Linear Array Sar Imaging via Compressed Sensing

In recent years, various attempts have been undertaken to obtain three-dimensional (3-D) reflectivity of observed scene from synthetic aperture radar (SAR) technique. Linear array SAR (LASAR) has been demonstrated as a promising technique to achieve 3-D imaging of earth surface. The common methods used for LASAR imaging are usually based on matched filter (MF) which obeys the traditional Nyquist sampling theory. However, due to limitation in the length of linear array and the “Rayleigh” resolution, the standard MFbased methods suffer from low resolution and high sidelobes. Hence, high resolution imaging algorithms are desired. In LASAR images, dominating scatterers are always sparse compared with the total 3D illuminated space cells. Combined with this prior knowledge of sparsity property, this paper presents a novel algorithm for LASAR imaging via compressed sensing (CS). The theory of CS indicates that sparse signal can be exactly reconstructed in high Signal-NoiseRatio (SNR) level by solving a convex optimization problem with a very small number of samples. To overcome strong noise and clutter interference in LASAR raw echo, the new method firstly achieves range focussing by a pulse compression technique, which can greatly improve SNR level of signal in both azimuth and crosstrack directions. Then, the resolution enhancement images of sparse targets are reconstructed by L1 norm regularization. High resolution properties and point localization accuracies are tested and verified by simulation and real experimental data. The results show that the CS method outperforms the conventional MF-based methods, even if very small random selected samples are used. Received 31 March 2011, Accepted 13 May 2011, Scheduled 10 June 2011 * Corresponding author: Shun-Jun Wei ([email protected]). 300 Wei, Zhang, and Shi