Ground Clutter Research Articles

Object-Orientated Filter Design in Spectral Domain for Polarimetric Weather Radar

Aiming at removing stationary and moving clutter while retaining precipitation for dual-polarization weather radar, a new clutter suppression method, named the object-orientated spectral polarimetric (OBSpol) filter, is put forward in this paper. Based on the spectral polarimetric feature and the range-Doppler continuity of precipitation, the OBSpol filter generates a filtering mask implemented on the raw range-Doppler spectrogram to mitigate the clutter and noise. The procedures are as follows. After the spectral polarimetric filtering and the mathematical morphology method, one binary mask where “1” indicates the precipitation is obtained. Then, the contiguous bins of the same value “1” in the range-Doppler domain are grouped in areas termed as objects. Whether the produced objects are precipitation or not will be further judged based on appropriate weather radar observable. Thus, combining all the filtered separate objects, a filtering mask can be obtained. In this paper, data collected by the polarimetric Doppler IRCTR drizzle radar are used to demonstrate and assess the performance of the proposed technique in the case of ground clutter, narrowband moving clutter, and noise. Two precipitation cases are examined: 1) moderate precipitation with large scale and 2) light precipitation with severe clutter contamination. In the range-Doppler spectrogram, both stationary and narrowband moving clutters are mitigated, while maintaining nonoverlapping precipitation signal. This helps to solve the problem when clutter and precipitation overlap in the time domain. In addition, the OBSpol filter is proven to be effective with different Doppler velocity resolutions. This technique can be applied in real-time due to its low computation complexity. Moreover, the spectral polarimetric filtering can be designed using the measurements of dual-polarization radar systems which do not have cross-polar measurements. Hence, the proposed clutter mitigation technique can be implemented for operational dual-polarization weather radar.

Moving Target Tracking Based on Improved GMPHD Filter in Circular SAR System

A new moving target tracking method based on an improved Gaussian mixture probability hypothesis density (GMPHD) filter in a circular synthetic aperture radar (SAR) system is proposed. In order to obtain the temporal dynamic behavior of moving targets in a SAR scene, a multiple-target observation is first extracted from a sequence of clutter-suppressed subaperture circular SAR images. Then, a new multiple-target tracking model for the circular SAR system is established based on the dynamic behavior of moving targets. An improved labeled GMPHD filter with an adaptive target birth intensity is proposed to realize the moving target detection and tracking in a strong ground clutter environment, which is suitable for the circular SAR system. This method can not only provide accurate estimates for the number and states of moving targets but also can extract their trajectories. The effectiveness of the proposed method is verified through simulations and experimental Gotcha data.

How Does Ground Clutter Affect CloudSat Snowfall Retrievals Over Ice Sheets?

CloudSat has provided the first spaceborne snowfall observations in polar regions. Nevertheless, CloudSat retrievals may be affected by ground clutter even if the snowfall rate at the surface is estimated from the reflectivity measured at about 1200 m above land/ice surface. In this study, the impact of ground clutter contamination on CloudSat snowfall retrievals over the Antarctic and Greenland ice sheets is investigated. Our results suggest that ground clutter affects CloudSat snowfall observations over some areas, particularly over complex terrain such as mountain ranges and fjords. Over these areas, the snowfall rates deduced from CloudSat observations can be, therefore, significantly overestimated. This has implications when developing snowfall climatologies from CloudSat products.

An Integrated Approach to Weather Radar Calibration and Monitoring Using Ground Clutter and Satellite Comparisons

