37 research outputs found
Three more Decades in Array Signal Processing Research: An Optimization and Structure Exploitation Perspective
The signal processing community currently witnesses the emergence of sensor
array processing and Direction-of-Arrival (DoA) estimation in various modern
applications, such as automotive radar, mobile user and millimeter wave indoor
localization, drone surveillance, as well as in new paradigms, such as joint
sensing and communication in future wireless systems. This trend is further
enhanced by technology leaps and availability of powerful and affordable
multi-antenna hardware platforms. The history of advances in super resolution
DoA estimation techniques is long, starting from the early parametric
multi-source methods such as the computationally expensive maximum likelihood
(ML) techniques to the early subspace-based techniques such as Pisarenko and
MUSIC. Inspired by the seminal review paper Two Decades of Array Signal
Processing Research: The Parametric Approach by Krim and Viberg published in
the IEEE Signal Processing Magazine, we are looking back at another three
decades in Array Signal Processing Research under the classical narrowband
array processing model based on second order statistics. We revisit major
trends in the field and retell the story of array signal processing from a
modern optimization and structure exploitation perspective. In our overview,
through prominent examples, we illustrate how different DoA estimation methods
can be cast as optimization problems with side constraints originating from
prior knowledge regarding the structure of the measurement system. Due to space
limitations, our review of the DoA estimation research in the past three
decades is by no means complete. For didactic reasons, we mainly focus on
developments in the field that easily relate the traditional multi-source
estimation criteria and choose simple illustrative examples.Comment: 16 pages, 8 figures. This work has been submitted to the IEEE for
possible publication. Copyright may be transferred without notice, after
which this version may no longer be accessibl
Ultrasound Signal Processing: From Models to Deep Learning
Medical ultrasound imaging relies heavily on high-quality signal processing
algorithms to provide reliable and interpretable image reconstructions.
Hand-crafted reconstruction methods, often based on approximations of the
underlying measurement model, are useful in practice, but notoriously fall
behind in terms of image quality. More sophisticated solutions, based on
statistical modelling, careful parameter tuning, or through increased model
complexity, can be sensitive to different environments. Recently, deep learning
based methods have gained popularity, which are optimized in a data-driven
fashion. These model-agnostic methods often rely on generic model structures,
and require vast training data to converge to a robust solution. A relatively
new paradigm combines the power of the two: leveraging data-driven deep
learning, as well as exploiting domain knowledge. These model-based solutions
yield high robustness, and require less trainable parameters and training data
than conventional neural networks. In this work we provide an overview of these
methods from the recent literature, and discuss a wide variety of ultrasound
applications. We aim to inspire the reader to further research in this area,
and to address the opportunities within the field of ultrasound signal
processing. We conclude with a future perspective on these model-based deep
learning techniques for medical ultrasound applications
Physical Layer Security in Integrated Sensing and Communication Systems
The development of integrated sensing and communication (ISAC) systems has been spurred by the growing congestion of the wireless spectrum. The ISAC system detects targets and communicates with downlink cellular users simultaneously. Uniquely for such scenarios, radar targets are regarded as potential eavesdroppers which might surveil the information sent from the base station (BS) to communication users (CUs) via the radar probing signal. To address this issue, we propose security solutions for ISAC systems to prevent confidential information from being intercepted by radar targets.
In this thesis, we firstly present a beamformer design algorithm assisted by artificial noise (AN), which aims to minimize the signal-to-noise ratio (SNR) at the target while ensuring the quality of service (QoS) of legitimate receivers. Furthermore, to reduce the power consumed by AN, we apply the directional modulation (DM) approach to exploit constructive interference (CI). In this case, the optimization problem is designed to maximize the SINR of the target reflected echoes with CI constraints for each CU, while constraining the received symbols at the target in the destructive region.
Apart from the separate functionalities of radar and communication systems above, we investigate sensing-aided physical layer security (PLS), where the ISAC BS first emits an omnidirectional waveform to search for and estimate target directions. Then, we formulate a weighted optimization problem to simultaneously maximize the secrecy rate and minimize the Cram\'er-Rao bound (CRB) with the aid of the AN, designing a beampattern with a wide main beam covering all possible angles of targets. The main beam width of the next iteration depends on the optimal CRB. In this way, the sensing and security functionalities provide mutual benefits, resulting in the improvement of mutual performances with every iteration of the optimization, until convergence.
