Phased-Array Radar System Simulator (PASIM): Development and Simulation Result Assessment

In this paper, a system-specific phased-array radar system simulator was developed, based on a time-domain modeling and simulation method, mainly for system performance evaluation of the future Spectrum-Efficient National Surveillance Radar (SENSR). The goal of the simulation study was to establish a complete data quality prediction method based on specific radar hardware and electronics designs. The distributed weather targets were modeled using a covariance matrix-based method. The data quality analysis was conducted using Next-Generation Radar (NEXRAD) Level-II data as a basis, in which the impact of various pulse compression waveforms and channel electronic instability on weather radar data quality was evaluated. Two typical weather scenarios were employed to assess the simulator’s performance, including a tornado case and a convective precipitation case. Also, modeling of some demonstration systems was evaluated, including a generic weather radar, a planar polarimetric phased-array radar, and a cylindrical polarimetric phased-array radar. Corresponding error statistics were provided to help multifunction phased-array radar (MPAR) designers perform trade-off studies.Funding: The work was supported by NOAA/NSSL through Grant # NA16OAR4320115.A Open access fees fees for this article provided whole or in part by OU Libraries Open Access Fund. Acknowledgments: We thank Ramesh Nepal from the Intelligent Aerospace Radar Team (IART) of School of Electrical and Computer Engineering, the University of Oklahoma as the initial user of the MATLAB Phased-Array System Toolbox for weather radar simulations at OU, who gave numerous discussions regarding PASIM implementation. We deeply thank Honglei Chen from MathWorks Inc., who provided important guidance and support to the weather radar signal statistical modeling and MATLAB tool.Ye

Simulation of Polarimetric Phased Array Weather Radars

Polarimetric phased array radars (PPARs) are a rapidly developing area of research interest in weather radar. However, they present intrinsic challenges for calibration and operation. Foremost among these are the adverse effects of copolar radiation pattern mismatch as well as cross-polar fields on polarimetric measurement accuracy. Characterization of the impact these effects have on weather radar observations and the effectiveness of proposed methods for mitigation of those impacts can be time-consuming and costly if conducted using radar hardware. Furthermore, few operational PPARs exist to serve as testbeds. Alternatively, the effects of copolar and cross-polar fields can be studied using numerical simulations. In that regard, this work outlines a simulation method that allows for the characterization of PPAR performance and the prototyping of techniques to mitigate cross-polar biases. To achieve this, a simulation volume is populated by thousands of scattering centers, whose movement and scattering characteristics at any point in space and time are governed by a high-resolution numerical weather prediction model. Each of these scattering centers has its own individually calculated Doppler spectrum in both the horizontal (H) and vertical (V) polarizations. These spectra are used to determine instantaneous scattering parameters that are combined with a highly flexible radar system model in order to compose time-series signals in H and V. This simulation method is used to evaluate and compare the performance of several bias mitigation techniques that have been previously proposed

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

Quantitative Analysis of Rapid-Scan Phased Array Weather Radar Benefits and Data Quality Under Various Scan Conditions

Currently, NEXRAD provides weather radar coverage for the contiguous United States. It is believed that a replacement system for NEXRAD will be in place by the year 2040, where a major goal of such a system is to provide improved temporal resolution compared to the 5-10-min updates of NEXRAD. In this dissertation, multiple projects are undertaken to help achieve the goals of improved temporal resolution, and to understand possible scanning strategies and radar designs that can meet the goal of improved temporal resolution while either maintaining (or improving) data quality. Chapter 2 of this dissertation uses a radar simulator to simulate the effect of various scanning strategies on data quality. It is found that while simply reducing the number of pulses per radial decreases data quality, other methods such as beam multiplexing and radar imaging/digital beamforming offer significant promise for improving data quality and/or temporal resolution. Beam multiplexing is found to offer a speedup factor of 1.7-2.9, while transmit beam spoiling by 10 degrees in azimuth can offer speedup factors up to ~4 in some regions. Due to various limitations, it is recommended that these two methods be used judiciously for rapid-scan applications. Chapter 3 attempts to quantify the benefits of a rapid-scan weather radar system for tornado detection. The first goal of Chapter 3 is to track the development of a common tornado signature (tornadic debris signature, or TDS) and relate it to developments in tornado strength. This is the first study to analyze the evolution of common tornado signatures at very high temporal resolution (6 s updates) by using a storm-scale tornado model and a radar emulator. This study finds that the areal extent of the TDS is correlated with both debris availability and with tornado strength. We also find that significant changes in the radar moment variables occur on short (sub-1-min) timescales. Chapter 3 also shows that the calculated improvement in tornado detection latency time (137-207 s) is greater than that provided by theory alone (107 s). Together, the two results from Chapter 3 emphasize the need for sub-1-min updates in some applications such as tornado detection. The ability to achieve these rapid updates in certain situations will likely require a combination of advanced scanning strategies (such as those mentioned in Chapter 2) and adaptive scanning. Chapter 4 creates an optimization-based model to adaptively reallocate radar resources for the purpose of improving data quality. This model is primarily meant as a proof of concept to be expanded to other applications in the future. The result from applying this model to two real-world cases is that data quality is successfully improved in multiple areas of enhanced interest, at the expense of worsening data quality in regions where data quality is not as important. This model shows promise for using adaptive scanning in future radar applications. Together, these results can help the meteorological community understand the needs, challenges, and possible solutions to designing a replacement system for NEXRAD. All of the techniques studied herein either rely upon (or are most easily achieved by) phased array radar (PAR), which further emphasizes the utility of PAR for achieving rapid updates with sufficient data quality. It is hoped that the results in this dissertation will help guide future decisions about requirements and design specifications for the replacement system for NEXRAD

