Advanced Signal Processing For Multi-Mission Airborne Radar

With the technological advancement of the 21st century, functions of different radars are being merged. A multi-functional system brings the technology of remote sensing to a wide array of applications while at the same time reduces costs of implementation and operation. Ground-based multi-mission radars have been studied in the past. The airborne counterpart deserves a through study with additional and stringent requirements of cost, size, weight, and power.In this dissertation, multi-mission functions in an airborne radar is performed using modular, software-based architecture. The software-based solution is chosen instead of proposing new hardware, primarily because evaluation, validation, and certification of new hardware is onerous and time consuming. The system implementations are validated using simulations as well as field measurements. The simulations are carried out using Mathworks® Phased Array System Toolbox. The field measurements are performed using an enhanced commercial airborne radar system called Polarimetric Airborne Radar Operating at X-band Version 1 (PARADOX1), which is an X-band, vertically polarized, solid state, pulsed radar.The shortcomings of PARADOX1 originate from small aperture size and low power. Various signal processing algorithms are developed and applied to PARADOX1 data to enhance the data quality. Super-resolution algorithms in range, angle, and Doppler domains, for example, have proven to effectively enhance the spatial resolution. An end-to-end study of single-polarized weather measurements is performed using PARADOX1 measurements. The results are compared with well established ground-based radars. The similarities, differences as well as limitations (of such comparisons) are discussed. Sense and Avoid (SAA) tracking is considered as a core functionality and presented in the context of safe integration of Unmanned Aerial Vehicles (UAV) in national airspace. A "nearly" constant acceleration motion model is used in conjunction with Kalman Filter and Joint Probabilistic Data Association (JPDA) to perform tracking operations. The basic SAA tracking function is validated through simulations as well as field measurements.The field-validations show that a modular, software-based enhancement to an existing radar system is a viable solution in realizing multi-mission functionalities in an airborne radar. The SAA tracking is validated in ground-based tests using an x86 based PC with a generic Linux operating system. The weather measurements from PARADOX1 and the subsequent data quality enhancements show that PARADOX1 data products are comparable to those of existing ground based radars

Precipitation observations from high frequency spaceborne polarimetric synthetic aperture radar and ground-based radar: theory and model validation

2010 Fall.Includes bibliographical references.Global weather monitoring is a very useful tool to better understand the Earth's hydrological cycle and provide critical information for emergency and warning systems in severe cases. Developed countries have installed numerous ground-based radars for this purpose, but they obviously are not global in extent. To address this issue, the Tropical Rainfall Measurement Mission (TRMM) was launched in 1997 and has been quite successful. The follow-on Global Precipitation Measurement (GPM) mission will replace TRMM once it is launched. However, a single precipitation radar satellite is still limited, so it would be beneficial if additional existing satellite platforms can be used for meteorological purposes. Within the past few years, several X-band Synthetic Aperture Radar (SAR) satellites have been launched and more are planned. While the primary SAR application is surface monitoring, and they are heralded as "all weather'' systems, strong precipitation induces propagation and backscatter effects in the data. Thus, there exists a potential for weather monitoring using this technology. The process of extracting meteorological parameters from radar measurements is essentially an inversion problem that has been extensively studied for radars designed to estimate these parameters. Before attempting to solve the inverse problem for SAR data, however, the forward problem must be addressed to gain knowledge on exactly how precipitation impacts SAR imagery. This is accomplished by simulating storms in SAR data starting from real measurements of a storm by ground-based polarimetric radar. In addition, real storm observations by current SAR platforms are also quantitatively analyzed by comparison to theoretical results using simultaneous acquisitions by ground radars even in single polarization. For storm simulation, a novel approach is presented here using neural networks to accommodate the oscillations present when the particle scattering requires the Mie solution, i.e., particle diameter is close to the radar wavelength. The process of transforming the real ground measurements to spaceborne SAR is also described, and results are presented in detail. These results are then compared to real observations of storms acquired by the German TerraSAR-X satellite and by one of the Italian COSMO-SkyMed satellites both operating in co-polar mode (i.e., HH and VV). In the TerraSAR-X case, two horizontal polarization ground radars provided simultaneous observations, from which theoretical attenuation is derived assuming all rain hydrometeors. A C-band fully polarimetric ground radar simultaneously observed the storm captured by the COSMO-SkyMed SAR, providing a case to begin validating the simulation model. While previous research has identified the backscatter and attenuation effects of precipitation on X-band SAR imagery, and some have noted an impact on polarimetric observations, the research presented here is the first to quantify it in a holistic sense and demonstrate it using a detailed model of actual storms observed by ground radars. In addition to volumetric effects from precipitation, the land backscatter is altered when water is on or near the surface. This is explored using TRMM, Canada's RADARSAT-1 C-band SAR and Level 3 NEXRAD ground radar data. A weak correlation is determined, and further investigation is warranted. Options for future research are then proposed

