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\"The story of the rise of modern navigation technology, from radio location to GPS-and the consequent decline of privacy What does it mean to never get lost? You Are Here examines the rise of our technologically aided era of navigational omniscience-or how we came to know exactly where we are at all times. In a sweeping history of the development of location technology in the past century, Bray shows how radio signals created to carry telegraph messages were transformed into invisible beacons to guide ships and how a set of rapidly-spinning wheels steered submarines beneath the polar ice cap. But while most of these technologies were developed for and by the military, they are now ubiquitous in our everyday lives. Our phones are now smart enough to pinpoint our presence to within a few feet-and nosy enough to share that information with governments and corporations. Filled with tales of scientists and astronauts, inventors and entrepreneurs, You Are Here tells the story of how humankind ingeniously solved one of its oldest and toughest problems-only to herald a new era in which it's impossible to hide\"-- Provided by publisher.

The paper provides a short history of the phasor measurement unit (PMU) concept. The origin of PMU is traced to the work on developing computer based distance relay using symmetrical component theory. PMUs evolved from a portion of this relay architecture. The need for synchronization using global positioning system (GPS) is discussed, and the wide area measurement system (WAMS) utilizing PMU signals is described. A number of applications of this technology are discussed, and an account of WAMS activities in many countries around the world are provided.

In navigation, the Twice the Distance Root Mean Square (2DRMS) is commonly used as a position accuracy measure. Its determination, based on statistical methods, assumes that the position errors are normally distributed and are often not reflected in actual measurements. As a result of the widespread adoption of this measure, the positioning accuracy of navigation systems is overestimated by 10–15%. In this paper, a new method is presented for determining the navigation system positioning accuracy based on a reliability model where the system’s operation and failure statistics are referred to as life and failure times. Based on real measurements, the method proposed in this article will be compared with the classical method (based on the 2DRMS measure). Real (empirical) measurements made by the principal modern navigation positioning systems were used in the analyses: Global Positioning System (GPS) (168’286 fixes), Differential Global Positioning System (DGPS) (900’000 fixes) and European Geostationary Navigation Overlay Service (EGNOS) (900’000 fixes). Research performed on real data, many of which can be considered representative, have shown that the reliability method provides a better (compared to the 2DRMS measure) estimate of navigation system positioning accuracy. Thanks to its application, it is possible to determine the position error distribution of the navigation system more precisely when compared to the classical method, as well as to indicate those applications that can be used by this system, ensuring the safety of the navigation process.

Surface-following buoys are widely used to collect routine ocean wave measurements. While accelerometer and tilt sensors have been used for decades to measure the wave-induced buoy displacements, alternative global positioning system (GPS) sensor packages have been introduced recently that are generally smaller, less expensive, and do not require calibration. In this study, the capabilities of several GPS sensors are evaluated with field observations in wind-sea and swell conditions off the California coast. The GPS buoys used in this study include Datawell Directional Waverider and Mini Directional Waverider buoys equipped with a specialized GPS Doppler shift sensor, and a low-cost experimental drifter equipped with an “off the shelf” GPS receiver for absolute position tracking. Various GPS position receivers were attached to the Waverider buoys to evaluate their potential use in low-cost wave-resolving drifters. Intercomparisons between the Datawell GPS-based buoys, the experimental GPS drifter, and a conventional Datawell buoy with an accelerometer–tilt–compass sensor package, show good agreement in estimates of wave frequency and direction spectra. Despite the limited (several meters) absolute accuracy of the GPS position receivers, the horizontal wave orbital displacements are accurately resolved, even in benign (significant wave height less than 1 m) swell conditions. Vertical sea surface displacements were not well resolved by the GPS position receivers with built-in or small patch antennas, but accurately measured when an external precision antenna was attached to the drifter. Overall, the field tests show excellent agreement between Datawell buoys using GPS and motion-sensor packages, and demonstrate the feasibility of observing ocean surface waves with low-cost GPS-tracked drifters.

Positioning systems are used to determine position coordinates in navigation (air, land, and marine). Statistical analysis of their accuracy assumes that the position errors (latitude—δφ and longitude—δλ) are random and that their distributions are consistent with the normal distribution. However, in practice, these errors do not appear in a random way, since the position determination in navigation systems is done with an iterative method. It causes so-called “Position Random Walk”, similar to the term “Random Walk” known from statistics. It results in the empirical distribution of δφ and δλ being inconsistent with the normal distribution, even for samples of up to several thousand measurements. This phenomenon results in a significant overestimation of the accuracy of position determination calculated from such a short series of measurements, causing these tests to lose their representativeness. This paper attempts to determine the length of a measurement session (number of measurements) that is representative of the positioning system. This will be a measurement session of such a length that the position error statistics (δφ and δλ) represented by the standard deviation values are close to the real values and the calculated mean values (φ¯ and λ¯) are also close to the real values. Special attention will also be paid to the selection of an appropriate (statistically reliable) number of measurements to be tested statistically to verify the hypothesis that the δφ and δλ distributions are consistent with the normal distribution. Empirical measurement data are taken from different positioning systems: Global Positioning System (GPS) (168′286 fixes), Differential Global Positioning System (DGPS) (864′000 fixes), European Geostationary Navigation Overlay Service (EGNOS) (928′492 fixes), and Decca Navigator system (4052 fixes). The analyses showed that all researched positioning systems (GPS, DGPS, EGNOS and Decca Navigator) are characterized by the Position Random Walk (PRW), which resulted in that the empirical distribution of δφ and δλ being inconsistent with the normal distribution. The size of the PRW depends on the nominal accuracy of position determination by the system. It was found that measurement sessions consisting of 1000 fixes (for the GPS system) overestimate the accuracy analysis results by 109.1% and cannot be considered representative. Furthermore, when analyzing the results of long measurement campaigns (GPS and DGPS), it was found that the representative length of the measurement session differs for each positioning system and should be determined for each of them individually.

