Airborne and Spaceborne Cloud Radar Designs
This paper presents some crucial design parameters and a strawman system design for a nadir-looking 94-GHz spaceborne cloud profiling radar.
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This paper presents some crucial design parameters and a strawman system design for a nadir-looking 94-GHz spaceborne cloud profiling radar.
Many radar detection algorithms that assume a stationary environment (clutter) have been proposed and analyzed over the years. However, in practice, changes in the nonstationary environment can perturb the parameters of the clutter distribution, or even alter the clutter distribution family, which can greatly deteriorate the target detection capability. To avoid such potential performance degradation, cognitive radar systems are envisioned which are required to rapidly realize the nonstationarity, accurately learn the new characteristics of the environments, and adaptively update the detector. In this paper, aiming to develop a fully cognitive radar for target detection in nonstationary environments, we propose a unifying framework that integrates (i) change-point detection of clutter distributions by using a data-driven cumulative sum (CUSUM) algorithm and its extended version, (ii) learning/identification of clutter distribution by applying sparse theory and kernel density estimation methods, and (iii) adaptive target detection by automatically modifying the likelihood-ratio test and corresponding detection threshold. Further, with extensive numerical examples, we demonstrate the achieved improvements in detection performance due to the proposed framework in comparison to a nonadaptive case, an adaptive matched filter (AMF) method, and the clairvoyant case. Herein, we also use Wilcoxon rank-sum tests to evaluate the statistical significance of the performance improvements
The statistical behavior of the radar backscattering coefficient of agricultural crops is examined. The data used were obtained by the microwave active spectrometer (MAS) systems in 1975 and 1976. Based on an evaluation of the angular and spectral variation of the mean, median, and 90-percent dynamic range of the coefficient (dB) histograms, empirical expressions describing the joint angular and frequency dependence of the mean and median were generated for each linear polarization configuration. The clutter model thus generated covers the angular range between 0 deg (nadir) and 80 deg and the frequency range between 1 GHz and 18 GHz. Decorrelation of the coefficient with frequency spacing was also evaluated and modeled.
Westinghouse conducted a flight test with its Sabreliner AN/APG-68 instrumented radar to assess the urban discrete/ground moving vehicle clutter environment. Glideslope approaches were flown into Washington National, BWI, and Georgetown, Delaware, airports employing radar mode timing, waveform, and processing configurations plausible for microburst windshear avoidance. The perceptions, both general and specific, of the clutter environment furnish an empirical foundation for beginning low false alarm detection algorithm development.
To accurately simulate and evaluate an airborne Doppler radar as a wind shear detection and avoidance sensor, the ground clutter surrounding a typical airport must be quantified. To do this, an imaging airborne Synthetic Aperture Radar (SAR) was employed to investigate and map the normalized radar cross sections (NRCS) of the ground terrain surrounding the Denver Stapleton Airport during November of 1988. Images of the Stapleton ground clutter scene were obtained at a variety of aspect and elevation angles (extending to near-grazing) at both HH and VV polarizations. Presented here, in viewgraph form with commentary, are the method of data collection, the specific observations obtained of the Denver area, a summary of the quantitative analysis performed on the SAR images to date, and the statistical modeling of several of the more interesting stationary targets in the SAR database. Additionally, the accompanying moving target database, containing NRCS and velocity information, is described.
The Mueller matrix and polarization covariance matrix for polarimetric radar systems were studied. The clutter is modelled by a layer of random permittivity, described by a three dimensional correlation function, with variance, and horizontal and vertical correlation lengths. This model is applied, using the wave theory with Born approximations carried to the second order, to find the backscattering elements of the polarimetric matrices. It is found that 8 out of 16 elements of the Mueller matrix are identically zero, corresponding to a covariance matrix with four zero elements. Theoretical predictions are matched with experimental data for vegetation fields. A general mixing formula is derived for discrete scatterers immersed in a host medium. The inclusion particles are assumed to be ellipsoidal. The electric field inside the scatterers is determined by quasistatic analysis, assuming the diameter of the inclusion particles to be much smaller than the wavelength. The results are applicable to general multiphase mixtures.
