Three-Dimensional Microwave Imaging for Concealed Weapon Detection Using Range Stacking Technique

Three-dimensional (3D) microwave imaging has been proven to be well suited for concealed weapon detection application. For the 3D image reconstruction under two-dimensional (2D) planar aperture condition, most of current imaging algorithms focus on decomposing the 3D free space Green function by exploiting the stationary phase and, consequently, the accuracy of the final imagery is obtained at a sacrifice of computational complexity due to the need of interpolation. In this paper, from an alternative viewpoint, we propose a novel interpolation-free imaging algorithm based on wavefront reconstruction theory. The algorithm is an extension of the 2D range stacking algorithm (RSA) with the advantages of low computational cost and high precision. The algorithm uses different reference signal spectrums at different range bins and then forms the target functions at desired range bin by a concise coherent summation. Several practical issues such as the propagation loss compensation, wavefront reconstruction, and aliasing mitigating are also considered. The sampling criterion and the achievable resolutions for the proposed algorithm are also derived. Finally, the proposed method is validated through extensive computer simulations and real-field experiments. The results show that accurate 3D image can be generated at a very high speed by utilizing the proposed algorithm

Detection of Isoflavones Content in Soybean Based on Hyperspectral Imaging Technology

Because of many important biological activities, Soybean isoflavones which has great potential for exploitation is significant to practical applications. Due to the conventional methods for determination of soybean isoflavones having long detection period, used too many reagents, couldn’t be detected on-line, and other issues, we propose hyperspectral imaging technology to detect the contents of soybean isoflavones. Based on the 40 varieties of soybeans produced in Heilongjiang province, we get the spectral reflection datum of soybean samples varied from the soybean’s hyperspectral images which are collected by the hyperspectral imaging system, and apply high performance liquid chromatography (HPLC) method to determine the true value of the selected samples of isoflavones. The feature wavelengths for isoflavones content prediction (1516, 1572, 1691, 1716 and 1760 nm) were selected based on correlation analysis. The prediction model was established by using the method of BP neural network in order to realize the prediction of soybean isoflavones content analysis. The experimental results show that, the ANN model could predict isoflavones content of soybean samples with of 0.9679, the average relative error is 0.8032 %, and the mean square error (MSE) is 0.110328, which indicates the effectiveness of the proposed method and provides a theoretical basis for the applications of hyerspectral imaging in non-destructive detection for interior quality of soybean

Airborne Downward Looking Sparse Linear Array 3-D SAR Heterogeneous Parallel Simulation

The airborne downward looking sparse linear array three dimensional synthetic aperture radar (DLSLA 3-D SAR) operates nadir observation with the along-track synthetic aperture formulated by platform movement and the cross-track synthetic aperture formulated by physical sparse linear array. Considering the lack of DLSLA 3-D SAR data in the current preliminary study stage, it is very important and essential to develop DLSLA 3-D SAR simulation (echo generation simulation and image reconstruction simulation, including point targets simulation and 3-D distributed scene simulation). In this paper, DLSLA 3-D SAR imaging geometry, the echo signal model and the heterogeneous parallel technique are discussed first. Then, heterogeneous parallel echo generation simulation with time domain correlation and the frequency domain correlation method is described. In the following, heterogeneous parallel image reconstruction simulation with two imaging algorithms, e.g., 3-D polar format algorithm, polar formatting and L1 regularization algorithm is discussed. Finally, the point targets and the 3-D distributed scene simulation are demonstrated to validate the effectiveness and performance of our proposed heterogeneous parallel simulation technique. The 3-D distributed scene employs airborne X-band DEM and P-band Circular SAR image of the same area as simulation scene input

Autonomous Navigation Airborne Forward-Looking SAR High Precision Imaging with Combination of Pseudo-Polar Formatting and Overlapped Sub-Aperture Algorithm

