Passive Multistatic Radar Imaging using an OFDM based Signal of Opportunity

This paper demonstrates a proof of concept in using an OFDM-based signal of opportunity for SAR imaging purposes within a passive, multistatic radar construct. Two signal processing methods have been proposed to create phase history data. The same methods are applied in both a simulated software model and an experimental data collection environment to produce simulated SAR images using the CBP imaging algorithm. The images generated from both the experimental and simulated data were observed to be consistent with each other and with expectations in terms of resolution. Coherent addition of the images results in improved image resolution due to the geometric and frequency diversity of the multistatic scenario compared to the individual bistatic pairs

Advances in Synthetic Aperture Radar from a Wavenumber Perspective

This dissertation examines the wavenumber domain of Synthetic Aperture Radar (SAR) images. This domain is the inverse Fourier transform domain of a SAR image. The dissertation begins with the radar receiver's signal model and develops equations describing the wavenumber domain of a SAR image produced by a generalized bistatic and monostatic SAR system. Then, closed form expressions for bistatic synthetic aperture radar spatial resolution of a generalized system from the wavenumber domain are developed. These spatial resolution equations have not previously appeared in the literature. From these equations, significant resolution is found in both range and cross-range forecasting a forward-scatter bistatic SAR image when the elevation angles of each bistatic platform are significantly different. Next, wavenumber and time domain image formation algorithms are discussed. Developed within this dissertation is a wavenumber preprocessing method that increases the speed of the Back Projection Algorithm (BPA). This preprocessing method takes advantage of deramped SAR radar returns and their polar wavenumber format. This new algorithm is called the Fast Decimated Wavenumber Back Projection Algorithm (FDWBPA). Matlab functions are included to implement this algorithm, simulate bistatic SAR images and process the data from anechoic chamber tests demonstrating forward scatter resolution

Metrics for Emitter Selection for Multistatic Synthetic Aperture Radar

A bistatic implementation of synthetic aperture radar (SAR) to form images of the ground from an aircraft makes use of separate emitters and receivers. When not using cooperative emitters, ground based communications systems can provide illumination. One way to improve performance of these waveforms, which are not designed for SAR, is a multistatic implementation, formed from multiple bistatic systems. This leads to the problem of selecting a subset from a potentially large set of emitters to use for image formation. A framework for this selection between sets of emitters is proposed using multiple objective optimization. This approach requires use of objective functions to score the inputs to the selection process. The four objective functions selected to score sets of emitters are: signal to noise ratio, waveform ambiguity function\u27s integrated sidelobes , effective multistatic resolution area, and contrast ratio. To speed calculations, an approximation is found for the point spread function. Simulation is used to compare approximation with theory, showing its utility for emitter selection. Finally a qualitative example of emitter selection is presented

Synthetic aperture source localization

2018 Summer.Includes bibliographical references.The detection and localization of sources of electromagnetic (EM) radiation has many applications in both civilian and defense communities. The goal of source localization is to identify the geographic position of an emitter of some radiation from measurements of the elds that the source produces. Although the problem has been studied intensively for many decades much work remains to be done. Many state-of-the-art methods require large numbers of sensors and perform poorly or require additional sensors when target emitters transmit highly correlated waveforms. Some methods also require a preprocessing step which attempts to identify regions of the data which come from emitters in the scene before processing the localization algorithm. Additionally, it has been proven that pure Angle of Arrival (AOA) techniques based on current methods are always suboptimal when multiple emitters are present. We present a new source localization technique which employs a cross correlation measure of the Time Dierence of Arrival (TDOA) for signals recorded at two separate platforms, at least one of which is in motion. This data is then backprojected through a Synthetic Aperture Radar (SAR)-like process to form an image of the locations of the emitters in a target scene. This method has the advantage of not requiring any a priori knowledge of the number of emitters in the scene. Nor does it rest on an ability to identify regions of the data which come from individual emitters, though if this capability is present it may improve image quality. Additionally we demonstrate that this method is capable of localizing emitters which transmit highly correlated waveforms, though complications arise when several such emitters are present in the scene. We discuss these complications and strategies to mitigate them. Finally we conclude with an overview of our method's performance for various levels of additive noise and lay out a path for advancing study of this new method through future work

