Global Positioning System Signals Research Articles

Smartphone-Based 3D Indoor Pedestrian Positioning through Multi-Modal Data Fusion.

Combining research areas of biomechanics and pedestrian dead reckoning (PDR) provides a very promising way for pedestrian positioning in environments where Global Positioning System (GPS) signals are degraded or unavailable. In recent years, the PDR systems based on a smartphone’s built-in inertial sensors have attracted much attention in such environments. However, smartphone-based PDR systems are facing various challenges, especially the heading drift, which leads to the phenomenon of estimated walking path passing through walls. In this paper, the 2D PDR system is implemented by using a pocket-worn smartphone, and then enhanced by introducing a map-matching algorithm that employs a particle filter to prevent the wall-crossing problem. In addition, to extend the PDR system for 3D applications, the smartphone’s built-in barometer is used to measure the pressure variation associated to the pedestrian’s vertical displacement. Experimental results show that the map-matching algorithm based on a particle filter can effectively solve the wall-crossing problem and improve the accuracy of indoor PDR. By fusing the barometer readings, the vertical displacement can be calculated to derive the floor transition information. Despite the inherent sensor noises and complex pedestrian movements, smartphone-based 3D pedestrian positioning systems have considerable potential for indoor location-based services (LBS).

Improved Multi-GNSS PPP Software for Upgrading the DEMETRA Project Time Monitoring Service

The H2020 DEMETRA project provides short latency clock monitoring services to the time users using the Atomium precise point positioning (PPP) software developed by the Royal Observatory of Belgium. In this paper, three recent updates of the current Atomium software are introduced: adding Galileo signals in the PPP computation; the option to constrain the receiver clock; PPP with integer ambiguity resolution. The advantages of these updates are demonstrated: Combining the Galileo and global positioning system (GPS) signals for PPP time transfer will further improve the frequency stability inside the computation batch; PPP with receiver clock constraint is not only used to reduce the short-term noise of the clock measurements but can also be used for some specific applications to a keep continuous clock solution in the computation batch or retrieve correct clock measurements from extremely noisy environments; the integer PPP allows a continuous clock solution, and improves the mid-term and long-term stability of the frequency transfer compared to the current PPP frequency transfer techniques.

Three-Dimensional Reconstruction of a Marine Floating Structure With an Unmanned Surface Vessel

This paper addresses 3-D reconstruction of a marine floating platform using an unmanned surface vessel (USV). Lidar and sonar sensors equipped on the USV are used to gather volumetric point cloud data of the floating platform structure both above and below the waterline. These measurements are collected in the vehicle-fixed frame, and thus, an accurate estimation of the vehicle's pose (i.e., position and attitude) is essential for satisfactory 3-D reconstruction. Because global positioning system signals are severely deteriorated or unavailable around large steel structures, relative navigation with respect to the planar surfaces of their hull structures is performed in the framework of simultaneous localization and mapping. To verify and demonstrate the feasibility of the proposed approach, a field experiment around a full-scale semisubmersible platform was conducted using a custom-designed USV.

Impact of GPS Processing on the Estimation of Snow Water Equivalent Using Refracted GPS Signals

Global navigation satellite system (GNSS) antennas buried underneath a snowpack have a high potential for in situ snow water equivalent (SWE) estimation. Automated and continuous SWE quantification independent of weather conditions could enhance snow hydrological monitoring and modeling. Accurate and reliable in situ data are needed for the calibration and validation of remote sensing data and snowpack modeling. A relative bias of less than 5% is achieved using sub-snow global positioning system (GPS) antennas (GPS refractometry) during a three full seasons time period in the Swiss Alps. A systematic overview regarding the temporal reliability of the sub-snow GPS derived results is, however, missing for this emerging technique. Moreover, GPS processing impacts the results significantly. Different GPS processing parameters are therefore selected and their influence on the SWE estimation is investigated. The impact of elevation-dependent weighting, the elevation cutoff angles, and the time intervals for SWE estimation are systematically assessed. The best results are achieved using all observations with an elevation-dependent weighting scheme. Moreover, the SWE estimation performance is equally accurate for hourly SWE estimation as for lower temporal resolutions up to daily estimates. The impact of snow on the coordinate solution is furthermore evaluated. While the east and north components are not systematically influenced by the overlying snowpack, the vertical component exhibits a significant variation and strongly depends on the SWE. The biased vertical component therefore provides an additional possibility to estimate SWE.

