scholarly journals Assessment of the position accuracy of a single-frequency GPS receiver designed for electromagnetic induction surveys

2018 ◽  
Vol 20 (1) ◽  
pp. 19-39 ◽  
Author(s):  
Sebastian Rudolph ◽  
Ben Paul Marchant ◽  
Lutz Weihermüller ◽  
Harry Vereecken
Keyword(s):  
Electromagnetic Induction ◽  
Single Frequency ◽  
Gps Receiver ◽  
Position Accuracy

scholarly journals High-precision coseismic displacement estimation with a single-frequency GPS receiver

2015 ◽  
Vol 202 (1) ◽  
pp. 612-623 ◽  
Author(s):  
Bofeng Guo ◽  
Xiaohong Zhang ◽  
Xiaodong Ren ◽  
Xingxing Li
Keyword(s):  
High Precision ◽  
Single Frequency ◽  
Gps Receiver ◽  
Coseismic Displacement ◽  
Displacement Estimation

Detection of Spoofing Attack using Machine Learning based on Multi-Layer Neural Network in Single-Frequency GPS Receivers

2017 ◽  
Vol 71 (1) ◽  
pp. 169-188 ◽  
Author(s):  
E. Shafiee ◽  
M. R. Mosavi ◽  
M. Moazedi
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Late Phase ◽  
Machine Learning Algorithms ◽  
Computational Method ◽  
Single Frequency ◽  
Detection Accuracy ◽  
Gps Receiver ◽  
Navigation Data ◽  
Spoofing Detection

The importance of the Global Positioning System (GPS) and related electronic systems continues to increase in a range of environmental, engineering and navigation applications. However, civilian GPS signals are vulnerable to Radio Frequency (RF) interference. Spoofing is an intentional intervention that aims to force a GPS receiver to acquire and track invalid navigation data. Analysis of spoofing and authentic signal patterns represents the differences as phase, energy and imaginary components of the signal. In this paper, early-late phase, delta, and signal level as the three main features are extracted from the correlation output of the tracking loop. Using these features, spoofing detection can be performed by exploiting conventional machine learning algorithms such as K-Nearest Neighbourhood (KNN) and naive Bayesian classifier. A Neural Network (NN) as a learning machine is a modern computational method for collecting the required knowledge and predicting the output values in complicated systems. This paper presents a new approach for GPS spoofing detection based on multi-layer NN whose inputs are indices of features. Simulation results on a software GPS receiver showed adequate detection accuracy was obtained from NN with a short detection time.

scholarly journals GPS Receiver Position Augmentation Using Correntropy Kalman Filter in Low Latitude Terrain

2022 ◽  
Author(s):  
Sirish Kumar Pagoti ◽  
Bala Sai Srilatha Indira Dutt Vemuri ◽  
Ganesh Laveti
Keyword(s):  
Kalman Filter ◽  
Indian Subcontinent ◽  
Estimation Algorithm ◽  
Position Estimation ◽  
Gps Receiver ◽  
Dual Frequency ◽  
Low Latitude ◽  
Position Accuracy ◽  
Aircraft Landing ◽  
Gps Accuracy

If any Global Positioning System (GPS) receiver is operated in low latitude regions or urban canyons, the visibility further reduces. These system constraints lead to many challenges in providing precise GPS position accuracy over the Indian subcontinent. As a result, the standalone GPS accuracy does not meet the aircraft landing requirements, such as Category I (CAT-I) Precision Approaches. However, the required accuracy can be achieved by augmenting the GPS. Among all these issues, the predominant factors that significantly influence the receiver position accuracy are selecting a user/receiver position estimation algorithm. In this article, a novel method is proposed based on correntropy and designated as Correntropy Kalman Filter (CKF) for precise GPS applications and GPS Aided Geosynchronous equatorial orbit Augmented Navigation (GAGAN) based aircraft landings over the low latitude Indian subcontinent. The real-world GPS data collected from a dual-frequency GPS receiver located in the southern region of the Indian subcontinent (IISc), Bangalore with Lat/Long: 13.021°N/ 77.5°E) is used for the performance evaluation of the proposed algorithm. Results prove that the proposed CKF algorithm exhibits significant improvement (up to 34%) in position estimation compared to the traditional Kalman Filter.

Extracting Ionosphere Scintillations Index Based on Single Frequency GPS Software Receiver

2012 ◽  
Vol 190-191 ◽  
pp. 1136-1143
Author(s):  
Zhi Huang ◽  
Hong Yuan ◽  
Qi Yao Zuo
Keyword(s):  
High Speed ◽  
Single Frequency ◽  
Scintillation Index ◽  
Ionospheric Scintillation ◽  
Signal Acquisition ◽  
Gps Receiver ◽  
Signal Loss ◽  
Software Receiver ◽  
Ionospheric Plasma Density ◽  
Gps Signal Acquisition

Scintillations are caused by ionospheric plasma-density irregularities and can lead to signal power fading, loss of lock of the carrier tracking loop in the GPS receiver. The traditional method of monitoring and mitigating scintillation is to transform commercial GPS receiver with modified hardware and embedded software. To better facilitate advance development GPS receiver under different condition, GPS software scintillation receiver is designed in this paper. The hardware scheme of high-speed GPS signal acquisition system is first discussed and implemented with FPGA and DSP architecture. Then, we describe receiver software processing algorithm, particularly the portion involving the scintillation signal acquisition and tracking, ionospheric scintillation index extracting and scintillation monitoring. The performance of software receiver is demonstrated under scintillation conditions. Relevant results show that software-receiver based approach can avoid weak signal loss and extract effectively ionospheric scintillation parameter compared with the traditional extracting method. Software receiver is suitable and reliable for the ionospheric scintillations monitoring, and can provide theoretical foundations and experimental preparations for future scintillation studies implemented with Chinese indigenous BeiDou-Ⅱ navigation and poisoning system.

