Modelagem em tempo real do erro sistemático das observáveis GPS devido à ionosfera

Single frequency GPS receivers have been many used in GPS surveys. Among the several applications, one can mention those that are to obtain the receiver's antenna coordinates in real time. One of the main error sources to these applications is the ionosphere systematic error. In the FCT/UNESP a regional ionosphere model (Mod_Ion) was developed. It has been implemented to execute after collecting of GPS data. At real time application two improvements in the Mod_Ion were introduced, consisting of an alteration of the function of modeling and implementation of the Kalman Filter. The results of the experiments showed that the modifications were the most effective in the ionosphere systematic effect's corrections, providing a improvement in the accuracy of point positioning, of 90,75%, in period of the highest ionosphere activity.

Correcão ionosférica utilizando os MAPAS: Globais do TEC DO IGS: Avaliação no posicionamento por ponto na região brasileira

One of the main drawbacks of the GPS accuracy for L1 users is the error due to ionosphere. This error depends on the total electron content presents in the ionosphere, as well as of the carrier frequency. Some models have been developed to correct GPS observables of the systematic error due to the ionosphere. The model more known and used is the Klobuchar model, which corrected 50-60% of the ionospheric error approximately. Alternatively, IGS (International GNSS Service) also has developed a model called Global Ionospheric Map (GIM). These maps, in format IONEX, are available in the site of the IGS, and one of the applications of them is to correct the GPS observables of the error due to ionosphere. This work aims at evaluating the quality of GPS point positioning using the IGS ionospheric model in the Brazilian region. Tests carried out had shown an average improvement in the horizontal and vertical determination of 72% and 26%, respectively, when GIM is used in the point positioning.

Posicionamento relativo na região equatorial em diversas condições ionosféricas

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Modelo regional da ionosfera (MOD_ION): implementação em tempo real

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Avaliação de funções para modelagem do efeito da refratação ionosférica na propagação dos sinais GPS

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Investigações preliminares sobre a influência do clima espacial no posicionamento relativo com GNSS

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Correlação do posicionamento por ponto GNSS com a ionosfera e com indices de atividade solar no periodo de 2002 a 2011

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Calcolo della posizione di reti di stazioni permanenti

