437 research outputs found

    Advancements in Measuring and Modeling the Mechanical and Hydrological Properties of Snow and Firn: Multi-sensor Analysis, Integration, and Algorithm Development

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    Estimating snow mechanical properties – such as elastic modulus, stiffness, and strength – is important for understanding how effectively a vehicle can travel over snow-covered terrain. Vehicle instrumentation data and observations of the snowpack are valuable for improving the estimates of winter vehicle performance. Combining in-situ and remotely-sensed snow observations, driver input, and vehicle performance sensors requires several techniques of data integration. I explored correlations between measurements spanning from millimeter to meter scales, beginning with the SnowMicroPenetrometer (SMP) and instruments applied to snow that were designed for measuring the load bearing capacity and the compressive and shear strengths of roads and soils. The spatial distribution of snow’s mechanical properties is still largely unknown. From this initial work, I determined that snow density remains a useful proxy for snowpack strength. To measure snow density, I applied multi-sensor electromagnetic methods. Using spatially distributed snowpack, terrain, and vegetation information developed in the subsequent chapters, I developed an over-snow vehicle performance model. To measure the vehicle performance, I joined driver and vehicle data in the coined Normalized Difference Mobility Index (NDMI). Then, I applied regression methods to distribute NDMI from spatial snow, terrain, and vegetation properties. Mobility prediction is useful for the strategic advancement of warfighting in cold regions. The security of water resources is climatologically inequitable and water stress causes international conflict. Water resources derived from snow are essential for modern societies in climates where snow is the predominant source of precipitation, such as the western United States. Snow water equivalent (SWE) is a critical parameter for yearly water supply forecasting and can be calculated by multiplying the snow depth by the snow density. In this work, I combined high-spatial resolution light detection and ranging (LiDAR) measured snow depths with ground-penetrating radar (GPR) measurements of two-way travel-time (TWT) to solve for snow density. Then using LiDAR derived terrain and vegetation features as predictors in a multiple linear regression, the density observations are distributed across the SnowEx 2020 study area at Grand Mesa, Colorado. The modeled density resolved detailed patterns that agree with the known interactions of snow with wind, terrain, and vegetation. The integration of radar and LiDAR sensors shows promise as a technique for estimating SWE across entire river basins and evaluating observational- or physics-based snow-density models. Accurate estimation of SWE is a means of water security. In our changing climate, snow and ice mass are being permanently lost from the cryosphere. Mass balance is an indicator of the (in)stability of glaciers and ice sheets. Surface mass balance (SMB) may be estimated by multiplying the thickness of any annual snowpack layer by its density. Though, unlike applications in seasonal snowpack, the ages of annual firn layers are unknown. To estimate SMB, I modeled the firn depth, density, and age using empirical and numerical approaches. The annual SMB history shows cyclical patterns representing the combination of atmospheric, oceanic, and anthropogenic climate forcing, which may serve as evaluation or assimilation data in climate model retrievals of SMB. The advancements made using the SMP, multi-channel GPR arrays, and airborne LiDAR and radar within this dissertation have made it possible to spatially estimate the snow depth, density, and water equivalent in seasonal snow, glaciers, and ice sheets. Open access, process automation, repeatability, and accuracy were key design parameters of the analyses and algorithms developed within this work. The many different campaigns, objectives, and outcomes composing this research documented the successes and limitations of multi-sensor estimation techniques for a broad range of cryosphere applications

    Detecting Targets above the Earth's Surface Using GNSS-R Delay Doppler Maps: Results from TDS-1

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    : Global Navigation Satellite System (GNSS) reflected signals can be used to remotely sense the Earth’s surface, known as GNSS reflectometry (GNSS-R). The GNSS-R technique has been applied to numerous areas, such as the retrieval of wind speed, and the detection of Earth surface objects. This work proposes a new application of GNSS-R, namely to detect objects above the Earth’s surface, such as low Earth orbit (LEO) satellites. To discuss its feasibility, 14 delay Doppler maps (DDMs) are first presented which contain unusually bright reflected signals as delays shorter than the specular reflection point over the Earth’s surface. Then, seven possible causes of these anomalies are analysed, reaching the conclusion that the anomalies are likely due to the signals being reflected from objects above the Earth’s surface. Next, the positions of the objects are calculated using the delay and Doppler information, and an appropriate geometry assumption. After that, suspect satellite objects are searched in the satellite database from Union of Concerned Scientists (UCS). Finally, three objects have been found to match the delay and Doppler conditions. In the absence of other reasons for these anomalies, GNSS-R could potentially be used to detect some objects above the Earth’s surface.Peer ReviewedPostprint (published version

