Robust Techniques for Bearing Estimation in Contaminated Gaussian Noise

The problem of estimating directions-of-arrival (DOA) of radiating sources from measurements provided by a passive array of sensors is frequently encountered in radar, sonar, radio astronomy and seismology. In this study various robust methods for the DOA estimation problem are developed, where the term robustness refers to insensitivity against small deviation in the underlying Gaussian noise assumption. The first method utilizes an eigenvector method and robust reconstruction of the correlation matrix by time series modeling of the array data; Secondly, a decentralized processing scheme is considered for geographically distributed array sites. The method provides reliable estimates even when a few of the subarray sites are malfunctioning. The above two techniques are useful for narrow band and incoherent sources. The third robust method, which utilizes Radon Transform, is capable of handling both the narrow band and wide band sources as well as the incoherent or coherent sources. The technique is also Useful in situations of very low SNR and colored noise with unknown correlation structure. The fourth method is an efficient narrow band robust maximum likelihood DOA estimation algorithm which is capable of handling coherent signals as well as the single snapshot cases. Furthermore, relationships between eigenvector methods and a ML DOA estimation, where the source signals are treated as sample functions of Gaussian random processes, are investigate

Earth Resources, A Continuing Bibliography with Indexes

This bibliography lists 460 reports, articles and other documents introduced into the NASA scientific and technical information system between July 1 and September 30, 1984. Emphasis is placed on the use of remote sensing and geophysical instrumentation in spacecraft and aircraft to survey and inventory natural resources and urban areas. Subject matter is grouped according to agriculture and forestry, environmental changes and cultural resources, geodesy and cartography, geology and mineral resources, hydrology and water management, data processing and distribution systems, instrumentation and sensors, and economical analysis

Geo-rectification and cloud-cover correction of multi-temporal Earth observation imagery

Over the past decades, improvements in remote sensing technology have led to mass proliferation of aerial imagery. This, in turn, opened vast new possibilities relating to land cover classification, cartography, and so forth. As applications in these fields became increasingly more complex, the amount of data required also rose accordingly and so, to satisfy these new needs, automated systems had to be developed. Geometric distortions in raw imagery must be rectified, otherwise the high accuracy requirements of the newest applications will not be attained. This dissertation proposes an automated solution for the pre-stages of multi-spectral satellite imagery classification, focusing on Fast Fourier Shift theorem based geo-rectification and multi-temporal cloud-cover correction. By automatizing the first stages of image processing, automatic classifiers can take advantage of a larger supply of image data, eventually allowing for the creation of semi-real-time mapping applications

Aboveground biomass corresponds strongly with drone-derived canopy height but weakly with greenness (NDVI) in a shrub tundra landscape

This is the author accepted manuscript. The final version is available from IOP Publishing via the DOI in this recordData accessibility: The data that support the findings of this study are openly available at the following DOI: https://doi.org/10.5285/61C5097B-6717-4692-A8A4-D32CCA0E61A9)Arctic landscapes are changing rapidly in response to warming, but future predictions are hindered by difficulties in scaling ecological relationships from plots to biomes. Unmanned aerial systems (UAS, hereafter 'drones') are increasingly used to observe Arctic ecosystems over broader extents than can be measured using ground-based approaches and facilitate the interpretation of coarse-grained remotely-sensed datasets. However, more information is needed about how drone-acquired remote sensing observations correspond with ecosystem attributes such as aboveground biomass. Working across a willow shrub-dominated alluvial fan at a focal study site in the Canadian Arctic, we conducted peak season drone surveys with a RGB camera and multispectral multi camera array to derive photogrammetric reconstructions of canopy and normalised difference vegetation index (NDVI) maps along with in situ point intercept measurements and biomass harvests from 36, 0.25 m2 plots. We found high correspondence between canopy height measured using in situ point intercept compared to drone-photogrammetry (concordance correlation coefficient = 0.808), although the photogrammetry heights were positively biased by 0.14 m relative to point intercept heights. Canopy height was strongly and linearly related to aboveground biomass, with similar coefficients of determination for point framing (R2 = 0.92) and drone-based methods (R2 = 0.90). NDVI was positively related to aboveground biomass, phytomass and leaf biomass. However, NDVI only explained a small proportion of the variance in biomass (R2 between 0.14 and 0.23 for logged total biomass) and we found moss cover influenced the NDVI-phytomass relationship. Biomass is challenging to infer from drone-derived NDVI, particularly in ecosystems where bryophytes cover a large proportion of the land surface. Our findings suggest caution with broadly attributing change in fine-grained NDVI to biomass differences across biologically and topographically complex tundra landscapes. By comparing structural, spectral and on-the-ground ecological measurements, we can improve understanding of tundra vegetation change as inferred from remote sensing.Natural Environment Research Council (NERC)Dartmouth CollegeAarhus University Research FoundationEuropean Union Horizon 202

Doctor of Philosophy

dissertationMagnetic resonance-guided focused ultrasound surgery (MRgFUS) is a noninvasive means of causing selective tissue necrosis using high-power ultrasound and MR temperature imaging. Inhomogeneities in the medium of propagation can cause significant distortion of the ultrasound beam, resulting in changes in focal-zone amplitude, location and shape. Current ultrasound beam simulation techniques are either only applicable to homogeneous media or are relatively slow in calculating power deposition patterns in inhomogeneous media. Further, these techniques use table-value estimates of the acoustic parameters for predicting ultrasound beam propagation in inhomogeneous media, resulting in at best an approximate power deposition pattern. This work improves numerical analysis of ultrasound beam propagation by developing techniques for: 1) fast, accurate predictions of ultrasound beam propagation in inhomogeneous media, 2) noninvasive estimation of acoustic parameters (speed of sound and attenuation coefficient) of tissue types present in inhomogeneous media, 3) noninvasive determination of changes in tissue acoustic properties due to treatment. These beam simulation techniques utilizing subject-specific tissue parameters will rapidly predict power deposition patterns in real patient geometries and estimate changes in tissue acoustic parameters during treatment, leading to treatment-responsive patientspecific treatment plans that will improve the safety, efficacy and effectiveness of MRgFUS

Connected Attribute Filtering Based on Contour Smoothness

A new attribute measuring the contour smoothness of 2-D objects is presented in the context of morphological attribute filtering. The attribute is based on the ratio of the circularity and non-compactness, and has a maximum of 1 for a perfect circle. It decreases as the object boundary becomes irregular. Computation on hierarchical image representation structures relies on five auxiliary data members and is rapid. Contour smoothness is a suitable descriptor for detecting and discriminating man-made structures from other image features. An example is demonstrated on a very-high-resolution satellite image using connected pattern spectra and the switchboard platform

Advanced technologies for the improvement of spray application techniques in spanish viticulture: an overview

Spraying techniques have been undergoing continuous evolution in recent decades. This paper presents part of the research work carried out in Spain in the field of sensors for characterizing vineyard canopies and monitoring spray drift in order to improve vineyard spraying and make it more sustainable. Some methods and geostatistical procedures for mapping vineyard parameters are proposed, and the development of a variable rate sprayer is described. All these technologies are interesting in terms of adjusting the amount of pesticides applied to the target canopy.Postprint (published version