Multistatic Passive Weather Radar

Practical and accurate estimation of three-dimensional wind fields is an ongoing challenge in radar meteorology. Multistatic (single transmitter / multiple receivers) radar architectures offer a cost effective solution for obtaining the multiple Doppler measurements necessary to achieve such estimates. In this work, the history and fundamental concepts of multistatic weather radar are reviewed. Several developments in multistatic weather radar enabled by recent technological progress, such as the widespread availability of high performance single-chip RF transceivers and the proliferation of phased array weather radars, are then presented. First, a network of compact, low-cost passive receiver prototypes is used to demonstrate a set of signal processing techniques that have been developed to enable transmitter / receiver synchronization through sidelobe radiation. Next, a pattern synthesis technique is developed which allows for the use of sidelobe whitening to mitigate velocity biases in multistatic radar systems. The efficacy of this technique is then demonstrated using a multistatic weather radar system simulator

Application of Machine Learning to Multiple Radar Missions and Operations

This dissertation investigated the application of Machine Learning (ML) in multiple radar missions. With the increasing computational power and data availability, machine learning is becoming a convenient tool in developing radar algorithms. The overall goal of the dissertation was to improve the transportation safety. Three specific applications were studied: improving safety in the airport operations, safer air travel and safer road travel. First, in the operations around airports, lightning prediction is necessary to enhance safety of the ground handling workers. Information about the future lightning can help the workers take necessary actions to avoid lightning related injuries. The mission was to investigate the use of ML algorithms with measurements produced by an S-band weather radar to predict the lightning flash rate. This study used radar variables, single pol and dual-pol, measured throughout a year to train the machine learning algorithm. The effectiveness of dual-pol radar variables for lighting flash rate prediction was validated, and Pearson's coefficient of about 0.88 was achieved in the selected ML scheme. Second, the detection of High Ice Water Content (HIWC),which impact the jet engine operations at high altitudes, is necessary to improve the safety of air transportation. The detection information help aircraft pilots avoid hazardous HIWC condition. The mission was to detect HIWC using ML and the X-band airborne weather radar. Due to the insufficiency of measured data, radar data was synthesized using an end-to-end airborne weather system simulator. The simulation employed the information about ice crystals' particle size distribution (PSDs), axial ratios, and orientation to generate the polarimetric radar variables. The simulated radar variables were used to train the machine learning to detect HIWC and estimate the IWC values. Pearson's coefficient of about 0.99 was achieved for this mission. The third mission included the improvement of angular resolution and explored the machine learning based target classification using an automotive radar. In an autonomous vehicle system, the classification of targets enhances the safety of ground transportation. The angular resolution was improved using Multiple Input Multiple Output (MIMO) techniques. The mission also involved classifying the targets (pedestrian vs. vehicle) using micro-Doppler features. The classification accuracy of about 94% was achieved