    Automated tracking of the Florida manatee (Trichechus manatus)

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    The electronic, physical, biological and environmental factors involved in the automated remote tracking of the Florida manatee (Trichechus manatus) are identified. The current status of the manatee as an endangered species is provided. Brief descriptions of existing tracking and position locating systems are presented to identify the state of the art in these fields. An analysis of energy media is conducted to identify those with the highest probability of success for this application. Logistic questions such as the means of attachment and position of any equipment to be placed on the manatee are also investigated. Power sources and manateeborne electronics encapsulation techniques are studied and the results of a compter generated DF network analysis are summarized

    THEORETICAL ASPECTS AND REAL ISSUES IN AN INTEGRATED MULTIRADAR SYSTEM

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    In the last few years Homeland Security (HS) has gained a considerable interest in the research community. From a scientific point of view, it is a difficult task to provide a definition of this research area and to exactly draw up its boundaries. In fact, when we talk about the security and the surveillance, several problems and aspects must be considered. In particular, the following factors play a crucial role and define the complexity level of the considered application field: the number of potential threats can be high and uncertain; the threat detection and identification can be made more complicated by the use of camouflaging techniques; the monitored area is typically wide and it requires a large and heterogeneous sensor network; the surveillance operation is strongly related to the operational scenario, so that it is not possible to define a unique approach to solve the problem [1]. Information Technology (IT) can provide an important support to HS in preventing, detecting and early warning of threats. Even though the link between IT and HS is relatively recent, sensor integration and collaboration is a widely applied technique aimed to aggregate data from multiple sources, to yield timely information on potential threats and to improve the accuracy in monitoring events [2]. A large number of sensors have already been developed to support surveillance operations. Parallel to this technological effort in developing new powerful and dedicated sensors, interest in integrating a set of stand-alone sensors into an integrated multi-sensor system has been increasing. In fact, rather than to develop new sensors to achieve more accurate tracking and surveillance systems, it is more useful to integrate existing stand-alone sensors into a single system in order to obtain performance improvements In this dissertation, a notional integrated multi-sensor system acting in a maritime border control scenario for HS is considered. In general, a border surveillance system is composed of multiple land based and moving platforms carrying different types of sensors [1]. In a typical scenario, described in [1], the integrated system is composed of a land based platform, located on the coast, and an airborne platform moving in front of the coast line. In this dissertation, we handle two different fundamental aspects. In Part I, we focus on a single sensor in the system, i.e. the airborne radar. We analyze the tracking performance of such a kind of sensor in the presence of two different atmospheric problems: the turbulence (in Chapter 1) and the tropospheric refraction (in Chapter 2). In particular, in Chapter 1, the losses in tracking accuracy of a turbulence-ignorant tracking filter (i.e. a filter that does not take into account the effects of the atmospheric turbulences) acting in a turbulent scenario, is quantified. In Chapter 2, we focus our attention on the tropospheric propagation effects on the radar electromagnetic (em) signals and their correction for airborne radar tracking. It is well known that the troposphere is characterized by a refractive index that varies with the altitude and with the local weather. This variability of the refractive index causes an error in the radar measurements. First, a mathematical model to describe and calculate the em radar signal ray path in the troposphere is discussed. Using this mathematical model, the errors due to the tropospheric propagation are evaluated and the corrupted radar measurements are then numerically generated. Second, a tracking algorithm, based on the Kalman Filter, that is able to mitigate the tropospheric errors during the tracking procedure, is proposed. In Part II, we consider the integrated system in its wholeness to investigate a fundamental prerequisite of any data fusion process: the sensor registration process. The problem of sensor registration (also termed, for naval system, the grid-locking problem) arises when a set of data coming from two or more sensors must be combined. This problem involves a coordinate transformation and the reciprocal alignment among the various sensors: streams of data from different sensors must be converted into a common coordinate system (or frame) and aligned before they could be used in a tracking or surveillance system. If not corrected, registration errors can seriously degrade the global system performance by increasing tracking errors and even introducing ghost tracks. A first basic distinction is usually made between relative grid-locking and absolute grid-locking. The relative grid-locking process aligns remote data to local data under the assumption that the local data are bias free and that all biases reside with the remote sensor. The problem is that, actually, also the local sensor is affected by bias. Chapter 3 of this dissertation is dedicated to the solution of the relative grid-locking problem. Two different estimation algorithms are proposed: a linear Least Squares (LS) algorithm and an Expectation-Maximization-based (EM) algorithm. The linear LS algorithm is a simple and fast algorithm, but numerical results have shown that the LS estimator is not efficient for most of the registration bias errors. Such non-efficiency could be caused by the linearization implied by the linear LS algorithm. Then, in order to obtain a more efficient estimation algorithm, an Expectation-Maximization algorithm is derived. In Chapter 4 we generalize our findings to the absolute grid-locking problem. Part III of this dissertation is devoted to a more theoretical aspect of fundamental importance in a lot of practical applications: the estimate of the disturbance covariance matrix. Due to its relevance, in literature it can be found a huge quantity of works on this topic. Recently, a new geometrical concept has been applied to this estimation problem: the Riemann (or intrinsic) geometry. In Chapter 5, we give an overview on the state of the art of the application of the Riemann geometry for the covariance matrix estimation in radar problems. Particular attention is given for the detection problem in additive clutter. Some covariance matrix estimators and a new decision rule based on the Riemann geometry are analyzed and their performance are compared with the classical ones. [1] Sofia Giompapa, “Analysis, modeling, and simulation of an integrated multi-sensor system for maritime border control”, PhD dissertation, University of Pisa, April 2008. [2] H. Chen, F. Y. Wang, and D. Zeng, “Intelligence and security informatics for Homeland Security: information, communication and transportation,” Intelligent Transportation Systems, IEEE Transactions on, vol. 5, no. 4, pp. 329-341, December 2004

