52 research outputs found
Interference Mitigation for FMCW Radar With Sparse and Low-Rank Hankel Matrix Decomposition
In this paper, the interference mitigation for Frequency Modulated Continuous
Wave (FMCW) radar system with a dechirping receiver is investigated. After
dechirping operation, the scattered signals from targets result in beat
signals, i.e., the sum of complex exponentials while the interferences lead to
chirp-like short pulses. Taking advantage of these different time and frequency
features between the useful signals and the interferences, the interference
mitigation is formulated as an optimization problem: a sparse and low-rank
decomposition of a Hankel matrix constructed by lifting the measurements. Then,
an iterative optimization algorithm is proposed to tackle it by exploiting the
Alternating Direction of Multipliers (ADMM) scheme. Compared to the existing
methods, the proposed approach does not need to detect the interference and
also improves the estimation accuracy of the separated useful signals. Both
numerical simulations with point-like targets and experiment results with
distributed targets (i.e., raindrops) are presented to demonstrate and verify
its performance. The results show that the proposed approach is generally
applicable for interference mitigation in both stationary and moving target
scenarios.Comment: 12 pages, 8 figure
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
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
Radio astronomical imaging in the presence of strong radio interference
Radio-astronomical observations are increasingly contaminated by
interference, and suppression techniques become essential. A powerful candidate
for interference mitigation is adaptive spatial filtering. We study the effect
of spatial filtering techniques on radio astronomical imaging. Current
deconvolution procedures such as CLEAN are shown to be unsuitable to spatially
filtered data, and the necessary corrections are derived. To that end, we
reformulate the imaging (deconvolution/calibration) process as a sequential
estimation of the locations of astronomical sources. This not only leads to an
extended CLEAN algorithm, the formulation also allows to insert other array
signal processing techniques for direction finding, and gives estimates of the
expected image quality and the amount of interference suppression that can be
achieved. Finally, a maximum likelihood procedure for the imaging is derived,
and an approximate ML image formation technique is proposed to overcome the
computational burden involved. Some of the effects of the new algorithms are
shown in simulated images. Keywords: Radio astronomy, synthesis imaging,
parametric imaging, interference mitigation, spatial filtering, maximum
likelihood, minimum variance, CLEAN.Comment: 27 pages, 7 figures. Paper with higher resolution color figures at
http://cobalt.et.tudelft.nl/~leshem/postscripts/leshem/imaging.ps.g
MIMO Systems
In recent years, it was realized that the MIMO communication systems seems to be inevitable in accelerated evolution of high data rates applications due to their potential to dramatically increase the spectral efficiency and simultaneously sending individual information to the corresponding users in wireless systems. This book, intends to provide highlights of the current research topics in the field of MIMO system, to offer a snapshot of the recent advances and major issues faced today by the researchers in the MIMO related areas. The book is written by specialists working in universities and research centers all over the world to cover the fundamental principles and main advanced topics on high data rates wireless communications systems over MIMO channels. Moreover, the book has the advantage of providing a collection of applications that are completely independent and self-contained; thus, the interested reader can choose any chapter and skip to another without losing continuity
Advanced RFI detection, RFI excision, and spectrum sensing : algorithms and performance analyses
Because of intentional and unintentional man-made interference, radio frequency interference (RFI) is causing performance loss in various radio frequency operating systems such as microwave radiometry, radio astronomy, satellite communications, ultra-wideband communications, radar, and cognitive radio. To overcome the impact of RFI, a robust RFI detection coupled with an efficient RFI excision are, thus, needed. Amongst their limitations, the existing techniques tend to be computationally complex and render inefficient RFI excision. On the other hand, the state-of-the-art on cognitive radio (CR) encompasses numerous spectrum sensing techniques. However, most of the existing techniques either rely on the availability of the channel state information (CSI) or the primary signal characteristics. Motivated by the highlighted limitations, this Ph.D. dissertation presents research investigations and results grouped into three themes: advanced RFI detection, advanced RFI excision, and advanced spectrum sensing.
Regarding advanced RFI detection, this dissertation presents five RFI detectors: a power detector (PD), an energy detector (ED), an eigenvalue detector (EvD), a matrix-based detector, and a tensor-based detector. First, a computationally simple PD is investigated to detect a brodband RFI. By assuming Nakagami-m fading channels, exact closed-form expressions for the probabilities of RFI detection and of false alarm are derived and validated via simulations. Simulations also demonstrate that PD outperforms kurtosis detector (KD). Second, an ED is investigated for RFI detection in wireless communication systems. Its average probability of RFI detection is studied and approximated, and asymptotic closed-form expressions are derived. Besides, an exact closed-form expression for its average probability of false alarm is derived. Monte-Carlo simulations validate the derived analytical expressions and corroborate that the investigated ED outperforms KD and a generalized likelihood ratio test (GLRT) detector. The performance of ED is also assessed using real-world RFI contaminated data. Third, a blind EvD is proposed for single-input multiple-output (SIMO) systems that may suffer from RFI. To characterize the performance of EvD, performance closed-form expressions valid for infinitely huge samples are derived and validated through simulations. Simulations also corroborate that EvD manifests, even under sample starved settings, a comparable detection performance with a GLRT detector fed with the knowledge of the signal of interest (SOI) channel and a matched subspace detector fed with the SOI and RFI channels. At last, for a robust detection of RFI received through a multi-path fading channel, this dissertation presents matrix-based and tensor-based multi-antenna RFI detectors while introducing a tensor-based hypothesis testing framework. To characterize the performance of these detectors, performance analyses have been pursued. Simulations assess the performance of the proposed detectors and validate the derived asymptotic characterizations.
Concerning advanced RFI excision, this dissertation introduces a multi-linear algebra framework to the multi-interferer RFI (MI-RFI) excision research by proposing a multi-linear subspace estimation and projection (MLSEP) algorithm for SIMO systems. Having employed smoothed observation windows, a smoothed MLSEP (s-MLSEP) algorithm is also proposed. MLSEP and s-MLSEP require the knowledge of the number of interferers and their respective channel order. Accordingly, a novel smoothed matrix-based joint number of interferers and channel order enumerator is proposed. Performance analyses corroborate that both MLSEP and s-MLSEP can excise all interferers when the perturbations get infinitesimally small. For such perturbations, the analyses also attest that s-MLSEP exhibits a faster convergence to a zero excision error than MLSEP which, in turn, converges faster than a subspace projection algorithm. Despite its slight complexity, simulations and performance assessment on real-world data demonstrate that MLSEP outperforms projection-based RFI excision algorithms. Simulations also corroborate that s-MLSEP outperforms MLSEP as the smoothing factor gets smaller.
With regard to advanced spectrum sensing, having been inspired by an F–test detector with a simple analytical false alarm threshold expression considered an alternative to the existing blind detectors, this dissertation presents and evaluates simple F–test based spectrum sensing techniques that do not require the knowledge of CSI for multi-antenna CRs. Exact and asymptotic analytical performance closed-form expressions are derived for the presented detectors. Simulations assess the performance of the presented detectors and validate the derived expressions. For an additive noise exhibiting the same variance across multiple-antenna frontends, simulations also corroborate that the presented detectors are constant false alarm rate detectors which are also robust against noise uncertainty
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
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
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