Multifractal Modelling of Aircraft Echoes from Low-resolution Radars Based on Structural Functions

As a kind of complex targets, the nonrigid vibration and attitude change of an aircraft as well as the rotation of its rotating parts will induce complex nonlinear modulation on its echo from low-resolution radars. If one performs the multifractal analysis of measures on an aircraft echo, it may offer a fine description of the dynamic characteristics which induce the echo structure. On basis of introducing multifractal theory based on structural functions, the paper models real recorded aircraft echo data from a low-resolution radar by using the random walk process and the incremental process respectively, and investigates the application of echo multifractal characteristics in aircraft target classification with low-resolution radars. The analysis shows that aircraft echoes from low-resolution radars have clear multifractal characteristics, and one should take an aircraft echo series as a random walk process to perform the multifractal analysis. The experimental results validate the classification method based on multifractal signatures.Defence Science Journal, 2013, 63(5), pp.515-520, DOI:http://dx.doi.org/10.14429/dsj.63.377

Self-affine Fractal Modelling of Aircraft Echoes from Low-resolution Radars

For complex targets, the non rigid vibration of an aircraft as well as its attitude changes and the rotation of its rotating parts will induce complex nonlinear modulation on its echo from low-resolution radars. If one performs the fractal analysis of measures on an aircraft echo, it may offer a fine description of the dynamic characteristics which induce the echo structure. On basis of introducing self-affine fractal theory, the paper models real recorded aircraft echo data from a low-resolution radar using the self-affine fractal representation, and investigates the application of echo self-affine fractal characteristics in aircraft target classification. Results analysis shows that aircraft echoes from low-resolution radars can be modelled by using the self-affine fractal method, and the self-affine fractal features can be effectively applied to target classification and recognition.

An Experimental Study of Different Signal Processing Methods on Ultrasonic Velocity Profiles in a Single Phase Flow

Ultrasonic velocity profile (UVP) measurement methods have been continuously developed in the field of engineering. A UVP can visualize a fluid flow along a benchmark line. This provides a significant advantage over other conventional methods such as differential pressure, turbine, and vortex. This paper presents an experimental study of using different signal processing methods including autocorrelation (AC), fast Fourier transform (FFT), maximum likelihood estimation (MLE), multiple signal classification (MUSIC), and Estimation of signal parameter via rotational invariance technique (ESPRIT) under diverse situations as the number of pulse repetitions (Nprf), frequency of repetitions (fprf), velocity profiles, computation – time requirements and flowrates. Experimental results express that there is an optimal number and frequency of pulse repetitions for each signal processing method that depended on fprf, Nprf, and flowrate. Moreover, computation-time and statistical tests were verified from experimental results. From the comparisons, MLE was experimentally the best algorithm even though the trade-off of moderate computation-time requirements was realized. However, considering the optimization of both accuracy and computation-time consumption, MLE was determined as the preferred signal processing method based on UVP for estimating flowrate in existing water reactors. &nbsp

Spectral LADAR: Active Range-Resolved Imaging Spectroscopy

Imaging spectroscopy using ambient or thermally generated optical sources is a well developed technique for capturing two dimensional images with high per-pixel spectral resolution. The per-pixel spectral data is often a sufficient sampling of a material's backscatter spectrum to infer chemical properties of the constituent material to aid in substance identification. Separately, conventional LADAR sensors use quasi-monochromatic laser radiation to create three dimensional images of objects at high angular resolution, compared to RADAR. Advances in dispersion engineered photonic crystal fibers in recent years have made high spectral radiance optical supercontinuum sources practical, enabling this study of Spectral LADAR, a continuous polychromatic spectrum augmentation of conventional LADAR. This imaging concept, which combines multi-spectral and 3D sensing at a physical level, is demonstrated with 25 independent and parallel LADAR channels and generates point cloud images with three spatial dimensions and one spectral dimension. The independence of spectral bands is a key characteristic of Spectral LADAR. Each spectral band maintains a separate time waveform record, from which target parameters are estimated. Accordingly, the spectrum computed for each backscatter reflection is independently and unambiguously range unmixed from multiple target reflections that may arise from transmission of a single panchromatic pulse. This dissertation presents the theoretical background of Spectral LADAR, a shortwave infrared laboratory demonstrator system constructed as a proof-of-concept prototype, and the experimental results obtained by the prototype when imaging scenes at stand off ranges of 45 meters. The resultant point cloud voxels are spectrally classified into a number of material categories which enhances object and feature recognition. Experimental results demonstrate the physical level combination of active backscatter spectroscopy and range resolved sensing to produce images with a level of complexity, detail, and accuracy that is not obtainable with data-level registration and fusion of conventional imaging spectroscopy and LADAR. The capabilities of Spectral LADAR are expected to be useful in a range of applications, such as biomedical imaging and agriculture, but particularly when applied as a sensor in unmanned ground vehicle navigation. Applications to autonomous mobile robotics are the principal motivators of this study, and are specifically addressed

Applicability of multispectral Sentinel data for mineral exploration by use of remote sensing and geospatial technologies:A case study in Northern Chile