Artifacts in Radar Imaging of Moving Targets

In this thesis, we study the artifacts that occur when a scene being imaged by radar contains moving targets. The physics of interaction between radar waves and moving targets were studied to develop a model using MATLAB for the received signal which does not make use of the start-stop approximation. The effects of target motion in the image formation process were studied for different radar configurations, including multistatic radars and Synthetic Aperture Radar. The key limitation of this model is its high computational resource requirements when simulating high bandwidth or long pulses. It was observed that range profiles may experience distortion due to the received waveforms differences from the matched filter. The exact outcome is waveform dependent; generally, both main lobe broadening and range errors were introduced by target motion. This leads to the wrong object localization and defocusing on the image. For SAR, a moving targets physical location varies throughout the imaging process. This means that standard backprojection fails to yield a focused image even if the range error due to the Doppler shift has been corrected, resulting in smearing. This is similar to motion blur experienced in optical cameras with a fast object.http://archive.org/details/artifactsinradar1094517470Civilian, Defence Science & Technology Agency, SingaporeApproved for public release; distribution is unlimited

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

Three Dimensional Bistatic Tomography Using HDTV

The thesis begins with a review of the principles of diffraction and reflection tomography; starting with the analytic solution to the inhomogeneous Helmholtz equation, after linearization by the Born approximation (the weak scatterer solution), and arriving at the Filtered Back Projection (Propagation) method of reconstruction. This is followed by a heuristic derivation more directly couched in the radar imaging context, without the rigor of the general inverse problem solution and more closely resembling an imaging turntable or inverse synthetic aperture radar. The heuristic derivation leads into the concept of the line integral and projections (the Radon Transform), followed by more general geometries where the plane wave approximation is invalid. We proceed next to study of the dependency of reconstruction on the space-frequency trajectory, combining the spatial aperture and waveform. Two and three dimensional apertures, monostatic and bistatic, fully and sparsely sampled and including partial apertures, with controlled waveforms (CW and pulsed, with and without modulation) define the filling of k-space and concomitant reconstruction performance. Theoretical developments in the first half of the thesis are applied to the specific example of bistatic tomographic imaging using High Definition Television (HDTV); the United States version of DVB-T. Modeling of the HDTV waveform using pseudonoise modulation to represent the hybrid 8VSB HDTV scheme and the move-stop-move approximation established the imaging potential, employing an idealized, isotropic 18 scatterer. As the move-stop-move approximation places a limitation on integration time (in cross correlation/pulse compression) due to transmitter/receiver motion, an exact solution for compensation of Doppler distortion is derived. The concept is tested with the assembly and flight test of a bistatic radar system employing software-defined radios (SDR). A three dimensional, bistatic collection aperture, exploiting an elevated commercial HDTV transmitter, is focused to demonstrate the principle. This work, to the best of our knowledge, represents a first in the formation of three dimensional images using bistatically-exploited television transmitters

Signal Theoretical Aspects of Bistatic SAR

Abstract-Bistatic SAR uses separated transmitter and receiver flying on different platforms. This configuration is envisaged to achieve benefits like the exploitation of additional information contained in the bistatic reflectivity of targets, reduced vulnerability in military systems or forward looking SAR imaging. The feasibility of the bistatic concept was already demonstrated by experimental investigations. Nevertheless, a closed satisfying theory reaching from signal modelling over the data collection strategies and the analysis of possible imaging performance to the specification of processors for practical use does not yet exist. The reason may be found in the non-standard geometry resulting in radar signals of high complexity. In this paper, we will start from a signal model for a rather general configuration. Since the changing imaging geometry makes it difficult to derive a general processor, we first look over the well known classes of monostatic SAR-processors. Then, the inversion problem is formulated for the bistatic case resulting in the matched filter processor. Emanating from this, two techniques are derived which are locally optimum either for short apertures or for small scenes. Special attention is turned to the transfer of range-migration type algorithms to the bistatic case

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