scholarly journals Detection and Parameter Extraction of Low Probability of Intercept Radar Signals using the Hough Transform

2016 ◽  
pp. 9-25
Author(s):  
Daniel L. Stevens ◽  
Stephanie A. Schuckers
Keyword(s):  
Hough Transform ◽  
Continuous Wave ◽  
Parameter Extraction ◽  
Time Frequency ◽  
Low Snr ◽  
Analysis Technique ◽  
Shift Keying ◽  
Low Probability ◽  
Low Probability Of Intercept ◽  
Radar Signals

Digital intercept receivers are currently moving away from Fourier-based analysis and towards classical time-frequency analysis techniques, such as the Wigner-Ville distribution, Choi-Williams distribution, spectrogram, and scalogram, for the purpose of analyzing low probability of intercept radar signals (e.g. triangular modulated frequency modulated continuous wave and frequency shift keying). Although these classical time-frequency techniques are an improvement over the Fourier-based analysis, they still suffer from a lack of readability, due to cross-term interference, and a mediocre performance in low SNR environments. This lack of readability may lead to inaccurate detection and parameter extraction of these radar signals. In this paper, the use of the Hough transform, because of its ability to suppresscross-term interference, separate signals from cross-terms, and perform well in the presence of noise, is proposed as an improved signal analysis technique. With these qualities, the Hough transform has the potential to produce better readability and consequently, more accurate signal detection and parameter extraction metrics.

Detection and Parameter Extraction of Low Probability of Intercept Frequency Hopping Signals using the Spectrogram and the Reassigned Spectrogram

2020 ◽  
pp. 11-20
Author(s):  
Daniel L. Stevens
Keyword(s):  
Frequency Analysis ◽  
Parameter Extraction ◽  
Frequency Hopping ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Analysis Techniques ◽  
Time Frequency Localization ◽  
Low Probability ◽  
Low Probability Of Intercept ◽  
Radar Signals

Low probability of intercept radar signals, which are often problematic to detect and characterize, have as their goal ‘to see and not be seen’. Digital intercept receivers are currently moving away from Fourier-based analysis and towards classical time-frequency analysis techniques for the purpose of analyzing these low probability of intercept radar signals. Although these classical time-frequency analysis techniques are an improvement over existing Fourier-based techniques, they still suffer from a lack of readability –which can be caused by poor time-frequency localization (such as the spectrogram), which may in turn lead to inaccurate detection and parameter extraction. In this study, the reassignment method, because of its ability to improve time-frequency localization, is proposed as an improved signal analysis technique to address the poor time-frequency localization deficiency of the spectrogram. This paper presents the novel approach of characterizing low probability of intercept frequency hopping radar signals through utilization and direct comparison of the spectrogram versus the reassigned spectrogram.

scholarly journals Low Probability of Intercept Triangular Modulated Frequency Modulated Continuous Wave Signal Characterization Comparison using the Spectrogram and the Scalogram

2017 ◽  
pp. 37-47
Author(s):  
Daniel L. Stevens ◽  
Stephanie A. Schuckers
Keyword(s):  
Continuous Wave ◽  
Signal To Noise Ratio ◽  
Time Frequency ◽  
Novel Approach ◽  
Wave Radar ◽  
Time Frequency Localization ◽  
Low Probability ◽  
Low Probability Of Intercept ◽  
Radar Signals ◽  
Continuous Wave Signal

Digital intercept receivers are currently moving away from Fourier-based analysis and towards classical time-frequency analysis techniques for the purpose of analyzing low probability of intercept radar signals. This paper presents the novel approach of characterizing low probability of intercept frequency modulated continuous wave radar signals through utilization and direct comparison of the Spectrogram versus the Scalogram. Two different triangular modulated frequency modulated continuous wave signals were analyzed. The following metrics were used for evaluation: percent error of: carrier frequency, modulation bandwidth, modulation period, chirp rate, and time-frequency localization (x and y direction). Also used were: percent detection, lowest signal-to-noise ratio for signal detection, and plot (processing) time. Experimental results demonstrate that overall, the Spectrogram produced more accurate characterization metrics than the Scalogram. An improvement in performance may well translate into saved equipment and lives.

