scholarly journals A Novel GNSS Interference Detection Method Based on Smoothed Pseudo-Wigner–Hough Transform

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4306
Author(s):  
Kewen Sun ◽  
Baoguo Yu ◽  
Mireille Elhajj ◽  
Washington Yotto Ochieng ◽  
Tengteng Zhang ◽  
...  
Keyword(s):  
Hough Transform ◽  
Detection Method ◽  
Detection Performance ◽  
Detection Methods ◽  
Energy Concentration ◽  
Limited Area ◽  
Interference Detection ◽  
Transform Method ◽  
Cross Term ◽  
Time Frequency

This paper develops novel Global Navigation Satellite System (GNSS) interference detection methods based on the Hough transform. These methods are realized by incorporating the Hough transform into three Time-Frequency distributions: Wigner–Ville distribution, pseudo -Wigner–Ville distribution and smoothed pseudo-Wigner–Ville distribution. This process results in the corresponding Wigner–Hough transform, pseudo-Wigner–Hough transform and smoothed pseudo-Wigner–Hough transform, which are used in GNSS interference detection to search for local Hough-transformed energy peak in a small limited area within the parameter space. The developed GNSS interference detection methods incorporate a novel concept of zero Hough-transformed energy distribution percentage to analyze the properties of energy concentration and cross-term suppression. The methods are tested with real GPS L1-C/A data collected in the presence of sweep interference. The test results show that the developed methods can deal with the cross-term problem with improved interference detection performance. In particular, the GNSS interference detection performance obtained with the smoothed pseudo-Wigner–Hough transform method is at least double that of the Wigner–Hough transform-based approach; the smoothed pseudo-Wigner–Hough transform-based GNSS interference detection method is improved at least 20% over the pseudo-Wigner–Hough transform-based technique in terms of the zero Hough-transformed energy percentage criteria. Therefore, the proposed smoothed pseudo-Wigner–Hough transform-based method is recommended in the interference detection for GNSS receivers, particularly in challenging electromagnetic environments.

scholarly journals Fault Detection Method Based on Global-Local Marginal Discriminant Preserving Projection for Chemical Process

Processes ◽  
2022 ◽  
Vol 10 (1) ◽  
pp. 122
Author(s):  
Yang Li ◽  
Fangyuan Ma ◽  
Cheng Ji ◽  
Jingde Wang ◽  
Wei Sun
Keyword(s):  
Feature Extraction ◽  
Fault Detection ◽  
Chemical Process ◽  
Detection Method ◽  
Detection Performance ◽  
Detection Methods ◽  
Normal Operation ◽  
Discriminative Feature ◽  
Inherent Feature ◽  
Global And Local

Feature extraction plays a key role in fault detection methods. Most existing methods focus on comprehensive and accurate feature extraction of normal operation data to achieve better detection performance. However, discriminative features based on historical fault data are usually ignored. Aiming at this point, a global-local marginal discriminant preserving projection (GLMDPP) method is proposed for feature extraction. Considering its comprehensive consideration of global and local features, global-local preserving projection (GLPP) is used to extract the inherent feature of the data. Then, multiple marginal fisher analysis (MMFA) is introduced to extract the discriminative feature, which can better separate normal data from fault data. On the basis of fisher framework, GLPP and MMFA are integrated to extract inherent and discriminative features of the data simultaneously. Furthermore, fault detection methods based on GLMDPP are constructed and applied to the Tennessee Eastman (TE) process. Compared with the PCA and GLPP method, the effectiveness of the proposed method in fault detection is validated with the result of TE process.

scholarly journals An API Semantics-Aware Malware Detection Method Based on Deep Learning

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Xin Ma ◽  
Shize Guo ◽  
Wei Bai ◽  
Jun Chen ◽  
Shiming Xia ◽  
...  
Keyword(s):  
Machine Learning ◽  
Detection Method ◽  
Malware Detection ◽  
Poor Performance ◽  
Detection Methods ◽  
Interference Detection ◽  
Machine Learning Method ◽  
Accuracy Rate ◽  
Malware Classification ◽  
Self Protection

