16 research outputs found
Sparsity driven ground moving target indication in synthetic aperture radar
Synthetic aperture radar (SAR) was first invented in the early 1950s as the remote surveillance
instruments to produce high resolution 2D images of the illuminated scene with weather-independent,
day-or-night performance. Compared to the Real Aperture Radar (RAR), SAR is
synthesising a large virtual aperture by moving a small antenna along the platform path. Typical
SAR imaging systems are designed with the basic assumption of a static scene, and moving targets
are widely known to induce displacements and defocusing in the formed images. While the
capabilities of detection, states estimation and imaging for moving targets with SAR are highly
desired in both civilian and military applications, the Ground Moving Target Indication (GMTI)
techniques can be integrated into SAR systems to realise these challenging missions. The state-of-the-
art SAR-based GMTI is often associated with multi-channel systems to improve the detection
capabilities compared to the single-channel ones. Motivated by the fact that the SAR imaging
is essentially solving an optimisation problem, we investigate the practicality to reformulate
the GMTI process into the optimisation form. Furthermore, the moving target sparsities and
underlying similarities between the conventional GMTI processing and sparse reconstruction
algorithms drive us to consider the compressed sensing theory in SAR/GMTI applications.
This thesis aims to establish an end-to-end SAR/GMTI processing framework regularised by
target sparsities based on multi-channel SAR models. We have explained the mathematical model
of the SAR system and its key properties in details. The common GMTI mechanism and basics
of the compressed sensing theory are also introduced in this thesis. The practical implementation
of the proposed framework is provided in this work. The developed model is capable of realising
various SAR/GMTI tasks including SAR image formation, moving target detection, target state
estimation and moving target imaging. We also consider two essential components, i.e. the data
pre-processing and elevation map, in this work. The effectiveness of the proposed framework is
demonstrated through both simulations and real data.
Given that our focus in this thesis is on the development of a complete sparsity-aided
SAR/GMTI framework, the contributions of this thesis can be summarised as follows. First, the
effects of SAR channel balancing techniques and elevation information in SAR/GMTI applications
are analysed in details. We have adapted these essential components to the developed framework
for data pre-processing, system specification estimation and better SAR/GMTI accuracies.
Although the purpose is on enhancing the proposed sparsity-based SAR/GMTI framework, the
exploitation of the DEM in other SAR/GMTI algorithms may be of independent interest.
Secondly, we have designed a novel sparsity-aided framework which integrates the
SAR/GMTI missions, i.e. SAR imaging, moving target and background decomposition, and
target state estimation, into optimisation problems. A practical implementation of the proposed
framework with a two stage process and theoretically/experimentally proven algorithms are
proposed in this work. The key novelty on utilising optimisations and target sparsities is explained
in details.
Finally, a practical algorithm for moving target imaging and state estimation is developed
to accurately estimate the full target parameters and form target images with relocation and
refocusing capabilities. Compared to the previous processing steps for practical applications, the
designed algorithm consistently relies on the exploitation of target sparsities which forms the final
processing stage of the whole pipeline. All the developed components contribute coherently to
establish a complete sparsity driven SAR/GMTI processing framework
The University Defence Research Collaboration In Signal Processing
This chapter describes the development of algorithms for automatic detection of anomalies from multi-dimensional, undersampled and incomplete datasets. The challenge in this work is to identify and classify behaviours as normal or abnormal, safe or threatening, from an irregular and often heterogeneous sensor network. Many defence and civilian applications can be modelled as complex networks of interconnected nodes with unknown or uncertain spatio-temporal relations. The behavior of such heterogeneous networks can exhibit dynamic properties, reflecting evolution in both network structure (new nodes appearing and existing nodes disappearing), as well as inter-node relations.