AbstractThe stability and accuracy of weather radar reflectivity calibration are imperative for quantitative applications, such as rainfall estimation, severe weather monitoring and nowcasting, and assimilation in numerical weather prediction models. Various radar calibration and monitoring techniques have been developed, but only recently have integrated approaches been proposed, that is, using different calibration techniques in combination. In this paper the following three techniques are used: 1) ground clutter monitoring, 2) comparisons with spaceborne radars, and 3) the self-consistency of polarimetric variables. These techniques are applied to a C-band polarimetric radar (CPOL) located in the Australian tropics since 1998. The ground clutter monitoring technique is applied to each radar volumetric scan and provides a means to reliably detect changes in calibration, relative to a baseline. It is remarkably stable to within a standard deviation of 0.1 dB. To obtain an absolute calibration value, CPOL observations are compared to spaceborne radars on board TRMM and GPM using a volume-matching technique. Using an iterative procedure and stable calibration periods identified by the ground echoes technique, we improve the accuracy of this technique to about 1 dB. Finally, we review the self-consistency technique and constrain its assumptions using results from the hybrid TRMM–GPM and ground echo technique. Small changes in the self-consistency parameterization can lead to 5 dB of variation in the reflectivity calibration. We find that the drop-shape model of Brandes et al. with a standard deviation of the canting angle of 12° best matches our dataset.

GLRT Detection of Micromotion Targets for the Multichannel SAR-GMTI System

This letter investigates the micromotion target detection problem for the multichannel synthetic aperture radar (SAR)- ground moving target indication system. The multichannel SAR signal models of the micromotion target and the ground clutter in the raw data domain are established firstly. Then the generalized likelihood ratio test (GLRT) of the micromotion target is derived. Based on the analysis of the probability density functions of the test statistics, theoretical detection performance dependent on the micromotion parameters is provided. Simulated heterogeneous SAR data validate the effectiveness of the GLRT detector.

Fast Prescreening for GPR Antipersonnel Mine Detection via Go Decomposition

Ground-penetrating radar (GPR) has been widely used for antipersonnel mine (APM) detection. However, its efficiency is often impaired by high false alarm rate (FAR) caused by the ground clutters. In this letter, a novel robust principal component analysis (RPCA)-based method is proposed for fast prescreening of APM in GPR image. Taking advantage of low rank and sparse structure of GPR image, the proposed method first adopts an efficient RPCA technique—Go Decomposition (GoDec)—to extract the target image. Then, thresholds are applied to the extracted image to detect the target and reject false alarms. The proposed method enjoys two advantages over traditional methods: 1) the ability of reducing FAR while maintaining high probability of detection (PD) in strong noise and clutter environment and 2) the fast detection guaranteed by the modified GoDec that yields results within several iterations. Extensive simulations and laboratory experiments are conducted to validate the proposed method, and the results are satisfactory (high PDs up to 99% and low FARs).

Comparative Evaluation of Snowfall Retrievals from the CloudSat W-band Radar Using Ground-Based Weather Radars

AbstractInstantaneous liquid-equivalent snowfall rates S retrieved from CloudSat W-band cloud radar reflectivity Ze measurements are compared to estimates of S from operational Weather Surveillance Radar-1988 Doppler (WSR-88D) systems when the CloudSat satellite overflew the ground-based radar sites during spatially extensive nimbostratus snowfall events. For these comparisons, the ground-based radar measurements are interpolated to closely match in space and time spaceborne radar resolution volumes above ground clutter, thus avoiding uncertainties in deriving near-surface snowfall rates from measurements aloft by both radar types. Although typical uncertainties of both ground-based and spaceborne snowfall-rate retrieval approaches are quite high, the results from the standard optimal estimation CloudSat 2C-SNOW-PROFILE algorithm are on average in good agreement with the WSR-88D default snowfall algorithm results with correlation coefficients being around 0.8–0.85. The CloudSat standard optimal estimation snowfall-rate products are also shown to be in satisfactory agreement with retrievals from several simple W-band Ze–S relations suggested earlier. The snowfall rate and snow/ice water content (IWC) parameters from the CloudSat 2C-SNOW-PROFILE algorithm are highly interdependent. A tight relation between S and IWC is apparently introduced through the ice particle fall velocity assumption that is made in the reflectivity-based snowfall retrieval algorithm. This suggests that ice sedimentation rate estimates can also be deduced from applications of numerous empirical IWC–reflectivity relations derived previously for different cloud conditions when appropriate assumptions about fall velocities are made. Intercomparisons between different CloudSat snow/ice water content products indicated significant discrepancies in IWC values from different standard CloudSat retrieval algorithms.

