149 research outputs found
Blur resolved OCT: full-range interferometric synthetic aperture microscopy through dispersion encoding
We present a computational method for full-range interferometric synthetic
aperture microscopy (ISAM) under dispersion encoding. With this, one can
effectively double the depth range of optical coherence tomography (OCT),
whilst dramatically enhancing the spatial resolution away from the focal plane.
To this end, we propose a model-based iterative reconstruction (MBIR) method,
where ISAM is directly considered in an optimization approach, and we make the
discovery that sparsity promoting regularization effectively recovers the
full-range signal. Within this work, we adopt an optimal nonuniform discrete
fast Fourier transform (NUFFT) implementation of ISAM, which is both fast and
numerically stable throughout iterations. We validate our method with several
complex samples, scanned with a commercial SD-OCT system with no hardware
modification. With this, we both demonstrate full-range ISAM imaging, and
significantly outperform combinations of existing methods.Comment: 17 pages, 7 figures. The images have been compressed for arxiv -
please follow DOI for full resolutio
Adaptive waveform design for SAR in a crowded spectrum
This thesis concerns the development of an adaptive waveform design scheme for synthetic
aperture radar (SAR) to support its operation in the increasingly crowded radio
frequency (RF) spectrum, focusing on mitigating the effects of external RF interference.
The RF spectrum is a finite resource and the rapid expansion of the telecommunications
industry has seen radar users face a significant restriction in the range of available
operational frequencies. This crowded spectrum scenario leads to increased likelihood
of RF interference either due to energy leakage from neighbouring spectral users or
from unlicensed transmitters.
SAR is a wide bandwidth radar imaging mode which exploits the motion of the radar
platform to form an image using multiple one dimensional profiles of the scene of interest
known as the range profile. Due to its wideband nature, SAR is particularly vulnerable
to RF interference which causes image impairments and overall reduction in quality.
Altering the approach for radar energy transmission across the RF spectrum is now
imperative to continue effective operation.
Adaptive waveforms have recently become feasible for implementation and offer the
much needed flexibility in the choice and control over radar transmission. However,
there is a critically small processing time frame between waveform reception and transmission,
which necessitates the use of computationally efficient processing algorithms
to use adaptivity effectively.
This simulation-based study provides a first look at adaptive waveform design for SAR
to mitigate the detrimental effects of RF interference on a pulse-to-pulse basis. Standard
SAR systems rely on a fixed waveform processing format on reception which restricts its
potential to reap the benefits of adaptive waveform design. Firstly, to support waveform
design for SAR, system identification techniques are applied to construct an alternative
receive processing method which allows flexibility in waveform type. This leads to the
main contribution of the thesis which is the formation of an adaptive spectral waveform
design scheme. A computationally efficient closed-form expression for the waveform spectrum that minimizes the error in the estimate of the SAR range profile on a pulse to pulse basis is
derived. The range profile and the spectrum of the interference are estimated at each
pulse. The interference estimate is then used to redesign the proceeding waveform for
estimation of the range profile at the next radar platform position. The solution necessitates
that the energy is spread across the spectrum such that it competes with the
interferer. The scenario where the waveform admits gaps in the spectrum in order to
mitigate the effects of the interference is also detailed and is the secondary major thesis
contribution. A series of test SAR images demonstrate the efficacy of these techniques
and yield reduced interference effects compared to the standard SAR waveform
Iterative synthetic aperture radar imaging algorithms
Synthetic aperture radar is an important tool in a wide range of civilian and military imaging
applications. This is primarily due to its ability to image in all weather conditions, during
both the day and the night, unlike optical imaging systems. A synthetic aperture radar system
contains a step which is not present in an optical imaging system, this is image formation.
This is required because the acquired data from the radar sensor does not directly correspond
to the image. Instead, to form an image, the system must solve an inverse problem. In
conventional scenarios, this inverse problem is relatively straight forward and a matched lter
based algorithm produces an image of suitable image quality. However, there are a number of
interesting scenarios where this is not the case.
Scenarios where standard image formation algorithms are unsuitable include systems with
data undersampling, errors in the system observation model and data that is corrupted by radio
frequency interference. Image formation in these scenarios will form the topics of this thesis
and a number of iterative algorithms are proposed to achieve image formation. The motivation
for these proposed algorithms is primarily from the eld of compressed sensing, which considers
the recovery of signals with a low-dimensional structure.
The rst contribution of this thesis is the development of fast algorithms for the system
observation model and its adjoint. These algorithms are required by large-scale gradient based
iterative algorithms for image formation. The proposed algorithms are based on existing fast
back-projection algorithms, however, a new decimation strategy is proposed which is more
suitable for some applications.
The second contribution is the development of a framework for iterative near- eld image
formation, which uses the proposed fast algorithms. It is shown that the framework can be used,
in some scenarios, to improve the visual quality of images formed from fully sampled data and
undersampled data, when compared to images formed using matched lter based algorithms.
The third contribution concerns errors in the system observation model. Algorithms that
correct these errors are commonly referred to as autofocus algorithms. It is shown that conventional
autofocus algorithms, which work as a post-processor on the formed image, are unsuitable
for undersampled data. Instead an autofocus algorithm is proposed which corrects errors within
the iterative image formation procedure. The proposed algorithm is provably stable and convergent with a faster convergence rate than previous approaches.
The nal contribution is an algorithm for ultra-wideband synthetic aperture radar image
formation. Due to the large spectrum over which the ultra-wideband signal is transmitted, there
is likely to be many other users operating within the same spectrum. These users can produce
signi cant radio frequency interference which will corrupt the received data. The proposed
algorithm uses knowledge of the RFI spectrum to minimise the e ect of the RFI on the formed
image
Machine Learning for Beamforming in Audio, Ultrasound, and Radar
Multi-sensor signal processing plays a crucial role in the working of several everyday technologies, from correctly understanding speech on smart home devices to ensuring aircraft fly safely. A specific type of multi-sensor signal processing called beamforming forms a central part of this thesis. Beamforming works by combining the information from several spatially distributed sensors to directionally filter information, boosting the signal from a certain direction but suppressing others. The idea of beamforming is key to the domains of audio, ultrasound, and radar.
Machine learning is the other central part of this thesis. Machine learning, and especially its sub-field of deep learning, has enabled breakneck progress in tackling several problems that were previously thought intractable. Today, machine learning powers many of the cutting edge systems we see on the internet for image classification, speech recognition, language translation, and more.
In this dissertation, we look at beamforming pipelines in audio, ultrasound, and radar from a machine learning lens and endeavor to improve different parts of the pipelines using ideas from machine learning. We start off in the audio domain and derive a machine learning inspired beamformer to tackle the problem of ensuring the audio captured by a camera matches its visual content, a problem we term audiovisual zooming. Staying in the audio domain, we then demonstrate how deep learning can be used to improve the perceptual qualities of speech by denoising speech clipping, codec distortions, and gaps in speech.
Transitioning to the ultrasound domain, we improve the performance of short-lag spatial coherence ultrasound imaging by exploiting the differences in tissue texture at each short lag value by applying robust principal component analysis. Next, we use deep learning as an alternative to beamforming in ultrasound and improve the information extraction pipeline by simultaneously generating both a segmentation map and B-mode image of high quality directly from raw received ultrasound data.
Finally, we move to the radar domain and study how deep learning can be used to improve signal quality in ultra-wideband synthetic aperture radar by suppressing radio frequency interference, random spectral gaps, and contiguous block spectral gaps. By training and applying the networks on raw single-aperture data prior to beamforming, it can work with myriad sensor geometries and different beamforming equations, a crucial requirement in synthetic aperture radar
- …