Overall, numerical results show the effectiveness of the ISAC security designs through the deployment of AN-aided secrecy rate maximization and CI techniques. The sensing-assisted PLS scheme offers a new approach for obtaining channel information of eavesdroppers, which is treated as a limitation of conventional PLS studies. This design gains mutual benefits in both single and multi-target scenarios
SPATIAL FILTERING OF CLUTTER USING PHASED ARRAY RADARS FOR OBSERVATIONS OF THE WEATHER
Phased array radars are attractive for weather surveillance primarily because of their capacity for extremely rapid scanning through electronic steering. When combined with the recently developed beam multiplexing technique, these radars can provide significantly improved update rates, which are necessary for monitoring rapidly evolving severe weather. A consequence of beam multiplexing, however, is that a small number of contiguous time series samples are typically used, creating a significant challenge for temporal/spectral filters typically used for clutter mitigation. As a result, the accurate extraction of weather products can become the limiting performance barrier for phased array radars that employ beam multiplexing in clutter-contaminated scattered fields. By exploiting the spatial correlation among the signals from the elements of the phased array antenna, the effect of clutter contamination can be reduced through a processed called spatial filtering . In contrast to conventional temporal filtering, spatial filtering is used to adaptively adjust the antenna beam pattern to produce lower gain in the directions of the undesired clutter signals. In this dissertation, the effect of clutter mitigation using spatial filtering was studied using numerical simulations of a tornadic environment and an array antenna configuration similar to the NSSL NWRT Phased Array Radar for changes in signal-to-noise ratio, clutter-to-signal ratio, number of time series samples, and diagonal loading for three types of clutter sources that include nearly stationary ground clutter, moving targets such as aircraft, and wind turbine clutter, which has recently been documented to be increasingly problematic for radars. Since such data are not currently available from a horizontally pointed phased array weather radar, experimental validation was applied to an existing data set from the Turbulent Eddy Profiler (TEP) developed at University of Massachusetts, which is a vertically pointed phased array radar. Results will show that spatial filtering holds promise for the future of phased array radars for the observation of the weather in a clutter environment
Sparse Representations & Compressed Sensing with application to the problem of Direction-of-Arrival estimation.
PhDThe significance of sparse representations has been highlighted in numerous signal processing
applications ranging from denoising to source separation and the emerging field
of compressed sensing has provided new theoretical insights into the problem of inverse
systems with sparsity constraints.
In this thesis, these advances are exploited in order to tackle the problem of direction-of-arrival (DOA) estimation in sensor arrays. Assuming spatial sparsity e.g. few sources
impinging on the array, the problem of DOA estimation is formulated as a sparse representation
problem in an overcomplete basis. The resulting inverse problem can be solved
using typical sparse recovery methods based on convex optimization i.e. `1 minimization.
However, in this work a suite of novel sparse recovery algorithms is initially developed,
which reduce the computational cost and yield approximate solutions. Moreover, the
proposed algorithms of Polytope Faces Pursuits (PFP) allow for the induction of structured
sparsity models on the signal of interest, which can be quite beneficial when dealing
with multi-channel data acquired by sensor arrays, as it further reduces the complexity
and provides performance gain under certain conditions.
Regarding the DOA estimation problem, experimental results demonstrate that the
proposed methods outperform popular subspace based methods such as the multiple
signal classification (MUSIC) algorithm in the case of rank-deficient data (e.g. presence
of highly correlated sources or limited amount of data) for both narrowband and wideband
sources. In the wideband scenario, they can also suppress the undesirable effects of spatial
aliasing.
However, DOA estimation with sparsity constraints has its limitations. The compressed
sensing requirement of incoherent dictionaries for robust recovery sets limits to
the resolution capabilities of the proposed method. On the other hand, the unknown
parameters are continuous and therefore if the true DOAs do not belong to the predefined discrete set of potential locations the algorithms' performance will degrade due to
errors caused by mismatches. To overcome this limitation, an iterative alternating descent
algorithm for the problem of off-grid DOA estimation is proposed that alternates
between sparse recovery and dictionary update estimates. Simulations clearly illustrate
the performance gain of the algorithm over the conventional sparsity approach and other
existing off-grid DOA estimation algorithms.EPSRC Leadership Fellowship EP/G007144/1; EU FET-Open Project FP7-ICT-225913