# A Novel GNSS Interference Detection Method Based on Smoothed Pseudo-Wigner-Hough Transform
## 6 Performance Evaluation  
The performances of the proposed GNSS interference detection methods based on the Hough transform are comprehensively analyzed in this section. The proposed GNSS interference detection methods are compared to the traditional time-frequency analysis-based techniques such as spectrogram and Wigner-Ville distribution. Then, according to the zero Hough-transformed energy distribution percentage criteria, the performances of GNSS interference detection are further analyzed and compared among the proposed Hough transform-based techniques. The scheme of the GNSS interference detection experiment is shown in Figure 7. In the GNSS interference detection experiment, a GNSS signal collector was used to collect GPS L1-C/A signal through a cable connected to a GNSS receiver antenna placed on the roof of the Feicui Building at Feicui Lake Campus, Hefei University of Technology, China. At the same time, a GNSS jammer controlled by a computer was used to generate a sweep interfering signal, which was simultaneously added to the real GPS L1-C/A signal through a signal combiner. Then, the combined GPS L1-C/A signal and sweep interference were collected by a signal collector, which was sent to a GNSS software receiver implemented on a display computer through a USB cable. The adopted experimental platform of the GNSS interference detection test is illustrated in Figure 8.  
The experimental scenario used in the GNSS interference detection test is characterized by the setup parameters shown in Table 1, which denote the collected GPS L1-C/A signal in zero-mean white Gaussian noise disturbed by a constant amplitude linear frequency modulation interference (i.e., linear chirp). For the experimental setup parameters, the carrier power-to-noise density ratio (C/N\({}_{0}\)) of the GPS L1-C/A signal is 46 dB-Hz; the jammer-to-noise ratio (JNR) of the linear chirp is \(-6\) dB; and the instantaneous frequency \(f_{inst}\) of the sweep interference linearly evolves with the time in the interval \([f_{L1}+\Delta f_{0},f_{L1}+\Delta f_{1}]\), where \(f_{L1}\) is the center frequency of the GPS L1-C/A signal, \(\Delta f_{0}\) is 6 MHz and \(\Delta f_{1}\) is \(-9\) MHz. Figure 7: The scheme of the GNSS interference detection experiment. Figure 9 depicts the spectrogram of the GPS L1-C/A signal in the presence of the sweep interference, and the Hamming type of the analysis window function is selected in the spectrogram analysis. In Figure 9a, the analysis window length is 31 samples. Although the sweep interference effect can be observed, it is impossible to correctly detect the sweep interference in the time-frequency plane since the spectrogram provides an insufficient time-frequency resolution. To analyze the time-frequency characteristics of the spectrogram approach by increasing the analysis window size, the spectrogram of the GPS L1-C/A signal in the presence of sweep interference is provided in Figure 9b, where the analysis window size is set to 63 samples; in this case, although the linear frequency modulation interference present in the received GPS L1-C/A signal can be more or less identified in the time-frequency plane, unsatisfactory time-frequency localization property in the time domain is obtained with the increased window width by using the spectrogram. Therefore, the spectrogram is not able to be practically used to provide a precise estimation of the characteristic parameters for the GNSS interfering signal, although an approximate evaluation can be made. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Sampling frequency, \(f_{s}\) & 24 MHz \\ Intermediate frequency, \(f_{IF}\) & 40.42 MHz \\ Code length & 1023 chips \\ Sweep Period & 0.5 ms \\ Analysis window & Hamming \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Setup parameters in the GNSS interference detection test. Figure 8: The experimental platform of the GNSS interference detection test.