The Experts below are selected from a list of 6915 Experts worldwide ranked by ideXlab platform
John J Soraghan - One of the best experts on this subject based on the ideXlab platform.
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small target detection in Sea Clutter
IEEE Transactions on Geoscience and Remote Sensing, 2004Co-Authors: S Panagopoulos, John J SoraghanAbstract:Sea Clutter in marine surveillance radar makes the task of detecting small targets a very challenging problem. In this paper, a set of three signal processing techniques designed to suppress unwanted Sea Clutter radar echo and achieve target detection with no prior knowledge of the ocean and environmental conditions is presented. These include signal averaging, time-frequency representation, and morphological filtering. Datasets from real marine radar operating in staring mode are used to illustrate the performance of the new approaches.
Wen-wen Tung - One of the best experts on this subject based on the ideXlab platform.
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Multiscale modeling of Sea Clutter to facilitate detection of low observable targets within Sea Clutter
Proceedings of SPIE, 2009Co-Authors: Jing Hu, Wen-wen Tung, Erik Blasch, Genshe ChenAbstract:Sea Clutter, the radar backscatter from the ocean surface, is highly complicated and non-stationary, due to multipath propagation of the radar returns and multiscale interactions at the air-Sea interface. To facilitate robust detection of low observable targets within Sea Clutter, which is an important issue in coastal security, navigation safety and environmental monitoring, we propose a systematic multiscale approach to the modeling of Sea Clutter. Specifically, we (i) develop new methods to better fit non-stationary and non-Gaussian Sea Clutter, (ii) fully characterize the correlation structure of Sea Clutter on multiple time scales, (iii) develop a highly accurate cascade model for Sea Clutter, and (iv) develop accurate and readily implementable methods to detect low observable targets within Sea Clutter.
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A New Way to Model Nonstationary Sea Clutter
IEEE Signal Processing Letters, 2009Co-Authors: Jing Hu, Wen-wen TungAbstract:Sea Clutter refers to the radar backscatter from a patch of ocean surface. To properly characterize radar Clutter returns, a lot of effort has been made to fit various distributions to the observed amplitude data of Sea Clutter. However, the fitting of real Sea Clutter data using those distributions is not satisfactory. This may be due to the fact that Sea Clutter data is highly nonstationary. This nonstationarity motivates us to perform distributional analysis on the data obtained by differentiating the amplitude data of Sea Clutter. By systematically analyzing differentiated data of 280 Sea Clutter time series measured under various Sea and weather conditions, we show that the Tsallis distribution fits Sea Clutter data much better than commonly used distributions for Sea Clutter such as the K distribution. We also find that the parameters from the Tsallis distribution are more effective than the ones from the K distribution for detecting low observable targets within Sea Clutter.
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On modeling Sea Clutter by diffusive models
Signal Processing Sensor Fusion and Target Recognition XVII, 2008Co-Authors: Jing Hu, Wen-wen Tung, Robert S. Lynch, Genshe ChenAbstract:Sea Clutter, the radar backscatter from the ocean surface, has been observed to be highly non-Gaussian. K distribution is among the best distributions proposed to fit non-Gaussian Sea Clutter data. Using diffusive models, K distributed Sea Clutter can be casted as a Gaussian speckle, with a de-correlation time of 0.1 s, modulated by a Gamma distribution, with a de-correlationtime of about 1 s, characterizingthe largescale structures of the Sea surface. Our analyses of large amounts of real Sea Clutter data suggest that between the time scales for the Gaussian speckle and large scale structures on the Sea surface to de-correlate, Sea Clutter can be characterized as multifractal 1/f processes. This is the feature that is not captured by diffusive models and underlies why K distribution cannot fit real Sea Clutter data sufficiently well. We surmise that by combining K distribution and associated diffusive models with multifractal formalism, the many different physical processes underlying Sea Clutter can be more comprehensively characterized.
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On modeling Sea Clutter by noisy chaotic dynamics
Signal Processing Sensor Fusion and Target Recognition XVII, 2008Co-Authors: Wen-wen Tung, Jing Hu, Robert S. Lynch, Genshe ChenAbstract:Modeling Sea Clutter by chaotic dynamics has been an exciting yet heatedly debated topic. To resolve controversies associated with this approach, we use the scale-dependent Lyapunov exponent (SDLE) to study Sea Clutter. The SDLE has been shown to be able to unambiguously distinguish chaos from noise. Our analyses of almost 400 Sea Clutter datasets measured by Professor Simon Haykin suggest that on very short time scales, Sea Clutter may be classified as noisy chaos, characterized by a parameter γ, which characterizes the speed of information loss. It is shown that γ can be used to very effectively detect low observable targets within Sea Clutter.
