As introduced in Chapter 3, inverse synthetic aperture radar (ISAR) data are arranged in a 2-D matrix, where the number of range cells is in its row and the number of pulses is in its column, or vice versa. To reconstruct an ISAR range-Doppler image, we first take range compression to obtain ISAR range profiles, which is a sequence of consecutive range profiles over the coherent processing interval (CPI). Range compression is usually performed through a matched filter. From the range profiles, we can see motion of targets; that is, targets appear at different positions in different range profiles. Then, motion compensation will be carried out, including translational motion compensation (TMC) and rotational motion compensation (RMC). Finally, after removing translational motion and rotational motion, by taking the Fourier transform along the number of pulses (slow-time domain), an ISAR range-Doppler image can be formed. In this chapter, we will introduce ISAR motion compensation methods, including the cross-correlation, range centroid, and minimum-entropy methods for range alignment and the minimum variance, Doppler centroid, phase gradient, and entropy methods for phase adjustment. Many motion compensation algorithms were developed for solving the image-smearing problem [1-12]. TMC includes range alignment and phase adjustment or phase correction. Range alignment is accomplished by tracking the movement of a prominent scatterer with strong peak in range profiles. This is called the coarse range alignment, which allows the prominent scatterer to be sorted into the same range cell across the range profiles. The accuracy of the alignment is limited by the range resolution cell. However, only the coarse range alignment is not sufficient for removing phase drift errors in the range profiles. Consequently, a suitable phase adjustment procedure must be carried out to remove the residual phase errors and drifts.