Autonomous navigation airborne forward-looking synthetic aperture radar (SAR) observes the anterior inferior wide area with a short cross-track dimensional linear array as azimuth aperture. This is an application scenario that is drastically different from that of side-looking space-borne or air-borne SAR systems, which acquires azimuth synthetic aperture with along-track dimension platform movement. High precision imaging with a combination of pseudo-polar formatting and overlapped sub-aperture algorithm for autonomous navigation airborne forward-looking SAR imaging is presented. With the suggested imaging method, range dimensional imaging is operated with wide band signal compression. Then, 2D pseudo-polar formatting is operated. In the following, azimuth synthetic aperture is divided into several overlapped sub-apertures. Intra sub-aperture IFFT (Inverse Fast Fourier Transform), wave front curvature phase error compensation, and inter sub-aperture IFFT are operated sequentially to finish azimuth high precision imaging. The main advantage of the proposed algorithm is its extremely high precision and low memory cost. The effectiveness and performance of the proposed algorithm are demonstrated with outdoor GBSAR (Ground Based Synthetic Aperture Radar) experiments, which possesses the same imaging geometry as the airborne forward-looking SAR (short azimuth aperture, wide azimuth swath). The profile response of the trihedral angle reflectors, placed in the imaging scene, reconstructed with the proposed imaging algorithm and back projection algorithm are compared and analyzed

Sparse Microwave Imaging Algorithm Based on L_1/2 Threshold Iteration and an Approximate Observation Operator

Aiming at the problems of synthetic-aperture radar (SAR), such as high sampling rate and vulnerability to noise interference in imaging, a sparse reconstruction algorithm based on approximate observation and L1/2 threshold iteration is proposed in this paper. To solve the problem of the large dimension and high computational complexity of the measurement matrix, a sparse reconstruction model based on approximate observation is constructed, and a threshold iterative algorithm to rapidly solve the L1/2 regularization problem is introduced, which can realize sparse signal reconstruction with fewer sampling data and quickly solve the problem. The simulation results of point targets and the measured data of spaceborne SAR show that compared with the traditional matched filtering algorithm and L1 threshold algorithm based on a two-step iteration, the proposed sparse reconstruction algorithm has a faster iteration speed and improves the image quality

Investigation of Wavenumber Domain Imaging Algorithm for Ground-Based Arc Array SAR

Ground-based synthetic aperture radar (GB-SAR) has become an important technique for remote sensing deformation monitoring. However, most of the existing GB-SAR systems realize synthetic aperture by exploiting two closely spaced horn antennas to move along a linear rail. In order to obtain higher data acquisition efficiency and a wider view angle, we introduce arc antenna array technology into the GB-SAR system, which realizes a novel kind of system: ground-based arc array SAR (GB-AA-SAR). In this paper, we analyze arc observation geometry and derive analytic expressions of sampling criteria. Then, we propose a novel wavenumber domain imaging algorithm for GB-AA-SAR, which can achieve high image reconstruction precision through numerical solutions in the wavenumber domain. The proposed algorithm can be applied in wide azimuth view angle scenarios, and the problem of azimuth mismatch caused by distance approximation in arc geometric efficient omega-k imaging can be solved successfully. Finally, we analyze the two-dimensional (2D) spatial resolution of GB-AA-SAR, and verify the effectiveness of the proposed algorithm through numerical simulation experiments

Research Progress on Three-dimensional SAR Imaging Techniques

Conventional Synthetic Aperture Radar (SAR) moves along a straight line and forms a linear synthetic apertures. It can only obtain the two-dimensional (2-D) image of illuminated scene that is the projection of the three-dimensional (3-D) real scene onto a slant plane. The slant plane 2-D SAR image, however, suffers from layover and foreshortening effects. 3-D SAR imaging enables 3-D resolving capability by extending the acquisition of frequency information from 2-D to 3-D. It can obtain the 3-D distribution of scattering centers; therefore, it solves the geometric deformation problems of layover and foreshortening. 3-D SAR imaging has become a trending topic in research on SAR techniques worldwide. In this paper, we first introduced the concept of 3-D SAR imaging and several typical 3-D SAR imaging modes. Furthermore, it provides a discussion on research progress at home and abroad, particularly focusing on the progress of our research team. Finally, future research prospects are presented