To improve the seamless navigation ability of an integrated Global Positioning System (GPS)/inertial navigation system in GPS-denied environments, a hybrid navigation strategy called the ...self-learning square-root- cubature Kalman filter (SL-SRCKF) is proposed in this article. The SL-SRCKF process contains two innovative steps: 1) it provides the traditional SRCKF with a self-learning ability, which means that navigation system observations can be provided continuously, even during long-term GPS outages; and 2) the relationship between the current Kalman filter gains and the optimal estimation error is established, which means that the optimal estimation accuracy can be improved by error compensation. The superiority of the proposed SL-SRCKF strategy is verified via experimental results and prominent advantages of this approach include: 1) the SL-SRCKF comprises two cycle filtering systems that work in a tightly coupled mode, and this allows more accurate error correction results to be obtained during GPS outages; 2) the system's error prediction ability is effectively improved by introducing a long short-term memory, which provides much better performance than other neural networks, such as random forest regression or the recursive neural network; and 3) under different (30, 60, and 100 s) GPS outage conditions, the long-term stability of SL-SRCKF is much better than that of other error correction approaches.
The theory and practice of unmanned aerial vehicle (UAV) capture and control via Global Positioning System (GPS) signal spoofing are analyzed and demonstrated. The goal of this work is to explore UAV ...vulnerability to deceptive GPS signals. Specifically, this paper (1) establishes the necessary conditions for UAV capture via GPS spoofing, and (2) explores the spoofer's range of possible post‐capture control over the UAV. A UAV is considered captured when a spoofer gains the ability to eventually specify the UAV's position and velocity estimates. During post‐capture control, the spoofer manipulates the true state of the UAV, potentially resulting in the UAV flying far from its flight plan without raising alarms. Both overt and covert spoofing strategies are considered, as distinguished by the spoofer's attempts to evade detection by the target GPS receiver and by the target navigation system's state estimator, which is presumed to have access to non‐GPS navigation sensor data. GPS receiver tracking loops are analyzed and tested to assess the spoofer's capability for covert capture of a mobile target. The coupled dynamics of a UAV and spoofer are analyzed and simulated to explore practical post‐capture control scenarios. A field test demonstrates capture and rudimentary control of a rotorcraft UAV, which results in unrecoverable navigation errors that cause the UAV to crash.
Landslides are natural phenomena, causing serious fatalities and negative impacts on socioeconomic. The Three Gorges Reservoir (TGR) area of China is characterized by more prone to landslides for the ...rainfall and variation of reservoir level. Prediction of landslide displacement is favorable for the establishment of early geohazard warning system. Conventional machine learning methods as forecasting models often suffer gradient disappearance and explosion, or training is slow. Hence, a dynamic method for displacement prediction of the step-wise landslide is provided, which is based on gated recurrent unit (GRU) model with time series analysis. The establishment process of this method is interpreted and applied to Erdaohe landslide induced by multi-factors in TGR area: the accumulative displacements of landslide are obtained by the global positioning system; the measured accumulative displacements is decomposed into the trend and periodic displacements by moving average method; the predictive trend displacement is fitted by a cubic polynomial; and the periodic displacement is obtained by the GRU model training. And the support vector machine (SVM) model and GRU model are used as comparisons. It is verified that the proposed method can quite accurately predict the displacement of the landslide, which benefits for effective early geological hazards warning system. Moreover, the proposed method has higher prediction accuracy than the SVM model.
Urban sensing plays a significant role in improving resource management, citizen engagement, environmental monitoring, urban planning, safety, and social equality. Global positioning system (GPS) is ...a crucial part in urban sensing as it provides accurate location tracking, real-time data collection, location-based services, mobility and transportation solutions, intelligent urban planning, and disaster management. However, there are various challenges associated with accurately estimating positions in urban environments due to various factors such as signal obstruction, urban canyons, multipath interference, noise and signal degradation, and differential GPS limitations. In the context of GPS-based urban sensing applications, the use of a navigation algorithm plays a critical role in extracting reliable data from corrupt sources, which can significantly impact inference performance for various signal processing applications. However, modeling all error sources that affect data quality can significantly increase system complexity, leading to challenges in terms of hardware and computation. To address this challenge, this article proposes a novel particle filter-based algorithm, called the double-resampling-based least-squares particle filter (DR-LPF), designed specifically for estimating the position of a GPS receiver without the need to model all error sources. By integrating current measurements (CMs) into the particle before resampling through the least-squares (LS) method, the DR-LPF allows the double-resampled particles to move toward high-likelihood regions, leading to improved estimation accuracy, reduced computation time, and reduced computational load. The application of the proposed DR-LPF algorithm finds wide applications in urban sensing environments where data quality can be affected by multiple error sources. By reducing the computational load and improving the estimation accuracy, the proposed DR-LPF algorithm can provide valuable insights into the movement and behavior of individuals and objects within an urban environment, enabling a wide range of smart city applications, such as traffic monitoring, environmental sensing, and crowd management.