    Feasibility of collision warning, precision approach and landing using GPS, volume 1

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    The use of GPS, with an appropriately configured data link, to enhance general aviation avionic functions encountered in the terminal area and on approach was investigated with emphasis on approach and landing guidance and collision warning. The feasibility of using differential GPS to obtain the precision navigation solutions required for landing was studied. Results show that the concept is sound. An experimental program was developed to demonstrate this concept. The collision avoidance/warning concept was examined through the development of a functional system specification

    Multi-Channel Ground-Penetrating Radar for the Continuous Quantification of Snow and Firn Density, Depth, and Accumulation

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    A priority of ice sheet surface mass balance (SMB) prediction is ascertaining the surface density and annual snow accumulation. These forcing data are inputs for firn density models and can be used to inform remotely sensed ice sheet surface processes and to assess Regional Climate Model (RCM) skill. The Greenland Traverse for Accumulation and Climate Studies (GreenTrACS) retrieved 16 shallow firn cores and dug 42 snow pits along the Western percolation zone of the Greenland Ice Sheet (GrIS) during May and June of 2016 and 2017. I deployed and maintained a multi-channel 500 MHz ground-penetrating radar in a multi-offset configuration throughout the two traverse campaigns. The multi-channel radar technique accurately and independently estimates density, depth, and annual snow accumulation -- between the firn core and snow pit sites -- by horizon velocity analysis of common midpoint radar reflections from the snow and shallow firn. I analyzed a 45 km section of the traverse in a high accumulation zone, known as the GreenTrACS Core 15 Western Spur. Deviations in surface density up to +- 15 kg/m3 from the transect mean correlate with surface elevation and surface slope angle. Spatial variation in mean annual accumulation of ~0.175 m w.e. ɑ-1 occurs across a trough in the surface topography ~5 km wide. The reported variability of density and accumulation demonstrates that RCMs must be down-scaled to resolutions within 5 km to assess subtle yet significant contributions to the GrIS SMB

    A Comparative Overview of Geophysical Methods

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    This report was prepared with support from the Air Force Research Laboratory, under contract FA8718-07-C-0021.The shallow subsurface structure of the Earth is important to understand for many economic and safety reasons. The problem is usually difficult due to complexity of the earth’s subsurface processes especially near the surface. A number of geophysical methods are used for this purpose using different physical characteristics of the Earth materials. A particular geophysical method illuminates part of the problem, but a reliable solution can only be found by combining results of different methods. In order to synthesize information from different geophysical methods, it is important to understand their similarities and differences. The aim of this study is to correlate the basic principles of geophysical methods side-by-side starting from fundamental equations. This study reveals that many analogies exist among these methods both in their mathematical formulation, and sometimes, in ways they are used in the geophysical applications

    Snow property controls on modelled Ku-band altimeter estimates of first-year sea ice thickness: Case studies from the Canadian and Norwegian Arctic

