Global Positioning System Signals Research Articles

Global Positioning System Signal Verification through Correlation Function Distortion and Received Power Tracking

Purpose: This study proposes a significantly improved method for detecting, classifying, and isolating Global Navigation Satellite System (GNSS) signals using the relationship between the measured power and the distortion of the correlation function to achieve the signal verification required for Global Positioning System (GPS) civil applications such as safe civil aircraft navigation. Methodology: The suggested approach uses power and distortion measurements in the received signal to identify it as jammed, multipath, spoofing, or no-interference. By adding an isolator scaling factor to the detector, the signal patterns will be induced with a unique temporary factor that will set it apart from the rest and make it possible to easily position each signal in its own zone. The detector divides the four signal types into distinct zones for verification. Findings: The sufficient signal data is analyzed and the extensive simulation conducted indicates that about 94% detection accuracy is achieved which is relatively high. Unique contribution to theory, practice and policy: This study is implemented through the development of relevant detection software tools with a user-friendly interface for GNSS signal detection, validation and analysis.

Intelligent jamming detection‐based robust principal components analysis in communication system for security and defense

AbstractThreats to physical layer security from jamming attacks make wireless cognitive communication systems vulnerable. Global Positioning System signal is vulnerable to these attacks. Over the last decade, several types of jamming detection techniques have been proposed, antijamming‐based classical and machine learning (ML) techniques. Most of these techniques are inefficient in detecting jammers. Thus, there is a great need for efficient and quickest jamming detection technique‐based classifier using receiver operating characteristic (ROC) curve for different threshold values with high accuracy. In this work, we compare the efficiency of the proposed orthogonal distance (OD) and score distance (SD) method‐based robust principal component analysis (PCA) in ML classification in detecting jamming signals. Two hypotheses are proposed to distinguish between the presence and absence attack problem. Using this compressed data matrix obtained from modulated wideband converter (MWC) structure via centralized cooperation directly as input of the proposed classifier combined‐based ROC curve for real‐time detection scenarios. The performance of this proposed algorithm‐based robust PCA was evaluated and compared using the detection anomaly rate (DAR%), and false alarm rate (FAR%), area under curve (AUC), and accuracy. The performance of obtained results is good.

Enhancing Soil Moisture Active–Passive Estimates with Soil Moisture Active–Passive Reflectometer Data Using Graph Signal Processing

The Soil Moisture Active–Passive (SMAP) mission has greatly contributed to the use of remote sensing technologies for monitoring the Earth’s land surface and estimating geophysical parameters that influence the climate system. Since the SMAP mission switched its radar receiver to allow the reception of Global Positioning System (GPS) signals, Global Navigation Satellite System Reflectometry (GNSS-R) configuration has been enabled, providing full polarimetric forward scattering measurements of the Earth’s surface, also known as SMAP Reflectometry or SMAP-R. Polarimetric GNSS-R is beneficial for sensing land surface properties, especially for more accurate estimations of soil moisture (SM) in densely vegetated areas. In this study, we explore the opportunity to enhance SMAP mission soil moisture estimates using reflected GNSS signals. We achieve this by interpolating the sparse reflectivity data with terrain information to disaggregate radiometer brightness temperatures. Our main objective is to present a novel algorithm based on Graph Signal Processing (GSP) that uses reflectometry data to enhance SMAP radiometer observations and ultimately improve SM retrievals. By implementing methods from the GSP field, we formulate the reflectivity interpolation problem as a signal reconstruction on a graph, where the weights of the edges between the nodes are chosen as a function of geophysical information. Subsequently, using the retrieved reflectivity maps, we increase the resolution of the brightness temperature data, leading to an improvement in the SM estimates. Initial findings indicate that our GSP method presents a promising alternative for analyzing sparse remote sensing observations, leveraging Earth’s surface geophysical information. This approach results in a notable improvement, with a reduced Root Mean Square Error (RMSE) of 11.8% compared to SMAP data and a reduction in unbiased RMSE (uRMSE) by 14.7% over vegetated areas.