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesThe objective of this MSc thesis is to prove that the Sentinel-2 satellite has the same capabilities for mineral exploration than another satellite considered the “reference technology” by the minerals industry. Since there have been an extensive use and applications of the Landsat-8 satellite for mineral exploration, this satellite is considered in this case the “reference technology”. To prove the capability of the Sentinel-2, a sequence of key applications applied on the Landsat-8 satellite for mineral exploration have been carried out using the Sentinel-2 on a specific mine site. Mine site for this investigation is the Escondida mine in northern Chile. Through several remote sensing applications such as band combinations, band ratios, PCA analyses, and pixel´s classification both satellites, Landsat-8 and Sentinel-2 have been tested on Escondida and results have been obtained and discussed. As a conclusion of this analysis, the capability of Sentinel-2 for mineral exploration has been proved, potential improvements have been identified and limitations in its prospective use have been indicated

Microwave imaging for security applications

Microwave imaging technologies have been widely researched in the biomedical field where they rely on the imaging of dielectric properties of tissues. Healthy and malignant tissue have different dielectric properties in the microwave frequency region, therefore, the dielectric properties of a human body’s tissues are generally different from other contraband materials. Consequently, dielectric data analysis techniques using microwave signals can be used to distinguish between different types of materials that could be hidden in the human body, such as explosives or drugs. Other concerns raised about these particular imaging systems were how to build them cost effectively, with less radiation emissions, and to overcome the disadvantages of X-ray imaging systems. The key challenge in security applications using microwave imaging is the image reconstruction methods adopted in order to gain a clear image of illuminated objects inside the human body or underneath clothing. This thesis will discuss in detail how microwave tomography scanning could overcome the challenge of imaging objects concealed in the human body, and prove the concept of imaging inside a human body using image reconstruction algorithms such as Radon transformation image reconstruction. Also, this thesis presents subspace based TR-MUSIC algorithms for point targets and extended targets. The algorithm is based on the collection of the dominant response matrix reflected by targets at the transducers in homogenous backgrounds, and uses the MUSIC function to image it. Lumerical FDTD solution is used to model the transducers and the objects to process its response matrix data in Matlab. Clear images of metal dielectric properties have been clearly detected. Security management understanding in airports is also discussed to use new scanning technologies such as microwave imaging in the future.The main contribution of this reseach is that microwave was proved to be able to image and detect illegal objects embedded or implanted inside human body

Remote Sensing of the Oceans

This book covers different topics in the framework of remote sensing of the oceans. Latest research advancements and brand-new studies are presented that address the exploitation of remote sensing instruments and simulation tools to improve the understanding of ocean processes and enable cutting-edge applications with the aim of preserving the ocean environment and supporting the blue economy. Hence, this book provides a reference framework for state-of-the-art remote sensing methods that deal with the generation of added-value products and the geophysical information retrieval in related fields, including: Oil spill detection and discrimination; Analysis of tropical cyclones and sea echoes; Shoreline and aquaculture area extraction; Monitoring coastal marine litter and moving vessels; Processing of SAR, HF radar and UAV measurements

GPR Method for the Detection and Characterization of Fractures and Karst Features: Polarimetry, Attribute Extraction, Inverse Modeling and Data Mining Techniques

The presence of fractures, joints and karst features within rock strongly influence the hydraulic and mechanical behavior of a rock mass, and there is a strong desire to characterize these features in a noninvasive manner, such as by using ground penetrating radar (GPR). These features can alter the incident waveform and polarization of the GPR signal depending on the aperture, fill and orientation of the features. The GPR methods developed here focus on changes in waveform, polarization or texture that can improve the detection and discrimination of these features within rock bodies. These new methods are utilized to better understand the interaction of an invasive shrub, Juniperus ashei, with subsurface flow conduits at an ecohydrologic experimentation plot situated on the limestone of the Edwards Aquifer, central Texas. First, a coherency algorithm is developed for polarimetric GPR that uses the largest eigenvalue of a scattering matrix in the calculation of coherence. This coherency is sensitive to waveshape and unbiased by the polarization of the GPR antennas, and it shows improvement over scalar coherency in detection of possible conduits in the plot data. Second, a method is described for full-waveform inversion of transmission data to quantitatively determine fracture aperture and electromagnetic properties of the fill, based on a thin-layer model. This inversion method is validated on synthetic data, and the results from field data at the experimentation plot show consistency with the reflection data. Finally, growing hierarchical self-organizing maps (GHSOM) are applied to the GPR data to discover new patterns indicative of subsurface features, without representative examples. The GHSOMs are able to distinguish patterns indicating soil filled cavities within the limestone. Using these methods, locations of soil filled cavities and the dominant flow conduits were indentified. This information helps to reconcile previous hydrologic experiments conducted at the site. Additionally, the GPR and hydrologic experiments suggests that Juniperus ashei significantly impacts infiltration by redirecting flow towards its roots occupying conduits and soil bodies within the rock. This research demonstrates that GPR provides a noninvasive tool that can improve future subsurface experimentation

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