MIMO-Based Forward-Looking SAR Imaging Algorithm and Simulation

Multiple-input multiple-output (MIMO) radar imaging can provide higher resolution and better sensitivity and thus can be applied to targets detection, recognition, and tracking. Missile-borne forward-looking SAR (MFL-SAR) is a new and special MIMO radar mode. It has advantage of two-dimensional (2D) imaging capability in forward direction over monostatic missile-borne SAR and airborne SAR. However, it is difficult to obtain accurate 2D frequency spectrum of the target echo signal due to the high velocity and descending height of this platform, which brings a lot of obstacles to imaging algorithm design. Therefore, a new imaging algorithm for MFL-SAR configuration based on the method of series reversion is proposed in this paper. This imaging method can implement range compression, secondary range compression (SRC), and range cell migration correction (RCMC) effectively. Finally, some simulations of point targets and comparison results confirm the efficiency of our proposed algorithm

FPGA Implementation of Real-Time Compressive Sensing with Partial Fourier Dictionary

This paper presents a novel real-time compressive sensing (CS) reconstruction which employs high density field-programmable gate array (FPGA) for hardware acceleration. Traditionally, CS can be implemented using a high-level computer language in a personal computer (PC) or multicore platforms, such as graphics processing units (GPUs) and Digital Signal Processors (DSPs). However, reconstruction algorithms are computing demanding and software implementation of these algorithms is extremely slow and power consuming. In this paper, the orthogonal matching pursuit (OMP) algorithm is refined to solve the sparse decomposition optimization for partial Fourier dictionary, which is always adopted in radar imaging and detection application. OMP reconstruction can be divided into two main stages: optimization which finds the closely correlated vectors and least square problem. For large scale dictionary, the implementation of correlation is time consuming since it often requires a large number of matrix multiplications. Also solving the least square problem always needs a scalable matrix decomposition operation. To solve these problems efficiently, the correlation optimization is implemented by fast Fourier transform (FFT) and the large scale least square problem is implemented by Conjugate Gradient (CG) technique, respectively. The proposed method is verified by FPGA (Xilinx Virtex-7 XC7VX690T) realization, revealing its effectiveness in real-time applications

An Ultra-high-resolution Microwave Photonic-based SAR Image Method Based on Space-variant Motion Error Analysis

An ultrahigh-resolution microwave photonic-based Synthetic Aperture Radar (SAR) imaging method based on space-variant motion error analysis is proposed to solve the influence of space-variant motion error on microwave photonic-based SAR imaging. First, the judgment rules of the influence of space-variant motion error are proposed to analyze the residual space-variant motion error. Second, on the basis of the different judgment results of microwave photonic-based SAR system conditions, the corresponding imaging process is proposed. Finally, the proposed judgment rules and imaging method are verified by point simulation, and the measured data of the 10 GHz microwave photonic-based ultrahigh-resolution SAR are analyzed and imaged. The experimental results show the effectiveness of the proposed method

Terahertz Image Detection with the Improved Faster Region-Based Convolutional Neural Network

In recent years, terahertz imaging systems and techniques have been developed and have gradually become a leading frontier field. With the advantages of low radiation and clothing-penetrable, terahertz imaging technology has been widely used for the detection of concealed weapons or other contraband carried on personnel at airports and other secure locations. This paper aims to detect these concealed items with deep learning method for its well detection performance and real-time detection speed. Based on the analysis of the characteristics of terahertz images, an effective detection system is proposed in this paper. First, a lots of terahertz images are collected and labeled as the standard data format. Secondly, this paper establishes the terahertz classification dataset and proposes a classification method based on transfer learning. Then considering the special distribution of terahertz image, an improved faster region-based convolutional neural network (Faster R-CNN) method based on threshold segmentation is proposed for detecting human body and other objects independently. Finally, experimental results demonstrate the effectiveness and efficiency of the proposed method for terahertz image detection

Refined Two-Stage Programming-Based Multi-Baseline Phase Unwrapping Approach Using Local Plane Model

The problem of phase unwrapping (PU) in synthetic aperture radar (SAR) interferometry (InSAR) is caused by the measured range differences being ambiguous with the wavelength. Therefore, multi-baseline (MB) is a key processing step of MB InSAR. Compared with the traditional single-baseline (SB) PU, MB PU is advantageous in solving steep terrain due to its ability to break through the constraint of the phase continuity assumption. However, the accuracy of most of the existing MB PU methods is still limited to its mathematical foundation, i.e., the Chinese remainder theorem (CRT) is too sensitive to measurement bias. To solve this issue, this paper presents a refined algorithm based on the two-stage programming MB PU approach (TSPA) proposed by H. Yu. The significant advantage of the refined TSPA method (abbreviated as LPM-TSPA) is that it improves the performance of stage 1 of TSPA through assuming terrain height surface in the neighborhood pixels can be approximated by a plane to combine more information of the interferometric phase in the local region to estimate the ambiguity number gradient. The experiment results indicate that the LPM-TSPA method can significantly improve the accuracy of the MB PU solution

Time-Frequency Reversion-Based Spectrum Analysis Method and Its Applications in Radar Imaging

Spectrum analysis (SA) plays an important role in radar signal processing, especially in radar imaging algorithm design. Because it is usually hard to obtain the analytical expression of spectrum by the Fourier integral directly, principle of stationary phase (POSP)-based SA is applied to approximate this integral. However, POSP requires the phase of the signal to vary rapidly, which is not the case in circular synthetic aperture radar (SAR) and turntable inverse SAR (ISAR). To solve this problem, a new SA method based on time-frequency reversion (TFRSA) is proposed, which utilizes the relationship of the Fourier transform pairs and their corresponding signal phases. In addition, the connection between the imaging geometry and time-frequency relationship is also analyzed and utilized to help solve the time-frequency reversion. When the TFRSA is applied to the linear trajectory SAR, the obtained spectrum expression is the same as the result of POSP. When it is applied to ISAR, the spectrum expressions of near-field and far-field are derived and their difference is found to be position-independent. Based on this finding, an extended polar format algorithm (EPFA) for near-field ISAR imaging is proposed, which can solve the distortion and defocusing problems caused by traditional ISAR imaging algorithms. When it is applied to the circular SAR (CSAR), a new and efficient imaging method based on EPFA is proposed, which can solve the low efficiency problem of conventional BP-based CSAR imaging algorithms. The simulations and real data processing results are provided to validate the effectiveness of proposed method