2,606 research outputs found

    Passport: Enabling Accurate Country-Level Router Geolocation using Inaccurate Sources

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    When does Internet traffic cross international borders? This question has major geopolitical, legal and social implications and is surprisingly difficult to answer. A critical stumbling block is a dearth of tools that accurately map routers traversed by Internet traffic to the countries in which they are located. This paper presents Passport: a new approach for efficient, accurate country-level router geolocation and a system that implements it. Passport provides location predictions with limited active measurements, using machine learning to combine information from IP geolocation databases, router hostnames, whois records, and ping measurements. We show that Passport substantially outperforms existing techniques, and identify cases where paths traverse countries with implications for security, privacy, and performance

    Passport: enabling accurate country-level router geolocation using inaccurate sources

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    When does Internet traffic cross international borders? This question has major geopolitical, legal and social implications and is surprisingly difficult to answer. A critical stumbling block is a dearth of tools that accurately map routers traversed by Internet traffic to the countries in which they are located. This paper presents Passport: a new approach for efficient, accurate country-level router geolocation and a system that implements it. Passport provides location predictions with limited active measurements, using machine learning to combine information from IP geolocation databases, router hostnames, whois records, and ping measurements. We show that Passport substantially outperforms existing techniques, and identify cases where paths traverse countries with implications for security, privacy, and performance.First author draf

    Orbit determination and orbit control for the Earth Observing System (EOS) AM spacecraft

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    Future NASA Earth Observing System (EOS) Spacecraft will make measurements of the earth's clouds, oceans, atmosphere, land and radiation balance. These EOS Spacecraft will be part of the NASA Mission to Planet Earth. This paper specifically addresses the EOS AM Spacecraft, referred to as 'AM' because it has a sun-synchronous orbit with a 10:30 AM descending node. This paper describes the EOS AM Spacecraft mission orbit requirements, orbit determination, orbit control, and navigation system impact on earth based pointing. The EOS AM Spacecraft will be the first spacecraft to use the TDRSS Onboard Navigation System (TONS) as the primary means of navigation. TONS flight software will process one-way forward Doppler measurements taken during scheduled TDRSS contacts. An extended Kalman filter will estimate spacecraft position, velocity, drag coefficient correction, and ultrastable master oscillator frequency bias and drift. The TONS baseline algorithms, software, and hardware implementation are described in this paper. TONS integration into the EOS AM Spacecraft Guidance, Navigation, and Control (GN&C) System; TONS assisted onboard time maintenance; and the TONS Ground Support System (TGSS) are also addressed

    HLOC: Hints-Based Geolocation Leveraging Multiple Measurement Frameworks

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    Geographically locating an IP address is of interest for many purposes. There are two major ways to obtain the location of an IP address: querying commercial databases or conducting latency measurements. For structural Internet nodes, such as routers, commercial databases are limited by low accuracy, while current measurement-based approaches overwhelm users with setup overhead and scalability issues. In this work we present our system HLOC, aiming to combine the ease of database use with the accuracy of latency measurements. We evaluate HLOC on a comprehensive router data set of 1.4M IPv4 and 183k IPv6 routers. HLOC first extracts location hints from rDNS names, and then conducts multi-tier latency measurements. Configuration complexity is minimized by using publicly available large-scale measurement frameworks such as RIPE Atlas. Using this measurement, we can confirm or disprove the location hints found in domain names. We publicly release HLOC's ready-to-use source code, enabling researchers to easily increase geolocation accuracy with minimum overhead.Comment: As published in TMA'17 conference: http://tma.ifip.org/main-conference

    A new iterative algorithm for geolocating a known altitude target using TDOA and FDOA measurements in the presence of satellite location uncertainty

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    AbstractThis paper considers the problem of geolocating a target on the Earth surface whose altitude is known previously using the target signal time difference of arrival (TDOA) and frequency difference of arrival (FDOA) measurements obtained at satellites. The number of satellites available for the geolocation task is more than sufficient and their locations are subject to random errors. This paper derives the constrained Cramér-Rao lower bound (CCRLB) of the target position, and on the basis of the CCRLB analysis, an approximately efficient constrained maximum likelihood estimator (CMLE) for geolocating the target is established. A new iterative algorithm for solving the CMLE is then proposed, where the updated target position estimate is shown to be the globally optimal solution to a generalized trust region sub-problem (GTRS) which can be found via a simple bisection search. First-order mean square error (MSE) analysis is conducted to quantify the performance degradation when the known target altitude is assumed to be precise but indeed has an unknown but deterministic error. Computer simulations are used to compare the performance of the proposed iterative geolocation technique with those of two benchmark algorithms. They verify the approximate efficiency of the proposed algorithm and the validity of the MSE analysis

    Non-Linear Optimization Applied to Angle-of-Arrival Satellite-Based Geolocation

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    Geolocation is a common application for satellite systems. This involves estimating an object\u27s location (herein called the subject) based on noisy satellite data. Many geolocation methods exist; however, none are tailored specifically for the unique problems faced by satellite systems. Some satellites are so far from the subject being localized that by the time the satellite receives a signal from the subject it might have moved appreciably. Furthermore, some satellites or terrestrial sensors may be much closer to the subject than others. Therefore, sensors may need to be weighted based upon their distance to the subject being localized. In addition, even if a subject can be localized, the confidence in this localization may be unknown. Non-linear optimization is proposed, implemented, and analyzed as a means of geolocating objects and providing confidence estimates from passive satellite line-of-sight data. Non-linear optimization requires an initial estimate. This estimate is provided by a triangulation method. The non-linear optimization then improves upon this estimate iteratively by finding estimates that are more likely to have produced the observed line-of-sight measurements. The covariance matrix of the geolocation parameters being estimated is naturally produced by the optimization which provides quantified confidence in the geolocation estimate. Simulations are developed to provide a means of evaluating the performance of the non-linear optimization algorithm. It was found that non-linear optimization can reduce the average error in geolocation estimates, provide improved estimation confidence, and accurately estimate its geolocation confidence for some subjects. The results from the theoretical development of the non-linear optimization algorithm and its simulated performance is quantified and discussed
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