The application of S-band polarimetric radar measurements toKa-band attenuation prediction

In September 1993, the National Aeronautics and Space Administration's Advanced Communications Technology Satellite (ACTS) was deployed into a geostationary orbit near 100° W longitude. The ACTS satellite employs two Ka-band beacons, one at 20.185 GHz and another at 27.505 GHz. Impairments due to rain attenuation and tropospheric scintillations will significantly affect new technologies for this spectrum. Heavy rain at Ka-band can easily produce 30 dB of attenuation along the propagation path. Propagation experiments being conducted in seven different climatic zones involve multiyear attenuation measurements along the satellite-Earth slant path. Measurements in the B2 climatic zone are made with an ACTS propagation terminal located in northeast Colorado. In order to gain move understanding about the physical processes that are responsible for Ka-band attenuation, the Colorado State University CHILL S-band polarimetric radar is used to take radar measurements along the slant path. The Colorado Front Range experiences a variety of weather conditions throughout the year ranging from upslope rain conditions to winter storms. Four such events measured along the slant path are illustrated in this paper. They include two convective cases and two “bright-band” cases. The S-band polarimetric radar data is used to initialize radar-based attenuation-prediction models, which are applied to the four precipitation events described. The comparisons of predicted attenuation to measured attenuation are quite good. It was also found during the course of the experiment that water droplets standing on the antenna surface can cause appreciable attenuation at Ka-band frequencies. That finding needs to be recognized in future model development and statistical analysis     

Estimation of raindrop size distribution from spaceborne Radar observations

The Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar uses surface reference method to estimate the attenuation encountered in the observation of radar reflectivity. The cumulative attenuation estimated from the surface reference method can be distributed along the radar range using a power law relation between the specific attenuation (k) and reflectivity factor (Z). A physical interpretation of the variability in the k-Z relation can be provided with the normalized drop size distributions. This paper describes an algorithm to estimate the drop size distribution (DSD) parameters from the measured attenuation and reflectivity values obtained from TRMM precipitation radar observations. Coincident data collected with ground polarimetric radar during the TRMM field campaigns is used to cross-validate the estimates of drop size distribution parameters obtained from the TRMM precipitation radar. The results of cross validation show fairly good agreement with the drop size distribution parameters retrieved from TRMM precipitation radar and the ground-radar-based estimates. The algorithm is subsequently used to generate monthly global maps of DSD. The global distribution of DSDs is critically important for development of retrieval algorithms used by the Global Precipitation Mission Radiometers.     

Application of neural networks for sea ice classification inpolarimetric SAR images

Several automatic methods have been developed to classify sea ice types from fully polarimetric synthetic aperture radar (SAR) images, and these techniques are generally grouped into supervised and unsupervised approaches. In previous work, supervised methods have been shown to yield higher accuracy than unsupervised techniques, but suffer from the need for human interaction to determine classes and training regions. In contrast, unsupervised methods determine classes automatically, but generally show limited ability to accurately divide terrain into natural classes. In this paper, a new classification technique is applied to determine sea ice types in polarimetric and multifrequency SAR images, utilizing an unsupervised neural network to provide automatic classification, and employing an iterative algorithm to improve the performance. The learning vector quantization (LVQ) is first applied to the unsupervised classification of SAR images, and the results are compared with those of a conventional technique, the migrating means method. Results show that LVQ outperforms the migrating means method, but performance is still poor. An iterative algorithm is then applied where the SAR image is reclassified using the maximum likelihood (ML) classifier. It is shown that this algorithm converges, and significantly improves classification accuracy. The new algorithm successfully identifies first-year and multiyear sea ice regions in the images at three frequencies. The results show that L- and P-band images have similar characteristics, while the C-band image is substantially different. Classification based on single features is also carried out using LVQ and the iterative ML method. It is found that the fully polarimetric classification provides a higher accuracy than those based on a single feature. The significance of multilook classification is demonstrated by comparing the results obtained using four-look and single-look classifications     