Dual-Polarized Radar Coverage in Terminal Airspaces and Its Effect on Interpretation of Winter Weather Signatures: Current Capabilities and Future Recommendations

AbstractThis is a feasibility study on the use of dual-polarized radars to infer icing in terminal airspaces (TASs) of commercial airports. The amount and quality of radar coverage in each TAS is quantified as a function of its location, traffic, and vulnerability to icing. No airport has 100% of the TAS covered, but most high-traffic or high-icing airports have comparatively good coverage (between 70% and 90%). A common occurrence during icing is anomalous propagation as 79% of events had an inversion within the TAS. This leads to overestimates in the elevations of icing layers and can cause significant ground-clutter contamination, which can overwhelm the echo produced by precipitation. The effects of beam broadening were also considered. Typical dendrite growth and melting layers can only be resolved in part of the TAS part of the time, or not at all, as these layers are often shallower than the radar beam. Because most airports have coverage from multiple radars, use of a three-dimensional mosaic was investigated. This allows for an increase in the TAS coverage (generally between 5% and 15%) and partly mitigates some of the resolution issues, but the maxima within individual layers are somewhat reduced in the interpolation process. A series of recommendations is made to address the concerns raised by this investigation. These include using only icing tops (not bottoms) to identify areas of icing, use of data mining to retrieve precipitation echo in the presence of ground clutter, and including the beamwidth in radar mosaics.

Bistatic Forward-Looking SAR Moving Target Detection Method Based on Joint Clutter Cancellation in Echo-Image Domain with Three Receiving Channels.

In bistatic forward-looking synthetic aperture radar (BFSAR) ground moving target detection (GMTD), the suppression of the strong and heterogeneous ground clutter is one of the most crucial and challenging issues. Due to the bistatic forward-looking mode and long observation time, Doppler ambiguity, range and Doppler cells migration and non-stationary characteristics will exist in clutter receives, which leads to severe performance degradation of the traditional method. Hence, this paper proposes a GMTD method based on joint clutter cancellation in echo-image domain for BFSAR to achieve effective GMTD in heterogeneous BFSAR clutter. First, the pre-filtering and keystone transform are applied to suppress Doppler ambiguity and correct range cell migration, respectively. Then, time-division space-time adaptive clutter cancellation is adopted to suppress clutter at the first time in the echo domain, which can eliminate the effect of the migration of Doppler cells. However, its performance will be severely degraded due to the strong non-stationary characteristic of BFSAR clutter. Finally, adaptive displaced phase center antenna is exploited to suppress the residual non-stationary BFSAR clutter in image domain. Experimental results have shown that the strong non-stationary clutter of BFSAR has been sufficiently suppressed by the proposed method and the SCNR provided is enough to detect a moving target well.

Assessment of Ground-Reference Data and Validation of the H-SAF Precipitation Products in Brazil

The uncertainties associated with rainfall estimates comprise various measurement scales: from rain gauges and ground-based radars to the satellite rainfall retrievals. The quality of satellite rainfall products has improved significantly in recent decades; however, such algorithms require validation studies using observational rainfall data. For this reason, this study aims to apply the H-SAF consolidated radar data processing to the X-band radar used in the CHUVA campaigns and apply the well established H-SAF validation procedure to these data and verify the quality of EUMETSAT H-SAF operational passive microwave precipitation products in two regions of Brazil (Vale do Paraíba and Manaus). These products are based on two rainfall retrieval algorithms: the physically based Bayesian Cloud Dynamics and Radiation Database (CDRD algorithm) for SSMI/S sensors and the Passive microwave Neural network Precipitation Retrieval algorithm (PNPR) for cross-track scanning radiometers (AMSU-A/AMSU-B/MHS sensors) and for the ATMS sensor. These algorithms, optimized for Europe, Africa and the Southern Atlantic region, provide estimates for the MSG full disk area. Firstly, the radar data was treated with an overall quality index which includes corrections for different error sources like ground clutter, range distance, rain-induced attenuation, among others. Different polarimetric and non-polarimetric QPE algorithms have been tested and the Vulpiani algorithm (hereafter, R q 2 V u 15 ) presents the best precipitation retrievals when compared with independent rain gauges. Regarding the results from satellite-based algorithms, generally, all rainfall retrievals tend to detect a larger precipitation area than the ground-based radar and overestimate intense rain rates for the Manaus region. Such behavior is related to the fact that the environmental and meteorological conditions of the Amazon region are not well represented in the algorithms. Differently, for the Vale do Paraíba region, the precipitation patterns were well detected and the estimates are in accordance with the reference as indicated by the low mean bias values.