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.
Global Positioning System (GPS) multipath signals can be used to infer volumetric soil moisture around a GPS antenna. While most GPS users concentrate on the signal that travels directly from the ...satellite to the antenna, the signal that is reflected by nearby surfaces contains information about the environment surrounding the antenna. The interference between the direct and reflected signals produces a modulation that can be observed in temporal variations of the signal-to-noise ratio (SNR) data recorded by the GPS receiver. Changes in the dielectric constant of the soil, which are associated with fluctuations in soil moisture, affect the effective reflector height, amplitude, and phase of the multipath modulation. Empirical studies have shown that these changes in SNR data are correlated with near-surface volumetric soil moisture. This study uses an electrodynamic single-scattering forward model to test the empirical relationships observed in field data. All three GPS interferogram metrics (effective reflector height, phase, and amplitude) are affected by soil moisture in the top 5 cm of the soil; surface soil moisture ( < 1\hbox{-}\hbox{cm} depth) exerts the strongest control. Soil type exerts a negligible impact on the relationships between GPS interferogram metrics and soil moisture. Phase is linearly correlated with surface soil moisture. The slope of the relationship is similar to that observed in field data. Amplitude and effective reflector height are also affected by soil moisture, although the relationship is nonlinear. Phase is the best metric derived from GPS data to use as a proxy for soil moisture variations.
The number of cities offering bikeshare has increased rapidly, from just a handful in the late 1990s to over 800 currently. This paper provides a review of recent bikeshare literature. Several themes ...have begun to emerge from studies examining bikeshare. Convenience is the major motivator for bikeshare use. Financial savings has been found to motivate those on a low income and the distance one lives from a docking station is an important predictor for bikeshare membership. In a range of countries, it has been found that just under 50% of bikeshare members use the system less than once a month. Men use bikeshare more than women, but the imbalance is not as dramatic as private bike riding (at least in low cycling countries). Commuting is the most common trip purpose for annual members. Users are less likely than private cyclists to wear helmets, but in countries with mandatory helmet legislation, usage levels have suffered. Bikeshare users appear less likely to be injured than private bike riders. Future directions include integration with e-bikes, GPS (global positioning system), dockless systems and improved public transport integration. Greater research is required to quantify the impacts of bikeshare, in terms of mode choice, emissions, congestion and health.
The growing commercial interest in indoor location-based services (ILBS) has spurred recent development of many indoor positioning techniques. Due to the absence of Global Positioning System (GPS) ...signal, many other signals have been proposed for indoor usage. Among them, Wi-Fi (802.11) emerges as a promising one due to the pervasive deployment of wireless LANs (WLANs). In particular, Wi-Fi fingerprinting has been attracting much attention recently because it does not require line-of-sight measurement of access points (APs) and achieves high applicability in complex indoor environment. This survey overviews recent advances on two major areas of Wi-Fi fingerprint localization: advanced localization techniques and efficient system deployment. Regarding advanced techniques to localize users, we present how to make use of temporal or spatial signal patterns, user collaboration, and motion sensors. Regarding efficient system deployment, we discuss recent advances on reducing offline labor-intensive survey, adapting to fingerprint changes, calibrating heterogeneous devices for signal collection, and achieving energy efficiency for smartphones. We study and compare the approaches through our deployment experiences, and discuss some future directions.
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-
and longitude-
...) 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 "
", similar to the term "
" known from statistics. It results in the empirical distribution of
and
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 (
and
) 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
and
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
and
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.
As Global Positioning System (GPS) cannot provide satisfying performance in indoor environments, indoor positioning technology, which utilizes indoor wireless signals instead of GPS signals, has ...grown rapidly in recent years. Meanwhile, visible light communication (VLC) using light devices such as light emitting diodes (LEDs) has been deemed to be a promising candidate in the heterogeneous wireless networks that may collaborate with radio frequencies (RF) wireless networks. In particular, light-fidelity has a great potential for deployment in future indoor environments because of its high throughput and security advantages. This paper provides a comprehensive study of a novel positioning technology based on visible white LED lights, which has attracted much attention from both academia and industry. The essential characteristics and principles of this system are deeply discussed, and relevant positioning algorithms and designs are classified and elaborated. This paper undertakes a thorough investigation into current LED-based indoor positioning systems and compares their performance through many aspects, such as test environment, accuracy, and cost. It presents indoor hybrid positioning systems among VLC and other systems (e.g., inertial sensors and RF systems). We also review and classify outdoor VLC positioning applications for the first time. Finally, this paper surveys major advances as well as open issues, challenges, and future research directions in VLC positioning systems.
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