Advanced polarimetric subsurface FM-CW radar

The subsurface radar suffers from two typical problems, i.e., strong clutter from surface and severe wave attenuation in the underground. This paper presents a unique countermeasure to these problems using a polarimetric FM-CW radar and an equivalent sensitivity time control (STC) technique. The authors apply the polarimetric filtering principle to suppress surface clutter either in the Co-pol channel or in the X-pol channel of synthetic aperture radar, yielding to polarimetric enhanced target image. This technique works when the surface clutter and target have different polarization properties. Moreover, they use an equivalent STC technique specially suited for FM-CW radar for a deep object sounding to compensate wave attenuation within the ground. These techniques contribute to a significant improvement of the radar performance and the detection image contrast, although the detection of the target is in general a much more complicated topic. The field experiments were carried out to show the usefulness of the method. Some detection results are presented     

Calibration of a polarimetric imaging SAR

Calibration using point targets is discussed. The fourport network calibration technique is used to describe the radar error model. The processor ambiguity function and the radar distortion matrices are combined to form a generalized polarimetric ambiguity function. The polarimetric ambiguity function of the SAR is found using a single point target, namely a trihedral corner reflector. Based on the resultant polarimetric ambiguity function, an estimate for the backscattering coefficient of the terrain is found using a modified version of the single target calibration technique (STCT). A radar image recorded by the JPL aircraft SAR, which includes a variety of point targets, is used for verification of the new calibration method. The calibrated responses of the point targets are compared both with theory and responses based on the POLCAL technique     

Application of neural networks to radar image classification

A number of methods have been developed to classify ground terrain types from fully polarimetric synthetic aperture radar (SAR) images, and these techniques are often grouped into supervised and unsupervised approaches. Supervised methods have yielded higher accuracy than unsupervised techniques, but suffer from the need for human interaction to determine classes and training regions. In contrast, unsupervised methods determine classes automatically, but generally show limited ability to accurately divide terrain into natural classes. In this paper, a new terrain classification technique is introduced to determine terrain classes in polarimetric SAR images, utilizing unsupervised neural networks to provide automatic classification, and employing an iterative algorithm to improve the performance. Several types of unsupervised neural networks are first applied to the classification of SAR images, and the results are compared to those of more conventional unsupervised methods. Results show that one neural network method-Learning Vector Quantization (LVQ)-outperforms the conventional unsupervised classifiers, but is still inferior to supervised methods. To overcome this poor accuracy, an iterative algorithm is proposed where the SAR image is reclassified using a maximum likelihood (ML) classifier. It is shown that this algorithm converges, and significantly improves classification accuracy     

A dual-polarization rain profiling algorithm

A new attenuation correction algorithm based on profiles of reflectivity, differential reflectivity, and differential propagation phase shift is presented. A solution for specific attenuation retrieval in rain medium is proposed, which solves the integral equations for reflectivity and differential reflectivity with cumulative differential propagation phase shift constraint. The conventional rain profiling algorithms that connect reflectivity and specific attenuation can retrieve specific attenuation values along the radar path assuming a constant intercept parameter of the normalized drop size distribution. However, in convective storms, the drop size distribution parameters can have significant variation along the path. This paper presents a dual-polarization rain profiling algorithm for horizontal looking radars incorporating reflectivity as well as differential reflectivity profiles. The dual-polarization rain profiling algorithm has been evaluated with X-band radar observations simulated from drop size distribution derived from high-resolution S-band measurements collected by the Colorado Statue University CHILL radar. The analysis shows that the retrieved specific attenuation, differential attenuation, reflectivity, and differential reflectivity from the dual-polarization rain profiling algorithm provide significant improvement over the current algorithms.     

Analysis of a Dual-Wavelength Surface Reference Radar Technique

Among the methods that have been proposed for estimating precipitation parameters from a down-looking radar are those that use the scattered power from the surface to infer path attenuation. In this paper, an analysis of a dual-wavelength surface reference technique (DSRT) is presented. The principal of the method is that the ratio of the backscattered power from the surface at one wavelength to that at a second wavelength is proportional to the differential attenuation of the signals through the intervening rain. To account for differences in the backscattering cross section of the surface a 0°, and in the radar calibration constant at the two wavelengths, measurements are made of the surface return power during clear air conditions. The characteristics of the method are best understood by comparing it to two closely related methods: the single-wavelength surface reference technique (SRT) and the standard dual-wavelength technique (DWT). A comparison between the DWT and the DSRT shows that the rain rate estimates are identical in form and that the surface reference can be obtained from the standard estimate by replacing the statistics of the rain reflectivity with those of ?°Advantages of the DSRT relative to its single wavelength counterpart occur when the wavelength correlation in ?° is high or when the mean values of ?° at the two wavelengths are nearly equal. To treat the methods under less restrictive assumptions a simulation is used. Error statistics of the rain rate estimates are presented for a space-borne radar operating at an attenuating wavelength.     