DNN-Based Relative Localization Technique for Real-Time Positioning of Moving Unmanned Swarm Robots

The unmanned swarm robot system, which enables multiple robots to collaborate and perform a variety of tasks, is extensively researched for its potential applications. Accurate determination of the location of swarm robots during operation is of paramount importance, and various positioning algorithms are employed to achieve this. Specifically, in situations where global positioning system (GPS) signals are unavailable, fixed anchor nodes with known location information can be utilized for localization. However, in scenarios where fixed anchor nodes are not present, and the robots operate in a swarm, applying this technology poses challenges, necessitating a localization technique that relies solely on distance information between the robots. This paper proposes a deep neural network (DNN) technique that utilizes only the distance information between moving nodes to predict the real-time relative coordinates of each node. It is assumed that the distances between nodes are updated sequentially and periodically according to a predetermined measurement cycle. A grid-based localization technique is used as the existing method for performance comparison. Computer simulation results demonstrate that the proposed DNN-based relative Localization technique exhibits superior localization performance compared to the existing Grid-based method. Furthermore, the proposed technique shows similar performance regardless of the distance measurement cycle, indicating that it is not significantly affected by the cycle. Therefore, applying the proposed relative Localization algorithm to swarm robots could enable real-time and accurate relative positioning, facilitating precise location tracking of the swarm.

Simplified Indoor Localization Using Bluetooth Beacons and Received Signal Strength Fingerprinting with Smartwatch.

Variations in Global Positioning Systems (GPSs) have been used for tracking users' locations. However, when location tracking is needed for an indoor space, such as a house or building, then an alternative means of precise position tracking may be required because GPS signals can be severely attenuated or completely blocked. In our approach to indoor positioning, we developed an indoor localization system that minimizes the amount of effort and cost needed by the end user to put the system to use. This indoor localization system detects the user's room-level location within a house or indoor space in which the system has been installed. We combine the use of Bluetooth Low Energy beacons and a smartwatch Bluetooth scanner to determine which room the user is located in. Our system has been developed specifically to create a low-complexity localization system using the Nearest Neighbor algorithm and a moving average filter to improve results. We evaluated our system across a household under two different operating conditions: first, using three rooms in the house, and then using five rooms. The system was able to achieve an overall accuracy of 85.9% when testing in three rooms and 92.106% across five rooms. Accuracy also varied by region, with most of the regions performing above 96% accuracy, and most false-positive incidents occurring within transitory areas between regions. By reducing the amount of processing used by our approach, the end-user is able to use other applications and services on the smartwatch concurrently.

Enhanced Deep Neural Networks for Traffic Speed Forecasting Regarding Sustainable Traffic Management Using Probe Data from Registered Transport Vehicles on Multilane Roads

Early forecasting of vehicle flow speeds is crucial for sustainable traffic development and establishing Traffic Speed Forecasting (TSF) systems for each country. While online mapping services offer significant benefits, dependence on them hampers the development of domestic alternative platforms, impeding sustainable traffic management and posing security risks. There is an urgent need for research to explore sustainable solutions, such as leveraging Global Positioning System (GPS) probe data, to support transportation management in urban areas effectively. Despite their vast potential, GPS probe data often present challenges, particularly in urban areas, including interference signals and missing data. This paper addresses these challenges by proposing a process for handling anomalous and missing GPS signals from probe vehicles on parallel multilane roads in Vietnam. Additionally, the paper investigates the effectiveness of techniques such as Particle Swarm Optimization Long Short-Term Memory (PSO-LSTM) and Genetic Algorithm Long Short-Term Memory (GA-LSTM) in enhancing LSTM networks for TSF using GPS data. Through empirical analysis, this paper demonstrates the efficacy of PSO-LSTM and GA-LSTM compared to existing methods and the state-of-the-art LSTM approach. Performance metrics such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Median Absolute Error (MDAE) validate the proposed models, providing insights into their forecasting accuracy. The paper also offers a comprehensive process for handling GPS outlier data and applying GA and PSO algorithms to enhance LSTM network quality in TSF, enabling researchers to streamline calculations and improve supposed model efficiency in similar contexts.

