Adaptive filtering approach to chirp estimation and inverse synthetic aperture radar imaging of maneuvering targets

Abstract

In inverse synthetic aperture radar (ISAR) imaging, due to the noncooperative motions of maneuvering targets, the Doppler shifts of scatterers are usually time-varying, and the radar return signals are usually chirps. Thus, chirp estimation plays an important role in the performance of ISAR imaging. Li and Stoica (1996) presented an adaptive finite impulse response (FIR) filtering approach to estimate the amplitudes and phases of sinusoidal signals and applied it to the synthetic aperture radar (SAR) imaging with the sinusoidal signal model. We extend the Li and Stoica's algorithm to estimate the chirps and then apply it to the ISAR imaging of maneuvering targets. The extended algorithm is verified by some raw ISAR data.