Diurnal, seasonal and solar cycle variation in total electron content and comparison with IRI-2016 model at Birnin Kebbi

Abstract. The ionosphere is the major error source for the signals of global positioning system (GPS) satellites. In the analysis of GPS measurements, ionospheric error is assumed to be somewhat of a nuisance. The error induced by the ionosphere is proportional to the number of electrons along the line of sight (LOS) from the satellite to receiver and can be determined in order to study the diurnal, seasonal, solar cycle and spatial variations in the ionosphere during quiet and disturbed conditions. In this study, we characterize the diurnal, seasonal and solar cycle variation in observed total electron content (OBS-TEC) and compare the results with the International Reference Ionosphere (IRI-2016) model. We obtained TEC from a dual-frequency GPS receiver located at Birnin Kebbi Federal Polytechnic (BKFP) in northern Nigeria (geographic location: 12.64∘ N, 4.22∘ E; 2.68∘ N dip) for the period 2011–2014. We observed differences between the diurnal variation in OBS-TEC and the IRI-2016 model for all hours of the day except during the post-midnight hours. Slight post-noon peaks in the daytime maximum and post-sunset decrease and enhancement are observed in the diurnal variation in OBS-TEC during the equinoxes. On a seasonal scale, we observed that OBS-TEC values were higher in the equinoxes than the solstices only in 2012. However, in 2011, the September equinox and December solstice recorded a higher magnitude, followed by the March equinox, and the magnitude was lowest in the June solstice. In 2013, the December solstice magnitude was highest, followed by the equinoxes, and it was lowest in the June solstice. In 2014, the March equinox and December solstice magnitudes were higher than the September equinox and June solstice magnitude. The June solstice consistently recorded the lowest values for all the years. OBS-TEC is found to increase from 2011 to 2014, thus revealing solar cycle dependence.

Efficient jamming signal against civilian GPS receivers

Civilian Global Positioning System (GPS) receivers are subjected to different types of jamming/interference impacts, which degrade its accuracy and reliability.It is well known that GPS signals are considered weak signals; thus, it can be efficiently jammed by using a GPS jammer.One of the paper goals is to increase the interference effectiveness in such a way being able to jam multiple satellites simultaneously, causing continues loss of lock for the GPS receiver tracking loop and decreasing the mitigation probability; a novel jamming signal is proposed which increases the interference influence zone and affects more than one satellite simultaneously. The proposed jamming signal has the ability to generate continuous loss of lock for the GPS receiver-tracking loop and to decrease the mitigation probability. The proposed jamming signal affects all GPS satellites with nearly small interference tolerance value and causes continuous loss of lock to the carrier-tracking loop of the GPS receiver.

A Robust Artificial Intelligence: Smart Indoor Positioning System

Over the previous few centuries, technology has converted massively from being a desktop personal computer to handheld mobile phones, with lower energy consumption of raw computing power. This computability is now incorporated with other systems as well as isolated to a single device. This paradigm was first noted in cyber-physical systems with the introduction of cloud services. The evolution of Artificial Intelligence(AI) with cloud computing and the importance of this field in human life, induce us to make simple and efficient talkative assistant robot for indoor navigation. The navigation system in outdoor typically rely upon Global Positioning System (GPS) but the indoor navigation systems have to rely on different technologies, as GPS signals cannot be received indoors. Thus, several technologies have been proposed and implemented over the past decade to improve navigation in indoors. But they were costly and less effective. Therefore, we have proposed a system that assists humans to find their location in a conversational manner. The suggested system was constructed by introducing the advantages of a personal assistant device, Amazon Alexa, the cloud services of Amazon and its voice services for indoor navigation. A Raspberry Pi 3 Model B is used as the element of the hardware to provide our system with intelligent characteristics. You can trigger the speech service using the "Alexa" keyword. Using the voice command, the skill / application we created can be initiated. It operates a script on the cloud once Alexa is enabled, which runs a subroutine on the Raspberry Pi 3 in-turn to provide a path for that specific place. Once the Raspberry Pi calculation is finished, it sends the message back to Alexa. Alexa transforms the text into a voice and informs the user path.

