The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Guifu Zhang - One of the best experts on this subject based on the ideXlab platform.
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detection of Ground Clutter for dual polarization weather radar using a novel 3d discriminant function
Journal of Atmospheric and Oceanic Technology, 2019Co-Authors: Mohammadhossein Golbonhaghighi, Guifu ZhangAbstract:AbstractA novel 3D discriminant function is introduced as part of a Ground Clutter detection algorithm for improving weather radar observations. The 3D discriminant function utilizes the phase fluc...
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Ground Clutter detection for weather radar using phase fluctuation index
IEEE Transactions on Geoscience and Remote Sensing, 2019Co-Authors: Mohammadhossein Golbonhaghighi, Guifu Zhang, Richard J. DoviakAbstract:Our aim, in this paper, is to develop a Clutter detection algorithm to provide more representative weather radar observations. The new discriminant function based on the phase fluctuation index (PFI) is introduced to achieve a better performance for Clutter detection algorithms. Statistical properties of the PFI for pure weather and Ground Clutter are presented. A Bayesian classifier is used to make an optimal decision to detect Clutter mixed with weather echoes. The performance improvements are demonstrated by applying the PFI detection algorithm to radar data collected by a WSR-88D polarimetric weather radar. Our proposed Clutter detection algorithm is compared to several other detection algorithms and reveals the PFI algorithm yields the highest probability of detection.
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Detection of Ground Clutter from Weather Radar Using a Dual-Polarization and Dual-Scan Method
Atmosphere, 2016Co-Authors: Mohammad-hossein Golbon-haghighi, Guifu Zhang, Yinguang Li, Richard J. DoviakAbstract:A novel dual-polarization and dual-scan (DPDS) classification algorithm is developed for Clutter detection in weather radar observations. Two consecutive scans of dual-polarization radar echoes are jointly processed to estimate auto- and cross-correlation functions. Discriminants are then defined and estimated in order to separate Clutter from weather based on their physical and statistical properties. An optimal Bayesian classifier is used to make a decision on Clutter presence from the estimated discriminant functions. The DPDS algorithm is applied to the data collected with the KOUN polarimetric radar and compared with the existing detection methods. It is shown that the DPDS algorithm yields a higher probability of detection and lower false alarm rate in Clutter detection.
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Ground Clutter detection using the statistical properties of signals received with a polarimetric radar
IEEE Transactions on Signal Processing, 2014Co-Authors: Guifu Zhang, Richard J. DoviakAbstract:Polarimetric weather radars provide additional measurements that allow better characterization of the targeted medium. Because Ground Clutter has different polarimetric characteristics from weather echoes, dual-polarization measurements can be used to distinguish one from the other. Ground Clutter and weather signals also have different statistical properties which can be utilized to distinguish one from the other. A test statistic, obtained from the generalized likelihood ratio test (GLRT), and a simple Bayesian classifier (SBC), with inputs from the mean and covariance of the received signals, are developed to detect Ground Clutter in the presence of weather signals. It is found that the test statistic produces false detections caused by narrow-band zero-velocity weather signals while the SBC can effectively neutralize them. This work is aimed at detecting Ground Clutter based solely on data from each resolution volume. The performances of the test statistic and SBC are shown by applying them to radar data collected with the University of Oklahoma-Polarimetric Radar for Innovation in Meteorology and Engineering.
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scan to scan correlation of weather radar signals to identify Ground Clutter
IEEE Geoscience and Remote Sensing Letters, 2013Co-Authors: Guifu Zhang, Richard J. Doviak, D S SaxionAbstract:The scan-to-scan correlation method to discriminate weather signals from Ground Clutter, described in this letter, takes advantage of the fact that the correlation time of radar echoes from hydrometeors is typically much shorter than that from Ground objects. In this letter, the scan-to-scan correlation method is applied to data from the WSR-88D, and its results are compared with those produced by the WSR-88D's Ground Clutter detector. A subjective comparison with an operational Clutter detection algorithm used on the network of weather radars shows that the scan-to-scan correlation method produces a similar Clutter field but presents Clutter locations with higher spatial resolution.
Richard J. Doviak - One of the best experts on this subject based on the ideXlab platform.