Reliable positioning and navigation is becoming imperative in more and more applications for public services, consumer products, and safety-critical purposes. Research for finding pervasive and robust positioning methodologies is critical for a growing amount of societal areas while making sure that navigation is trustworthy and the risks and threats of especially satellite navigation are accounted for. This book provides a comprehensive survey of the effect of radio-frequency interference (RFI) on the Global Navigation Satellite Systems (GNSS) as well as of the spoofing threats. Through case studies and practical implementation/applications, this resource presents engineers and scientists with a better understanding of interference and spoofing threats, ultimately helping them to design and implement robust systems.

Despite the importance of glacial lake development in ice dynamics and glacier thinning, in situ and satellite-based measurements from lake-terminating glaciers are sparse in the Bhutanese Himalaya, where a number of proglacial lakes exist. We acquired in situ and satellite-based observations across lake- and land-terminating debris-covered glaciers in the Lunana region, Bhutanese Himalaya. A repeated differential global positioning system survey reveals that thickness change of the debris-covered ablation area of the lake-terminating Lugge Glacier (-4.67±0.07 m a−1) is more than 3 times more negative than that of the land-terminating Thorthormi Glacier (-1.40±0.07 m a−1) for the 2004–2011 period. The surface flow velocities decrease down-glacier along Thorthormi Glacier, whereas they increase from the upper part of the ablation area to the terminus of Lugge Glacier. Numerical experiments using a two-dimensional ice flow model demonstrate that the rapid thinning of Lugge Glacier is driven by both a negative surface mass balance and dynamically induced ice thinning. However, the thinning of Thorthormi Glacier is minimised by a longitudinally compressive flow regime. Multiple supraglacial ponds on Thorthormi Glacier have been expanding since 2000 and have merged into a single proglacial lake, with the glacier terminus detaching from its terminal moraine in 2011. Numerical experiments suggest that the thinning of Thorthormi Glacier will accelerate with continued proglacial lake development.

Unmanned aerial vehicles (UAVs) are vulnerable to global positioning system (GPS) spoofing attacks, which can mislead their navigation systems and result in unpredictable catastrophic consequences. To address this issue, we propose a detection method based on stacked ensemble learning that combines convolutional neural network (CNN) and extreme gradient boosting (XGBoost) to detect spoofing signals in the GPS data received by UAVs. First, we applied the synthetic minority oversampling (SMOTE) technique to the dataset to address the issue of class imbalance. Then, we used a CNN model to extract high-level features, combined with the original features as input for the stacked model. The stacked model employs XGBoost as the base learner, which is optimized through five-fold cross-validation, and utilizes logistic regression for the final prediction. Furthermore, we incorporated magnetic field data to enhance the system’s robustness, thereby further improving the accuracy and reliability of GPS spoofing attack detection. Experimental results indicate that the proposed model achieved a high accuracy of 99.79% in detecting GPS spoofing attacks, demonstrating its potential effectiveness in enhancing UAV security.

Self-Supervised Representation Learning (SSRL) has become a potent strategy for addressing the growing threat of Global Positioning System (GPS) spoofing to small Unmanned Aerial Vehicles (UAVs) by capturing more abstract and high-level contributing features. This study focuses on enhancing attack detection capabilities by incorporating SSRL techniques. An innovative hybrid architecture integrates Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) models to detect attacks on small UAVs alongside two additional architectures, LSTM-Recurrent Neural Network (RNN) and Deep Neural Network (DNN), for detecting GPS spoofing attacks. The proposed model leverages SSRL, autonomously extracting meaningful features without the need for many labelled instances. Key configurations include LSTM-GRU, with 64 neurons in the input and concatenate layers and 32 neurons in the second layer. Ablation analysis explores various parameter settings, with the model achieving an impressive 99.9% accuracy after 10 epoch iterations, effectively countering GPS spoofing attacks. To further enhance this approach, transfer learning techniques are also incorporated, which help to improve the adaptability and generalisation of the SSRL model. By saving and applying pre-trained weights to a new dataset, we leverage prior knowledge to improve performance. This integration of SSRL and transfer learning yields a validation accuracy of 79.0%, demonstrating enhanced generalisation to new data and reduced training time. The combined approach underscores the robustness and efficiency of GPS spoofing detection in UAVs.

This paper presents two origin-destination flow estimation models using sampled GPS positions of probe vehicles and link flow counts. The first model, named as SPP model (scaled probe OD as prior OD), uses scaled probe vehicle OD matrix as prior OD matrix and applies conventional generalized least squares (GLS) framework to conduct OD correction using link counts; the second model, PRA model (probe ratio assignment), is an extension of SPP in which the observed link probe ratios are also included as additional information in the OD estimation process. For both models, the study explored a new way to construct assignment matrices directly from sampled probe trajectories to avoid sophisticated traffic assignment process. Then, for performance evaluation, a comprehensive numerical experiment was conducted using simulation dataset. The results showed that when the distribution of probe vehicle ratios is homogeneous among different OD pairs, both proposed models achieved similar degree of improvement compared with the prior OD pattern. However, under the case that the distribution of probe vehicle ratios is heterogeneous across different OD pairs, PRA model achieved more significant reduction on OD flow estimations compared with SPP model. Grounded on both theoretical derivations and empirical tests, the study provided in-depth discussions regarding the strengths and challenges of probe vehicle based OD estimation models.