Reducing GPS Error for Smart Collars Based on Animal’s Behavior

Global Positioning Systems (GPS) are successfully used in many fields such as navigation, meteorology, military tasks, mapping, virtual fencing, and more. Smart collars are currently the most convenient device for determining animal location in virtual fencing systems, however; these systems are still suffering from environmental effects and propagation in direct visibility. These types of side effects may reduce the work of GPS receivers. The current article defines a method for improving animal location accuracy using a virtual fence smart collar worn around the animal’s neck by the aid of maximum probability of movement from one point to another. The proposed approach first checks the current position of the animal, and after receiving a GPS signal from satellites it calculates the distance between the two GPS signals. Secondly, the method checks the animal’s behavior for the receiving period of the two points. Finally, the approach calculates a probability of maximum animal movement for the two-point receiving period. If the animal can pass the distance in the time frame of the two signals, then the second signal is taken as the correct position; otherwise, the point is taken which the animal could pass. Real-time animal behavior is classified using Support Vector Machines (SVM). The proposed method was verified within seven days of experiments. Consequently, the proposed approach experiments were sufficiently successful. The recreated locations from our approach appeared very close to the real point. The mean average of passed distance by the marked line decreased to 16.2, 5, 0 m for running, walking, and resting conditions, respectively. On the other hand, the unfiltered geolocations of the GPS receiver, give results significantly further from the animal’s actual position such as 148.8, 182.7, 136.2 m for running, walking, and resting conditions.

A Deep Learning-Based Approach to Forecast Ionospheric Delays for GPS Signals

This letter proposes the implementation of ionospheric forecasting model based on the long short-term memory (LSTM) networks. Ionospheric region produces time delay for radio wave propagation of global positioning system (GPS) satellites. The ionospheric delays for GPS signals degrade the position accuracy in the measurements for precise navigation and positioning services. Utilizing the emerging artificial intelligence mathematical tools to forecast ionospheric disturbances using GPS-estimated total electron content (TEC) observations is decisive. In this letter, multi-input LSTM forecasting technique is investigated and tested for evaluating its capability in forecasting the ionospheric delays over Bengaluru station (16.26° N, 80.44° E) using eight years (2009–2016) of GPS measured vertical TEC (VTEC) time-series data. The assessment of the LSTM model performance during geomagnetic quiet and disturbed conditions is carried out in comparison with artificial neural networks model and International Reference Ionosphere (IRI-2016) model based on statistical parameters like root-mean-square error and coefficient of determination ( $R^{2}$ ). The experimental analysis delineates that the proposed LSTM model has provided the correlation of 0.99 with the GPS-measured VTEC and with a forecasting error of 1–2 TEC units.

LIDAR 센서 기반 자율주차를 위한 인지 시스템

In this paper, we propose a perception system based on a LIDAR(Light Detection and Ranging) sensor for auto valet parking. The system uses LIDAR SLAM(Simultaneous Localization and Mapping) to create a map without GPS(Global Positioning System) signals, which allows the vehicle to generate a path to move. It can find an empty parking space and park the vehicle in it. This is more economical than the current valet systems, as it does not require any additional infrastructure. It also saves time because the driver does not need to find parking spaces directly, and it can prevent accidents in the parking lot. The map building of the parking lot was carried out with the LOAM(LIDAR Odometry and Mapping) algorithm of Carnegie Mellon University using the ICP(Iterative Closest Point) algorithm, and DBSCAN(Density Based Spatial Clustering of Applications with Noise) clustering was performed by voxelling data with the outliers removed. Using this process, the proposed perception system exhibited approximately 95% accuracy in detecting parking spaces.

