Point Spread Function Characterization of a Radially Displaced Scatterer Using Circular Synthetic Aperture Radar

This research effort investigated characterizing the point spread function (PSF) behavior of radially displaced point scatterers using circular synthetic aperture radar (CSAR). Thus far, research has been conducted to understand PSF of a scatterer located at the imaging scene center. An analytic closed-form solution has been derived assuming the scatterer is located at the origin of the CSAR imaging geometry. However, it is difficult to derive an analytic PSF solution for a scatterer that is radially displaced from the imaging scene center. Using the back projection image formation algorithm, PSF responses are generated at various point target locations. Consistent with previous studies, the three dimensional PSF for a point target located at the image center is cone shaped and serves as the basis for comparing and characterizing the PSFs of radially displaced scatterers. Simulated results show the impulse response of a radially displaced point scatterer is asymmetric and tends to exhibit increased ellipticity as it moves further from the scene center

A review of synthetic-aperture radar image formation algorithms and implementations: a computational perspective

Designing synthetic-aperture radar image formation systems can be challenging due to the numerous options of algorithms and devices that can be used. There are many SAR image formation algorithms, such as backprojection, matched-filter, polar format, Range–Doppler and chirp scaling algorithms. Each algorithm presents its own advantages and disadvantages considering efficiency and image quality; thus, we aim to introduce some of the most common SAR image formation algorithms and compare them based on these two aspects. Depending on the requisites of each individual system and implementation, there are many device options to choose from, for in stance, FPGAs, GPUs, CPUs, many-core CPUs, and microcontrollers. We present a review of the state of the art of SAR imaging systems implementations. We also compare such implementations in terms of power consumption, execution time, and image quality for the different algorithms used.info:eu-repo/semantics/publishedVersio

Convolution Backprojection for SAR Image Formation

Convolution Back Projection (CBP) is an imaging algorithm, which can be applied to data gathered by a Synthetic Aperture Radar (SAR) system to produce high resolution images. The Mathematics of CBP was first studied in the context of tomographic image reconstruction for medical applications. CBP image processing has also been applied to a variety of other fields, such as seismic imaging, sonar, and radio astronomy. In terms of SAR image processing algorithms, CBP is far less efficient than direct Fourier Inversion Algorithms. The purpose of this thesis is to study the CBP algorithm as it is applied to SAR image formation. Specifically, thesis will provide the formulation of the CBP algorithm for a circular SAR geometry. The starting point for the development of this algorithm is a common radar wave model, which can be derived from Maxwells Equation’s

Comparison of Image Processing Techniques Using Random Noise Radar

Radar imaging is a tool used by our military to provide information to enhance situational awareness for both war fighters on the front lines and military leaders planning and forming strategies from afar. Noise radar technology is especially exciting as it has properties of covertness as well as the ability to see through walls, foliage, and other types of cover. In this thesis, AFIT\u27s NoNet was used to generate images utilizing a random noise radar waveform as the transmission signal. The NoNet was arranged in four configurations: arc, line, cluster, and surround. Images were formed using three algorithms: multilateration and the SAR imaging techniques, convolution backprojection, and polar format algorithm. Each configuration was assessed based on image quality, in terms of its resolution, and computational complexity, in terms of its execution time. Experiments revealed tradeoffs between computational complexity and achieving fine resolutions. Depending on image size, the multilateration algorithm was approximately 6 to 35 faster than polar format and 16 to 26 times faster than convolution backprojection. Backprojection yielded images with resolutions up to approximately 11 times finer in range and 18 times finer in cross-range for the surround configuration, over multilateration images. Pixel size in polar format images made comparisons of resolution unusable. This thesis provides information on the performance of imaging algorithms given a configuration of nodes. The information will provide groundwork for future use of the AFIT NoNet as a covertly operating imaging radar in dynamic applications

