17 research outputs found
Sub-aperture SAR Imaging with Uncertainty Quantification
In the problem of spotlight mode airborne synthetic aperture radar (SAR)
image formation, it is well-known that data collected over a wide azimuthal
angle violate the isotropic scattering property typically assumed. Many
techniques have been proposed to account for this issue, including both
full-aperture and sub-aperture methods based on filtering, regularized least
squares, and Bayesian methods. A full-aperture method that uses a hierarchical
Bayesian prior to incorporate appropriate speckle modeling and reduction was
recently introduced to produce samples of the posterior density rather than a
single image estimate. This uncertainty quantification information is more
robust as it can generate a variety of statistics for the scene. As proposed,
the method was not well-suited for large problems, however, as the sampling was
inefficient. Moreover, the method was not explicitly designed to mitigate the
effects of the faulty isotropic scattering assumption. In this work we
therefore propose a new sub-aperture SAR imaging method that uses a sparse
Bayesian learning-type algorithm to more efficiently produce approximate
posterior densities for each sub-aperture window. These estimates may be useful
in and of themselves, or when of interest, the statistics from these
distributions can be combined to form a composite image. Furthermore, unlike
the often-employed lp-regularized least squares methods, no user-defined
parameters are required. Application-specific adjustments are made to reduce
the typically burdensome runtime and storage requirements so that appropriately
large images can be generated. Finally, this paper focuses on incorporating
these techniques into SAR image formation process. That is, for the problem
starting with SAR phase history data, so that no additional processing errors
are incurred
Fast Monostatic Scattering Analysis Based on Bayesian Compressive Sensing
The Bayesian compressive sensing algorithm
is utilized together with the method of moments to
fast analyze the monostatic electromagnetic scattering
problem. Different from the traditional compressive
sensing based fast monostatic scattering analysis method
which cannot determine the required measurement times,
the proposed method adopts the Bayesian framework
to recover the underlying signal. Error bars of the signal
can be obtained in the recovery procedure, which
provides a means to adaptively determine the number of
compressive-sensing measurements. Numerical results
are given to demonstrate the accuracy and effectiveness
of proposed method
Sparse Representation Denoising for Radar High Resolution Range Profiling
Radar high resolution range profile has attracted considerable attention in radar automatic target recognition. In practice, radar return is usually contaminated by noise, which results in profile distortion and recognition performance degradation. To deal with this problem, in this paper, a novel denoising method based on sparse representation is proposed to remove the Gaussian white additive noise. The return is sparsely described in the Fourier redundant dictionary and the denoising problem is described as a sparse representation model. Noise level of the return, which is crucial to the denoising performance but often unknown, is estimated by performing subspace method on the sliding subsequence correlation matrix. Sliding window process enables noise level estimation using only one observation sequence, not only guaranteeing estimation efficiency but also avoiding the influence of profile time-shift sensitivity. Experimental results show that the proposed method can effectively improve the signal-to-noise ratio of the return, leading to a high-quality profile
Semi-local scaling exponent estimation with box-penalty constraints and total-variation regularisation
We here establish and exploit the result that 2-D isotropic self-similar fields beget quasi-decorrelated wavelet coefficients and that the resulting localised log sample second moment statistic is asymptotically normal. This leads to the development of a semi-local scaling exponent estimation framework with optimally modified weights. Furthermore, recent interest in penalty methods for least squares problems and generalised Lasso for scaling exponent estimation inspires the simultaneous incorporation of both bounding box constraints and total variation smoothing into an iteratively reweighted least-squares estimator framework. Numerical results on fractional Brownian fields with global and piecewise constant, semi-local Hurst parameters illustrate the benefits of the new estimators
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