scholarly journals Space-Time-Frequency Adaptive Processor for Multiple Interference Suppression in GNSS Applications

2018 ◽  
Vol 2018 ◽  
pp. 1-9
Author(s):  
Qiang Guo ◽  
Lian-gang Qi ◽  
Jianhong Xiang
Keyword(s):  
Interference Suppression ◽  
Satellite System ◽  
Space Time ◽  
Similar Frequency ◽  
Time Frequency ◽  
Weight Calculation ◽  
Global Navigation Satellite ◽  
Short Time ◽  
Multiple Interference ◽  
Adaptive Processor

To enhance the multiple interference suppression performance of global navigation satellite system (GNSS) receivers without extra antenna elements, a space-time-frequency adaptive processor (STFAP) is investigated. Firstly, based on the analysis of the autocorrelation function of the multicomponent signal, we propose a common period estimation and data block technique to segment the received signal data into blocks. Secondly, the signal data in each block are short-time Fourier transformed into time-frequency (TF) domain, and the corresponding TF points with similar frequency characteristics are regrouped to structure space-time-frequency (STF) data matrixes. Finally, a space-time-frequency minimum output power- (STF-MOP) based weight calculation method is introduced to suppress multiple interfering signals according to their sparse characteristics in TF and space domains. Simulation results show that the proposed STFAP can effectively combat more wideband periodic frequency-modulated (WBPFM) interferences even some of them arriving from the same direction as GNSS signals without increasing the number of antenna elements.

scholarly journals A Joint Method Based on Time-Frequency Distribution to Detect Time-Varying Interferences for GNSS Receivers with a Single Antenna

Sensors ◽  
2019 ◽  
Vol 19 (8) ◽  
pp. 1946 ◽  
Author(s):  
Qingshui Lv ◽  
Honglei Qin
Keyword(s):  
Hough Transform ◽  
Frequency Estimation ◽  
Satellite System ◽  
Time Varying ◽  
Actual Performance ◽  
Time Frequency ◽  
Single Antenna ◽  
Global Navigation Satellite ◽  
Mean Square Errors ◽  
Joint Method

In this paper, a joint method combining Hough transform and reassigned smoothed pseudo Wigner-Ville distribution (RSPWVD) is presented to detect time-varying interferences with crossed frequency for a Global Navigation Satellite System (GNSS) receiver with a single antenna. The proposed method can prevent the cross-term interference and detect the time-varying interferences with crossed frequency which cannot be achieved by the classical time-frequency (TF) analysis with the peak detection method. The actual performance of the developed method has been evaluated by experiments with conditions where the real BeiDou system (BDS) B1I signals are corrupted by the simulated chirp interferences. The results of experiments show that the introduced method is effectively able to detect chirp interferences with crossed frequency and provide the same root mean square errors (RMSE) of the parameter estimation for chirp one and the improved initial frequency estimation for chirp two compared with the Hough transform of Wigner-Ville distribution (WVD) when the jamming to noise ratio (JNR) equals or surpasses 4 dB.

scholarly journals Estimation of diameter at breast height from mobile laser scanning data collected under a heavy forest canopy

2017 ◽  
Vol 63 (No. 9) ◽  
pp. 433-441 ◽  
Author(s):  
Čerňava Juraj ◽  
Tuček Ján ◽  
Koreň Milan ◽  
Mokroš Martin
Keyword(s):  
Laser Scanning ◽  
Forest Canopy ◽  
Mean Squared Error ◽  
Satellite System ◽  
Tree Level ◽  
Efficient Technology ◽  
Mobile Mapping ◽  
Mobile Mapping System ◽  
Global Navigation Satellite ◽  
Short Time

Mobile laser scanning (MLS) is time-efficient technology of geospatial data collection that proved its ability to provide accurate measurements in many fields. Mobile innovation of the terrestrial laser scanning has a potential to collect forest inventory data on a tree level from large plots in a short time. Valuable data, collected using mobile mapping system (MMS), becomes very difficult to process when Global Navigation Satellite System (GNSS) outages become too long. A heavy forest canopy blocking the GNSS signal and limited accessibility can make mobile mapping very difficult. This paper presents processing of data collected by MMS under a heavy forest canopy. DBH was estimated from MLS point cloud using three different methods. Root mean squared error varied between 2.65 and 5.57 cm. Our research resulted in verification of the influence of MLS coverage of tree stem on the accuracy of DBH data.

scholarly journals Cascaded Multitype Interferences Suppression Method Using Sparse Representation and Array Processing for GNSS Receiver

2017 ◽  
Vol 2017 ◽  
pp. 1-17 ◽  
Author(s):  
Qiang Guo ◽  
Liangang Qi
Keyword(s):  
Sparse Representation ◽  
Array Processing ◽  
Matching Pursuit ◽  
Interference Suppression ◽  
Satellite System ◽  
Signal Interference ◽  
Wideband Interference ◽  
Interference Signals ◽  
Suppression Method ◽  
Global Navigation Satellite