A framework for preventing unauthorized drone intrusions through radar detection and GPS spoofing

The increasing use of Global Positioning System (GPS)-based autonomous drones in various civilian and military applications has raised concerns about malicious or unintentionally harmful activities that can be carried through them. It is necessary to detect these intruding drones within protected areas and prevent their unauthorized access by denying them entry. We propose a framework that combines the detection of intruding drones using an L-band radar and then counters by transmitting fake GPS coordinates toward the drones, effectively redirecting them. This article explains the setup required to add to an existing monostatic radar that provides two-dimensional information, i.e., range and azimuth information, to enable the proposed setup to get the elevation angle of the drone. We propose a linear array design using digital receive-only beamforming techniques in the elevation domain to compute the elevation angle in addition to the range, velocity, and azimuth information being provided by the monostatic radar to get complete information about the intruding drone. The simulation of drone detection is followed by an examination of the impact of transmitting fabricated GPS coordinates to the drone. Experimental verification has been conducted to validate both the digital beamforming algorithm and the spoofing technique. This approach blocks the reception of actual GPS signals in the drone and replaces the drone's GPS coordinates with alternative, desired coordinates. The proposed framework can be used to prevent unauthorized drone intrusions in the protected area.

Retrieval of sea ice thickness from FY-3E data using Random Forest method

In this study, we employ a Random Forest approach to estimate sea ice thickness (SIT) using Fengyun-3E (FY-3E) and Soil Moisture Ocean Salinity (SMOS) data. This method relies on four input parameters: incidence angle (θ), reflectivity (Γ), sea ice salinity (S), and sea ice temperature (T). In addition, FY-3E can receive both Global Positioning System (GPS) and Beidou Navigation Satellite System (BDS) reflected signals. Evaluation for the Arctic region based on data spanning from October 2022 to April 2023 reveals that the proposed models trained on GPS and BDS signals from FY-3E achieve high consistency and low error. Take GPS signals as an example, coefficients of determination are 0.97 and 0.91 and mean absolute errors are 0.019 m and 0.032 m for the training and test sets, respectively. In general, SIT inversion based on GPS signals slightly exhibits a higher accuracy than that based on BDS signals, but both approaches display high performances. The areas with the highest accuracy of SIT estimation based on GPS and BDS signals are the Shelikhov Bay and the Okhotsk Sea, followed by the Bering Sea and the Bering Strait. We conclude that machine learning and data fusion are effective for SIT estimation.

Enhancing the integration of the GPS/INS during GPS outage using LWT-IncRGRU

A common technique for navigation and positioning applications is the Global Positioning System (GPS)/Inertial Navigation System (INS) integration, which combines the strengths of GPS and INS to offer accurate and reliable information. However, the performance of the GPS/INS integration deteriorates during a GPS outage, which happens when natural or artificial factors block the GPS signal. The novelty of this paper is improving GPS/INS integration performance during GPS outages using Incremental Regularized Gated Recurrent Unit (IncRGRU) learning and Lifting Wavelet Transform (LWT). Incremental learning is a learning paradigm that can update the model parameters online from streaming data without forgetting the previous ones. Moreover, regularization is a technique that improves the network's generalization and avoids overfitting by adding some constraints or penalties to the model. In this way, the GPS signal is modeled by IncRGRU learning, and the Kalman filter corrects the INS output. Furthermore, LWT removes the noise from the sensors' signals. This algorithm has lower complexity and can work in real-time compared to conventional wavelet transforms. The performance of GPS/INS integration during GPS outages and the accuracy and robustness of GPS/INS integration is significantly improved by using LWT-IncRGRU on real-world datasets. The positioning errors are reduced by an average of 76% during GPS outages, and an average of 69% improves the GPS/INS integration compared to existing methods.

Multi-constellation GNSS interferometric reflectometry for tidal analysis: mitigations for K1 and K2 biases due to GPS geometrical errors

It has been observed that when using sea levels derived from GPS (Global Positioning System) signal-to-noise ratio (SNR) data to perform tidal analysis, the luni-solar semidiurnal (K2) and the luni-solar diurnal (K1) constituents are biased due to geometrical errors in the reflection data, which result from their periods coinciding with the GPS orbital period and revisit period. In this work, we use 18 months of GNSS SNR data from multiple frequencies and multiple constellations at three sites to further investigate the biases and how to mitigate them. We first estimate sea levels using SNR data from the GPS, GLONASS, and Galileo signals, both individually and by combination. Secondly, we conduct tidal harmonic analysis using these sea-level estimates. By comparing the eight major tidal constituents estimated from SNR data with those estimated from the co-located tide-gauge records, we find that the biases in the K1 and K2 amplitudes from GPS S1C, S2X and S5X SNR data can reach 5 cm, and they can be mitigated by supplementing GLONASS- and Galileo-based sea-level estimates. With a proper combination of sea-level estimates from GPS, GLONASS, and Galileo, SNR-based tidal constituents can reach agreement at the millimeter level with those from tide gauges.

