Full waveform inversion (FWI) is an iterative method of data-fitting, aiming at high resolution recovery of the unknown model parameters. However, it is a cumbersome process, requiring a long computational time and large memory space/disc storage. One of the reasons for this computational limitation is the gradient calculation step. Based on the adjoint state method, it involves the temporal cross-correlation of the forward propagated source wavefield with the backward propagated residuals, in which we usually need to store the source wavefield, or include an extra extrapolation step to propagate the source wavefield from its storage at the boundary. We propose, alternatively, an amplitude excitation gradient calculation based on the excitation imaging condition concept that represents the source wavefield history by a single, specifically the most energetic arrival. An excitation based Born modeling allows us to derive the adjoint operation. In this case, the source wavelet is injected by a cross-correlation step applied to the data residual directly. Representing the source wavefield through the excitation amplitude and time, we reduce the large requirements for both storage and the computational time. We demonstrate the application of this approach on a 2-layer model with an anomaly and the Marmousi II model.