Interference suppression techniques have been intensively studied in nearly two decades due to their importance for maintaining the integrity and functionality of global navigation satellite system (GNSS). However, the interference suppression method applicable for the complex receiving environment in which there are multitype interfering signals has not been considered in most of the researches. To deal with this problem better, a cascaded multitype interferences suppression method using sparse representation and array processing is proposed. In the first stage, according to the sparsity of the narrowband and modulated wideband interference signals, a novel parallel multichannel signal interference suppression method based on matching pursuit (MP) algorithm and a design strategy for the overcomplete dictionary are proposed to mitigate the interferences with sparse features. Then, the minimum power distortionless response (MPDR) beamformer is employed in the second stage to suppress the residuary interferences (such as Gaussian noise interferences). Compared with existing algorithms, the proposed method can not only effectively suppress the interference arriving from the same direction with the desired signal and increase the Degree of Freedom (DoF) of the array antenna, but also introduce no distortion into the navigation signal. The effectiveness of the proposed method is illustrated by theoretical analysis and several simulation results.

scholarly journals Implication between Geophysical Events and the Variation of Seasonal Signal Determined in GNSS Position Time Series

2021 ◽  
Vol 13 (17) ◽  
pp. 3478
Author(s):  
Sorin Nistor ◽  
Norbert-Szabolcs Suba ◽  
Ahmed El-Mowafy ◽  
Michal Apollo ◽  
Zinovy Malkin ◽  
...  
Keyword(s):  
Time Series ◽  
Satellite System ◽  
Phase Lag ◽  
Time Frequency ◽  
Richter Scale ◽  
Different Types ◽  
Before And After ◽  
Position Time Series ◽  
Global Navigation Satellite ◽  
Seasonal Signal

The seasonal signal determined by the Global Navigation Satellite System (GNSS), which is captured in the coordinate time series, exhibits annual and semi-annual periods. This signal is frequently modelled by two periodic signals with constant amplitude and phase-lag. The purpose of this study is to explore the implication of different types of geophysical events on the seasonal signal in three stages—in the time span that contains the geophysical events, before and after the geophysical event, but also the stationarity phenomena, which is analysed on approximately 200 reference stations from the EPN network since 1995. The novelty of the article is demonstrated by correlating three different types of geophysical events, such as earthquakes with a magnitude greater than 6° on the Richter scale, landslides, and volcanic activity, and analysing the variation in amplitude of the seasonal signal. The geophysical events situated within a radius of 30 km from the epicentre showed a higher seasonal value than when the timespan did not contain a geophysical event. The presence of flicker and random walk noise was computed using overlapping Hadamard variance (OHVAR) and the non-stationary behaviour of the time series of the CORS coordinates in the time frequency analysis was done using continuous wavelet transform (CWT).

scholarly journals Speed Adaptation in Urban Road Network Management

2016 ◽  
Vol 17 (2) ◽  
pp. 111-121 ◽  
Author(s):  
Jamal Raiyn
Keyword(s):  
Travel Time ◽  
Real Time ◽  
Traffic Congestion ◽  
Intelligent Transportation System ◽  
Moving Average ◽  
Road Networks ◽  
Satellite System ◽  
Global Navigation Satellite ◽  
Short Time ◽  
Gnss Data

Abstract Various forecasting schemes have been proposed to manage traffic data, which is collected by videos cameras, sensors, and mobile phone services. However, these are not sufficient for collecting data because of their limited coverage and high costs for installation and maintenance. To overcome the limitations of these tools, we introduce a hybrid scheme based on intelligent transportation system (ITS) and global navigation satellite system (GNSS). Applying the GNSS to calculate travel time has proven efficient in terms of accuracy. In this case, GNSS data is managed to reduce traffic congestion and road accidents. This paper introduces a short-time forecasting model based on real-time travel time for urban heterogeneous road networks. Travel time forecasting has been achieved by predicting travel speeds using an optimized exponential moving Average (EMA) model. Furthermore for speed adaptation in heterogeneous road networks, it is necessary to introduce asuitable control strategy for longitude, based on the GNSS. GNSS products provide worldwide and real-time services using precise timing information and, positioning technologies.

scholarly journals Jammer Classification in GNSS Bands Via Machine Learning Algorithms

Sensors ◽  
2019 ◽  
Vol 19 (22) ◽  
pp. 4841 ◽  
Author(s):  
Ruben Morales Ferre ◽  
Alberto de la Fuente ◽  
Elena Simona Lohan
Keyword(s):  
Machine Learning ◽  
Classification Problem ◽  
Satellite System ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Learning Approaches ◽  
Time Frequency ◽  
Black And White ◽  
Image Mapping ◽  
Global Navigation Satellite

This paper proposes to treat the jammer classification problem in the Global Navigation Satellite System bands as a black-and-white image classification problem, based on a time-frequency analysis and image mapping of a jammed signal. The paper also proposes to apply machine learning approaches in order to sort the received signal into six classes, namely five classes when the jammer is present with different jammer types and one class where the jammer is absent. The algorithms based on support vector machines show up to 94 . 90 % accuracy in classification, and the algorithms based on convolutional neural networks show up to 91 . 36 % accuracy in classification. The training and test databases generated for these tests are also provided in open access.

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