The Atmospheric Imaging Radar for High Resolution Observations of Severe Weather

Mobile weather radars often utilize rapid scan strategies when collecting obser- vations of severe weather. Various techniques have been used to improve volume update times, including the use of agile and multi-beam radars. Imaging radars, similar in some respects to phased arrays, steer the radar beam in software, thus requiring no physical motion. In contrast to phased arrays, imaging radars gather data for an entire volume simultaneously within the field-of-view of the radar, which is defined by a broad transmit beam. As a result, imaging radars provide update rates significantly exceeding those of existing mobile radars, including phased arrays. The Atmospheric Radar Research Center at the University of Oklahoma is engaged in the design, construction and testing of a mobile imaging weather radar system called the Atmospheric Imaging Radar (AIR).Initial tests performed with the AIR demonstrate the benefits and versatility of utilizing beamforming techniques to achieve high spatial and temporal resolution. Specifically, point target analysis was performed using several digital beamform- ing techniques. Adaptive algorithms allow for the improved resolution and clutter rejection when compared to traditional techniques. Additional experiments were conducted during three severe weather events in Oklahoma, including an isolated cell event with high surface winds, a squall line, and a non-tornadic cyclone. Sev- eral digital beamforming techniques were tested and analyzed, producing unique, simultaneous multi-beam measurements using the AIR.The author made specific contributions to the field of radar meteorology in several areas. Overseeing the design and construction of the AIR was a signif- icant effort and involved the coordination of many smaller teams. Interacting with the members of each group and ensuring the success of the project was a primary focus throughout the venture. Meteorological imaging radars of the past have typically focused on boundary layer or upper atmospheric phenomena. The AIR's primary focus is to collect precipitation data from severe weather. Ap- plying well defined beamforming techniques, ranging from Fourier to adaptive algorithms like robust Capon and Amplitude and Phase Estimation (APES), to precipitation phenomena was a unique effort and has served to advance the use of adaptive array processing in radar meteorology. Exploration of irregular antenna spacing and drawing from the analogies between temporal and spatial process- ing led to the development of a technique that reduced the impact of grating lobes by unwrapping angular ambiguities. Ultimately, the author leaves having created a versatile platform capable of producing some of the highest resolution weather data available in the research community today, with opportunities to significantly advance the understanding of rapidly evolving weather phenomena and severe storms

Airborne Radar Observations of Severe Hailstorms: Implications for Future Spaceborne Radar

A new dual-frequency (Ku and Ka band) nadir-pointing Doppler radar on the high-altitude NASA ER-2 aircraft, called the High-Altitude Imaging Wind and Rain Airborne Profiler (HIWRAP), has collected data over severe thunderstorms in Oklahoma and Kansas during the Midlatitude Continental Convective Clouds Experiment (MC3E). The overarching motivation for this study is to understand the behavior of the dualwavelength airborne radar measurements in a global variety of thunderstorms and how these may relate to future spaceborne-radar measurements. HIWRAP is operated at frequencies that are similar to those of the precipitation radar on the Tropical Rainfall Measuring Mission (Ku band) and the upcoming Global Precipitation Measurement mission satellite's dual-frequency (Ku and Ka bands) precipitation radar. The aircraft measurements of strong hailstorms have been combined with ground-based polarimetric measurements to obtain a better understanding of the response of the Ku- and Ka-band radar to the vertical distribution of the hydrometeors, including hail. Data from two flight lines on 24 May 2011 are presented. Doppler velocities were approx. 39m/s2at 10.7-km altitude from the first flight line early on 24 May, and the lower value of approx. 25m/s on a second flight line later in the day. Vertical motions estimated using a fall speed estimate for large graupel and hail suggested that the first storm had an updraft that possibly exceeded 60m/s for the more intense part of the storm. This large updraft speed along with reports of 5-cm hail at the surface, reflectivities reaching 70 dBZ at S band in the storm cores, and hail signals from polarimetric data provide a highly challenging situation for spaceborne-radar measurements in intense convective systems. The Ku- and Ka-band reflectivities rarely exceed approx. 47 and approx. 37 dBZ, respectively, in these storms