The Mueller matrix and polarization covariance matrix for polarimetric radar systems was studied. The clutter was modeled by a layer of random permittivity, described by a three-dimensional correlation function, with variance, and horizontal and vertical correlation lengths. This model was applied, using the wave theory with Born approximations carried to the second order, to find the backscattering elements of the polarimetric matrices. Theoretical predictions are matched with experimental data for vegetation fields. The strong fluctuation theory was used to derive the backscattering cross sections. A two-layer anisotropic random medium model was developed for the active and passive microwave remote sensing of ice fields. A three-layer random medium model was adopted to study the volume scattering effects for the active and passive microwave remote sensing of snow-covered ice fields.
The Mueller matrix and polarization covariance matrix for polarimetric radar systems was studied. The clutter is modeled by a layer of random permittivity, described by a three-dimensional correlation function, with variance, and horizontal and vertical correlation lengths. A general mixing formula was derived for discrete scatters immersed in a host medium. The results are applicable to general multiphase mixtures. The strong fluctuation theory was used to derive the backscattering cross sections, and was further extended to include higher order co-polarized and cross-polarized moments. A two-layer anisotropic random medium model was developed for the active and passive remote sensing of ice fields. A three-layer random medium model was adopted to study the volume scattering effects for the active and passive microwave remote sensing of snow-covered ice fields. The snow layer was simulated by an isotropic random medium and the ice layer by an anisotropic random medium. The vegetation canopy and snow-covered ice field were studied with a three-layer model, an isotropic random medium layer overlying an anisotropic random medium. The dyadic Green's function of the three-layer medium and the scattered electromagnetic intensities with Born approximation were calculated.
The POLCAL crosstalk removal algorithm is based on the statistical properties of the image background and does not need any corner reflector or active radar calibrator deployed in the scene. The advantage of being able to remove the crosstalk contamination without using external calibration targets gives rise to the consideration of algorithms based on clutter statistics in polarimetric synthetic aperture radar (SAR) images in more detail. The basic assumption of the POLCAL procedure is the decorrelation of copolarized and cross-polarized backscatter, which is valid for natural targets with azimuthal symmetry. This assumption and others regarding the properties of the imaged surface are examined. An improved version called POLCALII is presented, and its performance is compared with POLCAL.
Wake vortices from a C-130 airplane were observed at the NASA Wallops Flight Facility with a ground-based, monostatic C-band radar and an antenna-mounted boresight video camera. The airplane wake was viewed from a distance of approximately 1 km, and radar scanning was adjusted to cross a pair of marker smoke trails generated by the C-130. For each airplane pass, changes in radar reflectivity were calculated by subtracting the signal magnitudes during an initial clutter scan from the signal magnitudes during vortex-plus-clutter scans. The results showed both increases and decreases in reflectivity on and near the smoke trails in a characteristic sinusoidal pattern of heightened reflectivity in the center and lessened reflectivity at the sides. Reflectivity changes in either direction varied from -131 to -102 dBm(exp -1); the vortex-plus-clutter to noise ratio varied from 20 to 41 dB. The radar recordings lasted 2.5 min each; evidence of wake vortices was found for up to 2 min after the passage of the airplane. Ground and aircraft clutter were eliminated as possible sources of the disturbance by noting the occurrence of vortex signatures at different positions relative to the ground and the airplane. This work supports the feasibility of vortex detection by radar, and it is recommended that future radar vortex detection be done with Doppler systems.
