Narrowband radar has been successfully used for high resolution imaging of fast rotating targets by exploiting their micro-motion features.In some practical situations,however,the target image may suffer from aliasing due to the fixed pulse repetition interval(PRI)of traditional radar scheme.In this work,the random PRI signal associated with compressed sensing(CS)theory was introduced for aliasing reduction to obtain high resolution images of fast rotating targets.To circumvent the large-scale dictionary and high computational complexity problem arising from direct application of CS theory,the low resolution image was firstly generated by applying a modified generalized Radon transform on the time-frequency domain,and then the dictionary was scaled down by random undersampling as well as the atoms extraction according to those strong scattering areas of the low resolution image.The scale-down-dictionary CS(SDD-CS)processing scheme was detailed and simulation results show that the SDD-CS scheme for narrowband radar can achieve preferable images with no aliasing as well as acceptable computational cost.
The theory of compressed sensing （CS） provides a new chance to reduce the data acquisition time and improve the data usage factor of the stepped frequency radar system. In light of the sparsity of radar target reflectivity, two imaging methods based on CS, termed the CS-based 2D joint imaging algorithm and the CS-based 2D decoupled imaging algorithm, are proposed. These methods incorporate the coherent mixing operation into the sparse dictionary, and take random measurements in both range and azimuth directions to get high resolution radar images, thus can remarkably reduce the data rate and simplify the hardware design of the radar system while maintaining imaging quality. Ex- periments from both simulated data and measured data in the anechoic chamber show that the proposed imaging methods can get more focused images than the traditional fast Fourier trans- form method. Wherein the joint algorithm has stronger robustness and can provide clearer inverse synthetic aperture radar images, while the decoupled algorithm is computationally more efficient but has slightly degraded imaging quality, which can be improved by increasing measurements or using a robuster recovery algorithm nevertheless.
