ISAR imaging and target characterization in the presence of multiplicative noise

ISAR imaging techniques are used to estimate the target spatial image from target backscatter data. In the ISAR measurement environment, the target is often modeled as a collection of point scatterers to take advantage of the Fourier relationship between scatterer location and measured backscatter data. Current ISAR imaging methods include range-Doppler processing, subspace techniques, and compressive sensing. Range-Doppler and subspace techniques are especially useful when imaging a target in the presence of AWGN (additive white Gaussian noise). Range-Doppler utilizes the processing gain of the Fourier transform to achieve a sign-to-noise gain while subspace techniques remove additive noise by separating signal and noise subspaces. Compressive sensing does not generally perform well in the presence of additive noise but can achieve very high resolution in low-noise conditions. These methods are well developed and proven in literature for the case of additive noise but do not address the case of multiplicative noise. This paper will present a method for extracting relative spatial information about the target in a computationally efficient manner.