Global Positioning System Failure Research Articles

Investigating the Implications of Sexual Assaults with Ride-Sharing: A Call for Research.

Sexual assault rates are increasing with ride-shares. Sexual violence data are often underestimated due to sexual violence cases going unreported and can lead to negative health outcomes. There is no research on the phenomenon of sexual assaults among ride-share users. This manuscript investigates sexual assaults from two ride-share companies and the policies/procedures in place to prevent sexual assaults. The data from two ride-share companies' previous Safety Reports were compared to see an increase in sexual assaults. The researchers looked at policies and regulations that ride-share companies have to prevent sexual assault. There is a call for research to investigate more in-depth the sexual assaults that have occurred including demographic data, geographic location, global positioning system failure, and the effectiveness of the criminal background checks.

Vehicle positioning in urban environments using particle filtering-based global positioning system, odometry, and map data fusion

<p>This article presents a new method for land vehicle navigation using global positioning system (GPS), dead reckoning sensor (DR), and digital road map information, particularly in urban environments where GPS failures can occur. The odometer sensors and map measure can be used to provide continuous navigation and correct the vehicle location in the presence of GPS masking. To solve this estimation problem for vehicle navigation, we propose to use particle filtering for GPS/odometer/map integration. The particle filter is a method based on the Bayesian estimation technique and the Monte Carlo method, which deals with non-linear models and is not limited to Gaussian statistics. When the GPS sensor cannot provide a location due to the number of satellites in view, the filter fuses the limited GPS pseudo-range data to enhance the vehicle positioning. The developed filter is then tested in a transportation network scenario in the presence of GPS failures, which shows the advantages of the proposed approach for vehicle location compared to the extended Kalman filter.</p>

INS/GPS Integrated Navigation for Unmanned Ships Based on EEMD Noise Reduction and SSA-ELM

The primary problem faced by the integrated navigation system based on the inertial navigation system (INS) and global positioning system (GPS) is providing reliable navigation and positioning solutions during GPS failure. Thus, this study proposes an innovative integrated navigation algorithm to address the limitation of precise positioning when GPS fails. First, for the limitation of noise interference in INS, noise reduction technology based on ensemble empirical mode decomposition (EEMD) is proposed to improve the quality of the INS signal and enhance the noise reduction effect. Second, an INS/GPS integrated framework based on the sparrow search algorithm (SSA) and extreme learning machine (ELM) is proposed. During normal GPS conditions, SSA-ELM is used to develop a high-precision prediction model to estimate differences between INS and GPS. When the GPS signal is interrupted, the difference predicted by SSA-ELM is used as the measurement input and the INS is corrected. To confirm the effectiveness of this method, a real ship experiment is conducted with other commonly used methods. The experimental results demonstrate that the proposed method can improve positioning accuracy and reliability when GPS is interrupted.

A Hybrid Unmanned Arieal Vehicle With A Rover For Disaster Rescue Management Operation

The present invention proposed in this paper is a multi-role UAV with in-built Rover for Disaster Rescue Management (DRM) operation. As we know, Disaster Rescue operation are very complex, difficult and time consuming. The loss of human life during such operations after a disaster has been an area of concern for various organizations. In places with heavy rainfall and bad weather, DRM operation becomes an uphill task. The proposed invention is a hybrid UAV with an in-built Rover capable of image capturing with live feeds to the remote central station and thermal image capturing capability. The system is built such that, in case of heavy debris and landslide where Rover operation can’t be initiated at starting due to blockage of path for rover to move, the UAV is initiated and the Rover takes over when is clear path is visible. The device is equipped with GPS (Global Positioning System) with sends the location to the central system or remote command Centre whenever the image capturing system detects a heat signature through thermal imaging. In case of GPS failure, the system is developed with secondary back up which drops a sensors marker which the remote unit can track. The system proposed here is developed with an aim of reducing the loss of human life and increase effective DRM operation which reduces the time taken for search and rescue operation.