Proposal for Generation of a Pseudo GPS Signal Used in Indoor Mode for Navigation Testing of Small Autonomous Flying Robot

In addition to the inertial navigation sensors embedded in an autonomous flying robot, Global Navigation Satellite Systems are most often used, in particular, the GPS (Global Positioning System). This offer information with an accuracy that depends on the arrangement of a constellation of satellites. It is very often that a measurement with errors of several meters is delivered by this system. With the need for agile decisions of the autonomous flying robot during its mission, especially in the case of rescuing people during disasters, it is not allowed for uncertainty to reign over the decision of the flying robot. In order to improve the estimators implemented experimentally, a secure laboratory test environment is more preferable. Here, we propose a way that helps researchers to conduct indoor experiments with a pseudo GPS that offers circumstances similar to outdoor navigation. The results that will be discussed are obtained with the Vicon motion tracker laboratory at Aalborg University, Denmark.

A NOVEL GPS FAULT DETECTION AND EXCLUSION ALGORITHM AIDED BY IMU AND VO DATA FOR VEHICLE INTEGRATED NAVIGATION IN URBAN ENVIRONMENTS

Abstract. High accuracy positioning is crucial for various applications that require accurate and reliable positioning data, such as autonomous vehicles (AVs), agriculture, and intelligent transportation. Global Positioning System (GPS) is widely used in integration with Inertial Measurement Unit (IMU) and Visual Odometry (VO) to implement an accurate and robust navigation system. However, the performance of the integrated system can be severely degraded in urban environments due to non-line-of-sight (NLOS) reception and multipath interference. To overcome these challenges, a novel Fault Detection and Exclusion (FDE) algorithm aided with IMU and VO measurements is proposed to identify and isolate the contaminated satellite signals. The algorithm utilizes instantaneous measurements from IMU and VO to predict the current vehicle position, enabling the estimation of pseudorange errors in GPS measurements. A FDE algorithm based on Hierarchical clustering is then developed to identify GPS signals with significant errors based on the predicted pseudorange error. An experimental field test was conducted using a land vehicle to evaluate the effectiveness of the proposed algorithm. The results show that the GPS/IMU/VO integrated navigation system with the proposed FDE algorithm has significantly improved the positioning accuracy and reliability compared to the traditional system. The proposed algorithm achieves a positioning accuracy with a 3D Root Mean Square Error (RMSE) of 11.18m in urban environment, making an improvement of 68.2% over the traditional GPS/IMU/VO integrated navigation system.

Detection of inland water bodies under dense biomass by CYGNSS

The reliable detection of inland water bodies under dense biomass is not possible with traditional spaceborne remote sensing systems. In this investigation, we demonstrate the capability of the National Aeronautics and Space Administration (NASA) Cyclone Global Navigation Satellite System (CYGNSS) mission to detect and quantify the extent of small water bodies under dense biomass up to 400 ton/ha in complex heterogenous scenes such as tropical rainforests. A novel inland water body product based on L1 CYGNSS data is developed using a combination of peak surface reflectivity Γp and its spread with respect to time delay and Doppler shift, as characterized by the power ratio Pratio. Only L1 CYGNSS mission data has the potential to study Earth's surface water dynamics because raw Intermediate Frequency (IF) data availability is limited. Several coherence indices are used to verify that the L-band Global Positioning System (GPS) signals are being coherently reflected by surface water bodies and attenuated by the intervening vegetation canopy. These indices include full and computationally fast versions of the entropy associated with Delay Doppler Maps (DDMs), Efull and Efast, as well as the time derivative of the phase φpeak of the reflected GPS signal. The capability to accurately resolve surface water extent under dense vegetation has been shown to improve the estimation by current state-of-art surface water extension datasets used by global methane CH4 emission models in wetlands.