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target detection within Sea Clutter a comparative study by fractal scaling analyses
Fractals, 2006Co-Authors: Jing Hu, Yi Zheng, Fred Posner, Wen-wen TungAbstract:Sea Clutter refers to the radar returns from a patch of ocean surface. Accurate modeling of Sea Clutter and robust detection of low observable targets within Sea Clutter are important problems in remote sensing and radar signal processing applications. Due to lack of fundamental understanding of the nature of Sea Clutter, however, no simple and effective methods for detecting targets within Sea Clutter have been proposed. To help solve this important problem, we apply three types of fractal scaling analyses, fluctuation analysis (FA), detrended fluctuation analysis (DFA), and the wavelet-based fractal scaling analysis to study Sea Clutter. Our analyses show that Sea Clutter data exhibit fractal behaviors in the time scale range of about 0.01 seconds to a few seconds. The physical significance of these time scales is discussed. We emphasize that time scales characterizing fractal scaling break are among the most important features for detecting patterns using fractal theory. By systematically studying 392 Sea Clutter time series measured under various Sea and weather conditions, we find very effective methods for detecting targets within Sea Clutter. Based on the data available to us, the accuracy of these methods is close to 100%.
Jing Hu - One of the best experts on this subject based on the ideXlab platform.
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Multiscale Characterization of Sea Clutter by Scale-Dependent Lyapunov Exponent
Mathematical Problems in Engineering, 2013Co-Authors: Jing HuAbstract:Determining whether Sea Clutter radar returns are stochastic or deterministic is crucial to the successful modelling of Sea Clutter as well as to facilitate target detection within Sea Clutter. Despite extensive studies of Sea Clutter using distributional analysis, chaos analysis, and fractal analysis, the nature of Sea Clutter is still not well understood. Realizing that the difficulty in Sea Clutter modeling is due to the multiscale nature of Sea Clutter, we employ a new multiscale complexity measure, the scale-dependent Lyapunov exponent (SDLE), to better characterize the nonstationary and multiscale nature of Sea Clutter. SDLE has been shown to readily characterize major models of complex time series, including deterministic chaos, noisy chaos, stochastic oscillations, random processes, random Levy processes, and complex time series with multiple scaling behaviors. With SDLE, we are able to directly show that Sea Clutter is not chaotic. More importantly, we find a new scaling law suggesting noisy dynamics for Sea Clutter. The new scaling law has an interesting interpretation in terms of intrinsic predictability of Sea Clutter, and provides an excellent new means of detecting targets within Sea Clutter.
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Multiscale modeling of Sea Clutter to facilitate detection of low observable targets within Sea Clutter
Proceedings of SPIE, 2009Co-Authors: Jing Hu, Wen-wen Tung, Erik Blasch, Genshe ChenAbstract:Sea Clutter, the radar backscatter from the ocean surface, is highly complicated and non-stationary, due to multipath propagation of the radar returns and multiscale interactions at the air-Sea interface. To facilitate robust detection of low observable targets within Sea Clutter, which is an important issue in coastal security, navigation safety and environmental monitoring, we propose a systematic multiscale approach to the modeling of Sea Clutter. Specifically, we (i) develop new methods to better fit non-stationary and non-Gaussian Sea Clutter, (ii) fully characterize the correlation structure of Sea Clutter on multiple time scales, (iii) develop a highly accurate cascade model for Sea Clutter, and (iv) develop accurate and readily implementable methods to detect low observable targets within Sea Clutter.
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A New Way to Model Nonstationary Sea Clutter
IEEE Signal Processing Letters, 2009Co-Authors: Jing Hu, Wen-wen TungAbstract:Sea Clutter refers to the radar backscatter from a patch of ocean surface. To properly characterize radar Clutter returns, a lot of effort has been made to fit various distributions to the observed amplitude data of Sea Clutter. However, the fitting of real Sea Clutter data using those distributions is not satisfactory. This may be due to the fact that Sea Clutter data is highly nonstationary. This nonstationarity motivates us to perform distributional analysis on the data obtained by differentiating the amplitude data of Sea Clutter. By systematically analyzing differentiated data of 280 Sea Clutter time series measured under various Sea and weather conditions, we show that the Tsallis distribution fits Sea Clutter data much better than commonly used distributions for Sea Clutter such as the K distribution. We also find that the parameters from the Tsallis distribution are more effective than the ones from the K distribution for detecting low observable targets within Sea Clutter.
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On modeling Sea Clutter by diffusive models
Signal Processing Sensor Fusion and Target Recognition XVII, 2008Co-Authors: Jing Hu, Wen-wen Tung, Robert S. Lynch, Genshe ChenAbstract:Sea Clutter, the radar backscatter from the ocean surface, has been observed to be highly non-Gaussian. K distribution is among the best distributions proposed to fit non-Gaussian Sea Clutter data. Using diffusive models, K distributed Sea Clutter can be casted as a Gaussian speckle, with a de-correlation time of 0.1 s, modulated by a Gamma distribution, with a de-correlationtime of about 1 s, characterizingthe largescale structures of the Sea surface. Our analyses of large amounts of real Sea Clutter data suggest that between the time scales for the Gaussian speckle and large scale structures on the Sea surface to de-correlate, Sea Clutter can be characterized as multifractal 1/f processes. This is the feature that is not captured by diffusive models and underlies why K distribution cannot fit real Sea Clutter data sufficiently well. We surmise that by combining K distribution and associated diffusive models with multifractal formalism, the many different physical processes underlying Sea Clutter can be more comprehensively characterized.
