Maximum Likelihood Estimation of Exponentials in Unknown Colored Noise for Target Identification in Synthetic Aperture Radar Images

This dissertation develops techniques for estimating exponential signals in unknown colored noise. The Maximum Likelihood (ML) estimators of the exponential parameters are developed. Techniques are developed for one and two dimensional exponentials, for both the deterministic and stochastic ML model. The techniques are applied to Synthetic Aperture Radar (SAR) data whose point scatterers are modeled as damped exponentials. These estimated scatterer locations (exponentials frequencies) are potential features for model-based target recognition. The estimators developed in this dissertation may be applied with any parametrically modeled noise having a zero mean and a consistent estimator of the noise covariance matrix. ML techniques are developed for a single instance of data in colored noise which is modeled in one dimension as (1) stationary noise, (2) autoregressive (AR) noise and (3) autoregressive moving-average (ARMA) noise and in two dimensions as (1) stationary noise, and (2) white noise driving an exponential filter. The classical ML approach is used to solve for parameters which can be decoupled from the estimation problem. The remaining nonlinear optimization to find the exponential frequencies is then solved by extending white noise ML techniques to colored noise. In the case of deterministic ML, the computationally efficient, one and two-dimensional Iterative Quadratic Maximum Likelihood (IQML) methods are extended to colored noise. In the case of stochastic ML, the one and two-dimensional Method of Direction Estimation (MODE) techniques are extended to colored noise. Simulations show that the techniques perform close to the Cramer-Rao bound when the model matches the observed noise

Maximum Likelihood Estimation of Exponentials in Unknown Colored Noise for Target in Identification Synthetic Aperture Radar Images

This dissertation develops techniques for estimating exponential signals in unknown colored noise. The Maximum Likelihood ML estimators of the exponential parameters are developed. Techniques are developed for one and two dimensional exponentials, for both the deterministic and stochastic ML model. The techniques are applied to Synthetic Aperture Radar SAR data whose point scatterers are modeled as damped exponentials. These estimated scatterer locations exponentials frequencies are potential features for model-based target recognition. The estimators developed in this dissertation may be applied with any parametrically modeled noise having a zero mean and a consistent estimator of the noise covariance matrix. ML techniques are developed for a single instance of data in colored noise which is modeled in one dimension as 1 stationary noise, 2 autoregressive AR noise and 3 autoregressive moving-average ARMA noise and in two dimensions as 1 stationary noise, and 2 white noise driving an exponential filter. The classical ML approach is used to solve for parameters which can be decoupled from the estimation problem. The remaining nonlinear optimization to find the exponential frequencies is then solved by extending white noise ML techniques to colored noise. In the case of deterministic ML, the computationally efficient, one and two-dimensional Iterative Quadratic Maximum Likelihood IQML methods are extended to colored noise. In the case of stochastic ML, the one and two-dimensional Method of Direction Estimation MODE techniques are extended to colored noise. Simulations show that the techniques perform close to the Cramer-Rao bound when the model matches the observed noise

Novel Hybrid-Learning Algorithms for Improved Millimeter-Wave Imaging Systems

Increasing attention is being paid to millimeter-wave (mmWave), 30 GHz to 300 GHz, and terahertz (THz), 300 GHz to 10 THz, sensing applications including security sensing, industrial packaging, medical imaging, and non-destructive testing. Traditional methods for perception and imaging are challenged by novel data-driven algorithms that offer improved resolution, localization, and detection rates. Over the past decade, deep learning technology has garnered substantial popularity, particularly in perception and computer vision applications. Whereas conventional signal processing techniques are more easily generalized to various applications, hybrid approaches where signal processing and learning-based algorithms are interleaved pose a promising compromise between performance and generalizability. Furthermore, such hybrid algorithms improve model training by leveraging the known characteristics of radio frequency (RF) waveforms, thus yielding more efficiently trained deep learning algorithms and offering higher performance than conventional methods. This dissertation introduces novel hybrid-learning algorithms for improved mmWave imaging systems applicable to a host of problems in perception and sensing. Various problem spaces are explored, including static and dynamic gesture classification; precise hand localization for human computer interaction; high-resolution near-field mmWave imaging using forward synthetic aperture radar (SAR); SAR under irregular scanning geometries; mmWave image super-resolution using deep neural network (DNN) and Vision Transformer (ViT) architectures; and data-level multiband radar fusion using a novel hybrid-learning architecture. Furthermore, we introduce several novel approaches for deep learning model training and dataset synthesis.Comment: PhD Dissertation Submitted to UTD ECE Departmen

