Sparse Apertures ISAR Imaging and Scaling for Maneuvering Targets

In advanced multifunctional radar, inverse synthetic aperture radar (ISAR) imaging of sparse apertures for maneuvering targets is a challenge problem. In general, the Doppler modulation of rotation motion can be modeled as linear frequency for uniformly accelerated rotation targets, which is spatial-variant in two-dimension (2-D). The signal diversity inherently reflects the maneuverability and provides a rationale of rotation motion estimation. In this paper, we focus on the problem of sparse apertures ISAR imaging and scaling for maneuvering targets. The maneuvering signal model is formulated as chirp code and represented using a chirp-Fourier basis. Then sparse representation is applied to realize range-Doppler (RD) imaging from the sparse apertures, where the superposition of chirp parameters is acquired using the modified discrete chirp Fourier transform (MDCFT). After preprocessing, such as sample selection, rotation center determination, and noise reduction, the chirp parameters are used to estimate the parameters of rotation motion using the weighted least square (WLS) method. Finally, a high-resolution scaled-ISAR image is achieved by rescaling the acquired RD image using the estimated rotation velocity. Experiments are performed to confirm the effectiveness of the proposal.