Con il trascorrere del tempo, le reti di stazioni permanenti GNSS (Global Navigation Satellite System) divengono sempre più un valido supporto alle tecniche di rilevamento satellitare. Esse sono al tempo stesso un’efficace materializzazione del sistema di riferimento e un utile ausilio ad applicazioni di rilevamento topografico e di monitoraggio per il controllo di deformazioni. Alle ormai classiche applicazioni statiche in post-processamento, si affiancano le misure in tempo reale sempre più utilizzate e richieste dall’utenza professionale. In tutti i casi risulta molto importante la determinazione di coordinate precise per le stazioni permanenti, al punto che si è deciso di effettuarla tramite differenti ambienti di calcolo. Sono stati confrontati il Bernese, il Gamit (che condividono l’approccio differenziato) e il Gipsy (che utilizza l’approccio indifferenziato). L’uso di tre software ha reso indispensabile l’individuazione di una strategia di calcolo comune in grado di garantire che, i dati ancillari e i parametri fisici adottati, non costituiscano fonte di diversificazione tra le soluzioni ottenute. L’analisi di reti di dimensioni nazionali oppure di reti locali per lunghi intervalli di tempo, comporta il processamento di migliaia se non decine di migliaia di file; a ciò si aggiunge che, talora a causa di banali errori, oppure al fine di elaborare test scientifici, spesso risulta necessario reiterare le elaborazioni. Molte risorse sono quindi state investite nella messa a punto di procedure automatiche finalizzate, da un lato alla preparazione degli archivi e dall’altro all’analisi dei risultati e al loro confronto qualora si sia in possesso di più soluzioni. Dette procedure sono state sviluppate elaborando i dataset più significativi messi a disposizione del DISTART (Dipartimento di Ingegneria delle Strutture, dei Trasporti, delle Acque, del Rilevamento del Territorio - Università di Bologna). E’ stato così possibile, al tempo stesso, calcolare la posizione delle stazioni permanenti di alcune importanti reti locali e nazionali e confrontare taluni fra i più importanti codici scientifici che assolvono a tale funzione. Per quanto attiene il confronto fra i diversi software si è verificato che: • le soluzioni ottenute dal Bernese e da Gamit (i due software differenziati) sono sempre in perfetto accordo; • le soluzioni Gipsy (che utilizza il metodo indifferenziato) risultano, quasi sempre, leggermente più disperse rispetto a quelle degli altri software e mostrano talvolta delle apprezzabili differenze numeriche rispetto alle altre soluzioni, soprattutto per quanto attiene la coordinata Est; le differenze sono però contenute in pochi millimetri e le rette che descrivono i trend sono comunque praticamente parallele a quelle degli altri due codici; • il citato bias in Est tra Gipsy e le soluzioni differenziate, è più evidente in presenza di determinate combinazioni Antenna/Radome e sembra essere legato all’uso delle calibrazioni assolute da parte dei diversi software. E’ necessario altresì considerare che Gipsy è sensibilmente più veloce dei codici differenziati e soprattutto che, con la procedura indifferenziata, il file di ciascuna stazione di ciascun giorno, viene elaborato indipendentemente dagli altri, con evidente maggior elasticità di gestione: se si individua un errore strumentale su di una singola stazione o se si decide di aggiungere o togliere una stazione dalla rete, non risulta necessario il ricalcolo dell’intera rete. Insieme alle altre reti è stato possibile analizzare la Rete Dinamica Nazionale (RDN), non solo i 28 giorni che hanno dato luogo alla sua prima definizione, bensì anche ulteriori quattro intervalli temporali di 28 giorni, intercalati di sei mesi e che coprono quindi un intervallo temporale complessivo pari a due anni. Si è così potuto verificare che la RDN può essere utilizzata per l’inserimento in ITRF05 (International Terrestrial Reference Frame) di una qualsiasi rete regionale italiana nonostante l’intervallo temporale ancora limitato. Da un lato sono state stimate le velocità ITRF (puramente indicative e non ufficiali) delle stazioni RDN e, dall’altro, è stata effettuata una prova di inquadramento di una rete regionale in ITRF, tramite RDN, e si è verificato che non si hanno differenze apprezzabili rispetto all’inquadramento in ITRF, tramite un congruo numero di stazioni IGS/EUREF (International GNSS Service / European REference Frame, SubCommission for Europe dello International Association of Geodesy).

Communication infrastructure study for precise positioning services in regional Queensland

Providing precise positioning services in regional areas to support agriculture, mining, and construction sectors depends on the availability of ground continuously operating GNSS reference stations and communications linking these stations to central computers and users. With the support of CRC for Spatial Information, a more comprehensive review has been completed recently to examine various wired and wireless communication links available for precise positioning services, in particular in the Queensland regional areas. The study covers a wide range of communication technologies that are currently available, including fixed, mobile wireless, and Geo-stationary and or low earth orbiting satellites. These technologies are compared in terms of bandwidth, typical latency, reliability, coverage, and costs. Additionally, some tests were also conducted to determine the performances of different systems in the real environment. Finally, based on user application requirements, the paper discusses the suitability of different communication links.

A Novel Slotless PMBLDC Motor for Precise Positioning Applications

This paper presents the design and analysis of a novel machine family of Siotiess Permanent Magnet Brushless DC motors (PMBLDC) for precise positioning applications of spacecrafts. Initial design, selection of major parameters and air gap magnetic flux density are estimated using the analytical model of the machine. The proportion of the halbach array in the machine was optimized using FE to obtain near trapezoidal flux pattern. The novel machine topology is found to deliver high torque density, high efficiency, zero cogging torque, better positional stability, high torque to inertia ratio and zero magnetic stiction suiting space requirements. The machine provides uniform air gap flux density along the radius thus avoiding circulating currents in stator conductors and hence reducing torque ripple

GPX - Gardens Point XML Information Retrieval at INEX 2004

Traditional information retrieval (IR) systems respond to user queries with ranked lists of relevant documents. The separation of content and structure in XML documents allows individual XML elements to be selected in isolation. Thus, users expect XML-IR systems to return highly relevant results that are more precise than entire documents. In this paper we describe the implementation of a search engine for XML document collections. The system is keyword based and is built upon an XML inverted file system. We describe the approach that was adopted to meet the requirements of Content Only (CO) and Vague Content and Structure (VCAS) queries in INEX 2004.