The objective of the research is to apply selected FAA certification techniques to the NASA experimental wind shear radar system. Although there is no intent to certify the NASA system, the procedures developed may prove useful to manufacturers that plan to undergo the certification process. The certification methodology for forward-looking wind shear detection radars will require estimation of system performance in several FAA-specified microburst/clutter scenarios as well as the estimation of probabilities of missed and false hazard alerts under general operational conditions. Because of the near-impossibility of obtaining these results experimentally, analytical and simulation approaches must be used. Hazard detection algorithms were developed that derived predictive estimates of aircraft hazard from basic radar measurements of weather reflectivity and radial wind velocity. These algorithms were designed to prevent false alarms due to ground clutter while providing accurate predictions of hazard to the aircraft due to weather. A method of calculation of the probability of missed and false hazard alerts has been developed that takes into account the effect of the various algorithms used in the system and provides estimates of the probability of missed and false alerts per microburst encounter under weather conditions found at Denver, Kansas City, and Orlando. Simulation techniques have been developed that permit the proper merging of radar ground clutter data (obtained from flight tests) with simulated microburst data (obtained from microburst models) to estimate system performance using the microburst/clutter scenarios defined by the FAA.
A pulsed/IF radar for compact range radar cross section measurements has been developed which converts RF returns to a fixed IF, so that amplification and grating may be performed at one frequency. This permits the use of components which have optimal performance at this frequency which results in a corresponding improvement in performance. Sensitivity and dynamic range are calculated for this system and compared with our old radar, and the effect of pulse width on clutter level is also studied. Sensitivity and accuracy tests are included to verify the performance of the radar.
This document describes the simulation approach used to test the performance of the NASA airborne windshear radar. An explanation of the actual radar hardware and processing algorithms provides an understanding of the parameters used in the simulation program. This report also contains a brief overview of the NASA airborne windshear radar experimental flight test results. A description of the radar simulation program shows the capabilities of the program and the techniques used for certification evaluation. Simulation of the NASA radar is comprised of three steps. First, the choice of the ground clutter data must be made. The ground clutter is the return from objects in or nearby an airport facility. The choice of the ground clutter also dictates the aircraft flight path since ground clutter is gathered while in flight. The second step is the choice of the radar parameters and the running of the simulation program which properly combines the ground clutter data with simulated windshear weather data. The simulated windshear weather data is comprised of a number of Terminal Area Simulation System (TASS) model results. The final step is the comparison of the radar simulation results to the known windshear data base. The final evaluation of the radar simulation is based on the ability to detect hazardous windshear with the aircraft at a safe distance while at the same time not displaying false alerts.
VIPR (Vapor Inside-cloud Profiling Radar) is a differential absorption radar operating over 155-175 GHz, covering a portion of the lower frequency flank of the 183 GHz water vapor absorption resonance. Transmitting at a ~1.9 mm wavelength, VIPR is highly sensitive to scattering from small particles comprising clouds and precipitation. Variation of VIPR’s cloud and precipitation echo power with frequency is often dominated by water vapor absorption, allowing humidity profiles to be retrieved along the radar’s beam path. One confounding effect in practical measurements is phase noise carried by the radar’s transmit signal, which can result in strong range sidelobes extending from bright targets and obscuring the echo signals of more weakly scattering clouds. Here we show that the magnitude and shape of phase-noise induced clutter from extended, bright cloud signals can be accurately modeled using a combination of empirical measurements of surface-echo phase noise and an analytic model based on the phase-noise sidelobe magnitude from a theoretical point target. This improved understanding of a potential clutter source in millimeter-wave imagine radar can lead to better performance modeling, and it provides motivation to improve the phase noise of very high frequency local oscillators.