Clutter Elimination and Random-Noise Denoising of GPR Signals Using an SVD Method Based on the Hankel Matrix in the Local Frequency Domain.

Ground-penetrating radar (GPR) is a kind of high-frequency electromagnetic detection technology. It is mainly used to locate targets and interfaces in underground structures. In addition to the effective signals reflected from the subsurface objects or interfaces, the GPR signals in field work also include noise and different clutters, such as antenna-coupled waves, ground clutters, and radio-frequency interference, which have similar wavelet spectral characteristics with the target signals. Clutter and noise seriously interfere with the target’s response signal. The singular value decomposition (SVD) filtering method can select appropriate singular values and characteristic components corresponding to the effective signals for signal reconstruction to filter the GPR data. However, the conventional time-domain SVD method introduces fake signals when eliminating direct waves, and does not have good suppression of random noise around non-horizontal phase axes. Here, an SVD method based on the Hankel matrix in the local frequency domain of GPR data is proposed. Different numerical models and real field GPR data were handled using the proposed method. Based on the power of fake signals introduced via different processes, qualitative and quantitative analyses were carried out. The comparison shows that the newly proposed method could improve efforts to suppress random noise around non-horizontal phase reflection events and weaken the horizontal fake signals introduced by eliminating clutter such as ground waves.

Ground Moving Target Detection Using Beam-Doppler Image Feature Recognition

An innovative moving target detection approach termed as Beam-Doppler Image Feature Recognition (BDIFR) is introduced based on the distinction between moving target and ground clutter features in the beam-Doppler domain. A novel minimum-distance-based region-growing method is developed for radar target feature extraction and detection. The proposed BDIFR algorithm is advantageous over conventional space-time adaptive processing in detecting ground moving targets in inhomogeneous clutter environments since it does not require secondary training data for clutter estimation.

Analysis of Bistatic Ground Clutter

Analysis of Bistatic Ground Clutter

Clutter mitigation, multiple peaks, and high-order spectral moments in 35 GHz vertically pointing radar velocity spectra

Abstract. This study presents and applies three separate processing methods to improve high-order moments estimated from 35 GHz (Ka band) vertically pointing radar Doppler velocity spectra. The first processing method removes Doppler-shifted ground clutter from spectra collected by a US Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) program Ka-band zenith pointing radar (KAZR) deployed at Oliktok Point (OLI), Alaska. Ground clutter resulted from multiple pathways through antenna side lobes and reflections off a rotating scanning radar antenna located 2 m away from KAZR, which caused Doppler shifts in ground clutter returns from stationary targets 2.5 km away. After removing clutter in the recorded velocity spectra, the second processing method identifies multiple separate and sub-peaks in the spectra and estimates high-order moments for each peak. Multiple peaks and high-order moments were estimated for both original 2 and 15 s averaged spectra. The third processing step improves the spectrum variance, skewness, and kurtosis estimates by removing velocity variability due to turbulent broadening during 15 s averaging intervals. Applying the multiple peak processing to Doppler velocity spectra during liquid-only clouds can identify cloud and drizzle particles and during mixed-phase clouds can identify liquid cloud and frozen hydrometeors. Consistent with previous studies, this work found that spectrum skewness assuming only a single spectral peak was a good indicator of two hydrometeor populations (for example, cloud and drizzle particles) being present in the radar pulse volume. Yet, after dividing the spectrum into multiple peaks, velocity spectrum skewness for individual peaks is near zero, indicating nearly symmetric peaks. This suggests that future studies should use velocity skewness of single-peak spectra as an indicator of possible multiple hydrometeor populations and then use multiple-peak moments for quantitative studies. Three future activities will continue this work. First, KAZR spectra from several ARM sites have been processed and are available in the ARM archive as a principal investigator (PI) product. ARM programmers are evaluating these processing methods as part of future multiple-peak products generated by ARM. Third, MATLAB code generating the Oliktok Point products has been uploaded as supplemental material for public dissemination.