The UDRC work has addressed not only the detection of anomalies, but also the identification of their nature and their statistical characteristics. Normal patterns and changes in behavior have been incorporated to provide an acceptable balance between true positive rate, false positive rate, performance and computational cost. Data quality measures have been used to ensure the models of normality are not corrupted by unreliable and ambiguous data. The context for the activity of each node in complex networks offers an even more efficient anomaly detection mechanism. This has allowed the development of efficient approaches which not only detect anomalies but which also go on to classify their behaviour
The University Defence Research Collaboration In Signal Processing: 2013-2018
Signal processing is an enabling technology crucial to all areas
of defence and security. It is called for whenever humans and
autonomous systems are required to interpret data (i.e. the signal)
output from sensors. This leads to the production of the
intelligence on which military outcomes depend. Signal processing
should be timely, accurate and suited to the decisions
to be made. When performed well it is critical, battle-winning
and probably the most important weapon which you’ve never
heard of.
With the plethora of sensors and data sources that are
emerging in the future network-enabled battlespace, sensing
is becoming ubiquitous. This makes signal processing more
complicated but also brings great opportunities.
The second phase of the University Defence Research Collaboration
in Signal Processing was set up to meet these complex
problems head-on while taking advantage of the opportunities.
Its unique structure combines two multi-disciplinary
academic consortia, in which many researchers can approach
different aspects of a problem, with baked-in industrial collaboration
enabling early commercial exploitation.
This phase of the UDRC will have been running for 5 years
by the time it completes in March 2018, with remarkable results.
This book aims to present those accomplishments and
advances in a style accessible to stakeholders, collaborators and
exploiters
High Dimensional Covariance Estimation for Spatio-Temporal Processes
High dimensional time series and array-valued data are ubiquitous in signal processing, machine learning, and science. Due to the additional (temporal) direction, the total dimensionality of the data is often extremely high, requiring large numbers of training examples to learn the distribution using unstructured techniques. However, due to difficulties in sampling, small population sizes, and/or rapid system changes in time, it is often the case that very few relevant training samples are available, necessitating the imposition of structure on the data if learning is to be done. The mean and covariance are useful tools to describe high dimensional distributions because (via the Gaussian likelihood function) they are a data-efficient way to describe a general multivariate distribution, and allow for simple inference, prediction, and regression via classical techniques.
In this work, we develop various forms of multidimensional covariance structure that explicitly exploit the array structure of the data, in a way analogous to the widely used low rank modeling of the mean. This allows dramatic reductions in the number of training samples required, in some cases to a single training sample. Covariance models of this form have been increasing in interest recently, and statistical performance bounds for high dimensional estimation in sample-starved scenarios are of great relevance.
This thesis focuses on the high-dimensional covariance estimation problem, exploiting spatio-temporal structure to reduce sample complexity. Contributions are made in the following areas: (1) development of a variety of rich Kronecker product-based covariance models allowing the exploitation of spatio-temporal and other structure with applications to sample-starved real data problems, (2) strong performance bounds for high-dimensional estimation of covariances under each model, and (3) a strongly adaptive online method for estimating changing optimal low-dimensional metrics (inverse covariances) for high-dimensional data from a series of similarity labels.PHDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/137082/1/greenewk_1.pd
Sidelobe suppression techniques for near-field multistatic SAR
Multirotor Unmanned Air Systems (UAS) represent a significant improvement in capability for Synthetic Aperture Radar (SAR) imaging when compared to traditional, fixed-wing, platforms. In particular, a swarm of UAS can generate significant measurement diversity through variation of spatial and frequency collections across an array of sensors. In such imaging schemes, the image formation step is challenging due to strong extended sidelobe; however, were this to be effectively managed, a dramatic increase in image quality is theoretically possible. Since 2015, QinetiQ have developed the RIBI system, which uses multiple UAS to perform short-range multistatic collections, and this requires novel near-field processing to mitigate the high sidelobes observed and form actionable imagery. This paper applies a number of algorithms to assess image reconstruction of simulated near-field multistatic SAR with an aim to suppress sidelobes observed in the RIBI system, investigating techniques including traditional SAR processing, regularised linear regression, compressive sensing. In these simulations presented, Elastic net, Orthogonal Matched Pursuit, and Iterative Hard Thresholding all show the ability to suppress sidelobes while preserving accuracy of scatterer RCS. This has also lead to a novel processing approach for reconstructing SAR images based on the observed Elastic net and Iterative Hard Thresholding performance, mitigating weaknesses to generate an improved combined approach. The relative strengths and weaknesses of the algorithms are discussed, as well as their application to more complex real-world imagery