New Approach for Unambiguous High-Resolution Wide-Swath SAR Imaging

The high-resolution wide-swath (HRWS) SAR system uses a small antenna for transmitting waveform and multiple antennas both in elevation and azimuth for receiving echoes. It has the potential to achieve wide spatial coverage and fine azimuth resolution, while it suffers from elevation pattern loss caused by the presence of topographic height and impaired azimuth resolution caused by nonuniform sampling. A new approach for HRWS SAR imaging based on compressed sensing (CS) is introduced. The data after range compression of multiple elevation apertures are used to estimate direction of arrival (DOA) of targets via CS, and the adaptive digital beamforming in elevation is achieved accordingly, which avoids the pattern loss of scan-on-receive (SCORE) algorithm when topographic height exists. The effective phase centers of the system are nonuniformly distributed when displaced phase center antenna (DPCA) technology is adopted, which causes Doppler ambiguities under traditional SAR imaging algorithms. Azimuth reconstruction based on CS can resolve this problem via precisely modeling the nonuniform sampling. Validation with simulations and experiment in an anechoic chamber are presented

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

Sensor Signal and Information Processing II

In the current age of information explosion, newly invented technological sensors and software are now tightly integrated with our everyday lives. Many sensor processing algorithms have incorporated some forms of computational intelligence as part of their core framework in problem solving. These algorithms have the capacity to generalize and discover knowledge for themselves and learn new information whenever unseen data are captured. The primary aim of sensor processing is to develop techniques to interpret, understand, and act on information contained in the data. The interest of this book is in developing intelligent signal processing in order to pave the way for smart sensors. This involves mathematical advancement of nonlinear signal processing theory and its applications that extend far beyond traditional techniques. It bridges the boundary between theory and application, developing novel theoretically inspired methodologies targeting both longstanding and emergent signal processing applications. The topic ranges from phishing detection to integration of terrestrial laser scanning, and from fault diagnosis to bio-inspiring filtering. The book will appeal to established practitioners, along with researchers and students in the emerging field of smart sensors processing

Looking beyond Pixels:Theory, Algorithms and Applications of Continuous Sparse Recovery

Sparse recovery is a powerful tool that plays a central role in many applications, including source estimation in radio astronomy, direction of arrival estimation in acoustics or radar, super-resolution microscopy, and X-ray crystallography. Conventional approaches usually resort to discretization, where the sparse signals are estimated on a pre-defined grid. However, sparse signals do not line up conveniently on any grid in reality. While the discrete setup usually leads to a simple optimization problem that can be solved with standard tools, there are two noticeable drawbacks: (i) Because of the model mismatch, the effective noise level is increased; (ii) The minimum reachable resolution is limited by the grid step-size. Because of the limitations, it is essential to develop a technique that estimates sparse signals in the continuous-domain--in essence seeing beyond pixels. The aims of this thesis are (i) to further develop a continuous-domain sparse recovery framework based on finite rate of innovation (FRI) sampling on both theoretical and algorithmic aspects; (ii) adapt the proposed technique to several applications, namely radio astronomy point source estimation, direction of arrival estimation in acoustics, and single image up-sampling; (iii) show that the continuous-domain sparse recovery approach can surpass the instrument resolution limit and achieve super-resolution. We propose a continuous-domain sparse recovery technique by generalizing the FRI sampling framework to cases with non-uniform measurements. We achieve this by identifying a set of unknown uniform sinusoidal samples and the linear transformation that links the uniform samples of sinusoids to the measurements. The continuous-domain sparsity constraint can be equivalently enforced with a discrete convolution equation of these sinusoidal samples. The sparse signal is reconstructed by minimizing the fitting error between the given and the re-synthesized measurements subject to the sparsity constraint. Further, we develop a multi-dimensional sampling framework for Diracs in two or higher dimensions with linear sample complexity. This is a significant improvement over previous methods, which have a complexity that increases exponentially with dimension. An efficient algorithm has been proposed to find a valid solution to the continuous-domain sparse recovery problem such that the reconstruction (i) satisfies the sparsity constraint; and (ii) fits the measurements (up to the noise level). We validate the flexibility and robustness of the FRI-based continuous-domain sparse recovery in both simulations and experiments with real data. We show that the proposed method surpasses the diffraction limit of radio telescopes with both realistic simulation and real data from the LOFAR radio telescope. In addition, FRI-based sparse reconstruction requires fewer measurements and smaller baselines to reach a similar reconstruction quality compared with conventional methods. Next, we apply the proposed approach to direction of arrival estimation in acoustics. We show that accurate off-grid source locations can be reliably estimated from microphone measurements with arbitrary array geometries. Finally, we demonstrate the effectiveness of the continuous-domain sparsity constraint in regularizing an otherwise ill-posed inverse problem, namely single-image super-resolution. By incorporating image edge models, the up-sampled image retains sharp edges and is free from ringing artifacts