A novel GPS/ RAVO/MEMS-INS smartphone-sensor-integrated method to enhance USV navigation systems during GPS outages

Most navigation systems of unmanned surface vehicles (USVs) are based on global position system (GPS)/inertial navigation system (INS) integrated methods to improve the accuracy of the navigation system. But in some places on the Earth’s surface, the GPS signal suffers from outages, interferences, and weakness, which affect the performance of the whole USV navigation system. This paper provides a continuous and accurate navigation solution via integrated GPS with micro-electro-mechanical (MEMS)-INS smartphone sensors and reduced-aided visual odometry (RAVO) using centralized Kalman filter (CKF) data fusion. The proposed RAVO is used to correct USV navigation system errors during GPS outages. It is based on integrated visual odometry (VO) with MEMS-INS smartphone sensors, the Doppler velocity log (DVL), depth and compass sensors. The DVL/depth/compass/MEMS-INS smartphone integrated solution is used to improve the performance of a VO system before data fusion processing using a CKF. The CKF is used as data fusion processing to estimate and correct USV navigation system errors.The efficiency of the USV navigation system is tested on a surface reference trajectory called Kur-Mukalla in Mukalla City, Yemen. During GPS outage for 83 s, the proposed method based on the GPS/RAVO/MEMS-INS smartphone integrated CKF method could provide a reliable USV navigation solution; it reduced the position error to 82.32% compared to the traditional GPS/MEMS-INS CKF method, and to about 70.72% compared to the GPS/Pure-VO/MEMS-INS CKF integrated method.

Pseudo DVL reconstruction by an evolutionary TS-fuzzy algorithm for ocean vehicles

By development of ocean exploration, autonomous vehicles are employed to perform on-water and underwater tasks. Using an extended Kalman filter, Inertial Navigation System/Doppler Velocity Log (INS/DVL) integrated systems are trying to navigate in oceans and underwater environments when Global Positioning System (GPS) signals are not accessible. The dependency of DVL signals on acoustic environments may cause any DVL malfunction due to sea creatures or strong wave-absorbing material. In this paper, an improved version of evolutionary TS-fuzzy (eTS) is proposed in order to predict DVL sensor outputs at DVL malfunction moment, by utilizing an artificial intelligent (AI) aided integrated system. According to lack of input selection and shrinking, while the classic eTS suffers from soaring prediction errors and may result in instability, by adding these properties to eTS, the performance increases in long-term DVL outage. The proposed eTS-aided system makes ocean navigation purposes possible during long-term and simultaneous outage of GPS and DVL. These evolutionary fuzzy systems change their structure depending on the path which makes the trained fuzzy system more flexible with non-stationary and varying environments. The real sensor data is collected online with a test setup on a lake and then the algorithms are applied. The powerful capacity of the proposed data fusion method is demonstrated in analysis results.

Design an Adjustable Narrow Correlator to Track GPS Signals

Multipath signals problem is still the main challenge for the global positioning system (GPS) receiver to obtain an accurate localization measurement. However, many techniques have been developed to mitigate the impact of these undesired signals at the expense of adding a burden to the GPS receiver design. Even though, none of the techniques in the works of literature was able to solve the loose signal lock issue efficiently. In this piece of work, we used a new approach to maintain the tracking synchronization when the receiver moves from outdoors to indoors /harsh environment. We design a dynamic narrow correlator in the delay lock loop, which made the space between the early and late correlators adjusted according to the receiver’s location. Consequently, this will enable the receiver to keep tracking the received GPS signal in a challenging environment and reduce the thrashing the available resources (power/processing) to acquire the GPS signal again. The results show that the ranging error has been reduced in comparison with a traditional narrow correlator, as well as the bit transition in the in-phase arm has been also maintained.