CloudSat’s Cloud Profiling Radar is a valuable tool for remotely monitoring high-latitude snowfall, but its ability to observe hydrometeor activity near the Earth’s surface is limited by a radar blind zone caused by ground clutter contamination. This study presents the development of a deeply supervised U-Net-style convolutional neural network to predict cold season reflectivity profiles within the blind zone at two Arctic locations. The network learns to predict the presence and intensity of near-surface hydrometeors by coupling latent features encoded in blind zone-aloft clouds with additional context from collocated atmospheric state variables (i.e., temperature, specific humidity, and wind speed). Results show that the U-Net predictions outperform traditional linear extrapolation methods, with low mean absolute error, a 38% higher Sørensen–Dice coefficient, and vertical reflectivity distributions 60% closer to observed values. The U-Net is also able to detect the presence of near-surface cloud with a critical success index (CSI) of 72% and cases of shallow cumuliform snowfall and virga with 18% higher CSI values compared to linear methods. An explainability analysis shows that reflectivity information throughout the scene, especially at cloud edges and at the 1.2-km blind zone threshold, along with atmospheric state variables near the tropopause, are the most significant contributors to model skill. This surface-trained generative inpainting technique has the potential to enhance current and future remote sensing precipitation missions by providing a better understanding of the nonlinear relationship between blind zone reflectivity values and the surrounding atmospheric state.
The primary motivation behind the Version 6X (V06X) Combined Radar-Radiometer Algorithm (CORRA) is to utilize high-sensitivity mode (HS) Ka-band data that had been moved to locations in the outer part of the GPM Dual-Frequency Precipitation Radar (DPR) swath on May 21, 2018. Presented at the Precipitation Measurement Missions (PMM) meeting are statistics of CORRA precipitation estimates across both the inner and outer portions of the DPR swath for the purpose of identifying potential discontinuities caused by the new HS data. Although none are found, further investigation reveals possible biases caused by assumptions regarding radar pulse attenuation within the ground clutter of the DPR.
The pulse pair method for spectrum parameter estimation is commonly used in pulse Doppler weather radar signal processing since it is economical to implement and can be shown to be a maximum likelihood estimator. With the use of airborne weather radar for windshear detection, the turbulent weather and strong ground clutter return spectrum differs from that assumed in its derivation, so the performance robustness of the pulse pair technique must be understood. Here, the effect of radar system pulse to pulse phase jitter and signal spectrum skew on the pulse pair algorithm performance is discussed. Phase jitter effect may be significant when the weather return signal to clutter ratio is very low and clutter rejection filtering is attempted. The analysis can be used to develop design specifications for airborne radar system phase stability. It is also shown that the weather return spectrum skew can cause a significant bias in the pulse pair mean windspeed estimates, and that the poly pulse pair algorithm can reduce this bias. It is suggested that use of a spectrum mode estimator may be more appropriate in characterizing the windspeed within a radar range resolution cell for detection of hazardous windspeed gradients.
Radar data collected during the 1991 NASA flight tests have been selectively analyzed to support research directed at developing both improved as well as new algorithms for detecting hazardous low-altitude windshear. Analysis of aircraft attitude data from several flights indicated that platform stability bandwidths were small compared to the data rate bandwidths which should support an assumption that radar returns can be treated as short time stationary. Various approaches at detection of weather returns in the presence of ground clutter are being investigated. Non-coventional clutter rejection through spectrum mode tracking and classification algorithms is a subject of continuing research. Based upon autoregressive modeling of the radar return time sequence, this approach may offer an alternative to overcome errors in conventional pulse-pair estimates. Adaptive filtering is being evaluated as a means of rejecting clutter with emphasis on low signal-to-clutter ratio situations, particularly in the presence of discrete clutter interference. An analysis of out-of-range clutter returns is included to illustrate effects of ground clutter interference due to range aliasing for aircraft on final approach. Data are presented to indicate how aircraft groundspeed might be corrected from the radar data as well as point to an observed problem of groundspeed estimate bias variation with radar antenna scan angle. A description of how recorded clutter return data are mixed with simulated weather returns is included. This enables the researcher to run controlled experiments to test signal processing algorithms. In the summary research efforts involving improved modelling of radar ground clutter returns and a Bayesian approach at hazard factor estimation are mentioned.