The explosive growth of malware variants poses a continuously and deeply evolving challenge to information security. Traditional malware detection methods require a lot of manpower. However, machine learning has played an important role on malware classification and detection, and it is easily spoofed by malware disguising to be benign software by employing self-protection techniques, which leads to poor performance for existing techniques based on the machine learning method. In this paper, we analyze the local maliciousness about malware and implement an anti-interference detection framework based on API fragments, which uses the LSTM model to classify API fragments and employs ensemble learning to determine the final result of the entire API sequence. We present our experimental results on Ali-Tianchi contest API databases. By comparing with the experiments of some common methods, it is proved that our method based on local maliciousness has better performance, which is a higher accuracy rate of 0.9734.

scholarly journals Adjacent Infrared Multitarget Detection Using Robust Background Estimation

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Sungho Kim ◽  
Kyung-Tae Kim
Keyword(s):  
False Alarm ◽  
False Alarm Rate ◽  
Target Detection ◽  
Detection Method ◽  
Detection Performance ◽  
Detection Methods ◽  
Constant False Alarm Rate ◽  
Cluttered Environments ◽  
Background Estimation ◽  
Cfar Detection

Small target detection is very important for infrared search and track (IRST) problems. Grouped targets are difficult to detect using the conventional constant false alarm rate (CFAR) detection method. In this study, a novel multitarget detection method was developed to identify adjacent or closely spaced small infrared targets. The neighboring targets decrease the signal-to-clutter ratio in hysteresis threshold-based constant false alarm rate (H-CFAR) detection, which leads to poor detection performance in cluttered environments. The proposed adjacent target rejection-based robust background estimation can reduce the effects of the neighboring targets and enhance the small multitarget detection performance in infrared images by increasing the signal-to-clutter ratio. The experimental results of the synthetic and real adjacent target sequences showed that the proposed method produces an upgraded detection rate with the same false alarm rate compared to the recent target detection methods (H-CFAR, Top-hat, and TDLMS).

scholarly journals A Novel Interference Detection Method of STAP Based on Simplified TT Transform

2017 ◽  
Vol 2017 ◽  
pp. 1-9
Author(s):  
Qiang Wang ◽  
Yongshun Zhang ◽  
Hanwei Liu ◽  
Yiduo Guo
Keyword(s):  
Detection Method ◽  
Physical Property ◽  
Training Sample ◽  
Space Time ◽  
Detection Performance ◽  
Inner Product ◽  
Interference Detection ◽  
Large Power ◽  
Training Samples ◽  
Performance Reduction

Training samples contaminated by target-like signals is one of the major reasons for inhomogeneous clutter environment. In such environment, clutter covariance matrix in STAP (space-time adaptive processing) is estimated inaccurately, which finally leads to detection performance reduction. In terms of this problem, a STAP interference detection method based on simplified TT (time-time) transform is proposed in this letter. Considering the sparse physical property of clutter in the space-time plane, data on each range cell is first converted into a discrete slow time series. Then, the expression of simplified TT transform about sample data is derived step by step. Thirdly, the energy of each training sample is focalized and extracted by simplified TT transform from energy-variant difference between the unpolluted and polluted stage, and the physical significance of discarding the contaminated samples is analyzed. Lastly, the contaminated samples are picked out in light of the simplified TT transform-spectrum difference. The result on Monte Carlo simulation indicates that when training samples are contaminated by large power target-like signals, the proposed method is more effective in getting rid of the contaminated samples, reduces the computational complexity significantly, and promotes the target detection performance compared with the method of GIP (generalized inner product).

