964 resultados para GPS


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In estuaries and natural water channels, the estimate of velocity and dispersion coefficients is critical to the knowledge of scalar transport and mixing. This estimate is rarely available experimentally at sub-tidal time scale in shallow water channels where high frequency is required to capture its spatio-temporal variation. This study estimates Lagrangian integral scales and autocorrelation curves, which are key parameters for obtaining velocity fluctuations and dispersion coefficients, and their spatio-temporal variability from deployments of Lagrangian drifters sampled at 10 Hz for a 4-hour period. The power spectral densities of the velocities between 0.0001 and 0.8 Hz were well fitted with a slope of 5/3 predicted by Kolmogorov’s similarity hypothesis within the inertial subrange, and were similar to the Eulerian power spectral previously observed within the estuary. The result showed that large velocity fluctuations determine the magnitude of the integral time scale, TL. Overlapping of short segments improved the stability of the estimate of TL by taking advantage of the redundant data included in the autocorrelation function. The integral time scales were about 20 s and varied by up to a factor of 8. These results are essential inputs for spatial binning of velocities, Lagrangian stochastic modelling and single particle analysis of the tidal estuary.

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Since a celebrate linear minimum mean square (MMS) Kalman filter in integration GPS/INS system cannot guarantee the robustness performance, a H(infinity) filtering with respect to polytopic uncertainty is designed. The purpose of this paper is to give an illustration of this application and a contrast with traditional Kalman filter. A game theory H(infinity) filter is first reviewed; next we utilize linear matrix inequalities (LMI) approach to design the robust H(infinity) filter. For the special INS/GPS model, unstable model case is considered. We give an explanation for Kalman filter divergence under uncertain dynamic system and simultaneously investigate the relationship between H(infinity) filter and Kalman filter. A loosely coupled INS/GPS simulation system is given here to verify this application. Result shows that the robust H(infinity) filter has a better performance when system suffers uncertainty; also it is more robust compared to the conventional Kalman filter.

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A scheme for integration of stand-alone INS and GPS sensors is presented, with data interchange over an external bus. This ensures modularity and sensor interchangeability. Use of a medium-coupled scheme reduces data flow and computation, facilitating use in surface vehicles. Results show that the hybrid navigation system is capable of delivering high positioning accuracy.

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The selection of different patch types for grazing by cattle in tropical savannas is well documented. Advances in high resolution satellite imagery and computing power now allow us to identify patch types over an entire paddock, combined with GPS collars as a non instrusive method of capturing positional data, an accurate and comprehensive picture of landscape use by cattle can be quantified.

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Patch selection by grazing animals is difficult to quantify, particularly in large, extensive paddocks like those in northern Australia. However, advances in high resolution satellite imagery now allow identification of patch types over an entire paddock which combined with GPS collars to capture positional data, can give an accurate and comprehensive picture of landscape use by cattle.

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There are some scenarios in which Unmmaned Aerial Vehicle (UAV) navigation becomes a challenge due to the occlusion of GPS systems signal, the presence of obstacles and constraints in the space in which a UAV operates. An additional challenge is presented when a target whose location is unknown must be found within a confined space. In this paper we present a UAV navigation and target finding mission, modelled as a Partially Observable Markov Decision Process (POMDP) using a state-of-the-art online solver in a real scenario using a low cost commercial multi rotor UAV and a modular system architecture running under the Robotic Operative System (ROS). Using POMDP has several advantages to conventional approaches as they take into account uncertainties in sensor information. We present a framework for testing the mission with simulation tests and real flight tests in which we model the system dynamics and motion and perception uncertainties. The system uses a quad-copter aircraft with an board downwards looking camera without the need of GPS systems while avoiding obstacles within a confined area. Results indicate that the system has 100% success rate in simulation and 80% rate during flight test for finding targets located at different locations.

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Accurate and stable time series of geodetic parameters can be used to help in understanding the dynamic Earth and its response to global change. The Global Positioning System, GPS, has proven to be invaluable in modern geodynamic studies. In Fennoscandia the first GPS networks were set up in 1993. These networks form the basis of the national reference frames in the area, but they also provide long and important time series for crustal deformation studies. These time series can be used, for example, to better constrain the ice history of the last ice age and the Earth s structure, via existing glacial isostatic adjustment models. To improve the accuracy and stability of the GPS time series, the possible nuisance parameters and error sources need to be minimized. We have analysed GPS time series to study two phenomena. First, we study the refraction in the neutral atmosphere of the GPS signal, and, second, we study the surface loading of the crust by environmental factors, namely the non-tidal Baltic Sea, atmospheric load and varying continental water reservoirs. We studied the atmospheric effects on the GPS time series by comparing the standard method to slant delays derived from a regional numerical weather model. We have presented a method for correcting the atmospheric delays at the observational level. The results show that both standard atmosphere modelling and the atmospheric delays derived from a numerical weather model by ray-tracing provide a stable solution. The advantage of the latter is that the number of unknowns used in the computation decreases and thus, the computation may become faster and more robust. The computation can also be done with any processing software that allows the atmospheric correction to be turned off. The crustal deformation due to loading was computed by convolving Green s functions with surface load data, that is to say, global hydrology models, global numerical weather models and a local model for the Baltic Sea. The result was that the loading factors can be seen in the GPS coordinate time series. Reducing the computed deformation from the vertical time series of GPS coordinates reduces the scatter of the time series; however, the long term trends are not influenced. We show that global hydrology models and the local sea surface can explain up to 30% of the GPS time series variation. On the other hand atmospheric loading admittance in the GPS time series is low, and different hydrological surface load models could not be validated in the present study. In order to be used for GPS corrections in the future, both atmospheric loading and hydrological models need further analysis and improvements.