High-Fidelity Inhomogeneous Ground Clutter Simulation of Airborne Phased Array PD Radar Aided by Digital Elevation Model and Digital Land Classification Data.

This paper presents a high-fidelity inhomogeneous ground clutter simulation method for airborne phased array Pulse Doppler (PD) radar aided by a digital elevation model (DEM) and digital land classification data (DLCD). The method starts by extracting the basic geographic information of the Earth’s surface scattering points from the DEM data, then reads the Earth’s surface classification codes of Earth’s surface scattering points according to the DLCD. After determining the landform types, different backscattering coefficient models are selected to calculate the backscattering coefficient of each Earth surface scattering point. Finally, the high-fidelity inhomogeneous ground clutter simulation of airborne phased array PD radar is realized based on the Ward model. The simulation results show that the classifications of landform types obtained by the proposed method are more abundant, and the ground clutter simulated by different backscattering coefficient models is more real and effective.

프랙탈 차원을 이용한 밀리미터파 탐색기 표적인식 알고리즘 연구

Many studies have been conducted on the accurate detection and identification of targets from ground clutter, in order to improve the accuracy rate of land guided weapons. Due to the variety and complicated characteristics of the ground clutter signal compared to the target, an active target identification technique is needed. In this paper, we propose a new algorithm to identify targets and divide them into different types by extracting the unique characteristics of the target through fractal dimension calculation with the characteristics of self-similarity. In the simulation using the algorithm, the probabilities of identifying the tank and truck were 100 % and 98.89 %, respectively, and the type of the target could be identified with a probability of 98 % or more.

Cyclostationary Phase Analysis on Micro-Doppler Parameters for Radar-Based Small UAVs Detection

Robust radar detection for small unmanned aerial vehicles (UAVs) is a challenging problem, as UAVs tend to fly at slow speed and low altitude, with a small radar cross section. Those properties may lead to difficulties in separating echoes from a significant clutter response. In this case, micro-Doppler (m-D) effect induced by the rotation of the rotor blades would be another preferable signature to enhance the discrimination between ground clutter and UAVs returns, as well as associated correct labels to small UAVs among others (such as birds). However, the detection presents two stubborn problems. First, the m-D signal always consists of multiple components, one of which is induced by the vibration of the platform. It is inevitable that the estimation precision would be affected by the m-D interference. Second, the m-D components would be rather weak. It could not always be observed due to the shelter of Doppler signal induced by the translation of the platform. In this paper, we propose the cyclostationary phase analysis (CPA) to estimate the m-D parameters for radar detection of small UAVs. This method utilizes the phase term of the returned signal as the input of the cyclostationary analysis. It could simplify the procedure of the parameter estimation, as well as reducing the m-D interference. Moreover, since the phase term of the Doppler components is not cyclostationary, the CPA could eliminate the impact of Doppler signal. Simulations and filed experiments are provided to showing an outranking performance than the existing methods in estimation precision.

