Performance analysis of different interference detection techniques for navigation with Indian constellation

Lineswala, Priyanka L.; Shah, Shweta N.

doi:10.1049/iet-rsn.2019.0091

Cited by 9 publications

(2 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The authors of this work state that narrowband interference and broadband interference are among the most dangerous interferences in aeronautical navigation systems. An overview of different interference detection techniques is given in [16]. The authors of this work emphasize that the elimination of interference is possible if a monitoring system is available, because commercial receivers do not have such a system.…”

Section: Introductionmentioning

confidence: 99%

The Influence of Narrowband Interference on DME System Operation

Džunda

Melníková

2023

Aerospace

View full text Add to dashboard Cite

Within the new air traffic management concept, using the Global Navigation Satellite System (GNSS), it is assumed that distance measurement equipment (DME) will be retained. The results of research carried out by several authors have confirmed that global navigation satellite systems (GNSS) can be put out of operation in cases of interference. Therefore, it is very timely to investigate the accuracy and resistance to interference of DME systems. The presented work contains the results of research in the field of assessment of the accuracy and resistance of a DME system, which works in conditions of narrowband interference, using modeling and simulation. Based on the derived model of the DME measurement signal, its parameters, and narrowband interference, algorithms for processing the measurement signals of a DME system in conditions of narrowband interference were derived. A quasi-optimal nonlinear filtering method was used in the derivation of the measurement signal-processing algorithms. A quadratic loss function was used as an optimality criterion, which allows us to obtain the results of measuring the parameters of the DME measurement signal as a minimum of the a posteriori mean error. Within this method, Gaussian approximation and large and small parameter methods were used. Simulation of the operation of the DME system confirmed that the measurement accuracy of this system depends on the stability of the frequency of the DME support generator, and also depends on the signal-to-noise ratio and the signal-to-interference ratio of the DME receiver input. Comparing the results of the DME system receiver designed by us with the parameters listed in in published works that discuss the accuracy of this system , we can conclude that its accuracy is much better. The simulation results confirmed that the potential accuracy of the distance measurement is equal to 2.2 m. However, the mentioned algorithms require substantial simplification to be used for real-time signal processing. This will be our next research direction.

show abstract

Section: Introductionmentioning

confidence: 99%

The Influence of Narrowband Interference on DME System Operation

Džunda

Melníková

2023

Aerospace

View full text Add to dashboard Cite

show abstract

“…The focus in the literature so far has encompassed all-but-jamming classification solutions. For example, jamming detection solutions were reported in [2,3,4]; jamming mitigation algorithms can be found in [2,5,6,7]; and jamming localization was addressed in [3,8]. However, very little effort has been dedicated so far to the jamming classification algorithms, which are also an important stage towards an efficient interference management solution.…”

Section: Introductionmentioning

confidence: 99%

Jammer Classification in GNSS Bands Via Machine Learning Algorithms

Ferre

Fuente

Lohan

2019

Sensors

View full text Add to dashboard Cite

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.

show abstract

Reduction in Complexity of Anti-jamming with Interference Approximation and Classification Algorithm

Ruparelia

Lineswala

2020

Communications in Computer and Information Science

View full text Add to dashboard Cite

Performance analysis of different interference detection techniques for navigation with Indian constellation

Cited by 9 publications

References 18 publications

The Influence of Narrowband Interference on DME System Operation

The Influence of Narrowband Interference on DME System Operation

Jammer Classification in GNSS Bands Via Machine Learning Algorithms

Reduction in Complexity of Anti-jamming with Interference Approximation and Classification Algorithm

Contact Info

Product

Resources

About