Efficient Imaging Algorithm Research Articles

A Novel Multistage Back Projection Fast Imaging Algorithm for Terahertz Video Synthetic Aperture Radar

Terahertz video synthetic aperture radar (THz-ViSAR) has tremendous research and application value due to its high resolution and high frame rate imaging benefits. However, it requires more efficient imaging algorithms. Thus, a novel multistage back projection fast imaging algorithm for the THz-ViSAR system is proposed in this paper to enable continuous playback of images like video. The radar echo data of the entire aperture is first divided into multiple sub-apertures, as with the fast-factorized back projection algorithm (FFBP). However, there are two improvements in sub-aperture imaging. On the one hand, the back projection algorithm (BPA) is replaced by the polar format algorithm (PFA) to improve the sub-aperture imaging efficiency. The imaging process, on the other hand, uses the global Cartesian coordinate system rather than the local polar coordinate system, and the wavenumber domain data of the full aperture are obtained step by step through simple splicing and fusion, avoiding the amount of two-dimensional (2D) interpolation operations required for local polar coordinate system transformation in FFBP. Finally, 2D interpolation for full-resolution images is carried out to image the ground object targets in the same coordinate system due to the geometric distortion caused by linear phase error (LPE) and the mismatch of coordinate systems in different imaging frames. The simulation experiments of point targets and surface targets both verify the effectiveness and superiority of the proposed algorithm. Under the same conditions, the running time of the proposed algorithm is only about 6% of FFBP, while the imaging quality is guaranteed.

Efficient Wavenumber Domain Processing for Near-Field Imaging With Polyline Arrays

We propose an efficient wavenumber domain approach for polyline array-based millimeter-wave imaging. The polyline multiple-input–multiple-output (MIMO) array provides more uniform illumination of the target than the planar MIMO array while enjoying the planar array’s easy manufacturing. However, no existing efficient imaging algorithms deal with such an array topology. This article presents an approach that first implements a polyline-to-arc transformation on the polyline array, accompanied by a phase calibration method to maintain the transformation precision. The wavenumber domain technique is then employed to perform image reconstruction. This processing enables the transformation of the polyline array scheme into a monostatic cylindrical aperture, for which efficient imaging algorithms exist. The imaging performance and computational efficiency of the proposed approach are demonstrated with both simulations and experiments.

A Modified Omega-K Algorithm for Near-Field Single-Frequency MIMO-Arc-Array-Based Azimuth Imaging

Inspired by the circular-aperture synthetic aperture radar (Circular-SAR) imaging regime that can provide higher azimuth image resolution, the imaging regime employing the multiple-input-multiple-output (MIMO) arc array is proposed. Moreover, no efficient imaging algorithm is now available for these MIMO arc array setups. This article proposes a modified Omega-K algorithm for near-field azimuth image reconstruction based on the geometry transformation from the single-frequency MIMO arc array to the equivalent MIMO linear array. Note that this geometry transformation relies on the calculation of the phase difference and the definition of the corresponding compensation function. Compared with the single-input-single-output (SISO) arc scan requiring more elements, simulations with good agreement were performed to validate the azimuth image reconstructions of single/multiple targets. Although the computing efficiency of the proposed algorithm for MIMO arc array is lower than that of the traditional Omega-K algorithm for the SISO arc scan, it still has an obvious advantage relative to the traditional backprojection (BP) algorithm that is always taken as the gold standard. Furthermore, three realistic imaging geometries, including the metal strips at different radii, the tilted metal plate, and the human body torso cross section, were simulated to test the fidelity of the proposed reconstruction algorithm. Proof-of-principle experiments were also carried out based on an MIMO arc array with the help of one arc scanner. Finally, both simulation and experimental results validate the effectiveness of the proposed algorithm on azimuth image reconstruction quality.

Simultaneous Moving and Stationary Target Imaging for Geosynchronous Spaceborne-Airborne Bistatic SAR Based on Sparse Separation

In synthetic aperture radar (SAR) imaging, moving target is generally mixed with stationary targets. Meanwhile, the image of a moving target is distorted and displaced due to the lack of its prior velocity information. Furthermore, imaging of a moving target for geosynchronous (GEO) spaceborne-airborne bistatic SAR (GEO SA-BiSAR) is a more challenging problem because the echo is sub-Nyquist sampled in azimuth. In this article, a simultaneous moving and stationary target imaging method for GEO SA-BiSAR is proposed. First, range models and the corresponding echo models of moving and stationary targets are established. The observation models for both moving and stationary targets with two receiving channels are derived based on the inverse of an efficient imaging algorithm. After that, the imaging problem of moving and stationary targets is modeled as a joint velocity estimation and sparse decomposition problem, which aims at optimizing the entropy of the moving target image and residual error of the formed images at the same time. Finally, a joint optimization method based on the particle swarm optimization (PSO) method and alternating direction method of multipliers (ADMM) is applied to achieve the imaging of moving and stationary targets and estimation of the moving target velocity. With two receiving channels, the accurate separation and focusing of stationary and moving targets as well as the precise estimation of moving target velocity can be achieved with sub-Nyquist sampling echo. Simulation results are presented to validate the effectiveness of the proposed method.