An integrated approach for precise road reconstruction from aerial imagery and LiDAR data

Accurate and detailed road models play an important role in a number of geospatial applications, such as infrastructure planning, traffic monitoring, and driver assistance systems. In this thesis, an integrated approach for the automatic extraction of precise road features from high resolution aerial images and LiDAR point clouds is presented. A framework of road information modeling has been proposed, for rural and urban scenarios respectively, and an integrated system has been developed to deal with road feature extraction using image and LiDAR analysis. For road extraction in rural regions, a hierarchical image analysis is first performed to maximize the exploitation of road characteristics in different resolutions. The rough locations and directions of roads are provided by the road centerlines detected in low resolution images, both of which can be further employed to facilitate the road information generation in high resolution images. The histogram thresholding method is then chosen to classify road details in high resolution images, where color space transformation is used for data preparation. After the road surface detection, anisotropic Gaussian and Gabor filters are employed to enhance road pavement markings while constraining other ground objects, such as vegetation and houses. Afterwards, pavement markings are obtained from the filtered image using the Otsu's clustering method. The final road model is generated by superimposing the lane markings on the road surfaces, where the digital terrain model (DTM) produced by LiDAR data can also be combined to obtain the 3D road model. As the extraction of roads in urban areas is greatly affected by buildings, shadows, vehicles, and parking lots, we combine high resolution aerial images and dense LiDAR data to fully exploit the precise spectral and horizontal spatial resolution of aerial images and the accurate vertical information provided by airborne LiDAR. Objectoriented image analysis methods are employed to process the feature classiffcation and road detection in aerial images. In this process, we first utilize an adaptive mean shift (MS) segmentation algorithm to segment the original images into meaningful object-oriented clusters. Then the support vector machine (SVM) algorithm is further applied on the MS segmented image to extract road objects. Road surface detected in LiDAR intensity images is taken as a mask to remove the effects of shadows and trees. In addition, normalized DSM (nDSM) obtained from LiDAR is employed to filter out other above-ground objects, such as buildings and vehicles. The proposed road extraction approaches are tested using rural and urban datasets respectively. The rural road extraction method is performed using pan-sharpened aerial images of the Bruce Highway, Gympie, Queensland. The road extraction algorithm for urban regions is tested using the datasets of Bundaberg, which combine aerial imagery and LiDAR data. Quantitative evaluation of the extracted road information for both datasets has been carried out. The experiments and the evaluation results using Gympie datasets show that more than 96% of the road surfaces and over 90% of the lane markings are accurately reconstructed, and the false alarm rates for road surfaces and lane markings are below 3% and 2% respectively. For the urban test sites of Bundaberg, more than 93% of the road surface is correctly reconstructed, and the mis-detection rate is below 10%.

Change Point Estimation in Monitoring Survival Time

Precise identification of the time when a change in a hospital outcome has occurred enables clinical experts to search for a potential special cause more effectively. In this paper, we develop change point estimation methods for survival time of a clinical procedure in the presence of patient mix in a Bayesian framework. We apply Bayesian hierarchical models to formulate the change point where there exists a step change in the mean survival time of patients who underwent cardiac surgery. The data are right censored since the monitoring is conducted over a limited follow-up period. We capture the effect of risk factors prior to the surgery using a Weibull accelerated failure time regression model. Markov Chain Monte Carlo is used to obtain posterior distributions of the change point parameters including location and magnitude of changes and also corresponding probabilistic intervals and inferences. The performance of the Bayesian estimator is investigated through simulations and the result shows that precise estimates can be obtained when they are used in conjunction with the risk-adjusted survival time CUSUM control charts for different magnitude scenarios. The proposed estimator shows a better performance where a longer follow-up period, censoring time, is applied. In comparison with the alternative built-in CUSUM estimator, more accurate and precise estimates are obtained by the Bayesian estimator. These superiorities are enhanced when probability quantification, flexibility and generalizability of the Bayesian change point detection model are also considered.