GPS RECEIVERS TIMING DATA PROCESSING USING NEURAL NETWORKS: OPTIMAL ESTIMATION AND ERRORS MODELING

2007 ◽  
Vol 17 (05) ◽  
pp. 383-393 ◽  
Author(s):  
M. R. MOSAVI
Keyword(s):  
Neural Networks ◽  
Outdoor Recreation ◽  
Optimal Estimation ◽  
Single Frequency ◽  
Gps Receiver ◽  
Commercial Aviation ◽  
Gps Receivers ◽  
Before And After ◽  
Timing Data

The Global Positioning System (GPS) is a network of satellites, whose original purpose was to provide accurate navigation, guidance, and time transfer to military users. The past decade has also seen rapid concurrent growth in civilian GPS applications, including farming, mining, surveying, marine, and outdoor recreation. One of the most significant of these civilian applications is commercial aviation. A stand-alone civilian user enjoys an accuracy of 100 meters and 300 nanoseconds, 25 meters and 200 nanoseconds, before and after Selective Availability (SA) was turned off. In some applications, high accuracy is required. In this paper, five Neural Networks (NNs) are proposed for acceptable noise reduction of GPS receivers timing data. The paper uses from an actual data collection for evaluating the performance of the methods. An experimental test setup is designed and implemented for this purpose. The obtained experimental results from a Coarse Acquisition (C/A)-code single-frequency GPS receiver strongly support the potential of methods to give high accurate timing. Quality of the obtained results is very good, so that GPS timing RMS error reduce to less than 120 and 40 nanoseconds, with and without SA.

scholarly journals Mapping of Agricultural Subsurface Drainage Systems Using a Frequency-Domain Ground Penetrating Radar and Evaluating Its Performance Using a Single-Frequency Multi-Receiver Electromagnetic Induction Instrument

2020 ◽  
Author(s):  
Triven Koganti ◽  
Ellen Van De Vijver ◽  
Barry J. Allred ◽  
Mogens H. Greve ◽  
Jørgen Ringgaard ◽  
...  
Keyword(s):  
Electrical Conductivity ◽  
Ground Penetrating Radar ◽  
Electromagnetic Induction ◽  
Subsurface Drainage ◽  
Single Frequency ◽  
Drainage Systems ◽  
Local Soil ◽  
3D Gpr ◽  
Ground Penetrating ◽  
Emi Sensor

Subsurface drainage systems remove excess water from the soil profile thereby improving crop yields in poorly drained farmland. Knowledge of the position of the buried drain lines is important: 1) to improve understanding of leaching and offsite release of nutrients and pesticides, and 2) for the installation of a new set of drain lines between the old ones for enhanced soil water removal efficiency. Traditional methods of drainage mapping involve the use of tile probes and trenching equipment. While these can be effective, they are also time-consuming, labor-intensive, and invasive, thereby entailing an inherent risk of damaging the drainpipes. Non-invasive geophysical soil sensors provide a potential alternative solution. Previous research has focused on the use of time-domain ground penetrating radar (GPR), with variable success depending on local soil and hydrological conditions and the central frequency of the specific equipment employed. The objectives of this study were 1) to test the use of a stepped-frequency continuous wave (SFCW) 3D-GPR (GeoScope Mk IV 3D-Radar with DXG1820 antenna array) for subsurface drainage mapping, and 2) to evaluate the performance of a 3D-GPR with the use of a single-frequency multi-receiver electromagnetic induction (EMI) sensor (DUALEM) in-combination. The 3D-GPR system offers more flexibility for application to different (sub)surface conditions due to the coverage of wide frequency bandwidth. The EMI sensor simultaneously provides information about the apparent electrical conductivity (ECa) for different soil volumes, corresponding to different depths. This sensor combination was evaluated on twelve different study sites with various soil types with textures ranging from sand to clay till. While the 3-D GPR showed a high success rate in finding the drainpipes at five sites (sandy, sandy loam, loamy sand, and organic topsoils), the results at the other seven sites were less successful due to limited penetration depth (PD) of the 3D-GPR signal. The results suggest that the electrical conductivity estimates produced by the inversion of ECa data measured by the DUALEM sensor could be a useful proxy to explain the success achieved by the 3D-GPR in finding the drain lines. The high attenuation of electromagnetic waves in highly conductive media limiting the PD of the 3D-GPR can explain the findings obtained in this research.

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