scholarly journals A Novel Approach for the Characterization of Triangular Modulated Frequency Modulated Continuous Wave Low Probability of Intercept Radar Signals via Application of the Wigner-Ville Distribution

2021 ◽  
pp. 19-28
Author(s):  
Daniel L. Stevens
Keyword(s):  
Signal Processing ◽  
Continuous Wave ◽  
Processing Technique ◽  
Signal Processing Technique ◽  
Time Frequency ◽  
Novel Approach ◽  
Wave Radar ◽  
Low Probability ◽  
Low Probability Of Intercept ◽  
Radar Signals

Digital intercept receivers are changing from Fourier-based analysis to classical time-frequency analysis techniques for analyzing low probability of intercept radar signals. This paper presents a novel approach of characterizing low probability of intercept triangular modulated frequency modulated continuous wave radar signals through utilization and direct comparison of the signal processing techniques Wigner-Ville Distribution versus the Reassigned Smooth Pseudo Wigner-Ville Distribution. The following metrics were used for evaluation: percent error of: carrier frequency, modulation bandwidth, modulation period, chirp rate, and time-frequency localization (x and y direction). Also used were: percent detection, lowest signal-to-noise ratio for signal detection, and plot (processing) time. Experimental results demonstrate that overall, the Reassigned Smooth Pseudo Wigner-Ville Distribution signal processing technique produced more accurate characterization metrics than the Wigner-Ville Distribution signal processing technique.

scholarly journals Low Probability of Intercept Triangular Modulated Frequency Modulated Continuous Wave Signal Characterization Comparison Using the Wigner Ville Distribution and the Choi Williams Distribution

2019 ◽  
pp. 35-44
Author(s):  
Daniel L. Stevens
Keyword(s):  
Continuous Wave ◽  
Signal To Noise Ratio ◽  
Time Frequency ◽  
Novel Approach ◽  
Wave Radar ◽  
Time Frequency Localization ◽  
Low Probability ◽  
Low Probability Of Intercept ◽  
Radar Signals ◽  
Continuous Wave Signal

Digital intercept receivers are currently moving away from Fourier-based analysis and towards classical time frequency analysis techniques for the purpose of analyzing low probability of intercept radar signals. This paper presents the novel approach of characterizing low probability of intercept triangular modulated frequency modulated continuous wave radar signals through utilization and direct comparison of the Wigner Ville Distribution versus the Choi Williams Distribution. The following metrics were used for evaluation: percent error of: carrier frequency, modulation bandwidth, modulation period, chirp rate, and time-frequency localization (x and y direction). Also used were: percent detection, lowest signal-to noise ratio for signal detection, and plot (processing) time. Experimental results demonstrate that overall, the Wigner Ville Distribution produced more accurate characterization metrics than the Choi Williams Distribution. An improvement in performance may well translate into an increase in personnel safety.

scholarly journals Low Probability of Intercept Frequency Hopping Signal Characterization Comparison Using the Wigner Ville Distribution and the Choi Williams Distribution

2018 ◽  
pp. 1-10
Author(s):  
Daniel L. Stevens ◽  
Stephanie A. Schuckers
Keyword(s):  
Frequency Hopping ◽  
The Novel ◽  
Modulation Bandwidth ◽  
Time Frequency ◽  
Novel Approach ◽  
Time Frequency Localization ◽  
Low Probability ◽  
Low Probability Of Intercept ◽  
Radar Signals ◽  
Signal Characterization

Low probability of intercept radar signals, which are often challenging to detect and characterize, have as their objective ‘to see and not be seen’. Digital intercept receivers are currently moving from Fourier-based techniques to classical time-frequency techniques for the analysis of low probability of intercept radar signals. This paper presents the novel approach of characterizing low probability of intercept frequency hopping radar signals through utilization and direct comparison of the Wigner Ville Distribuion versus the Choi Williams Distribution. Two different frequency hopping low probability of intercept radar signals were analyzed (4-component and 8-component). The following metrics were used for evaluation: percent error of:carrier frequency, modulation bandwidth, modulation period, and time-frequency localization. Also used were: percent detection, lowest signaltonoise ratio for signal detection, and plot (processing) time.