WIND TURBINE CLUTTER IN WEATHER RADAR: CHARACTERIZATION AND MITIGATION

With the rapid growth of the wind power industry, many commercial utility-scale wind turbines have been built across the country. These extremely large man-made structures are reported to have negative impact on nearby radars due to their complex scattering mechanisms. Various forms of clutter effect caused by wind turbines in the radar vicinity are generally referred to as the Wind Turbine Clutter (WTC). Due to the lack of awareness on this newly recognized clutter, many wind farms have been built in the Line of Sight (LOS) coverage of operational radars, potentially affecting their performance. Weather radar is the one affected most by WTC because the target of interest is precipitation particles, which is spatially inseparable from the wind turbine within the clutter contaminated resolution volume. Our study thus focuses on analyzing the cause of different types of clutter effects by wind turbines, characterizing the radar signatures of such clutter and mitigating the clutter effect for weather radar. The Micro-Doppler signature of the WTC reveals interesting time-variant spectrum features which are closely related to the instantaneous motions of the wind turbine. The complex motions of a wind turbine can be mostly characterized by three rotations: roll, pitch and yaw. Electromagnetic (EM) characterization of such a dynamic electrical large target is challenging. Various scattering mechanisms are analyzed and the back scattered field and RCS of the wind turbine are computed using commercial EM solver and a hybrid high-frequency approximation approach developed from our study. Field measurements were carried out by deploying the mobile radar to wind farms. The measurements give us the first non-aliased Doppler spectrum of wind turbines. In order to synchronize the wind turbine motion with radar data acquisition, the Radar Wind Turbine Testbed (RWT$^2$) was developed for indoor scaled measurements, which includes the scaled wind turbine model and the scatterometer. Both frequency and time domain measurements were made to characterize the statistics of return signal from the wind turbine model. Several mitigation schemes developed from our study will be discussed, including the telemetry based method, the Adaptive Spectrum Processing (ASP) and the mitigation scheme for moment data based on the Maximum A Posterior (MAP) criteria. A thorough analysis of utilizing LOS avoidance to prevent WTC at the first place will be presented at the end

Annual report for the CSU-CHILL radar facility

Submitted to the National Science Foundation, Division of Atmospheric Sciences.1 February 1994.Cooperative agreement no. ATM-8919080

Bayesian Approaches to Detect and Mitigate Ground Clutter mixed with Weather Signals

Ground clutter is a long standing issue in radar meteorology, considering that it can bring significant bias to the estimations of weather moments, polarimetric parameters, rainfall rate, hydrometeor identification, etc. Bayes' theorem is introduced and applied to signal processing of weather radar signals which distinguishes it from existing empirical methods to improve data quality. Five ground clutter detection algorithms are discussed, which are the Spectrum Clutter Identification (SCI), Simple Bayesian Classifier applied to the Dual-Scan discriminants (SBC-DS), test statistic obtained from the Generalized Likelihood Ratio Test (GLRT), Simple Bayesian Classifier applied to the Dual-Pol discriminants (SBC-DP), and Simple Bayesian Classifier applied to the Dual-Pol Dual-Scan discriminants (SBC-DPDS). One ground clutter filtering algorithm is developed, which is the Bi-Gaussian Model Adaptive Processing (BGMAP). The BGMAP algorithm will be applied to the clutter contaminated gates identified by ground clutter detection algorithms. The performances of the clutter detection and filtering algorithms are evaluated using the data collected by the OU-PRIME (University of Oklahoma-Polarimetric Radar for Innovation in Meteorology and Engineering) 5-cm polarimetric radar and PX-1000 3-cm polarimetric transportable radar

Analysis of the Dynamics and Microphysics of a Wet Downburst Case Using Dual-Polarization Radar Data