A novel GPS/DVL/MEMS-INS smartphone sensors integrated method to enhance autonomous navigation, guidance and control system of AUSVs based on ADSF Combined Filter

A reliable navigation, guidance and control system of Autonomous Unmanned Surface Vehicle (AUSV) is the major challenge for most researchers, especially when Global Positioning System (GPS) signal outages or its accuracy decreases in some places on the Earth’s surface. Our paper introduces a novel navigation, guidance and control system of AUSV as follows; the proposed navigation system of AUSV is based on integrated Micro Electric Mechanical System –Inertial Navigation System (MEMS-INS) smartphone sensors with GPS and Doppler Velocity Log (DVL) to correct AUSV navigation system errors. The guidance and control system of AUSV is based on Ultrasonic sensors, Raspberry Pi microcontroller, and digital compass sensor. The ultrasonic sensor is used to detect and avoid obstacles on the path of AUSV. The Raspberry Pi microcontroller and digital compass are used to guide AUSV in the required path.Since the GPS and DVL signals are used as the reference sources to correct errors of MEMS-INS system, the accuracy of GPS and DVL decreases due to a bad weather and due to associated noise of DVL measurement, which effects on a whole AUSV navigation system. To resolve this problem, the Adaptive Data Sharing Factor (ADSF) using Combined Filter (CF) is used as integrated method. According to proposed ADSF CF integrated method, the reference source (GPS or DVL) which has the beast accurate system will be used to correct the navigation errors. At same time the least accuracy system will detect and avoid.The efficiency of navigation, guidance and control system of AUSV based on proposed GPS/DVL/MEMS-INS ADFS CF integrated method is tested on surface reference trajectory with four obstacles located on the path of AUSV. During GPS outages, the AUSV was able to reach the target location to implement several tasks such as monitoring, exploring, tracking and returned back to base station depending on the stored path in its memory with high efficiency. Based on estimated results, the proposed navigation system could provide continuous navigation solution and reduce the navigation error to about 85.43% compared to GPS/DVL/MEMS CKF method and to about 68.14% compared to GPS/DVL/MEMS Constant DSF Combined Filter.

Vibration monitoring of a small concrete bridge using wavelet transforms on GPS data

Small concrete bridges are important roadway infrastructures in Brazil, where they serve as crucial links in the transportation network. Monitoring and maintenance of these bridges are essential for prolonging their lifetimes. The original work presented in this paper is based on the use of global positioning system (GPS) as a trustworthy tool for characterizing the dynamic behavior of small concrete bridges under normal operating conditions. The proposed inspection procedure includes the analysis of only two GPS signals with a method named the phase residual method, which is based on phase interferometry. Analytical methods, namely, the fast Fourier transform, continuous wavelet transform, and Chebyshev, are effective signal processing techniques for visualizing variations in system frequencies. The object of the study was the bridge over the Jaguari River, which is located in Brazil. The description of the dynamic behavior of the middle span of the concrete bridge was obtained by analyzing data obtained at a sampling frequency of 100 Hz collected for only 1 min. The results provided a clear picture of the energy distribution in the time–frequency plane during forced vibration: (1) the maximum deformation of the main span was pronounced at 3.5–8 mm; (2) the vibration frequency ranged between 4 and 8 Hz; and (3) the values obtained coincided with the values indicated by regular the monitoring sensors, such as accelerometers and transducers of displacements. The advantages of the use of GPS and the contribution to the geotechnical short- or long-term monitoring of road bridges are the use of a reference outside the structure, use under inclement weather conditions, and lack of disruption of traffic.

