Utilizing Near-Field Measurements to Characterize Far-Field Radar Signatures

The increased need for stealth aircraft requires an on-site Far-Field (FF) Radar Cross-Section (RCS) measurement process. Conducting these measurements in on-site Near-Field (NF) monostatic facilities results in significant savings for manufacturers and acquisition programs. However, NF measurements are not directly extended to a FF RCS. Therefore, a large target Near-Field to Far-Field Transformation (NFFFT) is needed for RCS measurements. One approach requires an Inverse Synthetic Aperture Radar (ISAR) process to create accurate scattering maps. The focus of this work is the development of accurate NF scattering maps generated by a monostatic ISAR process. As a first look, the process is isolated to a simulated environment to avoid the uncontrollable effects of real measurement environments. The simulation begins with a NF Synthetic Target Generator (STG) which approximates a target using scattering centers illuminated by spherical electromagnetic waves to approximating NF scattering. The resulting NF In-phase and Quadrature (IQ) data is used in a Trapezoidal ISAR process to create spatially distorted images that are accurately corrected within the ISAR process resolution using a newly developed NF correction. The resulting spatially accurate ISAR images do not complete the NFFFT. However, accurate scattering maps are essential for process development

Autofocus for inverse synthetic aperture radar (ISAR) imaging

Autofocus is a key step of inverse synthetic aperture radar (ISAR) imaging. In this paper four new approaches to autofocussing based on the application of beamforming and subspace concepts to ISAR imaging are developed. Their relations to maximum likelihood (ML) estimation are identified. A common feature of these techniques is the estimation of the complex vector formed by the exponential function of phase rather than phase itself so that phase unwrapping is obviated. The Cramer Rao lower bound (CRLB) of the estimated complex vector corresponding to translational motion and the CRLB of the estimated distance between two scatterers are derived. The results of processing simulated and real data confirm the validity of proposed approaches

Improvement of Continuous Wave Radar Measurements in a Partially Controlled Environment

A continuous wave (CW) radar system within a partially controlled environment measures scale model aircraft for mono-static and fully polarimetric radar imaging. Due to a pseudo-far-field setup, wavefront curvature manifests primarily as geometric distortion. Recently proposed phase error models show induced geometric distortion to be independent of aperture size which are verified via measurement for Sensors and Signals Exploitation Laboratory (SSEL) collections. The partially controlled nature of the SSEL introduces stray infrastructural reflections into the measured data. Three methods to reduce stray signals are explored namely: true background subtraction (TBS), running average (RA), and spatial filtering (SF). Of the three methods, SF provides 15 dB improvement in dynamic range revealing underlying SSEL structure. Defocus due to quadratic phase error (QPE) is considered, but shown to be negligible for typical aperture sizes of 20 degrees

A very efficient RCS data compression and reconstruction technique, volume 4

A very efficient compression and reconstruction scheme for RCS measurement data was developed. The compression is done by isolating the scattering mechanisms on the target and recording their individual responses in the frequency and azimuth scans, respectively. The reconstruction, which is an inverse process of the compression, is granted by the sampling theorem. Two sets of data, the corner reflectors and the F-117 fighter model, were processed and the results were shown to be convincing. The compression ratio can be as large as several hundred, depending on the target's geometry and scattering characteristics

Three Dimensional Inverse Synthetic Aperture Radar Imaging

This research investigates the generation, display, and interpretation of three-dimensional (3-D) Synthetic Aperture Radar images. Three-dimensional assumes that the data collected consists of one temporal dimension and two orthogonal angular dimensions. From this data, a three dimensional reflectivity map, or 3-D image, of a target can be constructed. This thesis effort develops and applies a three-dimensional imaging algorithm on actual radar data measured on a one-third scale model of a C-29 aircraft. Two-dimensional slices of the three-dimensional image as well as three-dimensional isosurfaces are compared to the physical properties of the target. The results demonstrate the ability to produce three-dimensional images given three-dimensional radar data