A significant, wet downburst affected Norman, Oklahoma, on 14 June 2011. Surface winds in excess of 35 m/s (>80 mph) and hailstones in excess of 4 cm diameter occurred during the downburst. The polarimetric S-band (~11.09-cm wavelength) KOUN Weather Surveillance Radar-1988 Doppler (WSR-88D) and the rapid-scan X-band (~3-cm wavelength) polarimetric, Doppler radar (RaXPol) collected nearly simultaneous polarimetric radar data (PRD) of the downburst. The focus of this dissertation is the characterization and analysis of the dynamics and microphysics of the 14 June 2011 downburst and parent thunderstorm using various analysis methods. Analysis of the PRD from both KOUN and RaXPol radars are conducted using low-level plan position indicator (PPI) elevation scans and reconstructed range–height indicator (RHI) data. A hydrometeor classification algorithm (HCA) is applied to the KOUN PRD to understand the microphysical evolution of hydrometeors in the downburst. Dual-Doppler analysis is conducted with KOUN and the KTLX WSR-88D. Dual-frequency analysis and a comparison of the PRD are conducted between KOUN and RaXPol. Finally, a variational retrieval algorithm of rain microphysics is developed and applied to KOUN based on S-band parametrized polarimetric observation operators. Through the above analyses, an understanding of the structure and evolution of the downburst and its potential driving mechanism(s) is developed. It is found that graupel aloft transitioned to nearly all rain and hail mixture above the 0 °C level. Eventually, this large area of rain and hail mixture (i.e., mixed-phase precipitation) descended to the ground with some melting of the hail, causing the downburst. The downburst grew from a microburst at ~2.1 km horizontal scale to a macroburst at ~6.4 km in less than 7 min. As the downburst expanded, its near-surface horizontal winds intensified from 23 m/s to 42 m/s. Descending surges of mixed-phase precipitation cores aloft, indicated by a reduction in co-polar correlation coefficient (ρhv), provided a continued stream of precipitation loading and melting hail that may have aided in the continued expansion and intensity of the downburst. The unique, rapid-scan observations also captured the development of features such as a horizontal rotor, vertical vortices, multiple gust front heads, and an elevated nose on the leading edge of the gust front. The structure of the downburst is compared to the current conceptual model of a downburst and to 1-min Oklahoma Mesonet observations that were nearly collocated with the radar as well

CYLINDRICAL POLARIMETRIC PHASED ARRAY RADAR DEMONSTRATOR: PERFORMANCE ASSESSMENT AND WEATHER MEASUREMENTS

A desirable candidate for future weather observation is a polarimetric phased array radar (PPAR), which is capable of both using polarimetry for multi-parameter measurements and the fast-scan proficiency of the PAR. However, it is challenging to collect high-quality polarimetric radar data of weather with a planar PPAR (PPPAR), whose beam and polarization characteristics change with the electronic beam direction, causing geometrically induced cross-polarization coupling, sensitivity losses, and measurement biases when the PPPAR beam is steered away from the broadside. As an alternative to PPPAR, the concept of cylindrical polarimetric phased array radar (CPPAR) was proposed, which has scan-invariant beam characteristics in azimuth and polarization purity in all directions using commutating scan, thus enables high quality polarimetric weather measurements. To validate the CPPAR concept, a small-scale CPPAR demonstrator has been jointly developed by the Advanced Radar Research Center (ARRC) at the University of Oklahoma (OU) and the National Severe Storms Laboratory (NSSL) of NOAA. This dissertation presents the results of initial weather measurements, shows the performance of the CPPAR demonstrator, and evaluates the polarimetric data quality that has been achieved. The system specifications and field tests of the CPPAR demonstrator are provided, including system overview, waveform design and verification, pattern optimization and far-field tests. In addition, three methods of system calibration are introduced and compared, including calibration with an external source, calibration with weather measurements of mechanical scan, and calibration with ground clutter. It is found that calibration with weather measurements of mechanical scan has the best performance and it is applied on the CPPAR demonstrator for the first time, which effectively improved the beam-to-beam consistency and radar data quality in commutating beam electronic scan by minimizing gain and beamwidth variations. Performance of the CPPAR is assessed through system simulation and weather measurements. The CPPAR is evaluated through an end-to-end phased array radar system simulator (PASIM). The simulation framework, weather returns modeling, antenna pattern, channel electronics, and simulation results of CPPAR, as well as comparison with those that would be obtained with a PPPAR, are provided. Also, weather measurements of a few convective precipitation cases and a stratiform precipitation case made with the CPPAR, employing the single beam mechanical scan and commutating beam electronic scan respectively, are presented. First, a qualitative comparison is made between the CPPAR and a nearby operational NEXRAD. Then a quantitative comparison is conducted between the mechanical scan and electronic scan, and error statistics are estimated and discussed. In addition, a theoretical explanation of a feature of the commutating beam electronic scan in clutter detection that is different from mechanical scan is presented and verified by measurements in clear air conditions with the CPPAR. Moreover, clutter detection results based on multi-lag phase structure function, dual-scan cross-correlation coefficient, copolar correlation coefficient, and differential reflectivity obtained from both electronic scan and mechanical scan modes of the CPPAR are compared