Development of a map-matching algorithm for dynamic-sampling-rate GPS signals to determine vehicle routes on a MATSim network

The rapid development and proliferation of global positioning system (GPS)-enabled systems and devices have led to a significant increase in the availability of transport data, more specifically GPS trajectories, that can be used in researching vehicle activities. In order to save data storage- and handling costs many vehicle tracking systems only store low-frequency trajectories for vehicles. A number of existing methods used to map GPS trajectories to a digital road network were analysed and such an algorithm was implemented in Multi-Agent Transport Simulation (MATSim), an open source collaborative simulation package for Java. The map-matching algorithm was tested on a simple grid network and a real and extensive network of the City of Cape Town, South Africa. Experimentation showed the network size has the biggest influence on algorithm execution time and that a network must be reduced to include only the links that the vehicle most likely traversed. The algorithm is not suited for trajectories with sampling rates less than 5 seconds as it can result in unrealistic paths chosen, but it manages to obtain accuracies of around 80% up until sampling sizes of around 50 seconds whereafter the accuracy decreases. Further experimentation also revealed optimal algorithm parameters for matching trajectories on the Cape Town network. The use case for the implementation was to infer basic vehicle travel information, such as route travelled and speed of travel, for municipal waste collection vehicles in the City of Cape Town, South Africa.

Correction of Phasor Measurements Independent of Transmission Line Parameters

Phase angle measurements from phasor measurement unit (PMU) data are sensitive to several issues, such as timing error, global positioning system (GPS) signal spoofing attack, transducer sensitivities and latency, and phasor computation algorithms. In some applications, highly accurate phase angle values are essential. In this paper, two methods are developed for correcting the phase angle difference (PAD) between the PMUs at both ends of a line. The method is based on the assumption that the line parameters are unchanged during the measurement period. The proposed scheme does not require knowing the parameters of the transmission line connecting the two PMUs, and uses multi-point measurements to estimate possible PAD bias. An additional benefit of the methods is that the parameters of the transmission line can also be calculated.

Smoothing and Interpolating Noisy GPS Data with Smoothing Splines

AbstractA comprehensive method is provided for smoothing noisy, irregularly sampled data with non-Gaussian noise using smoothing splines. We demonstrate how the spline order and tension parameter can be chosen a priori from physical reasoning. We also show how to allow for non-Gaussian noise and outliers that are typical in global positioning system (GPS) signals. We demonstrate the effectiveness of our methods on GPS trajectory data obtained from oceanographic floating instruments known as drifters.

Measurements and model for the satellite‐to‐aircraft channel in L‐band

Wireless radio transmission from a satellite-based emitter to a receiver located on an aircraft is of interest for many applications such as internet access for passengers, air traffic management or positioning by global navigation satellite systems especially when a worldwide service shall be granted. In particular, the last two mentioned applications are related to the safety of life requiring realistic and accurate channel models for software-based system testing. State-of-the-art channel models for the satellite-to-aircraft case lack of accuracy in terms of modelling all propagation impairments. In this contribution, the authors describe airborne propagation experiments using Global Positioning System signals for channel sounding, the data evaluation and the derived channel model.

Multi-View and Multi-Scale Localization for Intelligent Vehicles in Underground Parking Lots

Because of limited access to global positioning system (GPS) signals, accurate and reliable localization for intelligent vehicles in underground parking lots is still an open problem. This paper proposes a multi-view and multi-scale localization method aiming at solving this problem. The proposed method is divided into an offline mapping stage and an online localization stage. In the mapping stage, the offline map is generated by fusing 3-D information, WiFi features, visual features, and trajectory from visual odometry (VO). In the localization stage, WiFi fingerprint matching is exploited for coarse localization. Based on the result of coarse localization, multi-view localization is exploited for image-level localization. Finally, metric localization is exploited to refine the localization results. By applying this multi-scale strategy, it is possible to fuse WiFi localization and visual localization and reduce the image matching and error rate to a great extent. Because of exploiting more information, multi-view localization is more robust and accurate than single-view localization. The method is tested in a 2,000 m2 underground parking lot. The result demonstrates that this method can achieve sub-meter localization on average. The proposed localization method can be a supplement to the existing intelligent vehicle localization techniques.