Respiratory organ motion in interventional MRI : tracking, guiding and modeling

Respiratory organ motion is one of the major challenges in interventional MRI, particularly in interventions with therapeutic ultrasound in the abdominal region. High-intensity focused ultrasound found an application in interventional MRI for noninvasive treatments of different abnormalities. In order to guide surgical and treatment interventions, organ motion imaging and modeling is commonly required before a treatment start. Accurate tracking of organ motion during various interventional MRI procedures is prerequisite for a successful outcome and safe therapy. In this thesis, an attempt has been made to develop approaches using focused ultrasound which could be used in future clinically for the treatment of abdominal organs, such as the liver and the kidney. Two distinct methods have been presented with its ex vivo and in vivo treatment results. In the first method, an MR-based pencil-beam navigator has been used to track organ motion and provide the motion information for acoustic focal point steering, while in the second approach a hybrid imaging using both ultrasound and magnetic resonance imaging was combined for advanced guiding capabilities. Organ motion modeling and four-dimensional imaging of organ motion is increasingly required before the surgical interventions. However, due to the current safety limitations and hardware restrictions, the MR acquisition of a time-resolved sequence of volumetric images is not possible with high temporal and spatial resolution. A novel multislice acquisition scheme that is based on a two-dimensional navigator, instead of a commonly used pencil-beam navigator, was devised to acquire the data slices and the corresponding navigator simultaneously using a CAIPIRINHA parallel imaging method. The acquisition duration for four-dimensional dataset sampling is reduced compared to the existing approaches, while the image contrast and quality are improved as well. Tracking respiratory organ motion is required in interventional procedures and during MR imaging of moving organs. An MR-based navigator is commonly used, however, it is usually associated with image artifacts, such as signal voids. Spectrally selective navigators can come in handy in cases where the imaging organ is surrounding with an adipose tissue, because it can provide an indirect measure of organ motion. A novel spectrally selective navigator based on a crossed-pair navigator has been developed. Experiments show the advantages of the application of this novel navigator for the volumetric imaging of the liver in vivo, where this navigator was used to gate the gradient-recalled echo sequence

Multifrequency Aperture-Synthesizing Microwave Radiometer System (MFASMR). Volume 1

Background material and a systems analysis of a multifrequency aperture - synthesizing microwave radiometer system is presented. It was found that the system does not exhibit high performance because much of the available thermal power is not used in the construction of the image and because the image that can be formed has a resolution of only ten lines. An analysis of image reconstruction is given. The system is compared with conventional aperture synthesis systems

High-precision RCS measurement of aircraft’s weak scattering source

AbstractThe radar cross section (RCS) of weak scattering source on the surface of an aircraft is usually less than −40dBsm. How to accurately measure the RCS characteristics of weak scattering source is a technical challenge for the aircraft’s RCS measurement. This paper proposes separating and extracting the two-dimensional (2D) reflectivity distribution of the weak scattering source with the microwave imaging algorithm and spectral transform so as to enhance its measurement precision. Firstly, we performed the 2D microwave imaging of the target and then used the 2D gating function to separate and extract the reflectivity distribution of the weak scattering source. Secondly, we carried out the spectral transform of the reflectivity distribution and eventually obtained the RCS of the weak scattering source through calibration. The prototype experimental results and their analysis show that the measurement method is effective. The experiments on an aircraft’s low-scattering conformal antenna verify that the measurement method can eliminate the clutter on the surface of aircraft. The precision of measuring a −40dBsm target is 3–5dB better than the existing RCS measurement methods. The measurement method can more accurately obtain the weak scattering source’s RCS characteristics

