Bailing Ren

MODIFIED WAVENUMBER DOMAIN ALGORITHM FOR THREE-DIMENSIONAL MILLIMETER-WAVE IMAGING

Millimeter-wave (MMW) imaging techniques have been used for the detection of concealed weapons and contraband carried on personnel at airports and other secure locations. The combination of frequency-modulated continuous-wave (FMCW) technology and MMW imaging techniques should lead to compact, light-weight, and low-cost systems which are especially suitable for security and detection application. However, the long signal duration time leads to the failure of the conventional stop-and-go approximation of the pulsed system. Therefore, the motion within the signal duration time needs to be taken into account. Analytical three- dimensional (3-D) backscattered signal model, without using the stop-and-go approximation, is developed in this paper. Then, a wavenumber domain algorithm, with motion compensation, is presented. In addition, conventional wavenumber domain methods use Stolt interpolation to obtain uniform wavenumber samples and compute the fast Fourier transform (FFT). This paper uses the 3- D nonuniform fast Fourier transform (NUFFT) instead of the Stolt interpolation and FFT. The NUFFT-based method is much faster than the Stolt interpolation-based method. Finally, point target simulations are performed to verify the algorithm.

Near-Field Radar Imaging via Compressive Sensing

A novel two-dimensional (2-D) compressive sensing (CS) based method is presented for near-field radar imaging. First, an accurate near-field approximation is proposed, based on which the circular wavefront curvature of spherical waves can be compensated by mapping the images to a rectified new grid. More importantly, the near-field approximation makes the two dimensions of the scattered data separable for the range and cross-range directions, which makes it possible to solve the 2-D reflectivity matrix for the image reconstruction directly. Then, a 2-D proximal subgradient algorithm for near-field radar imaging based on a fast iterative shrinkage/thresholding algorithm (FISTA) is introduced to resolve the memory usage and computation time issues. Simulation and experimental results are provided to demonstrate the performance of the proposed method with comparisons to the traditional Fourier-based method and to the conjugate gradient (CG) based method, which proves that the proposed method is an effective way to solve the near-field radar imaging problem.

Modified Cylindrical Holographic Algorithm for Three-Dimensional Millimeter-Wave Imaging

Millimeter-wave (MMW) imaging techniques have been developed for the detection of concealed weapons and plastic explosives carried on personnel at major transportation hubs and secure locations. The combination of frequency-modulated continuous-wave (FMCW) technology and MMW imaging techniques leads to wideband, compact, and cost-efiective systems which are especially suitable for security detection. Cylindrical three-dimensional (3-D) imaging technique, with the ability of viewing multiple sides, is an extension of rectilinear 3-D imaging technique only viewing a single side. Due to the relatively long signal sweep time, the conventional stop-and-go approximation of the pulsed systems is not suitable for FMCW systems. Therefore, a 3-D backscattered signal model including the efiects of the continuous motion within the signal duration time is developed for cylindrical imaging systems. Then, a holographic image reconstruction algorithm, with motion compensation, is presented and demonstrated by means of numerical simulations.

Cylindrical Three-Dimensional Millimeter-Wave Imaging via Compressive Sensing

Millimeter-wave (MMW) imaging techniques have been used for the detection of concealed weapons and contraband carried by personnel. However, the future application of the new technology may be limited by its large number of antennas. In order to reduce the complexity of the hardware, a novel MMW imaging method based on compressive sensing (CS) is proposed in this paper. The MMW images can be reconstructed from the significantly undersampled backscattered data via the CS approach. Thus the number of antennas and the cost of system can be further reduced than those based on the traditional imaging methods that obey the Nyquist sampling theorem. The effectiveness of the proposed method is validated by numerical simulations as well as by real measured data of objects.

Raw data compress method of Synthetic Aperture Radar based on compressive sensing

Nowadays, due to the need of high resolution imaging, huge amount of data are needed to be collected base on Nyquist sampling theorem. However, the acquisition platform cannot afford the computation requirement to process on board, so those data must be sent to the ground so that it can be processed. This paper is focused on the compression of the Synthetic Aperture Radar (SAR) raw data to send as less data as we can ease the burden on the system and reduce the time for transmission. In this paper, we compressed the SAR raw data using compressive sensing method, and we train the sparse basis through K-SVD method. First we use the raw data that we collected to train the sparse basis using K-SVD method, when we get the trained sparse basis, we only need to send part of the raw data with the basis to the ground and the raw data can be recovered perfectly. The result of recovered data and imaging results are given. This method can help us to perform further application research of SAR imaging.

The NUFFT-based algorithm for 3-D near-field ISAR imaging

This paper presents a new three-dimensional (3-D) near-field synthetic aperture imaging technique based on the nonuniform fast Fourier transformation (NUFFT). A 3-D ISAR image can be obtained by processing coherently the backscattered fields as a function of the frequency and two rotation angles about axes which are mutually orthogonal. Comparisons of the NUFFT-based technique and the Cartesian tomography-based technique are demonstrated by means of numerical simulations. The simulation results demonstrate the proposed algorithm has the advantage of better reconstruction accuracy compared to the Cartesian tomography-based method.

Comparison of two near-field imaging algorithms for synthetic aperture radar

Tomographic method is a common near-field imaging algorithm for synthetic aperture radar (SAR). However it is too slow for real time imaging when the span of the angle is large in azimuth. In order to alleviate the computational load, the algorithm based on the azimuth convolution has been proposed. This paper compares two different near-field imaging algorithms: the tomography-based method and the fast cyclical convolution algorithm based on linear interpolation and FFT. Through numerical simulations and experimental results, we show that the fast cyclical convolution algorithm based on linear interpolation and FFT is faster than the near-field tomographic method.

A fast circular convolution algorithm based on NUFFT for near-field SAR imaging

A fast cyclical convolution algorithm based on the two dimensional (2-D) nonuniform fast Fourier transformation (NUFFT) is presented in this paper. This method combines the azimuth convolution with min-max Nonuniform Fast Fourier Transform (min-max NUFFT) approach which has been shown to provide considerably improved image quality relative to the traditional interpolation algorithm. Through numerical simulations and experimental results, we show that the proposed algorithm can provide images with lower sidelobes but requires more computational time than that of the cyclical convolution method based on the linear interpolation and FFT. The algorithm presented in this paper is also suitable for the bistatic antenna array.

An algorithm based on the compressed sensing for near range two dimensional imaging

Planar array two dimensional (2D) imaging is an important technology. It saves scanning time, but needs a large number of antenna elements. In order to save the cost, we hope to use less antennas to get the same (or better) image results. In this paper, we use sparse planar array and reconstruct the image by the two dimensional fast smoothed L0 (2D-SL0) algorithm. Paraxial Green function is used as sensing matrix. Comparisons of the algorithms based on 2D-SL0 algorithm and the matched filter processing (MFP) are demonstrated by means of numerical simulations. It is obvious that the imaging results by the 2D-SL0 algorithm is much clearer. And the focusing performance of the algorithm based on the 2D-SL0 algorithm is very well, even when we use the sparse antenna array.