Near-Field 3D SAR Imaging Using a Scanning Linear MIMO Array With Arbitrary Topologies

For near-field synthetic aperture radar (SAR) imaging, frequency domain imaging algorithms typically require the array elements to be uniformly arranged to facilitate the application of fast Fourier transform (FFT) operations; this greatly restricts the array types that can be used for practical applications. For arrays with non-uniformly positioned antennas, no general and efficient imaging algorithm is available yet. To address this issue, an effective algorithm for efficient three-dimensional (3D) SAR imaging with arbitrary linear multi-input-multi-output (MIMO) array topologies is proposed. Specifically, non-uniform FFT (NUFFT) technology is utilized to tackle the non-uniform sampling issues in both the spatial and the wavenumber domain. Despite the intuitive idea, to achieve satisfied imaging, some issues are non-trivial and should be carefully addressed. Both simulations and experiments show the superiority of the proposed algorithm on computational complexity while maintain the same high imaging precision compared with the back-projection algorithm (BPA). Moreover, we suggest that the concept of NUFFT-based imaging can also be generalized to other imaging regimes with arbitrary array configurations.

Design and implementation of MIMO radar real‐time imaging system based on multicore DSP

Multiple-input multiple-output (MIMO) imaging radar is a new kind of new imaging radar in recent years. It can achieve faster data acquisition speed than the traditional ground-based SAR due to the ability to get a large number of multi-channel data in a short period of time. However, traditional single-core digital signal processors (DSPs) have limited space and ability to deal with such large amount of data. To solve this problem, a real-time image processing software architecture for MIMO imaging radar algorithm is designed and carried out based on TMS320C6678 DSP of Texas Instruments (TI), which is the highest-performance fixed/floating-point DSP based on TI's KeyStone multicore architecture. The efficient imaging algorithms mapping from Matlab to DSP is realised, with efficient task allocation, resource reuse of echo data, and processing variable, and using the method of multicore parallel processing.

An Efficient Algorithm for MIMO Cylindrical Millimeter-Wave Holographic 3-D Imaging

A novel cylindrical millimeter-wave holography regime using a multi-input-multi-output (MIMO) array is first introduced. More importantly, an accurate and fast 3-D imaging algorithm is developed for this regime. Although many efficient imaging algorithms for synthetic aperture radar (SAR) or circular SAR have been developed, they can hardly be used for this new regime due to the compound effects of a spherical wavefront, MIMO geometry, and cylindrical observation aperture. In this paper, we develop an efficient algorithm mainly based on the ideas of a spherical wave decomposition, circular convolution, and nonuniform fast Fourier transform. The proposed algorithm achieves similar imaging quality with the golden-standard back-projection algorithm (BPA) while costs much less time. Compared with the BPA, the proposed algorithm belongs to a kind of the frequency-domain method built upon range migration techniques. Detailed derivations are first presented. Then, several important issues including resolutions, sampling criteria, and computational complexities are analyzed. Finally, a series of simulations and experiments are carried out, and all the results verify the effectiveness of the proposed algorithm on both accuracy and efficiency.

P and S Wave Receiver Function Imaging of Subduction With Scattering Kernels

AbstractA method for scattered wave imaging in 3‐D with both teleseismic P and S wave receiver function data is introduced. The approach relies on body‐wave scattering kernels that are derived from the adjoint data sensitivity kernels which are typically used for full waveform tomography. The forward problem is approximated using ray theory, yielding a computationally efficient imaging algorithm that can resolve dipping and discontinuous interfaces using both P and S wave receiver functions. Traveltime fields for the incident teleseismic arrivals and the receiver point‐sources are obtained by solving the Eikonal equation using a Fast Marching code which can handle a 3‐D reference velocity model. An energy stable finite‐difference method is used to simulate elastic wave propagation in a 2‐D hypothetical subduction zone model. The resulting synthetic P and S wave receiver function data sets are used to validate the imaging method. The kernel images are compared with those generated by the Generalized Radon Transform and Common Conversion Point Stacking methods. These results demonstrate the potential of the kernel imaging approach for constraining lithospheric structure in complex geologic environments with sufficiently dense recordings of teleseismic data. Potential imaging targets include short‐wavelength compositional variations in the mantle wedge and the slab's lithosphere‐asthenosphere boundary.