Dual Radar SAR Controller

The following is a user guide for the Dual Radar SAR Controller graphical user interface (GUI) to operate the dual radar synthetic aperture radar (SAR) scanner. The scanner was designed in the Spring semester of 2022 by Josiah Smith (RA), Yusef Alimam (UG), and Geetika Vedula (UG) with multiple axes of motion for the radar and target under test. The system is operated by a personal computer (PC) running MATLAB. An AMC4030 motion controller is employed to control the mechanical system. An ESP32 microcontroller synchronizes the mechanical motion and radar frame firing to achieving precise positioning at high movement speeds; the software was designed by Josiah Smith (RA) and Benjamin Roy (UG). A second system is designed that employs 3-axes of motion (X-Y + rotation) for fine control over the location of the target under test. The entire system is capable of efficiently collecting data from colocated and non-colocated radars for multiband fusion imaging in addition to simple single radar imaging

적응형 레인지 셀 포커싱 기법 기반의 3차원 전자파 이미징 연구

학위논문 (박사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2015. 8. 정현교.본 논문에서는 3차원 전자파 이미징 알고리즘(microwave imaging algorithm)에 관한 연구를 살펴보았다. 기존의 시간 영역에서의 이미징 알고리즘 접근법과 주파수 영역에서의 이미징 알고리즘 접근법들을 검토하고 문제점들을 분석하였으며, 이를 개선하기 위해 적응형 기법 (adaptive technique)이 적용된 새로운 알고리즘을 제안하였다. 알고리즘 해석은 2차원부터 시작하여 3차원으로 확장하였다. 먼저 시간 영역 접근법(time-domain approach)에서의 대표적인 방법이라고 할 수 있는 후광 영사 (BP : back-projection) 방법을 검토하고 문제점을 제시하였으며 이를 개선하기 위해 최근 제안되고 있는 몇 가지 방법에 대해서도 알아보았다. 다음으로는 주파수 영역 접근법(frequency-domain approach)에서의 대표적인 방법이라고 할 수 있는 거리 천이 알고리즘(RMA : range migration algorithm)을 살펴보았다. 기본적으로 시간 영역 접근법보다는 빠른 처리를 할 수 있다는 장점을 지니지만 알고리즘의 처리 과정 중에서 적용되는 보간 계산상의 문제점을 가지고 있다. 기존의 방법들에 대한 장, 단점을 분석하여 본 논문에서는 이미지의 품질 향상을 위해 최근 연구되고 있는 초해상도 (super-resolution) 기법 중 하나인 강화된 뮤직 (eMUSIC : enhanced multi-signal classification) 알고리즘을 가중 함수로 사용하는 것을 제안하였다. 제안한 방법으로부터 얻은 변환 데이터를 거리 천이 알고리즘에 적용하여 본래의 원시 데이터(raw data)를 이용한 이미징 결과와 비교를 함으로써 가중 함수 적용에 대한 타당성도 증명하였다. 아울러, 주파수 영역에서의 이미징 접근법에서 나타나는 문제점인 보간 계산을 생략하면서, 각 레인지 셀 별로 단계적인 거리 천이를 할 수 있는 레인지 셀 포커싱 기법(RCF : range cell focusing technique)을 제안하였다. 레인지 셀 별로 변화하는 정합 필터 함수(matched filter function)를 생성하였고 이를 적용하여 레인지 굴곡을 보상하였다. 제안된 알고리즘은 모든 레인지 셀에서 정합 필터 함수를 생성하고 계산하기 때문에 시간적인 효율이 떨어지지만 몬테카를로 방법이 이용된 적응형 기법을 적용함으로써 해상도뿐 만 아니라, 알고리즘 처리상의 시간 효율도 개선하였다. 이를 적응형 레인지 셀 포커싱 (ARCF : adaptive range cell focusing) 기법이라 명하며 먼저, 이상적인 점타깃에 대한 시뮬레이션 데이터를 이용하여 이미징을 구현하였다. 1차원 선형 스캔(linear scan)을 통한 2차원 원시 데이터의 2차원 이미징과 2차원 스캔을 통한 3차원 원시 데이터의 3차원 이미징을 통해 알고리즘의 효율성을 검증하였다. 실험 환경은 포지셔너(positioner), X-Band 더블 릿지드 안테나(X-Band double ridged antenna), 벡터 네트워크 어널라이져(VNA : vector network analyzer)로 구성되어있다. 송·수신 안테나를 이용하여 이동하면서 원시 데이터를 획득하였고 복원하고자 하는 타깃(target)으로는 불연속적 타깃과 연속적인 타깃 두 종류를 사용하였다. 불연속적인 타깃으로는 직경 6cm의 금속 구로 이루어진 임의의 알파벳 모양을 이용하였으며 연속적인 타깃으로는 총기류와 유사한 모형과 하드디스크, 텀블러 등을 이용하였다. 다양한 실험 결과로부터 본 논문에서 제안한 알고리즘의 타당성이 검증되었으며, 이는 향후 3차원 고해상도의 실시간 전자파 이미징 알고리즘을 구현하는데 있어 초석이 되고자 한다.제 1 장 서 론 .................................................................... 01 1.1 연구의 배경 .......................................................................... 01 1.2 논문의 구성 .......................................................................... 03 제 2 장 전자파 이미징 시스템 및 알고리즘 ............................. 04 2.1 신호 모델 .............................................................................. 04 2.2 전자파 이미징 알고리즘 ........................................................ 08 제 3 장 원시 데이터 변환 및 검증 .......................................... 19 3.1 원시 데이터 변환의 필요성 ................................................... 19 3.2 가중 함수 정의 ..................................................................... 22 3.3 가중된 원시데이터 검증 ........................................................ 32 3.4 변환 데이터를 이용한 거리천이 알고리즘 ............................. 34 제 4 장 3차원 적응형 레인지 셀 포커싱 기법 ......................... 38 4.1 레인지 셀 포커싱 기법의 개요 .............................................. 38 4.2 2차원 레인지 셀 포커싱 기법의 수치적 해석 ........................ 44 4.3 2차원 적응형 레인지 셀 포커싱 기법 .................................... 52 4.4 3차원 적응형 레인지 셀 포커싱 기법 .................................... 61 제 5 장 제안 알고리즘 검증 .................................................... 68 5.1 시뮬레이션 데이터 이용 ........................................................ 68 5.2 실험 데이터 이용 .................................................................. 74 제 6 장 결 론 ................................................................... 85 6.1 결론 및 토의 ......................................................................... 85 6.2 향후 연구 .............................................................................. 87 참고문헌 .................................................................................. 88 Abstract ................................................................................. 98Docto