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Ground Clutter detection for weather radar using phase fluctuation index
IEEE Transactions on Geoscience and Remote Sensing, 2019Co-Authors: Mohammadhossein Golbonhaghighi, Guifu Zhang, Richard J. DoviakAbstract:Our aim, in this paper, is to develop a Clutter detection algorithm to provide more representative weather radar observations. The new discriminant function based on the phase fluctuation index (PFI) is introduced to achieve a better performance for Clutter detection algorithms. Statistical properties of the PFI for pure weather and Ground Clutter are presented. A Bayesian classifier is used to make an optimal decision to detect Clutter mixed with weather echoes. The performance improvements are demonstrated by applying the PFI detection algorithm to radar data collected by a WSR-88D polarimetric weather radar. Our proposed Clutter detection algorithm is compared to several other detection algorithms and reveals the PFI algorithm yields the highest probability of detection.
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Detection of Ground Clutter from Weather Radar Using a Dual-Polarization and Dual-Scan Method
Atmosphere, 2016Co-Authors: Mohammad-hossein Golbon-haghighi, Guifu Zhang, Yinguang Li, Richard J. DoviakAbstract:A novel dual-polarization and dual-scan (DPDS) classification algorithm is developed for Clutter detection in weather radar observations. Two consecutive scans of dual-polarization radar echoes are jointly processed to estimate auto- and cross-correlation functions. Discriminants are then defined and estimated in order to separate Clutter from weather based on their physical and statistical properties. An optimal Bayesian classifier is used to make a decision on Clutter presence from the estimated discriminant functions. The DPDS algorithm is applied to the data collected with the KOUN polarimetric radar and compared with the existing detection methods. It is shown that the DPDS algorithm yields a higher probability of detection and lower false alarm rate in Clutter detection.
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Ground Clutter detection using the statistical properties of signals received with a polarimetric radar
IEEE Transactions on Signal Processing, 2014Co-Authors: Guifu Zhang, Richard J. DoviakAbstract:Polarimetric weather radars provide additional measurements that allow better characterization of the targeted medium. Because Ground Clutter has different polarimetric characteristics from weather echoes, dual-polarization measurements can be used to distinguish one from the other. Ground Clutter and weather signals also have different statistical properties which can be utilized to distinguish one from the other. A test statistic, obtained from the generalized likelihood ratio test (GLRT), and a simple Bayesian classifier (SBC), with inputs from the mean and covariance of the received signals, are developed to detect Ground Clutter in the presence of weather signals. It is found that the test statistic produces false detections caused by narrow-band zero-velocity weather signals while the SBC can effectively neutralize them. This work is aimed at detecting Ground Clutter based solely on data from each resolution volume. The performances of the test statistic and SBC are shown by applying them to radar data collected with the University of Oklahoma-Polarimetric Radar for Innovation in Meteorology and Engineering.
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scan to scan correlation of weather radar signals to identify Ground Clutter
IEEE Geoscience and Remote Sensing Letters, 2013Co-Authors: Guifu Zhang, Richard J. Doviak, D S SaxionAbstract:The scan-to-scan correlation method to discriminate weather signals from Ground Clutter, described in this letter, takes advantage of the fact that the correlation time of radar echoes from hydrometeors is typically much shorter than that from Ground objects. In this letter, the scan-to-scan correlation method is applied to data from the WSR-88D, and its results are compared with those produced by the WSR-88D's Ground Clutter detector. A subjective comparison with an operational Clutter detection algorithm used on the network of weather radars shows that the scan-to-scan correlation method produces a similar Clutter field but presents Clutter locations with higher spatial resolution.
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A New Approach to Detect Ground Clutter Mixed With Weather Signals
IEEE Transactions on Geoscience and Remote Sensing, 2013Co-Authors: Yinguang Li, Guifu Zhang, Richard J. DoviakAbstract:Considering that the statistics of the phase and the power of weather signals in the spectral domain are different from those statistics for echoes from stationary objects, a spectrum Clutter identification (SCI) algorithm has been developed to detect Ground Clutter using single polarization radars, but SCI can be extended for dual-pol radars. SCI examines both the power and phase in the spectral domain and uses a simple Bayesian classifier to combine four discriminants: spectral power distribution, spectral phase fluctuations, spatial texture of echo power, and spatial texture of spectrum width to make decisions as to the presence of Clutter that can corrupt meteorological measurements. This work is focused on detecting Ground Clutter mixed with weather signals, even if the Clutter power to signal power ratio is low. The performance of the SCI algorithm is shown by applying it to radar data collected by University of Oklahoma-Polarimetric Radar for Innovation in Meteorology and Engineering.
James Ward - One of the best experts on this subject based on the ideXlab platform.