On the Potential of Adaptive Beamforming for Phased-Array Weather Radar

As the Weather Surveillance Radar 1988 Doppler network reaches the end of its expected life, a network of multifunction phased-array radars (MPAR) supporting both aircraft and weather surveillance missions has been proposed. A phased-array system should match the sensitivity, spatial resolution, and data quality of the WSR-88D while having a update time of 60 seconds for weather surveillance. Since an MPAR system must complete both weather and aircraft surveillance missions, the update time reduction provided by having multiple faces is insufficient to achieve the desired 60 second update time for weather surveillance. Therefore, it is likely that multiple simultaneous beams would be needed per face to meet the timeline requirements. An approach to achieve multiple receive beams is to use a spoiled transmit beam and to form a cluster of simultaneous receive beams. However, a significant challenge for this approach is the potential of high sidelobe levels in the two-way radiation pattern, which can result in significantly biased estimates of the radar variables in situations where the signal power has large spatial variation. This dissertation proposes an adaptive beamspace algorithm designed for phased-array weather radar that utilizes a spoiled transmit beam and a cluster of simultaneous receive beams to achieve the desired timeline. Taking advantage of the adaptive algorithm's ability to automatically adjust sidelobe levels to match the scene, the high-sidelobe problem associated with a spoiled transmit beam is mitigated. Through extensive simulations, it is shown that adaptive beamspace processing can produce accurate and calibrated estimates of weather radar variables. Furthermore, it is demonstrated that the adaptive beamspace algorithm can automatically reject interference signals and reduce their impact on the radar-variable estimates. Additionally, it is shown that, despite higher sidelobe levels, the adaptive beamspace algorithm can perform similarly to a conventional system based on a dish antenna in terms of biases when reflectivity gradients are present. Finally, the adaptive beamspace algorithm is shown to compare favorably to some alternative solutions that can also achieve the desired MPAR timeline requirement while preserving data quality

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

Observations and Simulations of a Multistatic Weather Radar Network

Multistatic radar architectures have the potential to provide a cost-effective source of 3D wind information from both operational and research radars, owing to a system design of one transmitter and several receivers. A prototype multistatic network consisting of two passive receivers and the KTLX WSR-88D has been constructed in the Oklahoma City metropolitan area. To achieve sufficiently precise Doppler frequency estimates while reducing cost, transmitter/receiver synchronization is done through measurements of the WSR-88D’s sidelobe radiation, rather than an expensive GPS-based system. This yields an exceptionally simple system capable of producing bistatic moment data with virtually no cooperation from the transmitting radar system. However, the main factor inhibiting the usage of such systems in 3D dual-Doppler wind retrievals is sidelobe contamination arising from the use of low-gain antennae with broad receive beams. Therefore, mitigation of sidelobe contamination should be paramount for those seeking to use this type of radar system. To this end, simulations of multistatic radar systems with varying receiver network layouts and transmitting techniques are performed to evaluate several strategies for reducing the effects of sidelobe contamination. One such strategy is to simply increase the number of receivers, which is shown to improve retrieval quality, albeit with diminishing returns. Another strategy is sidelobe whitening, which uses varying sidelobe phases to greatly reduce the coherent signal from the sidelobes. This technique alone is shown to markedly improve measured Doppler velocities and subsequent retrievals, especially in simulations of convective systems. Since sidelobe whitening can only be done with a phased array weather radar, the potential associated with a phased array-bistatic radar system is tremendous, particularly when coupled with the rapid-scan capabilities intrinsic to phased array systems. Since the initial deployment of the prototype multistatic system, several datasets of severe convection have been collected, including several instances of quasi-linear convective systems (QLCSs) and supercells. Multi-Doppler retrievals done with the multistatic data are able to resolve important structures in the horizontal and vertical wind fields, including mesocyclones and horizontal rotors. These retrievals are shown to be comparable in accuracy to simultaneous multi-Doppler retrievals done with only monostatic radar data, though the deleterious effects of sidelobe contamination are apparent in the multistatic retrievals in some cases

All-Weather Sense and Avoid (SAA) Radar Clutter Modeling and Control

The background of this thesis is related to the enhancement and optimization of the Pulsed-Doppler Radar sensor for the need of Detect and Avoid (DAA), or Sense and Avoid (SAA), for both weather and air-traffic (collision aircraft) detection and monitoring. Such radars are used in both manned and unmanned aircraft for the situation awareness of pilot navigation operations. The particular focus of this study is to develop a simulation model that is based on MATLAB's phased array toolbox and use that simulation model to predict the performance of an end-to-end radar signal processing chain for all-weather, multi-mission DAA. To achieve this goal, we developed an airborne system model based on MATLAB toolboxes, NASA’s airborne radar flight test data, and NEXRAD radar data. The measured data from airborne and ground-based radars are used as the “truth field” for the weather. During the modeling and verification process, we primarily investigated the impact of ground or surface clutters on the radar outputs and results, which include the testing of the constant-gamma model using actual measured radar data and improved system and sensor modeling based on the clutter geometry. Evaluation of various moving target indication (MTI) techniques were tested with the simulation model