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Objectives: GPS technology enables the visualisation of a map reader s location on a mobile map. Earlier research on the cognitive aspects of map reading identified that searching for map-environment points is an essential element for the process of determining one s location on a mobile map. Map-environment points refer to objects that are visualized on the map and are recognizable in the environment. However, because the GPS usually adds only one point to the map that has a relation to the environment, it does not provide a sufficient amount of information for self-location. The aim of the present thesis was to assess the effect of GPS on the cognitive processes involved in determining one s location on a map. Methods: The effect of GPS on self-location was studied in a field experiment. The subjects were shown a target on a mobile map, and they were asked to point in the direction of the target. In order for the map reader to be able to deduce the direction of the target, he/she has to locate himself/herself on the map. During the pointing tasks, the subjects were asked to think aloud. The data from the experiment were used to analyze the effect of the GPS on the time needed to perform the task. The subjects verbal data was used to assess the effect of the GPS on the number of landmark concepts mentioned during a task (landmark concepts are words referring to objects that can be recognized both on the map and in the environment). Results and conclusions: The results from the experiment indicate that the GPS reduces the time needed to locate oneself on a map. The analysis of the verbal data revealed that the GPS reduces the number of landmark concepts in the protocols. The findings suggest that the GPS guides the subject s search for the map-environment points and narrows the area on the map that must be searched for self-location.

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Following rising demands in positioning with GPS, low-cost receivers are becoming widely available; but their energy demands are still too high. For energy efficient GPS sensing in delay-tolerant applications, the possibility of offloading a few milliseconds of raw signal samples and leveraging the greater processing power of the cloud for obtaining a position fix is being actively investigated. In an attempt to reduce the energy cost of this data offloading operation, we propose Sparse-GPS(1): a new computing framework for GPS acquisition via sparse approximation. Within the framework, GPS signals can be efficiently compressed by random ensembles. The sparse acquisition information, pertaining to the visible satellites that are embedded within these limited measurements, can subsequently be recovered by our proposed representation dictionary. By extensive empirical evaluations, we demonstrate the acquisition quality and energy gains of Sparse-GPS. We show that it is twice as energy efficient than offloading uncompressed data, and has 5-10 times lower energy costs than standalone GPS; with a median positioning accuracy of 40 m.

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全球定位系统(GPS)是一种全天候、高精度的连续定位系统,它以速度快、方法灵活多样、操作简便等优势被广泛应用于工程测量和变形监测中。结合水厂铁矿GPS边坡变形监测实例,对GPS监测网的星历预报、基线向量平差计算、网平差计算、结果及残差不确定度进行了细致分析研究,以验证GPS技术在边坡变形监测中的可靠性和精度。

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在对编队飞行GPS/INS相对导航当前的主要滤波算法进行总结和分析的基础上,提出了分布滤波的算法,并建立了GPS/INS相对导航仿真系统,对该算法进行验证。仿真结果表明该算法能够适应大型、紧密编队飞行的需求,在减小计算量、降低通讯数据量、允许编队做大的机动飞行的同时,能得到较高的估计精度。

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El presente proyecto analiza la tecnología utilizada por los sistemas GNSS, su funcionamiento y composición. Estudia varios sistemas GPS comerciales sus características y protocolos utilizados y diferentes programas, para el análisis, gestión y visualización de los datos asociados. El proyecto se centra en estudiar posibles métodos que mejoran la precisión de posicionamiento obtenida por los receptores GPS y propone, después de las correspondientes pruebas, dos métodos basados en DGPS con los que se han obtenido precisiones de unos pocos centímetros.

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[ES]este proyecto trata sobre el desarrollo de un core en una FPGA para conseguir, gracias a un módulo GPS, una referencia temporal precisa, necesaria para un equipo PTP master (IEEE-1588), a bajo coste y con calidad Grand Master.

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[ES]El proyecto presentado a continuación muestra la elaboración de un core para ser embebido dentro de las denominadas FPGAs (Field Programmable Gate Array), cuya finalidad es la creación de una referencia temporal, en arquitectura de 64bits, gracias a un módulo GPS (Global Positioning System), lo más cercana posible al orden de las decenas de nano-segundos, para poder ser insertado en un equipo PTP-Master (Precision Time Protocol - Master) (IEEE (Institute of Electrical and Electronics Engineers) - 1588), a bajo coste y con calidad comparable a la de los dispositivos Grand Master.