Spectral Correlation Density Estimation Using 2N-FFT Interpolation

2021 ◽  
Author(s):  
Timofey Shevgunov
Keyword(s):  
Density Estimation ◽  
Spectral Correlation ◽  
Spectral Correlation Density ◽  
Fft Interpolation

GPU Based Quarter Spectral Correlation Density Function

2018 ◽  
Author(s):  
Scott Marshall ◽  
Garrett Vanhoy ◽  
Ali Akoglu ◽  
Tamal Bose ◽  
Bo Ryu
Keyword(s):  
Density Function ◽  
Spectral Correlation ◽  
Spectral Correlation Density

ATSC digital television signal detection with spectral correlation density

2014 ◽  
Vol 16 (6) ◽  
pp. 600-612 ◽  
Author(s):  
Do-Sik Yoo ◽  
Jongtae Lim ◽  
Min-Hong Kang
Keyword(s):  
Signal Detection ◽  
Digital Television ◽  
Spectral Correlation ◽  
Television Signal ◽  
Spectral Correlation Density

Estimation of intrapulse modulation parameters of LPI radar under noisy conditions

2021 ◽  
pp. 1-18
Author(s):  
Chilukuri Raja Kumari ◽  
Hari Kishore Kakarla ◽  
K. Subbarao
Keyword(s):  
Frequency Modulation ◽  
Precise Measurement ◽  
Signal To Noise Ratio ◽  
Nonlinear Frequency ◽  
Pulse Modulation ◽  
Spectral Correlation ◽  
Electronic Warfare ◽  
Spectral Correlation Density ◽  
Low Probability ◽  
Better Than

Abstract Low probability of intercept (LPI) radars utilize specially designed waveforms for intra-pulse modulation and hence LPI radars cannot be easily intercepted by passive receivers. The waveforms include linear frequency modulation, nonlinear frequency modulation, polyphase, and polytime codes. The advantages of LPI radar are wide bandwidth, frequency variability, low power, and the ability to hide their emissions. On the other hand, the main purpose of intercept receiver is to classify and estimate the parameters of the waveforms even when the signals are contaminated with noise. Precise measurement of the parameters will provide necessary information about a threat to the radar so that the electronic attack or electronic warfare support system could take instantaneous counter action against the enemy. In this work, noisy polyphase and polytime coded waveforms are analyzed using cyclostationary (CS) algorithm. To improve the signal quality, the noisy signal is pre-processed using two types of denoising filters. The denoised signal is analyzed using CS techniques and the coefficients of spectral correlation density are computed. With this method, modulation parameters of nine types of waveforms up to −12 dB signal-to-noise ratio with an accuracy of better than 95% are extracted. When compared with literature values, it is found that the results are superior.

GPGPU Based Parallel Implementation of Spectral Correlation Density Function

2019 ◽  
Vol 92 (1) ◽  
pp. 71-93
Author(s):  
Scott Marshall ◽  
Garrett Vanhoy ◽  
Ali Akoglu ◽  
Tamal Bose ◽  
Bo Ryu
Keyword(s):  
Density Function ◽  
Parallel Implementation ◽  
Spectral Correlation ◽  
Spectral Correlation Density

Cyclostationary Analysis Based Gear Fault Diagnosis

2012 ◽  
Vol 459 ◽  
pp. 190-194
Author(s):  
Zhen Tao Li ◽  
Hui Li
Keyword(s):  
Vibration Signal ◽  
Random Processes ◽  
Spectral Structure ◽  
Spectral Correlation ◽  
Detection And Identification ◽  
Spectral Correlation Density ◽  
Fault Detection And Identification ◽  
Gear Fault ◽  
Cyclostationary Signals ◽  
Fault Characteristic

Gearbox vibrations acquired by sensors are random cyclostationary signals, which are a combination of periodic and random processes due to the machine’s rotation cycle and interaction with the real world. Since the spectral structure of a gear vibration signal is mainly characterized by the interaction between the meshing harmonics and their sidebands, the spectral correlation density (SCD) function has been applied to gear monitoring. This approach is capable of completely extracting the fault characteristic frequencies related to the defect. This gives a desirable ability to detect the singularity characteristic of a signal precisely. This technique permits both fault detection and identification of the damaged gear. The experimental results show that the proposed method based on cyclostationary analysis can effectively diagnose the faults of gear.

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