Yueguan Lin

Random noise SAR based on compressed sensing

Recent theory of compressed sensing (CS) suggested that exact recovery of an unknown sparse signal can be achieved from few measurements with overwhelming probability. In this paper, we combine CS technology with a random noise SAR and proposed the concept of random noise SAR based on CS. The block diagram of the radar system and the collected data processing procedure was presented. Theoretic analysis show that the sensing matrix of the random noise SAR exhibits good restricted isometry property (RIP).When the target scene is sparse or sparse in any basis, the random noise radar based on CS can get high accuracy image by collecting far less amount of echo data than traditional noise radar does. The conclusions are all demonstrated by simulation experiments.

Multi-channel SAR imaging based on distributed compressive sensing

The rapid development of compressive sensing (CS) shows that it is possible to recover a sparse signal from very limited measurements. Synthetic aperture radar (SAR) imaging based on CS can reconstruct the target scene with a reduced number of collected samples by solving an optimization problem. For multichannel SAR imaging based on CS, each channel requires sufficient samples for separate imaging and the total number of samples could still be large. We propose an imaging algorithm based on distributed compressive sensing (DCS) that reconstructs scenes jointly under multiple channels. Multi-channel SAR imaging based on DCS not only exploits the sparsity of the target scene, but also exploits the correlation among channels. It requires significantly fewer samples than multi-channel SAR imaging based on CS. If multiple channels offer different sampling rates, DCS joint processing can reconstruct target scenes with a much more flexible allocation of the number of measurements offered by each channel than that used in separate CS processing.

MIMO SAR processing with azimuth nonuniform sampling

This paper analyses ambiguity suppression caused by multiple-input multiple-output (MIMO) SAR azimuth nonuniform samplings. Two methods are analyzed: azimuth spectrum reconstruction algorithm and minimum mean square error (MMSE) imaging algorithm. The azimuth spectrum reconstruction algorithm can reconstruct the scene fine resolution, while the nonideal orthogonality of multi-channel encoding waveforms causes azimuth ambiguous in SAR imaging. The MMSE imaging algorithm can perfectly reconstruct, while it requires high SNR.

Displaced phase center antenna SAR imaging based on compressed sensing

The displaced phase center antenna (DPCA) synthetic aperture radar (SAR) has the potential to achieve high azimuth resolution and wide swath. Its pulse repletion frequency (PRF) has to be selected such that SAR platform moves just one half of its total antenna length between subsequent radar pulses. If this condition is not satisfied, there will be nonuniform sampling in azimuth and azimuth ambiguities will appear when traditional imaging algorithms based on matched filter are used. We propose an innovative imaging algorithm based on compressed sensing (CS) which can reconstruct the scene well even though this rigid condition is not satisfied.

MIMO SAR Using Orthogonal Coding: Design, Performance Analysis, and Verifications

Multiple-input multiple-output (MIMO) synthetic aperture radar (SAR) is a promising technology in radar imaging which provides a better balance of azimuth resolution and swath width compared with traditional single-input single-output (SISO) SAR. It has the potential to help scientists and engineers to design ambitious SAR system with higher resolution and wider swath. This paper studies the principle of MIMO SAR using orthogonal coding waveform and then provides the performance analysis in resolution and swath width. By using orthogonal coding waveform, lower channel interference is obtained, which makes MIMO SAR achieve wider unambiguous range swath and lower azimuth ambiguity. Simulations are carried out by means of the system parameters of real spaceborne SAR platform. A ground-based MIMO SAR imaging system with up and down chirp modulation is also designed. The performances of MIMO SAR and SISO SAR are compared, and the validity and advantage of MIMO SAR are verified.

New Approach for Unambiguous High-Resolution Wide-Swath SAR Imaging

The high-resolution wide-swath (HRWS) SAR system uses a small antenna for transmitting waveform and multiple antennas both in elevation and azimuth for receiving echoes. It has the potential to achieve wide spatial coverage and fine azimuth resolution, while it suffers from elevation pattern loss caused by the presence of topographic height and impaired azimuth resolution caused by nonuniform sampling. A new approach for HRWS SAR imaging based on compressed sensing (CS) is introduced. The data after range compression of multiple elevation apertures are used to estimate direction of arrival (DOA) of targets via CS, and the adaptive digital beamforming in elevation is achieved accordingly, which avoids the pattern loss of scan-on-receive (SCORE) algorithm when topographic height exists. The effective phase centers of the system are nonuniformly distributed when displaced phase center antenna (DPCA) technology is adopted, which causes Doppler ambiguities under traditional SAR imaging algorithms. Azimuth reconstruction based on CS can resolve this problem via precisely modeling the nonuniform sampling. Validation with simulations and experiment in an anechoic chamber are presented.

Distinguishing ability analysis of compressed sensing radar imaging based on information theory model

Recent theory of compressed sensing (CS) has been widely used in many application areas. In this paper, we mainly concentrate on the CS in radar and analyze the distinguishing ability of CS radar image based on information theory model. The information content contained in the CS radar echoes is analyzed by simplifying the information transmission channel as a parallel Gaussian channel, and the relationship among the signal-to-noise ratio (SNR) of the echo signal, the number of required samples, the length of the sparse targets and the distinguishing level of the radar image is gotten. Based on this result, we introduced the distinguishing ability of the CS radar image and some of its properties are also gotten. Real IECAS advanced scanning two-dimensional railway observation (ASTRO) data experiment demonstrates our conclusions.