scholarly journals Detection of Polyphase Codes Radar Signals in Low SNR

2016 ◽  
Vol 2016 ◽  
pp. 1-6 ◽  
Author(s):  
Runlan Tian ◽  
Guoyi Zhang ◽  
Rui Zhou ◽  
Wei Dong
Keyword(s):  
Hough Transform ◽  
Signal To Noise Ratio ◽  
Superior Performance ◽  
Radar Signal ◽  
Time Frequency ◽  
Low Snr ◽  
Radar Signals ◽  
Core Idea ◽  
Code Type ◽  
Electronic Intelligence

A novel effective detection method is proposed for electronic intelligence (ELINT) systems detecting polyphase codes radar signal in the low signal-to-noise ratio (SNR) scenario. The core idea of the proposed method is first to calculate the time-frequency distribution of polyphase codes radar signals via Wigner-Ville distribution (WVD); then the modified Hough transform (HT) is employed to cumulate all the energy of WVD’s ridges effectively to achieve signal detection. Compared with the generalised Wigner Hough transform (GWHT) method, the proposed method has a superior performance in low SNR and is not sensitive to the code type. Simulation results verify the validity of the proposed method.

scholarly journals Low Probability of Intercept Frequency Hopping Signal Characterization Comparison using the Spectrogram and the Scalogram

2016 ◽  
pp. 13-23 ◽  
Author(s):  
Daniel L. Stevens ◽  
Stephanie A. Schuckers
Keyword(s):  
Signal To Noise Ratio ◽  
Frequency Hopping ◽  
The Novel ◽  
Time Frequency ◽  
Novel Approach ◽  
Time Frequency Localization ◽  
Low Probability ◽  
Low Probability Of Intercept ◽  
Radar Signals ◽  
Signal Characterization

Low probability of intercept radar signals, which are often problematic to detect and characterize, have as their goal ‘to see and not be seen’. Digital intercept receivers are currently moving away from Fourier-based analysis and towards classical time-frequency analysis techniques for the purpose of analyzing these low probability of intercept radar signals. This paper presents the novel approach of characterizing low probability of intercept frequency hopping radar signals through utilization and direct comparison of the Spectrogram versus the Scalogram. Two different frequency hopping low probability of intercept radar signals were analyzed(4-component and 8-component). The following metrics were used for evaluation: percent error of:carrier frequency, modulation bandwidth, modulation period, and time-frequency localization. Also used were: percent detection, lowest signal-to-noise ratio for signal detection, and plot (processing) time. Experimental results demonstrate that overall,theScalogram produced more accurate characterization metrics than the Spectrogram. An improvement in performance may well translate into saved equipment and lives.

scholarly journals A Radar Signal Recognition Approach via IIF-Net Deep Learning Models

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Ji Li ◽  
Huiqiang Zhang ◽  
Jianping Ou ◽  
Wei Wang
Keyword(s):  
Recognition Accuracy ◽  
Signal To Noise Ratio ◽  
Recognition Rate ◽  
Software Radio ◽  
Radar Signal ◽  
Signal Recognition ◽  
Electromagnetic Environment ◽  
Time Frequency ◽  
Low Snr ◽  
Radar Signals

In the increasingly complex electromagnetic environment of modern battlefields, how to quickly and accurately identify radar signals is a hotspot in the field of electronic countermeasures. In this paper, USRP N210, USRP-LW N210, and other general software radio peripherals are used to simulate the transmitting and receiving process of radar signals, and a total of 8 radar signals, namely, Barker, Frank, chaotic, P1, P2, P3, P4, and OFDM, are produced. The signal obtains time-frequency images (TFIs) through the Choi–Williams distribution function (CWD). According to the characteristics of the radar signal TFI, a global feature balance extraction module (GFBE) is designed. Then, a new IIF-Net convolutional neural network with fewer network parameters and less computation cost has been proposed. The signal-to-noise ratio (SNR) range is −10 to 6 dB in the experiments. The experiments show that when the SNR is higher than −2 dB, the signal recognition rate of IIF-Net is as high as 99.74%, and the signal recognition accuracy is still 92.36% when the SNR is −10 dB. Compared with other methods, IIF-Net has higher recognition rate and better robustness under low SNR.

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