A Real-Time Interference Monitoring Technique for GNSS Based on a Twin Support Vector Machine Method

Li, Wutao; Huang, Zhigang; Lang, Rongling; Qin, Honglei; Zhou, Kai; Cao, Yunxia

doi:10.3390/s16030329

Cited by 19 publications

(16 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The classification algorithms are very helpful to predict the weather pattern. SVM is one of the highly developed classification technique [13].…”

Section: Support Vector Machinementioning

confidence: 99%

Performance Analysis on Decision Tree and SVM Classification With Reference to Flood Occurrences in India

Mohankumar¹,

Sangeetha²

2018

ijarcs

View full text Add to dashboard Cite

Floods are pervasive catastrophic disasters which cause financial damages, lost life and environmental scarcity. Flood damage estimation is one of the important factors to the way in the depth of the flood and predicts the future damages. Data mining classification techniques to discover patterns and sequences will use to predict the zones that depiction to flood. A data of flood damages for twelve years collected from various sources. The classification models of Decision Trees and Support Vector Machine with different kernel functions are taken for the prediction models. The Matlab analytics compare the prediction models indicates the performance of the algorithm has much better accuracy. Experiments confirm that the Support Vector Machine with Quadratic kernel function is more accurate in finding the prediction pattern. The accuracy and visualization also suggest that flood prediction which the outcome will goal to better manage floods all the way through preclusion, fortification and catastrophe.

show abstract

“…The classification algorithms are very helpful to predict the weather pattern. SVM is one of the highly developed classification technique [13].…”

Section: Support Vector Machinementioning

confidence: 99%

Performance Analysis on Decision Tree and SVM Classification With Reference to Flood Occurrences in India

Mohankumar¹,

Sangeetha²

2018

ijarcs

View full text Add to dashboard Cite

show abstract

“…In the signal features method, the features of the spoofing signal are quite similar to those of the actual signal and there is no sudden change in the transition process; but still, the signal feature method has not proven to work well. In addition, some crossing methods were proposed to detect spoofing, for instance machine learning [32], maximum likelihood estimation [33], and cooperation of multiple detections [34]. However, these methods are still dependent on prior information or actual signal features [35].…”

Section: Introductionmentioning

confidence: 99%

A GPS Spoofing Generator Using an Open Sourced Vector Tracking-Based Receiver

Meng

Hsu

et al. 2019

Sensors

View full text Add to dashboard Cite

Spoofing can seriously threaten the use of the Global Positioning System (GPS) in critical applications such as positioning and navigation of autonomous vehicles. Research into spoofing generation will contribute to assessment of the threat of possible spoofing attacks and help in the development of anti-spoofing methods. However, the recent commercial off-the-shelf (COTS) spoofing generators are expensive and the technology implementation is complicated. To address the above problem and promote the GPS safety-critical applications, a spoofing generator using a vector tracking-based software-defined receiver is proposed in this contribution. The spoofing generator aims to modify the raw signals by cancelling the actual signal component and adding the spoofing signal component. The connections between the spreading code and carrier, and the states of the victim receiver are established through vector tracking. The actual signal can be predicted effectively, and the spoofing signal will be generated with the spoofing trajectory at the same time. The experimental test results show that the spoofing attack signal can effectively mislead the victim receiver to the designed trajectory. Neither the tracking channels nor the positioning observations have abnormal changes during this processing period. The recent anti-spoofing methods cannot detect this internal spoofing easily. The proposed spoofing generator can cover all open-sky satellites with a high quality of concealment. With the superiority of programmability and diversity, it is believed that the proposed method based on an open source software-defined receiver has a great value for anti-spoofing research of different GNSS signals.

show abstract

“…In Ref. [ 22 ], the authors proposed a method to monitor the spoofing signal based on machine learning and signal processing. Other methods can also detect spoofing signals to a certain extent, but still have limitations.…”

Section: Introductionmentioning

confidence: 99%

Spoofing Detection Algorithm Based on Pseudorange Differences

Liu

Zhi-jia

et al. 2018

Sensors

View full text Add to dashboard Cite

Intentional spoofing interference can cause damage to the navigation terminal and threaten the security of a global navigation satellite system (GNSS). For spoofing interference, an anti-spoofing algorithm based on pseudorange differences for a single receiver is proposed, which can be used to detect simplistic and intermediate spoofing attacks, as well as meaconing attacks. Double-difference models using the pseudorange of two adjacent epochs are established followed by the application of Taylor expansion to the position relationship between the satellite and the receiver (or the spoofer). The authenticity of the signal can be verified by comparing the results of the proposed spoofing detection algorithm with the traditional least squares method. The results will differ when spoofing is present. The parameter setting of the proposed algorithm is introduced. The algorithm has the advantage of both simplicity and efficiency and needs only a single receiver and pseudorange data. A NovAtel receiver is adopted for the actual experiments. The Texas spoofing test battery (TEXBAT), as well as two other simulation experiments are used to verify the performance of the algorithm. The simulation results validate the feasibility and effectiveness of the algorithm.

show abstract

A Real-Time Interference Monitoring Technique for GNSS Based on a Twin Support Vector Machine Method

Cited by 19 publications

References 8 publications

Performance Analysis on Decision Tree and SVM Classification With Reference to Flood Occurrences in India

Performance Analysis on Decision Tree and SVM Classification With Reference to Flood Occurrences in India

A GPS Spoofing Generator Using an Open Sourced Vector Tracking-Based Receiver

Spoofing Detection Algorithm Based on Pseudorange Differences

Contact Info

Product

Resources

About