Radar Image Processing and Its Applications Based on Convolution Back Projection

A general synthetic aperture radar (SAR) signal model is derived from the Maxwell’s equations, and a SAR image processing algorithm called Convolution Back Projection (CBP) will be introduced in this thesis, which can be applied to data gathered by a Synthetic Aperture Radar (SAR) system to produce high resolution images. The purpose of this thesis is starting from Maxwell’s equations to study the CBP algorithm as it is applied to SAR image processing. Two different image simulation results will be provided by this method

Using synthetic aperture radar data-dome collections for building feature analysis

Low-frequency synthetic aperture radar (LF-SAR) is a remote sensing measurement technique that can aid in covert intelligence gathering capabilities for detecting concealed targets in building, and obscured phenomena in general. The Airbus Defence and Space Ltd LF-SAR data dome project has provided a coherently collected three-dimensional data set using airborne circular SAR (CSAR) trajectories, with the potential of providing volumetric SAR imagery of obscured regions inside buildings. Preliminary results of this collection are presented. Both the linear strip-map and CSAR datasets provided contain a great deal of information. Early results show promise, but have revealed the fundamental challenge with low-frequency remote sensing, that being the presence of radio-frequency interference, which reduces the quality of SAR image products

Implementation and Performance of Factorized Backprojection on Low-cost Commercial-Off-The-Shelf Hardware

Traditional Synthetic Aperture Radar (SAR) systems are large, complex, and expensive platforms that require significant resources to operate. The size and cost of the platforms limits the potential uses of SAR to strategic level intelligence gathering or large budget research efforts. The purpose of this thesis is to implement the factorized backprojection SAR image processing algorithm in the C++ programming language and test the code\u27s performance on a low cost, low size, weight, and power (SWAP) computer: a Raspberry Pi Model B. For a comparison of performance, a baseline implementation of filtered backprojection is adapted to C++ from pre-existing MATLAB® code. The factorized backprojection algorithm shows a computational improvement factor of 2-3 compared to filtered backprojection. Execution on a single Raspberry Pi is too slow for real-time imaging. However, factorized backprojection is easily parallelized, and we include a discussion of parallel implementation across multiple Pis

Synthetic Aperture Radar Algorithms on Transport Triggered Architecture Processors using OpenCL

Live SAR imaging from small UAVs is an emerging field. On-board processing of the radar data requires high-performance and energy-efficient platforms. One candidate for this are Transport Triggered Architecture (TTA) processors. We implement Backprojection and Backprojection Autofocus on a TTA processor specially adapted for this task using OpenCL. The resulting implementation is compared to other platforms in terms of energy efficiency. We find that the TTA is on-par with embedded GPUs and surpasses other OpenCL-based platforms. It is outperformed only by a dedicated FPGA implementation. © 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

Convex Model-Based Synthetic Aperture Radar Processing

The use of radar often conjures up images of small blobs on a screen. But current synthetic aperture radar (SAR) systems are able to generate near-optical quality images with amazing benefits compared to optical sensors. These SAR sensors work in all weather conditions, day or night, and provide many advanced capabilities to detect and identify targets of interest. These amazing abilities have made SAR sensors a work-horse in remote sensing, and military applications. SAR sensors are ranging instruments that operate in a 3D environment, but unfortunately the results and interpretation of SAR images have traditionally been done in 2D. Three-dimensional SAR images could provide improved target detection and identification along with improved scene interpretability. As technology has increased, particularly regarding our ability to solve difficult optimization problems, the 3D SAR reconstruction problem has gathered more interest. This dissertation provides the SAR and mathematical background required to pose a SAR 3D reconstruction problem. The problem is posed in a way that allows prior knowledge about the target of interest to be integrated into the optimization problem when known. The developed model is demonstrated on simulated data initially in order to illustrate critical concepts in the development. Then once comprehension is achieved the processing is applied to actual SAR data. The 3D results are contrasted against the current gold- standard. The results are shown as 3D images demonstrating the improvement regarding scene interpretability that this approach provides