Improving Vehicle Localization in a Smart City with Low Cost Sensor Networks and Support Vector Machines

A smart city’s main purpose is to provide intelligent responses to different problems of the rapid urban population growth. For instance, integrating fleet management solutions into intelligent transportation systems (ITS) can efficiently resolve transportation problems relying on each vehicle information. Usually, the position estimate is ensured by the integration of the Global Positioning System (GPS) and Inertial Navigation Systems (INS). For multisensor data fusion, the Extended Kalman Filter (EKF) is generally applied using the sensor’s measures and the GPS position as a helper. However, the INS are expensive and require more complex computing which induces restrictions on their implementation. Furthermore, the EKF performance depends on the vehicle dynamic variations and may quickly diverge because of environmental changes. In this paper, we present a robust low cost approach using EKF and Support Vector Machines (SVM) to reliably estimate the vehicle position by limiting the EKF drawbacks. The sensors used are a GPS augmented by a low cost wireless sensor network. When GPS signals are available, we train SVM on different dynamics and outage times to learn the position errors so we can correct the future EKF predictions during GPS signal outages. We obtain empirically an improvement of up to 94% over the simple EKF predictions in case of GPS failures.

Attitude Determination Method by Fusing Single Antenna GPS and Low Cost MEMS Sensors Using Intelligent Kalman Filter Algorithm

For meeting the demands of cost and size for micronavigation system, a combined attitude determination approach with sensor fusion algorithm and intelligent Kalman filter (IKF) on low cost Micro-Electro-Mechanical System (MEMS) gyroscope, accelerometer, and magnetometer and single antenna Global Positioning System (GPS) is proposed. The effective calibration method is performed to compensate the effect of errors in low cost MEMS Inertial Measurement Unit (IMU). The different control strategies fusing the MEMS multisensors are designed. The yaw angle fusing gyroscope, accelerometer, and magnetometer algorithm is estimated accurately under GPS failure and unavailable sideslip situations. For resolving robust control and characters of the uncertain noise statistics influence, the high gain scale of IKF is adjusted by fuzzy controller in the transition process and steady state to achieve faster convergence and accurate estimation. The experiments comparing different MEMS sensors and fusion algorithms are implemented to verify the validity of the proposed approach.

On the enhanced detectability of GPS anomalous behavior with relative entropy

A standard receiver autonomous integrity monitoring (RAIM) technique for the global positioning system (GPS) has been dedicated to provide an integrity monitoring capability for safety-critical GPS applications, such as in civil aviation for the en-route (ER) through non-precision approach (NPA) or lateral navigation (LNAV). The performance of the existing RAIM method, however, may not meet more stringent aviation requirements for availability and integrity during the precision approach and landing phases of flight due to insufficient observables and/or untimely warning to the user beyond a specified time-to-alert in the event of a significant GPS failure. This has led to an enhanced RAIM architecture ensuring stricter integrity requirement by greatly decreasing the detection time when a satellite failure or a measurement error has occurred. We thus attempted to devise a user integrity monitor which is capable of identifying the GPS failure more rapidly than a standard RAIM scheme by incorporating the RAIM with the relative entropy, which is a likelihood ratio approach to assess the inconsistence between two data streams, quite different from a Euclidean distance. In addition, the delay-coordinate embedding technique needs to be considered and preprocessed to associate the discriminant measure obtained from the RAIM with the relative entropy in the new RAIM design. In simulation results, we demonstrate that the proposed user integrity monitor outperforms the standard RAIM with a higher level of detection rate of anomalies which could be hazardous to the users in the approach or landing phase and is a very promising alternative for the detection of deviations in GPS signal. The comparison also shows that it enables to catch even small anomalous gradients more rapidly than a typical user integrity monitor.