Measurements for GPS meteorological applications

The Global Positioning System (GPS) is at present used for the location and the synchronization of the ground, marine and air meteorological measurements as well as for wind measurements. In the first part of the paper, are presented results of measurements of the positioning of a point known with the Algerian National Center of Space Techniques, CNTS [1] of Arzew and it is included of the national geodesic network and the world network Tyrgeonet : 305 site. The purpose is the study of the total electron content (TEC) using measurements of GPS signal code. For that, two methods are used: Dual-frequency and Klobuchar model method. To eliminate possible skews from the satellite and the receiver. For a GPS receiver alone, one can use the model of Klobuchar to determine the vertical time on the code transmitted by the signal L1. A comparison between the two models will be made to choose that which characterizes best the ionospheric effect. In the second part of this article, we describe the principle of a new technique which is the radio occultation allowing to sound the troposphere by using the GPS signal. Finally, the study of the ionosphere has for application the spatial telecommunications, whereas the study of the troposphere has for application the meteorological phenomena.

Quality Assessment of the Atmospheric Radio Occultation Profiles from FY-3E/GNOS-II BDS and GPS Measurements

The Fengyun-3E (FY-3E) satellite carrying the advanced Global Navigation Satellite System (GNSS) Radio Occultation Sounder-II (GNOS-II) is already in operation for radio occultation (RO) observation, with the BeiDou Navigation Satellite System (BDS-2 and BDS-3) and Global Positioning System (GPS) signals tracking capability. FY-3E BDS and GPS RO signals tracking capability were first evaluated by comparing their penetration depths, and then the quality of the refractivity, temperature, and specific humidity profiles was analyzed with the fifth-generation European Centre for Medium-Range Weather Forecasts reanalysis (ERA5) data. Results show the mean penetration depth of BDS occultations was 1.65 km compared to 1.09 km of GPS occultations. Between 5 and 25 km, the mean refractivity bias of the BDS (GPS) was −0.14% (0.01%) with the mean standard deviation (SD) being 1.11% (1.52%); the mean temperature biases of both were within ±0.1 K, and the mean SD of BDS was 1.1 K compared to 1.2 K for the GPS; BDS/GPS specific humidity bias was within ±0.3 g/kg with corresponding SD being less than 1.3 g/kg. Seasonal deviations of specific humidities were largest in summer and smallest in winter. Latitudinal deviations over the tropics were generally higher than in other areas. Enriched quantity and high accuracy and precision after careful calibration will promote the FY-3E RO profiles as a reliable data source for the RO community.

An improved indoor positioning based on crowd-sensing data fusion and particle filter

Due to the lack of global positioning system (GPS) signals in some enclosed areas, indoor localisation has recently gained significant importance for academics. However, indoor localisation has a number of challenges and defects, including accuracy, cost, coverage, and ease of use. This paper explores the integration between the inertial measurement unit (IMU) and Wi-Fi-based received signal strength indicator (RSSI) measurements, demonstrating their combined potential for robust indoor localisation. IMUs excel at capturing precise short-term motion dynamics, offering insights into an object’s acceleration and orientation. Conversely, RSSI measurements serve as valuable indicators for relative positioning within indoor environments. By fusing data from these sources, our approach compensates for the inherent weaknesses of each sensor type. To achieve accurate indoor positioning, we employ techniques such as sensor fusion, Wi-Fi fingerprinting, and dead reckoning. Wi-Fi fingerprinting allows us to create a database that maps RSSI measurements to specific locations, while dead reckoning helps mitigate drift and inaccuracies. By combining these methods, we estimate a device’s position with increased precision. Through experimental evaluation, we assess the performance and efficiency of our integrated approach, comparing the estimated path or new location with a predefined reference path. The findings emphasise a significant improvement in accuracy, with the integration of crowd-sensing, particle filtering, and magnetic fingerprinting techniques resulting in a notable increase from 80.49% to 96.32% accuracy.