Statistical rainstorm models: Their theoretical and physical foundations

Precipitation has become a serious source of attenuation as higher frequencies are being employed for microwave communications. System performance is strongly influenced by the quantity and character of precipitation that occurs over the links of the system. Rain appears to be the precipitation form that accounts for most of the serious attenuation occurrences. It is also for rain that the Scattering theory is most complete. Rain attenuation can be accurately predicted if the drop-size distribution along the propagation path is known. The drop spectrum determines as well the rainfall rate, radar reflectivity, and microwave emission of rain. Consequently, it is possible to make estimates of rain attenuation through indirect measurements by raingauge, radar, and radiometer. Recent experiments have confirmed that these estimates are sufficiently accurate for practical purposes. From propagation experiments and studies of the fine-scale structure of rain, data are becoming available on the horizontal extent of heavy rain areas and the way this structure influences system performance. These data have been used to formulate statistical raincell models that permit prediction of the performance of single-path and path-diversity systems. The current status of raincell models is reviewed and suggestions for future research are offered.     

Development of a neural network based algorithm for rainfallestimation from radar observations

Rainfall estimation based on radar measurements has been an important topic in radar meteorology for more than four decades. This research problem has been addressed using two approaches, namely a) parametric estimates using reflectivity-rainfall relation (Z-R relation) or equations using multiparameter radar measurements such as reflectivity, differential reflectivity, and specific propagation phase, and b) relations obtained by matching probability distribution functions of radar based estimates and ground observations of rainfall. In this paper the authors introduce a neural network based approach to address this problem by taking into account the three-dimensional (3D) structure of precipitation. A three-layer perceptron neural network is developed for rainfall estimation from radar measurements. The neural network is trained using the radar measurements as the input and the ground raingage measurements as the target output. The neural network based estimates are evaluated using data collected during the Convection and Precipitation Electrification (CaPE) experiment conducted over central Florida in 1991. The results of the evaluation show that the neural network can be successfully applied to obtain rainfall estimates on the ground based on radar observations. The rainfall estimates obtained from neural network are shown to be better than those obtained from several existing techniques. The neural network based rainfall estimate offers an alternate approach to the rainfall estimation problem, and it can be implemented easily in operational weather radar systems     

一种极化-多普勒气象雷达的射频干扰滤波方法

为了滤除极化-多普勒气象雷达中的射频干扰,该文提出利用谱极化滤波器,适用于同时发射同时接收(STSR)和分时发射同时接收(ATSR)体制的极化气象雷达。首先利用C波段STSR气象雷达的实测数据研究射频干扰的时域、频域和极化域特性,建立射频干扰信号模型。然后,在X波段ATSR雷达的数据中仿真加入射频干扰,验证谱极化滤波器的有效性。总体看来,在ATSR雷达中利用谱极化滤波器可以有效保留降雨目标并且滤除射频干扰。最后,针对STSR雷达提出利用数据分集的方法,STSR雷达的实测数据可以模拟ATSR雷达数据,再利用谱极化滤波器实现射频干扰滤除,同样可以取得较好的滤波效果。      

Radiowave propagation research in the tropics using a transportablemultiparameter radar system

Although accurate models for Earth-space microwave propagation, based on extensive measured data, exist for temperate regions of the world, this is not the case for the tropics. With the proliferation of satellite communications in these areas, it has become important to test the validity of currently recommended techniques for calculating circuit availability and co-channel interference levels due to attenuation by precipitation and scattering from precipitation, respectively. Multiparameter radar measurements, employing both polarimetric and Doppler techniques and made over a significant period of time on a statistically-valid basis, provide an ideal tool for these investigations. The paper describes the design considerations for, and construction of such a radar. A selection of its data products are presented, illustrating its potential for radio propagation and meteorological research     

