Jamming Detection using Wavelet Transforms
Ramesh, Aravind (2019)
2019
Electrical Engineering
Informaatioteknologian ja viestinnän tiedekunta - Faculty of Information Technology and Communication Sciences
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Hyväksymispäivämäärä
2019-05-22
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:tty-201905211721
https://urn.fi/URN:NBN:fi:tty-201905211721
Tiivistelmä
Modern Global Navigation Satellite Systems (GNSS), such as GPS and Galileo, play vital role in providing high precision navigation and positioning services for civilian and military applications. The high precision feature of these systems is compromised in the presence of interference, particularly intentional narrowband interference otherwise commonly known as jamming. To ensure the sustainability of high precision, removal of jamming components is necessary. In order to achieve successful elimination of jamming components, efficient detection and understanding of the nature of jamming signals are vital.
In practice, signals are finite in nature and vary over time. Mathematical tools such as Fourier transforms assume signals are infinite (periodic), thereby fail to capture accurate time-related information. To overcome this situation, a sophisticated technique that captures valuable information in both time and frequency domains is required. One such technique is the wavelet transform.
Wavelet transform involves successive scaling of fast decaying wavelike oscillations known as wavelets in time and shifting it along the duration of an incoming signal. This process results in either stretching or shrinking of wavelets. Stretched wavelet facilitates the extraction of slow variations in a signal and compressed wavelet facilitates the extraction of abrupt variations.
The conceived algorithm detects the presence of jamming signals, simultaneously capturing features such as frequency, bandwidth and duration. The operational capability of the algorithm was tested for GNSS signals operating in L1 frequency band (1575.42MHz) such as GPS L1 and Galileo E1. The parameters defined to measure the efficiency of the algorithm are detection probability (Pd) and false alarm probability (Pfa). Pd is estimated for different values of jammer to signal ratio (JSR) with fixed signal to noise ratio (SNR) and Pfa depends on the choice of detection threshold (T). T is chosen such that Pfa is as low as possible. The detector works better in low noise and high jammer power scenarios.
Keywords: Jamming, Wavelets, GPS, Galileo, SNR, JSR, L1
In practice, signals are finite in nature and vary over time. Mathematical tools such as Fourier transforms assume signals are infinite (periodic), thereby fail to capture accurate time-related information. To overcome this situation, a sophisticated technique that captures valuable information in both time and frequency domains is required. One such technique is the wavelet transform.
Wavelet transform involves successive scaling of fast decaying wavelike oscillations known as wavelets in time and shifting it along the duration of an incoming signal. This process results in either stretching or shrinking of wavelets. Stretched wavelet facilitates the extraction of slow variations in a signal and compressed wavelet facilitates the extraction of abrupt variations.
The conceived algorithm detects the presence of jamming signals, simultaneously capturing features such as frequency, bandwidth and duration. The operational capability of the algorithm was tested for GNSS signals operating in L1 frequency band (1575.42MHz) such as GPS L1 and Galileo E1. The parameters defined to measure the efficiency of the algorithm are detection probability (Pd) and false alarm probability (Pfa). Pd is estimated for different values of jammer to signal ratio (JSR) with fixed signal to noise ratio (SNR) and Pfa depends on the choice of detection threshold (T). T is chosen such that Pfa is as low as possible. The detector works better in low noise and high jammer power scenarios.
Keywords: Jamming, Wavelets, GPS, Galileo, SNR, JSR, L1