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    Uncertainty in snow properties impacts the accuracy of Arctic sea ice thickness estimates from radar altimetry. On firstyear sea ice (FYI), spatiotemporal variations in snow properties can cause the Ku-band main radar scattering horizon to appear above the snow/sea ice interface. This can increase the estimated sea ice freeboard by several centimeters, leading to FYI thickness overestimations. This study examines the expected changes in Kuband main scattering horizon and its impact on FYI thickness estimates, with variations in snow temperature, salinity and density derived from 10 naturally occurring Arctic FYI Cases encompassing saline/non-saline, warm/cold, simple/complexly layered snow (4 cm to 45 cm) overlying FYI (48 cm to 170 cm). Using a semi-empirical modeling approach, snow properties from these Cases are used to derive layer-wise brine volume and dielectric constant estimates, to simulate the Ku-band main scattering horizon and delays in radar propagation speed. Differences between modeled and observed FYI thickness are calculated to assess sources of error. Under both cold and warm conditions, saline snow covers are shown to shift the main scattering horizon above from the snow/sea ice interface, causing thickness retrieval errors. Overestimates in FYI thicknesses of up to 65% are found for warm, saline snow overlaying thin sea ice. Our simulations exhibited a distinct shift in the main scattering horizon when the snow layer densities became greater than 440 kg/m3 , especially under warmer snow conditions. Our simulations suggest a mean Ku-band propagation delay for snow of 39%, which is higher than 25%, suggested in previous studies

    Assessing the Viability of Complex Electrical Impedance Tomography (EIT) with a Spatially Distributed Sensor Array for Imaging of River Bed Morphology: a Proof of Concept (Study)

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    This report was produced as part of a NERC funded ‘Connect A’ project to establish a new collaborative partnership between the University of Worcester (UW) and Q-par Angus Ltd. The project aim was to assess the potential of using complex Electrical Impedance Tomography (EIT) to image river bed morphology. An assessment of the viability of sensors inserted vertically into the channel margins to provide real-time or near real-time monitoring of bed morphology is reported. Funding has enabled UW to carry out a literature review of the use of EIT and existing methods used for river bed surveys, and outline the requirements of potential end-users. Q-par Angus has led technical developments and assessed the viability of EIT for this purpose. EIT is one of a suite of tomographic imaging techniques and has already been used as an imaging tool for medical analysis, industrial processing and geophysical site survey work. The method uses electrodes placed on the margins or boundary of the entity being imaged, and a current is applied to some and measured on the remaining ones. Tomographic reconstruction uses algorithms to estimate the distribution of conductivity within the object and produce an image of this distribution from impedance measurements. The advantages of the use of EIT lie with the inherent simplicity, low cost and portability of the hardware, the high speed of data acquisition for real-time or near real-time monitoring, robust sensors, and the object being monitored is done so in a non-invasive manner. The need for sophisticated image reconstruction algorithms, and providing images with adequate spatial resolution are key challenges. A literature review of the use of EIT suggests that to date, despite its many other applications, to the best of our knowledge only one study has utilised EIT for river survey work (Sambuelli et al 2002). The Sambuelli (2002) study supported the notion that EIT may provide an innovative way of describing river bed morphology in a cost effective way. However this study used an invasive sensor array, and therefore the potential for using EIT in a non-invasive way in a river environment is still to be tested. A review of existing methods to monitor river bed morphology indicates that a plethora of techniques have been applied by a range of disciplines including fluvial geomorphology, ecology and engineering. However, none provide non-invasive, low costs assessments in real-time or near real-time. Therefore, EIT has the potential to meet the requirements of end users that no existing technique can accomplish. Work led by Q-par Angus Ltd. has assessed the technical requirements of the proposed approach, including probe design and deployment, sensor array parameters, data acquisition, image reconstruction and test procedure. Consequently, the success of this collaboration, literature review, identification of the proposed approach and potential applications of this technique have encouraged the authors to seek further funding to test, develop and market this approach through the development of a new environmental sensor

    Further SEASAT SAR coastal ocean wave analysis

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    Analysis techniques used to exploit SEASAT synthetic aperture radar (SAR) data of gravity waves are discussed and the SEASAT SAR's ability to monitor large scale variations in gravity wave fields in both deep and shallow water is evaluated. The SAR analysis techniques investigated included motion compensation adjustments and the semicausal model for spectral analysis of SAR wave data. It was determined that spectra generated from fast Fourier transform analysis (FFT) of SAR wave data were not significantly altered when either range telerotation adjustments or azimuth focus shifts were used during processing of the SAR signal histories, indicating that SEASAT imagery of gravity waves is not significantly improved or degraded by motion compensation adjustments. Evaluation of the semicausal (SC) model using SEASAT SAR data from Rev. 974 indicates that the SC spectral estimates were not significantly better than the FFT results
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