Efficient imaging algorithm for spaceborne synthetic aperture radar/ground moving target indication systems

The use of spaceborne synthetic aperture radar (SAR) for ground moving target indication (GMTI) has recently attracted a great deal of interest. In this study, an efficient imaging algorithm for spaceborne SAR/GMTI systems is proposed. The proposed imaging algorithm first implements the secondary range compression and the range cell migration correction in the two-dimensional frequency domain and then the azimuth compression in the range Doppler domain. The proposed algorithm can finely focus the stationary scene and coarsely focus multiple slow-moving targets simultaneously without a priori knowledge of targets’ motion parameters and position parameters. Moreover, the proposed algorithm is interpolation free and can be efficiently implemented by using only complex multiplications and fast Fourier transforms. These advantages make the proposed algorithm useful in practice. Experimental results from both simulated data and real data validate the proposed algorithm.

A High-Order Imaging Algorithm for High-Resolution Spaceborne SAR Based on a Modified Equivalent Squint Range Model

Two challenges have been faced in signal processing of ultrahigh-resolution spaceborne synthetic aperture radar (SAR). The first challenge is constructing a precise range model, and the second one is to develop an efficient imaging algorithm since traditional algorithms fail to process ultrahigh-resolution spaceborne SAR data effectively. In this paper, a novel high-order imaging algorithm for high-resolution spaceborne SAR is presented. First, a modified equivalent squint range model (MESRM) is developed by introducing equivalent radar acceleration into the equivalent squint range model, and it is more suitable for high-resolution spaceborne SAR. The signal model based on the MESRM is also presented. Second, a novel high-order imaging algorithm is derived. The insufficient pulse-repetition frequency problem is solved by an improved subaperture method, and accurate focusing is achieved through an extended hybrid correlation algorithm. Simulations are performed to validate the presented algorithm.

Efficient imaging algorithm for airborne CSSAR/GMTI systems

Airborne circular stripmap synthetic aperture radar (CSSAR) has the advantages of short revisit time and large coverage, and thus is an attractive tool for wide-area surveillance and ground moving-target indication (GMTI). To enhance the CSSAR/GMTI system's surveillance capability and moving-target detection performance, an efficient imaging algorithm is proposed. The proposed algorithm can finely focus the stationary scene and coarsely focus multiple moving targets simultaneously without a priori knowledge of the targets’ motion parameters and position parameters. Moreover, the proposed algorithm is interpolation free and can be efficiently implemented using only complex multiplications and fast Fourier transforms. Simulation results validate the proposed algorithm.

Efficient Nonuniform Fourier Reconstruction for Spaceborne/Airborne Bistatic SAR

In this letter, an efficient imaging algorithm is proposed for reconstructing the spaceborne/airborne bistatic synthetic aperture radar (SA-BSAR) data acquired with a high squint angle. By utilizing the Taylor expansion and the chain rule for derivatives of composite function, the expression of the SA-BSAR ideal matching filter is converted to a 2-D nonuniform discrete Fourier transform (NUDFT). Due to neither input nor output data of the NUDFT being equispaced, nonuniform fast Fourier transform of type 3 (NUFFT-3) is implemented to speed up the SA-BSAR image reconstruction. The computation complexity of the proposed imaging algorithm is O(MNlogMN), where MN is the number of the image pixels. Simulation results demonstrate the validity of the proposed algorithm.

Watershed Functions Applied to a 3D Image Segmentation Problem for the Analysis of Packed Particle Beds

AbstractIn an effort to advance the area of particle science and technology the University of Utah has been using cone beam X‐ray microtomography as a major tool for the quantitative three dimensional (3D) analysis of packed beds of multiphase particles. In this regard, efficient imaging algorithms have been developed and applied in order to extend two dimensional (2D) image analysis techniques for the solution of three 3D problems. Based on this research, image analyses of different particle samples were carried out for identification, segmentation, and quantitative volume characterization of irregularly shaped particles. The whole analysis involves acquisition of a 3D image by X‐ray microtomography and a sequence of image processing tools for thresholding, distance transformation, maxima detection using reconstruction by dilation, and a watershed 3D algorithm for segmentation and identification of irregularly shaped particles in three dimensions. Several examples of the 3D analysis for irregularly shaped particles are presented and the results discussed. Application of these algorithms is demonstrated by the determination of coal washability curves.

Neonatal gastrointestinal emergencies

Gastrointestinal emergencies in neonates often demand a quick and efficient diagnostic imaging. The procedures have to take the special diseases and conditions in these babies and preterm children into account. This paper summarises the most common causes for gastrointestinal neonatal emergencies and discusses the indication and performance as well as the diagnostic value of the commonly used modalities, giving some suggestions for an efficient imaging algorithm. Most of the time conventional plain abdominal films and sonography can answer the clinically important questions, however, in certain conditions fluoroscopy with contrast administration (enema, etc.) is mandatory. Only rarely is CT, MRI or Angiography indicated.