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space time adaptive processing for airborne radar
Space-Time Adaptive Processing (Ref. No. 1998 241) IEE Colloquium on, 1998Co-Authors: James WardAbstract:Advanced airborne radar systems are required to detect targets in the presence of both Clutter and jamming. Ground Clutter is extended in both angle and range, and is spread in Doppler frequency because of the platform motion. Space-time adaptive processing (STAP) refers to the simultaneous processing of the signals from an array antenna during a multiple pulse coherent waveform. STAP can provide improved detection of targets obscured by mainlobe Clutter, sidelobe Clutter, and jamming. This paper provides an overview of partially adaptive STAP approaches. Analysis of the Clutter covariance matrix rank provides insight and conditions for preprocessor design. As the filters used for detection in a STAP radar depend on the backGround interference estimates, the approaches used for parameter estimation must be modified for a STAP radar. The effect of STAP on angle and Doppler accuracy is described, and an approach for joint angle and Doppler estimation in a STAP radar is described.
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space time adaptive processing for airborne radar
International Conference on Acoustics Speech and Signal Processing, 1995Co-Authors: James WardAbstract:Advanced airborne radar systems are required to detect targets in the presence of both Clutter and jamming. Ground Clutter is extended in both angle and range, and is spread in Doppler frequency because of the platform motion. Space-time adaptive processing (STAP) refers to the simultaneous processing of the signals from an array antenna during a multiple pulse coherent waveform. STAP can provide improved detection of targets obscured by mainlobe Clutter, defection of targets obscured by sidelobe Clutter, and detection in combined Clutter and jamming environments. Fully adaptive STAP is impractical for reasons of computational complexity and estimation with limited data, so partially adaptive approaches are required. The paper presents a taxonomy of partially adaptive STAP approaches that are classified according to the type of preprocessor, or equivalently, by the domain in which adaptive weighting occurs. Analysis of the rank of the Clutter covariance matrix in each domain provides insight and conditions for preprocessor design.
J B Billingsley - One of the best experts on this subject based on the ideXlab platform.
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Validation of windblown radar Ground Clutter spectral shape
IEEE Transactions on Aerospace and Electronic Systems, 2001Co-Authors: M. Greco, A Farina, F. Gini, J B BillingsleyAbstract:We investigate the robustness of the linear matched filter (MF) operating in a Gaussian environment in the presence of a mismatch between the design Clutter-power spectral density (PSD) shape and the actual one. The Gaussian, the power-law (PL), and the double-exponential spectral models have been considered with the goal of investigating which one fits best for windblown foliage. We analyze the MF performance in terms of improvement factor, probability of false alarm, and probability of detection by making use of the theoretical models and measured X-band Ground Clutter data. The numerical results validate the double-exponential spectral model for windblown foliage by showing that the differences in performance prediction between using measured Clutter data and modeled Clutter data of various spectral shapes (viz., Gaussian, FL, and double-exponential) are minimized when the spectral model employed is of double-exponential shape.
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A new model for the Doppler spectrum of windblown radar Ground Clutter
Proceedings of the 1999 IEEE Radar Conference. Radar into the Next Millennium (Cat. No.99CH36249), 1999Co-Authors: Pierfrancesco Lombardo, J B BillingsleyAbstract:A phenomenological model is derived for the Doppler spectrum of radar Clutter echoes from windblown vegetation. The family of spectral shapes obtained, denoted K-PSD, shows an exponential decay of the spectral tails that gives a good fit to the spectra observed in MIT-LL Ground Clutter measurements. The exponential shape provides tails that decay much faster than in the usual power-law model, but that remain wider than in the Gaussian model. The derived model provides a theoretical basis for the spectral shapes observed in the measurements.
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statistical analyses of measured radar Ground Clutter data
IEEE Transactions on Aerospace and Electronic Systems, 1999Co-Authors: J B Billingsley, Fulvio Gini, Alfonso Farina, Maria Greco, L VerrazzaniAbstract:The performance of Ground-based surveillance radars strongly depends on the distribution and spectral characteristics of Ground Clutter. To design signal processing algorithms that exploit the knowledge of Clutter characteristics, a preliminary statistical analysis of Ground-Clutter data is necessary. We report the results of a statistical analysis of X-band Ground-Clutter data from the MIT Lincoln Laboratory Phase One program. Data non-Gaussianity of the in-phase and quadrature components was revealed, first by means of histogram and moments analysis, and then by means of a Gaussianity test based on cumulants of order higher than the second; to this purpose parametric autoregressive (AR) modeling of the Clutter process was developed. The test is computationally attractive and has constant false alarm rate (CFAR). Incoherent analysis has also been carried out by checking the fitting to Rayleigh, Weibull, log-normal, and K-distribution models. Finally, a new modified Kolmogorov-Smirnoff (KS) goodness-of-fit test is proposed; this modified test guarantees good fitting in the distribution tails, which is of fundamental importance for a correct design of CFAR processors.