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On modeling Sea Clutter by noisy chaotic dynamics
Signal Processing Sensor Fusion and Target Recognition XVII, 2008Co-Authors: Wen-wen Tung, Jing Hu, Robert S. Lynch, Genshe ChenAbstract:Modeling Sea Clutter by chaotic dynamics has been an exciting yet heatedly debated topic. To resolve controversies associated with this approach, we use the scale-dependent Lyapunov exponent (SDLE) to study Sea Clutter. The SDLE has been shown to be able to unambiguously distinguish chaos from noise. Our analyses of almost 400 Sea Clutter datasets measured by Professor Simon Haykin suggest that on very short time scales, Sea Clutter may be classified as noisy chaos, characterized by a parameter γ, which characterizes the speed of information loss. It is shown that γ can be used to very effectively detect low observable targets within Sea Clutter.
S Panagopoulos - One of the best experts on this subject based on the ideXlab platform.
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small target detection in Sea Clutter
IEEE Transactions on Geoscience and Remote Sensing, 2004Co-Authors: S Panagopoulos, John J SoraghanAbstract:Sea Clutter in marine surveillance radar makes the task of detecting small targets a very challenging problem. In this paper, a set of three signal processing techniques designed to suppress unwanted Sea Clutter radar echo and achieve target detection with no prior knowledge of the ocean and environmental conditions is presented. These include signal averaging, time-frequency representation, and morphological filtering. Datasets from real marine radar operating in staring mode are used to illustrate the performance of the new approaches.
Wenguang Wang - One of the best experts on this subject based on the ideXlab platform.
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ICCAIS - Sea Clutter Suppression using EMD-SVD- FRFT Filtering
2019 International Conference on Control Automation and Information Sciences (ICCAIS), 2019Co-Authors: Wenguang Wang, Cheng Chen, Yilin WangAbstract:Sea Clutter suppression is one of the major measures to improve target detection from Sea Clutter. Singular value decomposition-fractional Fourier transform (SVD-FRFT) filtering is conducted to suppress Sea Clutter without impairing the target even when the target and first-order Sea Clutter are mixed in the Doppler spectrum. However, for the range cells with only Sea Clutter, the SVD-FRFT filtering cannot thoroughly suppress the Sea Clutter owe to energy concentration of Sea Clutter to some extent in FRFT domain, which gives rise to false alarm in target detection. To address this problem, the combination of empirical mode decomposition (EMD) and SVD-FRFT is proposed to separate target from Sea Clutter and pre-suppress the Sea Clutter. The experiment based on real measured Sea echo shows that the novel method can obtain superior performance.
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Sea Clutter Suppression using EMD-SVD- FRFT Filtering
2019 International Conference on Control Automation and Information Sciences (ICCAIS), 2019Co-Authors: Wenguang Wang, Cheng Chen, Yilin WangAbstract:Sea Clutter suppression is one of the major measures to improve target detection from Sea Clutter. Singular value decomposition-fractional Fourier transform (SVD-FRFT) filtering is conducted to suppress Sea Clutter without impairing the target even when the target and first-order Sea Clutter are mixed in the Doppler spectrum. However, for the range cells with only Sea Clutter, the SVD-FRFT filtering cannot thoroughly suppress the Sea Clutter owe to energy concentration of Sea Clutter to some extent in FRFT domain, which gives rise to false alarm in target detection. To address this problem, the combination of empirical mode decomposition (EMD) and SVD-FRFT is proposed to separate target from Sea Clutter and pre-suppress the Sea Clutter. The experiment based on real measured Sea echo shows that the novel method can obtain superior performance.
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A Novel Method of Small Target Detection in Sea Clutter
International Scholarly Research Notices, 2011Co-Authors: Peng Wu, Jun Wang, Wenguang WangAbstract:Detecting low observable targets within Sea Clutter at low grazing angle is one of the reSearch hotspots in radar signal processing community. In this paper, we have proposed a novel method based on polarimetric decomposition theorem. The polar characteristics of Sea Clutter has been analyzed, with the parameters after the decomposition of target scattering matrix. The scattering entropy and the scattering angle are the key parameters to discriminate the target from the Sea Clutter. The technique is designed to suppress unwanted Sea Clutter at polarimetric domain. Datasets from real marine radar are used to illustrate the performance of the new approach.