GPS Receiver Simplification for Low cost Applications and Multipath Mitigation Analysis on SDR based Re configurable Software Receiver

Many modern position-based applications rely heavily on the Global Navigation Satellite System (GNSS). Most applications require precise position data obtained through sophisticated hardware with a high computational capacity in the receiver. Some cost-effective applications may not require precise position data and require less complex signal processing. The use of efficient hardware and signal processing techniques to reduce the overall cost of a GNSS receiver is an active research topic. This paper considers Global Positioning System (GPS) constellation and proposes two factors to reduce the receiver complexity: sampling frequency and the number of tracking channels. A Keysight GNSS signal generator to record GPS signals, a Software Defined Radio board and a software-based GPS receiver are used in the experimentation. The sampling frequencies are 40, 20, 10 and 5 MHz considered, and tracking channels are reduced from 12 to 6 and then 4. The increase of error in the receiver position with 6 and 4 satellites is considerably small, but the number of tracking channels and signal processing requirements are reduced considerably. The GPS signals are affected by many errors; one of the significant sources of error is multipath propagation. Three distinct GPS multipath scenarios are generated for four satellite signal combinations with the GNSS simulator for the receiver performance analysis. Three multipath mitigation techniques, namely Early Minus Late (EML), Narrow correlator (NC) and strobe correlator (SC) methods, are considered because of their simple structure and fewer signal processing requirements. The error reductions of three multipath scenarios are compared, and the SC method performs better in all three multipath scenarios.

IBeacon-based indoor positioning system: from theory to practical deployment

<span lang="EN-US">Developing an indoor positioning system became essential when global positioning system signals could not work well in indoor environments. Mobile positioning can be accomplished via many radio frequency technology such as Bluetooth low energy (BLE), wireless fidelity (Wi-Fi), ultra-wideband (UWB), and so on. With the pressing need for indoor positioning systems, we, in this work, present a deployment scheme for smartphone using Bluetooth iBeacons. Three main parts, hardware deployment, software deployment, and positioning accuracy assessment, are discussed carefully to find the optimal solution for a complete indoor positioning system. Our application and experimental results show that proposed solution is feasible and indoor positioning system is completely attainable.</span>

A Visual Odometry Pipeline for Real-Time UAS Geopositioning

The state-of-the-art geopositioning is the Global Navigation Satellite System (GNSS), which operates based on the satellite constellation providing positioning, navigation, and timing services. While the Global Positioning System (GPS) is widely used to position an Unmanned Aerial System (UAS), it is not always available and can be jammed, introducing operational liabilities. When the GPS signal is degraded or denied, the UAS navigation solution cannot rely on incorrect positions GPS provides, resulting in potential loss of control. This paper presents a real-time pipeline for geopositioning functionality using a down-facing monocular camera. The proposed approach is deployable using only a few initialization parameters, the most important of which is the map of the area covered by the UAS flight plan. Our pipeline consists of an offline geospatial quad-tree generation for fast information retrieval, a choice from a selection of landmark detection and matching schemes, and an attitude control mechanism that improves reference to acquired image matching. To evaluate our method, we collected several image sequences using various flight patterns with seasonal changes. The experiments demonstrate high accuracy and robustness to seasonal changes.

A Jamming Aware Artificial Potential Field Method to Counter GPS Jamming for Unmanned Surface Ship Path Planning

Global positioning system (GPS) plays an important role in the path planning of unmanned surface ships. As we know, the civil GPS signal is not encrypted, and its structure is open. Meanwhile, the power strength of GPS signals arriving at receivers is weak. All of these make GPS vulnerable to jamming. GPS jamming attacks adversely affect the accuracy of the received GPS signals, thus affecting the path planning results of unmanned surface ships and even causing collisions. This article presents a jamming aware artificial potential field (JA-APF) method. The JA-APF method first determines whether GPS jamming exists in unmanned surface ships' surrounding environment by detecting the receivers' received signal strength and calculating the packet error rate. Then, the JA-APF method calculates the position range of the jamming source. Finally, the JA-APF method replans the path according to the resultant force of the attraction around the goal, the repulsion around obstacles, and the repulsion around multiple GPS jammers. Through the simulation, we determine the value of the repulsive potential factor of the jammer to optimize the path planning results of the JA-APF method and analyze the influence of the jamming power on the path planning results. The simulation results show that the JA-APF method can effectively solve the impact of GPS jamming on the results in the path planning process and recover as soon as possible.