Intensity-driven adaptive-neighborhood technique for polarimetric and interferometric SAR parameters estimation

In this paper, a new method to filter coherency matrices of polarimetric or interferometric data is presented. For each pixel, an adaptive neighborhood (AN) is determined by a region growing technique driven exclusively by the intensity image information. All the available intensity images of the polarimetric and interferometric terms are fused in the region growing process to ensure the validity of the stationarity assumption. Afterward, all the pixels within the obtained AN are used to yield the filtered values of the polarimetric and interferometric coherency matrices, which can be derived either by direct complex multilooking or from the locally linear minimum mean-squared error (LLMMSE) estimator. The entropy/alpha/anisotropy decomposition is then applied to the estimated polarimetric coherency matrices, and coherence optimization is performed on the estimated polarimetric and interferometric coherency matrices. Using this decomposition, unsupervised classification for land applications by an iterative algorithm based on a complex Wishart density function is also applied. The method has been tested on airborne high-resolution polarimetric interferometric synthetic aperture radar (POL-InSAR) images (Oberpfaffenhofen area-German Space Agency). For comparison purposes, the two estimation techniques (complex multilooking and LLMMSE) were tested using three different spatial supports: a fix-sized symmetric neighborhood (boxcar filter), directional nonsymmetric windows, and the proposed AN. Subjective and objective performance analysis, including coherence edge detection, receiver operating characteristics plots, and bias reduction tables, recommends the proposed algorithm as an effective POL-InSAR postprocessing technique.     

联合AC-ICF和TR技术的多功能波形设计方法

要: 分布式网络雷达的多功能承载是目前雷达领域研究的一个热点,基于OFDM信号的雷达通信共享的多功能波形设计受到人们广泛关注,其核心问题之一是较高的包络峰均比（PMEPR）。基于OFDM信号,本文联合自适应限幅门限的迭代限幅滤波技术（AC-ICF）和子载波预留（TR）技术研究了一种面向分布式组网雷达的多功能波形设计方法。首先,建立雷达网络的OFDM信号模型。其次,联合AC-ICF和TR技术研究了低PMEPR的多功能波形设计方法。最后,通过计算机仿真验证了多功能波形设计方法的可行性,并对其中面临的问题进行了分析与讨论,同时对优化设计后波形的雷达和通信性能进行了简要分析。      

Scattering of radiowaves by a melting layer of precipitation inbackward and forward directions

A melting layer of precipitation is composed of melting snowflakes (snow particles); the assumption of spherical particles along with mass conservation is used. The melting layer is studied by deriving the size distribution of the melting snow particles, the thickness of a melting layer, the density of a dry snow particle, and the average dielectric constant of a melting snow particle. Vertical profiles of radar reflectivity and specific attenuation are computed at 1-100 GHz by using the Mie theory for five raindrop size distributions at rain rates below 12.5 mm/h. The radar bright band is explained with computed radar reflectivities at 3-10 GHz. It is shown that the radar bright band can be absent in the melting layer at frequencies above 20 GHz. This agrees with radar observations at 35 and 94 GHz. The specific attenuation, as well as the average specific attenuation of the melting layer, is divided into absorption part and scattering part. The latter is increasingly significant with the increase of frequency. The total zenith attenuation due to stratiform rain is divided into the rain zenith attenuation and the additional zenith attenuation, which is the difference between zenith attenuation, due to the melting layer, and attenuation, due to the same path length of the resulting rain. The additional zenith attenuation increases with the increase of rain rate even at frequencies above 20 GHz. This should be taken into account in radar remote sensing and satellite-Earth communications     