Radar signature characterization from wind turbine scattering

textThe growth in the number of wind farms has raised significant concerns in the radar community due to their potential interference on radar systems. The motion of the turbine blades creates unwanted Doppler clutter that can interfere in the tracking of moving targets. Large turbine structures can also produce electromagnetic shadows that may make observing objects behind a wind farm difficult. Detailed characterization of the clutter is the first step towards effective mitigation techniques. The goal of this dissertation research is to gain a better understanding of the dynamic radar signatures resulting from scattering by wind turbines. First, the scattering characteristics of turbines in the presence of ground surface are studied. Image theory in conjunction with a shooting-and-bouncing ray code, Ahilo, is used to carry out the dynamic signature simulation. The observed features in the simulation are corroborated with laboratory model measurements. Second, the effects of higher order motions of a turbine undergoing rotation on the radar signatures are investigated and characterized. Mathematical models for the motions are proposed and used to simulate the joint time-frequency and inverse synthetic aperture radar characteristics of the turbine undergoing these motions. The motions are studied for an isolated turbine as well as for a turbine rotating above a ground. Selected motions are corroborated by laboratory model measurements. Next, a method to remove the dynamic clutter produced by wind turbines is presented. A physics-based basis is constructed to model the radar backscattering from a wind turbine. This basis is used in conjunction with the matching pursuit algorithm to iteratively remove the Doppler clutter due to wind turbines. The algorithm is tested using radar return generated using Ahilo. Finally, radar features of wind turbines are simulated and studied in the HF (high frequency) band. The features are presented in the range-Doppler plane for single as well as arrays of turbines. Doppler aliasing due to the limited pulse repetition frequency of HF radars is examined. Shadowing characteristics of arrays of turbines are simulated and analyzed. Electromagnetic modeling details including effects of thin-wire modeling, non-conducting turbine components, and the presence of a conducting ground surface are discussed.Electrical and Computer Engineerin

On the usage of GRECOSAR, an orbital polarimetric SAR simulator of complex targets, to vessel classification studies

This paper presents a synthetic aperture radar (SAR) simulator that is able to generate polarimetric SAR (POLSAR) and polarimetric inverse SAR data of complex targets. It solves the electromagnetic problem via high-frequency approximations, such as physical optics and the physical theory of diffraction, with notable computational efficiency. In principle, any orbital monostatic sensor working at any band, resolution, and operating mode can be modeled. To make simulations more realistic, the target’s bearing and speed are considered, and for the particular case of vessels, even the translational and rotational movements induced by the sea state. All these capabilities make the simulator a powerful tool for supplying large amounts of data with precise scenario information and for testing future sensor configurations. In this paper, the usefulness of the simulator on vessel classification studies is assessed. Several simulated polarimetric images are presented to analyze the potentialities of coherent target decompositions for classifying complex geometries, thus basing an operational algorithm. The limitations highlighted by the results suggest that other approaches, like POLSAR interferometry, should be explored.Peer Reviewe

On the usage of GRECOSAR: an orbital polarimetric SAR simulator of complex targets for vessel classification studies

This paper presents a synthetic aperture radar (SAR) simulator that is able to generate polarimetric SAR (POLSAR) and polarimetric inverse SAR data of complex targets. It solves the electromagnetic problem via high-frequency approximations, such as physical optics and the physical theory of diffraction, with notable computational efficiency. In principle, any orbital monostatic sensor working at any band, resolution, and operating mode can be modeled. To make simulations more realistic, the target’s bearing and speed are considered, and for the particular case of vessels, even the translational and rotational movements induced by the sea state. All these capabilities make the simulator a powerful tool for supplying large amounts of data with precise scenario information and for testing future sensor configurations. In this paper, the usefulness of the simulator on vessel classification studies is assessed. Several simulated polarimetric images are presented to analyze the potentialities of coherent target decompositions for classifying complex geometries, thus basing an operational algorithm. The limitations highlighted by the results suggest that other approaches, like POLSAR interferometry, should be explored.Peer Reviewe

Self-correcting multi-channel Bussgang blind deconvolution using expectation maximization (EM) algorithm and feedback

A Bussgang based blind deconvolution algorithm called self-correcting multi-channel Bussgang (SCMB) blind deconvolution algorithm was proposed. Unlike the original Bussgang blind deconvolution algorithm where the probability density function (pdf) of the signal being recovered is assumed to be completely known, the proposed SCMB blind deconvolution algorithm relaxes this restriction by parameterized the pdf with a Gaussian mixture model and expectation maximization (EM) algorithm, an iterative maximum likelihood approach, is employed to estimate the parameter side by side with the estimation of the equalization filters of the original Bussgang blind deconvolution algorithm. A feedback loop is also designed to compensate the effect of the parameter estimation error on the estimation of the equalization filters. Application of the SCMB blind deconvolution framework for binary image restoration, multi-pass synthetic aperture radar (SAR) autofocus and inverse synthetic aperture radar (ISAR) autofocus are exploited with great results.Ph.D.Committee Chair: Dr. Russell Mersereau; Committee Member: Dr. Doug Willams; Committee Member: Dr. Mark Richards; Committee Member: Dr. Xiaoming Huo; Committee Member: Dr. Ye (Geoffrey) L