Clutter removal of near-field UWB SAR imaging for pipeline penetrating radar

Recently, ultrawideband (UWB) near-field synthetic aperture radar (SAR) imaging has been proposed for pipeline penetrating radar applications thanks to its capability in providing suitable resolution and penetration depth. Because of geometrical restrictions, there are many complicated sources of clutter in the pipe. However, this issue has not been investigated yet. In this article, we investigate some well-known clutter removal algorithms using full-wave simulated data and compare their results considering image quality, signal to clutter ratio and contrast. Among candidate algorithms, two-dimensional singular spectrum analysis (2-D SSA) shows a good potential to improve the signal to clutter ratio. However, basic 2-D SSA produces some artifacts in the image. Therefore, to mitigate this issue, we propose “modified 2-D SSA.” After developing the suitable clutter removal algorithm, wepropose a complete algorithm chain for pipeline imaging. An UWB nearfieldSARmonitoring system including anUWBM-sequence sensor and automatic positioner are implemented and the image of drilled perforations in a concrete pipe mimicking oil well structure as a case study is reconstructed to test the proposed algorithm. Compared to the literature, a comprehensive near-field SAR imaging algorithm including new clutter removal is proposed and its performance is verified by obtaining high-quality images in experimental results

Photoacoustic tomography and sensing in biomedicine

Photoacoustics has been broadly studied in biomedicine, for both human and small animal tissues. Photoacoustics uniquely combines the absorption contrast of light or radio frequency waves with ultrasound resolution. Moreover, it is non-ionizing and non-invasive, and is the fastest growing new biomedical method, with clinical applications on the way. This review provides a brief recap of recent developments in photoacoustics in biomedicine, from basic principles to applications. The emphasized areas include the new imaging modalities, hybrid detection methods, photoacoustic contrast agents and the photoacoustic Doppler effect, as well as translational research topics