The Multi-Scale Hough Transform Lane Detection Method Based on the Algorithm of Otsu and Canny

2014 ◽  
Vol 1042 ◽  
pp. 126-130 ◽  
Author(s):  
Yu Chai ◽  
Su Jing Wei ◽  
Xin Chun Li
Keyword(s):  
Hough Transform ◽  
Detection Method ◽  
Lane Detection ◽  
Transform Method ◽  
Canny Operator ◽  
The Road ◽  
Multi Scale ◽  
Lane Departure ◽  
Lane Line

In order to improve the accuracy of detecting lane for automatic vehicle driving, a method for detecting the straight part of Lane is proposed, which is the Multi-Scale Hough transform method for lane detection based on the algorithm of Otsu and Canny. First of all, by the methods of Otsu to segment image and use the morphology operation of erode and dilate to wipe off the information of roadside trees and fences to strengthen the road boundary characteristics.Then the lane edge and feature is gained by the canny operator. At last, using Standard Hough Transform, Progressiveness Probabilities Hough Transform and Multi-Scale Hough Transform complete the detection of lane’s straight part. The experimental results show that, Multi-Scale Hough Transform method can accurately detect the lane line and provide the reliable basis for the path planning, automatic follow-up vehicle driving and lane departure warning.

Mask filtering to the Wigner-Ville distribution

Geophysics ◽  
2021 ◽  
pp. 1-30
Author(s):  
Haifa Alsalmi ◽  
Yanghua Wang
Keyword(s):  
High Resolution ◽  
Seismic Data ◽  
Energy Concentration ◽  
Cross Term ◽  
Time Frequency ◽  
Stationary Signals ◽  
Spectral Analysis Method ◽  
The Cross ◽  
Data Adaptive ◽  
3D Seismic Data

The Wigner-Ville distribution (WVD) is a high-resolution time-frequency spectral analysis method for non-stationary signals, and yet it suffers from cross-term interference among signal components. We proposed applying a masking filter directly to the WVD time-frequency spectrum to suppress the cross-term interference. Conventional methods for suppressing the interference include the smoothed-pseudo WVD (SP-WVD) method, which incorporates smooth filtering in both time and frequency directions. We exploited the SP-WVD spectrum as a reference to design the masking filter, and thus the mask filtered WVD (MF-WVD) procedure is data adaptive. The MF-WVD method preserves the high-resolution energy concentration in the spectrum portrayed by the standard WVD, while suppressing the cross-term interference cleanly like in the SP-WVD method. Applying the MF-WVD method to field 3D seismic data generates high-resolution spectral cubes for various frequencies, and these spectral cubes may be used intuitively for detecting reservoir-related characteristics.

scholarly journals A New GNSS Interference Detection Method Based on Rearranged Wavelet–Hough Transform

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1714
Author(s):  
Kewen Sun ◽  
Tengteng Zhang
Keyword(s):  
Hough Transform ◽  
Continuous Wave ◽  
Detection Method ◽  
Satellite System ◽  
Interference Detection ◽  
Radio Frequency Interference ◽  
Aggregation Property ◽  
Gnss Receivers ◽  
Bench Experiment ◽  
Global Navigation Satellite

Since radio frequency interference (RFI) seriously degrades the performance of a global navigation satellite system (GNSS) receiver, interference detection becomes very important for GNSS receivers. In this paper, a novel rearranged wavelet–Hough transform (RWHT) method is proposed in GNSS interference detection, which is obtained by the combination of rearranged wavelet transform and Hough transform (HT). The proposed RWHT method is tested for detecting sweep interference and continuous wave (CW) interference, the major types of GNSS interfering signals generated by a GNSS jammer in a controlled test bench experiment. The performance of the proposed RWHT method is compared with the conventional techniques such as Wigner–Ville distribution (WVD) and Wigner–Hough transform (WHT). The analysis results show that the proposed RWHT method reduces the influence of cross-item problem and improves the energy aggregation property in GNSS interference detection. When compared with the WHT approach, this proposed RWHT method presents about 90.3% and 30.8% performance improvement in the initial frequency and chirp rate estimation of the GNSS sweep interfering signal, respectively. These results can be further considered to be the proof of the validity and effectiveness of the developed GNSS interference detection method using RWHT.

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