A hybrid clustering technique using quantitative and qualitative data for wireless sensor networks

Clustering is an efficient method to organize sensor nodes in Wireless Sensor Networks (WSNs) for data transmissions and energy saving. To perform clustering, many methods require geographic location data for calculating the distance between sensor nodes. But location data may not always be available due to Global Positioning System (GPS) failures or may not be practical in consideration of all sensor nodes due to the high cost and energy consumption of GPS. Alternatively, Received Signal Strength (RSS) or RSS Indicator (RSSI) has been used to estimate the distance. But many studies have shown that RSS or RSSI is not reliable in practice. In order to mitigate these realistic problems, this paper proposes a hybrid clustering protocol – Hybrid Distributed Hierarchical Agglomerative Clustering (H-DHAC) – which uses both quantitative location data and binary qualitative connectivity data in clustering for WSNs. Our simulation results reveal that H-DHAC only has a slightly lower percentage of compromise in performance in terms of network life time and total transmitted data compared to similar approaches that use complete location data. However, H-DHAC still outperforms the well known clustering protocols, e.g., LEACH and LEACH-C. On the other hand, the cost of H-DHAC can be significantly lower in comparison to those approaches that use complete quantitative location data, as GPS is not required for all sensor nodes. In addition, H-DHAC still can be operational in the presence of GPS failures.

A T-DMB navigation system for seamless positioning in both indoor and outdoor environments

Abstract The conventional global positioning system (GPS) can often fail to provide position determination for a mobile user in indoor and urban environments. To cope with GPS failure in such environments, a new navigation system which utilizes a terrestrial digital multimedia broadcasting (T-DMB) signal to obtain the mobile user's position is presented. Since the T-DMB transmitters in Korea construct a single frequency network (SFN), which forces the transmitters to be synchronized, the mobile user can measure a time difference of arrival (TDOA) for all audible T-DMB transmitter pairs. The time difference between T-DMB transmitters is converted to a distance difference by multiplying the time difference by the speed of light. Using these measurements and a TDOA positioning method, the mobile user position can be estimated. An experiment with a T-DMB receiver and a data acquisition (DAQ) board is performed in Seoul to analyze the error characteristic of TDOA measurements. It is certified that the measurement error is bounded under 300 m and can be used to determine the mobile user's position with a small standard deviation.

Simulating GPS radio signal to synchronize network--a new technique for redundant timing.

Currently, many distributed systems such as 3G mobile communications and power systems are time synchronized with a Global Positioning System (GPS) signal. If there is a GPS failure, it is difficult to realize redundant timing, and thus time-synchronized devices may fail. In this work, we develop time transfer by simulating GPS signals, which promises no extra modification to original GPS-synchronized devices. This is achieved by applying a simplified GPS simulator for synchronization purposes only. Navigation data are calculated based on a pre-assigned time at a fixed position. Pseudo-range data which describes the distance change between the space vehicle (SV) and users are calculated. Because real-time simulation requires heavy-duty computations, we use self-developed software optimized on a PC to generate data, and save the data onto memory disks while the simulator is operating. The radio signal generation is similar to the SV at an initial position, and the frequency synthesis of the simulator is locked to a pre-assigned time. A filtering group technique is used to simulate the signal transmission delay corresponding to the SV displacement. Each SV generates a digital baseband signal, where a unique identifying code is added to the signal and up-converted to generate the output radio signal at the centered frequency of 1575.42 MHz (L1 band). A prototype with a field-programmable gate array (FPGA) has been built and experiments have been conducted to prove that we can realize time transfer. The prototype has been applied to the CDMA network for a three-month long experiment. Its precision has been verified and can meet the requirements of most telecommunication systems.

Mid-Latitude Amplitude Scintillations of GPS Signals and GPS Failures at the Auroral Oval Boundary

Analyzing ionospheric effects of magnetic storms on July 15, 2000 and September 26, 2001, we show that a region with intense small-scale electron density irregularities emerges in the main phase of magnetic storms on the southern boundary of the auroral oval as it expands to the mid-latitudes. This assumption is supported by the presence of powerful backscattering signals recorded on July 15, 2000 by the incoherent-scatter radars in the eastern and western hemispheres. Such irregularities cause strong scintillations of signals of the navigation Global Positioning System (GPS), which leads to a breakdown of signal tracking and an increase in the positioning errors of the GPS.