3D reconstruction and motion estimation using forward looking sonar

Autonomous Underwater Vehicles (AUVs) are increasingly used in different domains including archaeology, oil and gas industry, coral reef monitoring, harbour’s security, and mine countermeasure missions. As electromagnetic signals do not penetrate underwater environment, GPS signals cannot be used for AUV navigation, and optical cameras have very short range underwater which limits their use in most underwater environments. Motion estimation for AUVs is a critical requirement for successful vehicle recovery and meaningful data collection. Classical inertial sensors, usually used for AUV motion estimation, suffer from large drift error. On the other hand, accurate inertial sensors are very expensive which limits their deployment to costly AUVs. Furthermore, acoustic positioning systems (APS) used for AUV navigation require costly installation and calibration. Moreover, they have poor performance in terms of the inferred resolution. Underwater 3D imaging is another challenge in AUV industry as 3D information is increasingly demanded to accomplish different AUV missions. Different systems have been proposed for underwater 3D imaging, such as planar-array sonar and T-configured 3D sonar. While the former features good resolution in general, it is very expensive and requires huge computational power, the later is cheaper implementation but requires long time for full 3D scan even in short ranges. In this thesis, we aim to tackle AUV motion estimation and underwater 3D imaging by proposing relatively affordable methodologies and study different parameters affecting their performance. We introduce a new motion estimation framework for AUVs which relies on the successive acoustic images to infer AUV ego-motion. Also, we propose an Acoustic Stereo Imaging (ASI) system for underwater 3D reconstruction based on forward looking sonars; the proposed system features cheaper implementation than planar array sonars and solves the delay problem in T configured 3D sonars

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

Development of new array signal processing techniques using swarm intelligence

Ankara : The Department of Electrical and Electronics Engineering and the Institute of Engineering and Sciences of Bilkent University, 2010.Thesis (Ph. D.) -- Bilkent University, 2010.Includes bibliographical references leaves 144-158.In this thesis, novel array signal processing techniques are proposed for identifi- cation of multipath communication channels based on cross ambiguity function (CAF) calculation, swarm intelligence and compressed sensing (CS) theory. First technique detects the presence of multipath components by integrating CAFs of each antenna output in the array and iteratively estimates direction-of-arrivals (DOAs), time delays and Doppler shifts of a known waveform. Second technique called particle swarm optimization-cross ambiguity function (PSO-CAF) makes use of the CAF calculation to transform the received antenna array outputs to delay-Doppler domain for efficient exploitation of the delay-Doppler diversity of the multipath components. Clusters of multipath components are identified by using a simple amplitude thresholding in the delay-Doppler domain. PSO is used to estimate parameters of the multipath components in each cluster. Third proposed technique combines CS theory, swarm intelligence and CAF computation. Performance of standard CS formulations based on discretization of the multipath channel parameter space degrade significantly when the actual channel parameters deviate from the assumed discrete set of values. To alleviate this “off-grid”problem, a novel technique by making use of the PSO, that can also be used in applications other than the multipath channel identification is proposed. Performances of the proposed techniques are verified both on sythetic and real data.Güldoğan, Mehmet BurakPh.D

Arrayed synthetic aperture radar

In this thesis, the use of array processing techniques applied to Single Input Multiple Output (SIMO) SAR systems with enhanced capabilities is investigated. In Single Input Single Output (SISO) SAR systems there is a high resolution, wide swath contradiction, whereby it is not possible to increase both cross-range resolution and the imaged swath width simultaneously. To overcome this, a novel beamformer for SAR systems in the cross-range direction is proposed. In particular, this beamformer is a superresolution beamformer capable of forming wide nulls using subspace based approaches. SIMO SAR systems also give rise to additional sets of received data, which includes geometrical information about the SAR and target environment, and can be used for enhanced target parameter estimation. In particular, this thesis looks at round trip delay, joint azimuth and elevation angle, and relative target power estimation. For round trip delay estimation, the use of the traditional matched filter with subspace partitioning is proposed. Then by using a joint 2D Multiple Signal Classification (MUSIC) algorithm, joint Direction of Arrival (DOA) estimation can be achieved. Both the use of range lines of raw SAR data and the use of a Region of Interest (ROI) of a SAR image are investigated. However in terms of imaging, MUSIC is not well-suited for SAR, due to its target response not corresponding to the target's true power return. Therefore a joint DOA and target power estimation algorithm is proposed to overcome this limitation. These algorithms provide the framework for the development of three processing techniques. These allow sidelobe suppression in the slant range direction, along with the reconstruction of undersampled data and region enhancement using MUSIC with power preservation.Open Acces