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exponential decay in windblown radar Ground Clutter doppler spectra multifrequency measurements and model
1996Co-Authors: J B BillingsleyAbstract:Abstract : A new empirical model for windblown radar Ground Clutter Doppler spectra is developed based on many Clutter spectral measurements obtained with Lincoln Laboratory's L-Band Clutter Experiment (LCE) and Phase One five-frequency (i.e., VHF, UHF, L-, S-, and X-band) instrumentation radars over spectral dynamic ranges reaching 60 to 80 dB below zero-Doppler peaks. The model includes both ac and dc spectral components. Ac spectral shape is specified as exponential, with the Doppler-velocity exponential shape factor strongly dependent on wind speed but independent of radar frequency, VHF to X-band. The exponential shape is intermediate in spectral extent between the Gaussian shape of historical usage (now acknowledged as being too narrow) and power-law shapes of more recent usage (which are too wide when extrapolated to levels 60 to 80 dB down). The ratio of dc to ac spectral power in the model is determined by an empirically derived analytic expression that captures the strong dependencies of dc/ac ratio on both wind speed and radar frequency in the measurement data. Many examples of windblown Clutter spectra are provided and compared with model predictions, encompassing variations in wind speed and radar frequency, as well as in other parameters such as polarization, range, cell size, grazing angle, wind direction, measurement site, and season of the year. Although the exponential model is explicitly derived to be applicable to windblown trees, examples are also provided of measured Clutter spectra from scrub desert, rangeland and cropland vegetations which indicate that the model can also perform adequately for other windblown vegetation types by suitably adjusting its dc/ac term.
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Ground Clutter measurements for surface sited radar
NASA STI Recon Technical Report N, 1993Co-Authors: J B BillingsleyAbstract:Abstract : A large volume of radar Ground Clutter measurement data has been collected from many sites that are widely dispersed over the North American continent. At each site backscatter was recorded from all the terrain within the field of view, typically to ranges extending to 25 or 50 km. As a result, in most of these measurements the angle of illumination of the earth's surface was usually very low, typically within a degree or so of grazing incidence, with much intermittent shadowing of low regions. This report examines the nature of low-angle radar Ground Clutter as it has come to be understood through analysis of this extensive new base of measurements. Depression angle, that is, the angle below the horizontal at which the backscattering terrain point is observed at the radar antenna, is shown to be the principal parametric influence on Clutter amplitude statistics, even for the very low angles and small (i.e., typically fractional) variations in angle that occur in surface-sited radar. This principal role of depression angle is the result of its effect on shadowing in a sea of patchy visibility and discrete or localized scattering sources. Following this understanding, a general predictive model for Ground Clutter spatial amplitude statistics is developed based on very precise computation of depression angle but on only relatively gross specification of terrain type. The important radar parameters entering the model are rf frequency as it affects mean strength of Clutter amplitude distributions and spatial resolution as it affects spread in these distributions.... Radar Ground Clutter, Terrain reflectivity, Land Clutter, electromagnetic propagation Clutter measurements, Low-angle, Radar Clutter, Multipath Clutter models, Multifrequency.
Ralf Bennartz - One of the best experts on this subject based on the ideXlab platform.
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uncertainty analysis for cloudsat snowfall retrievals
Journal of Applied Meteorology and Climatology, 2011Co-Authors: Michael J Hiley, Mark S Kulie, Ralf BennartzAbstract:Abstract A new method to derive radar reflectivity–snow rate (Ze–S) relationships from scattering properties of different ice particle models is presented. Three statistical Ze–i relationships are derived to characterize the best estimate and uncertainties due to ice habit. The derived relationships are applied to CloudSat data to derive near-surface snowfall retrievals. Other uncertainties due to various method choices, such as vertical continuity tests, the near-surface reflectivity threshold used for choosing snowfall cases, and correcting for attenuation, are also explored on a regional and zonally averaged basis. The vertical continuity test in particular is found to have interesting regional effects. Although it appears to be useful for eliminating Ground Clutter over land, it also masks out potential lake-effect-snowfall cases over the Southern Ocean storm-track region. The choice of reflectivity threshold is found to significantly affect snowfall detection but is insignificant in terms of the mean...
