesis density filter, amplitude information cardinality balanced multitarget multiBernoulli filter and the standard GLMB filter.