Unsupervised segmentation of polarimetric SAR data using thecovariance matrix

A method for unsupervised segmentation of polarimetric synthetic aperture radar (SAR) data into classes of homogeneous microwave polarimetric backscatter characteristics is presented. Classes of polarimetric backscatter are selected on the basis of a multidimensional fuzzy clustering of the logarithm of the parameters composing the polarimetric covariance matrix. The clustering procedure uses both polarimetric amplitude and phase information, is adapted to the presence of image speckle, and does not require an arbitrary weighting of the different polarimetric channels; it also provides a partitioning of each data sample used for clustering into multiple clusters. Given the classes of polarimetric backscatter, the entire image is classified using a maximum a posteriori polarimetric classifier. Four-look polarimetric SAR complex data of lava flows and of sea ice acquired by the NASA/JPL airborne polarimetric radar (AIRSAR) are segmented using this technique     

运动估计误差对同时极化测量影响和分析

同时全极化雷达是面向空中运动目标动态散射矩阵测量的一种全极化雷达，针对同时全极化雷达系统特点，对同时全极化雷达系统中匹配滤波器引入的误差进行了分析，并仿照分时全极化雷达系统模型的发射和接收失真矩阵模型，给出了匹配滤波器引入的失真矩阵及各矩阵元素的物理意义。分析了目标运动估计对目标动态散射矩阵测量的影响机理，提出了运动估计误差矩阵，利用该矩阵对同时全极化雷达测量影响进行了定量仿真分析，从而为同时全极化雷达系统运动估计方法的性能要求提供理论依据。     

Polarimetric Detection of Targets in Heavy Inhomogeneous Clutter

Polarization diversity has proved to be a useful tool for radar detection, especially when discrimination by Doppler effect is not possible. In this paper, we address the problem of improving the performance of polarimetric detectors for targets in heavy inhomogeneous clutter. First, we introduce a new polarimetric radar model that includes the realistic dependence of the clutter reflections on the transmitted signal. Then, we develop a polarimetric detection test that is robust to inhomogeneous clutter. We run this polarimetric test against synthetic and real data to assess its performance in comparison with existing polarimetric detectors. Finally, we propose a polarimetric waveform-design algorithm to further improve the target-detection performance. A numerical analysis is presented to demonstrate the potential performance improvement that can be achieved with this algorithm.     

A radar cross-section model for power lines at millimeter-wave frequencies

The knowledge of radar backscatter characteristics of high-voltage power lines is of great importance in the development of a millimeter-wave wire detection system. In this paper, a very high-frequency technique based on an iterative physical optics approach is developed for predicting polarimetric radar backscattering behavior of power lines of arbitrary strand arrangement. In the proposed scattering model the induced surface current is obtained using the tangent plane approximation in an iterative manner where the first-order current, obtained from the incident wave, is used as the source for the second-order current and so on. The approximation is valid for frequencies where the cable strand diameter is on the order of or larger than the wavelength. It is shown that the copolarized backscatter is dominated by the contribution from the first-order PO currents, whereas the cross-polarized backscatter is generated by the second- and higher order PO currents. Using this model, the effects of radar antenna footprint, surface irregularities, and cable sag (when suspended between towers) on radar backscatter are studied. To verify the validity of the proposed model, theoretical results are compared at 94 GHz with experimental results and are found to be in good agreement.     

An ultrawideband, polarimetric radar for the study of sea scatter

An ultrawideband radar system is described which has the capability of making pulse-to-pulse polarimetric measurements of the dynamic water features responsible for radar backscatter from the sea. The fast risetime voltage step produced by a Tektronix time-domain reflectometer (TDR) is used to excite a 6-12-GHz amplifier, producing a short (15 cm) radar pulse, A pair of 2- to 18-GHz antennas and appropriate pulse-to-pulse transmit and receive switching capability allows the collection of four consecutive equivalent-time-sampled pulses, one for each combination of the linear transmit and receive antenna polarizations. A polarimetric scattering matrix is then obtained at a sequence of frequencies across the 6-12-GHz band through the Fourier transform of each of the four waveforms and the application of an ultrawideband, polarimetric calibration procedure. The effect of motion on the computed scattering matrix is discussed and quantified, as this is an important consideration for polarimetric investigations of the water features responsible for radar sea scatter, A technique is then presented which compensates for the effects of target translation during the sampling interval. Scattering measurements of several rigid targets and of small breaking waves in a wave tank are used to illustrate the unique capabilities of this system and its applicability to sea scatter studies