In many multi-targets tracking scenarios, the amplitude returning from targets are typically stronger than those from clutters. This information could be used to enhance the tracking performance by establishing more accurate clutter and target likelihoods. In this paper, we incorporate the amplitude information into the δ-Generalized Labeled Multi-Bernoulli (δ-GLMB) filter, which is based on the random finite set (RFS), and can output target tracks. In addition, we present the novel update equation of the δ-GLMB filter using amplitude likelihood function. Simulation results demonstrate the better performance via a linear multi-target scenario.