Improved Algorithms for Removing Isolated Non-Meteorological Echoes and Ground Clutters in CINRAD

Using China New Generation Weather Radar (CINRAD) level-II data, the original algorithms for removing isolated non-meteorological echoes and ground clutters in radar data, which have been applied to Weather Surveillance Radar-1988 Doppler (WSR-88D) in the USA and Severe Weather Automatic Nowcast (SWAN) system in China, are modified and improved. To remove isolated non-meteorological echoes, the new algorithm introduces a constraint parameter (Po) to distinguish whether a window of 5 × 5 points is isolated as external echoes. A statistical analysis of 150 radar scans (5 cases, with each case comprising 30 scans) under three different echo types (small-scale convection, typhoon, and large-scale synoptic system) shows that the constraint parameter Po ⩽ 0.167 is suitable for removing isolated non-meteorological echoes while preserving the edge of meteorological echoes. A new parameter, NDZ, which promotes the ability of the algorithm to identify the ground clutters appearing at two adjacent elevation angles, is constructed based on the vertical continuity of reflectivity. These improved algorithms are tested for four cases (three cases of isolated non-meteorological echoes and one case of ground clutters). Based on the statistics of 232 volume scans of radar data (on a temporal resolution of 1 h) measured at Nanchang station from 0000 UTC 5 to 1600 UTC 14 March 2015, it is found that the improved algorithms not only eliminate most (over 95% under clear-sky conditions) of the isolated non-meteorological echoes and ground clutters (including those appearing at two adjacent elevation angles), but also well preserve the structure of meteorological echoes (storms). Key words: radar, isolated non-meteorological echoes, ground clutter, quality control

Knowledge-Aided Bayesian Space-Time Adaptive Processing

Ground moving target indicator (GMTI) radar processing attempts to distinguish between radar returns emanating from moving targets and stationary ground clutter. The task is confounded by the relative motion between the radar platform and the scene, as well as by the strength of clutter returns. Techniques such as space-time adaptive processing require an unknown interference covariance describing clutter, jammers, and thermal noise. The covariance is estimated from training data not under test, but, heterogeneous, contaminated, or limited training data degrade the covariance estimate and reduce the detection performance. State-of-the-art techniques for interference covariance estimation reduce the required amount of training data by imposing assumed structure on the covariance matrix. Here, a Bayesian signal model is adopted for jointly estimating targets and clutter in a single cell under test, allowing GMTI processing without training data. The approach incorporates the knowledge of an approximate digital elevation map, platform kinematics (velocity, crab angle, and antenna spacings), and the belief that moving targets are sparse in the scene. Low-complexity computation with the Bayesian model is enabled by recent algorithm developments for fast inference on linear mixing models. Results from the KASSPER I dataset show improved detection performance compared to existing techniques using scores or even hundreds of training bins.

A Method for Identifying Midlatitude Mesoscale Convective Systems in Radar Mosaics. Part I: Segmentation and Classification

AbstractThis research evaluates the ability of image-processing and select machine-learning algorithms to identify midlatitude mesoscale convective systems (MCSs) in radar-reflectivity images for the conterminous United States. The process used in this study is composed of two parts: segmentation and classification. Segmentation is performed by identifying contiguous or semicontiguous regions of deep, moist convection that are organized on a horizontal scale of at least 100 km. The second part, classification, is performed by first compiling a database of thousands of precipitation clusters and then subjectively assigning each sample one of the following labels: 1) midlatitude MCS, 2) unorganized convective cluster, 3) tropical system, 4) synoptic system, or 5) ground clutter and/or noise. The attributes of each sample, along with their assigned label, are used to train three machine-learning algorithms: random forest, gradient boosting, and “XGBoost.” Results using a testing dataset suggest that the algorithms can distinguish between MCS and non-MCS samples with a high probability of detection and low probability of false detection. Further, the trained algorithm predictions are well calibrated, allowing reliable probabilistic classification. The utility of this two-step procedure is illustrated by generating spatial frequency maps of automatically identified precipitation clusters that are stratified by using various reflectivity and probabilistic prediction thresholds. These results suggest that machine learning can add value by limiting the amount of false-positive (non-MCS) samples that are not removed by segmentation alone.