—Amplitude modulation of the broadband propeller noise as a result of the cavitation yields a sec... more —Amplitude modulation of the broadband propeller noise as a result of the cavitation yields a second-order cyclosta-tionary ship-radiated noise. The spectrum of the modulating signal, consisting of the so-called propeller (or cavitation) tonals, enables the detection and the classification of the submarines or surface ships. However, data acquisition for this purpose causes vast data sizes due to high sampling rates and multiple sensor deployment. To mitigate the negative effects of this acquisition process such as on energy efficiency, hardware complexity, and storage capacity, we propose a scheme for compressive sensing of propeller tonals. We show that the spectral correlation function of cyclostation-ary propeller noise is sparse and derive a linear relationship between the compressive and Nyquist-rate cyclic modulation spectra, i.e., the approximation of spectral correlation function that allows utilizing matrix representations required in compressive sensing. It also enables use of the cyclic modulation coherence, i.e., the normalized cyclic modulation spectrum, to demonstrate the effect of compressive sensing in terms of statistical detection. We compare the recovery and detection performance results of the sparse approximation algorithms based on the so-called iterative hard thresholding and compressive sampling matching pursuit. Results show that compression is achievable without affecting the detection performance negatively. The main challenges are the weak modulation, low signal-to-noise ratio, and nonstationarity of the ambient noise, all of which reduce the sparsity level, hence causing degraded recovery and detection performance. Index Terms—Compressive sampling matching pursuit (CoSaMP), compressive sensing (CS), cyclostationarity, iterative hard thresholding (IHT), propeller tonals, ship detection.
Narrow-band spectral components of ship radiated noise are of crucial importance for sonar operat... more Narrow-band spectral components of ship radiated noise are of crucial importance for sonar operators in order to detect and classify possible targets. Classical spectral estimation techniques have been widely used to fulfill the requirements for narrow-band signal detection. However, classical methods may not be sufficient when signal-to-noise ratio is relatively low. In this paper we propose to utilize the subspace based multiple signal classification for the cavitation related narrow-band ship radiated noise detection. In addition we propose the use of fractional lower order statistics instead of second order statistics when the ship radiated noise exhibits an impulsive behavior leading to drifting apart from Gaussianity. We present the results of a real ship noise data gathered from the Strait of Istanbul. We also investigate and present the 1/f -type noise characteristics of a sample ship noise segment and discuss how we can use this information for robust estimation of cavitation related narrow-band ship noise spectrum.
Depending strongly on the wind speed, wind power exhibits significant intermittencies and fluctua... more Depending strongly on the wind speed, wind power exhibits significant intermittencies and fluctuations, which may affect adversely the smooth operation of the grid. Therefore, in order to provide the security of energy supply and operate the power system in accordance with the regulations, an effective wind forecast is necessary. Persistence type forecasting cannot guarantee to predict the variations in wind power within the acceptable range in medium and long terms. In this paper, a new statistical approach for wind speed forecasting is presented based on independent component analysis (ICA) and autoregressive (AR) model. The purpose of using ICA is to investigate the possibility that the wind speed time-series may contain some underlying hidden factors. It is understood that ICA, especially ICA methods based on exploiting the time structure like second order blind identification (SOBI) can be used as a preliminary step in wind speed forecasting.
—Amplitude modulation of the broadband propeller noise as a result of the cavitation yields a sec... more —Amplitude modulation of the broadband propeller noise as a result of the cavitation yields a second-order cyclosta-tionary ship-radiated noise. The spectrum of the modulating signal, consisting of the so-called propeller (or cavitation) tonals, enables the detection and the classification of the submarines or surface ships. However, data acquisition for this purpose causes vast data sizes due to high sampling rates and multiple sensor deployment. To mitigate the negative effects of this acquisition process such as on energy efficiency, hardware complexity, and storage capacity, we propose a scheme for compressive sensing of propeller tonals. We show that the spectral correlation function of cyclostation-ary propeller noise is sparse and derive a linear relationship between the compressive and Nyquist-rate cyclic modulation spectra, i.e., the approximation of spectral correlation function that allows utilizing matrix representations required in compressive sensing. It also enables use of the cyclic modulation coherence, i.e., the normalized cyclic modulation spectrum, to demonstrate the effect of compressive sensing in terms of statistical detection. We compare the recovery and detection performance results of the sparse approximation algorithms based on the so-called iterative hard thresholding and compressive sampling matching pursuit. Results show that compression is achievable without affecting the detection performance negatively. The main challenges are the weak modulation, low signal-to-noise ratio, and nonstationarity of the ambient noise, all of which reduce the sparsity level, hence causing degraded recovery and detection performance. Index Terms—Compressive sampling matching pursuit (CoSaMP), compressive sensing (CS), cyclostationarity, iterative hard thresholding (IHT), propeller tonals, ship detection.
Narrow-band spectral components of ship radiated noise are of crucial importance for sonar operat... more Narrow-band spectral components of ship radiated noise are of crucial importance for sonar operators in order to detect and classify possible targets. Classical spectral estimation techniques have been widely used to fulfill the requirements for narrow-band signal detection. However, classical methods may not be sufficient when signal-to-noise ratio is relatively low. In this paper we propose to utilize the subspace based multiple signal classification for the cavitation related narrow-band ship radiated noise detection. In addition we propose the use of fractional lower order statistics instead of second order statistics when the ship radiated noise exhibits an impulsive behavior leading to drifting apart from Gaussianity. We present the results of a real ship noise data gathered from the Strait of Istanbul. We also investigate and present the 1/f -type noise characteristics of a sample ship noise segment and discuss how we can use this information for robust estimation of cavitation related narrow-band ship noise spectrum.
Depending strongly on the wind speed, wind power exhibits significant intermittencies and fluctua... more Depending strongly on the wind speed, wind power exhibits significant intermittencies and fluctuations, which may affect adversely the smooth operation of the grid. Therefore, in order to provide the security of energy supply and operate the power system in accordance with the regulations, an effective wind forecast is necessary. Persistence type forecasting cannot guarantee to predict the variations in wind power within the acceptable range in medium and long terms. In this paper, a new statistical approach for wind speed forecasting is presented based on independent component analysis (ICA) and autoregressive (AR) model. The purpose of using ICA is to investigate the possibility that the wind speed time-series may contain some underlying hidden factors. It is understood that ICA, especially ICA methods based on exploiting the time structure like second order blind identification (SOBI) can be used as a preliminary step in wind speed forecasting.
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