WAVEFORM AND TRANSCEIVER OPTIMIZATION FOR MULTI-FUNCTIONAL AIRBORNE RADAR THROUGH ADAPTIVE PROCESSING

Pulse compression techniques have been widely used for target detection and remote sensing. The primary concern for pulse compression is the sidelobe interference. Waveform design is an important method to improve the sidelobe performance. As a multi-functional aircraft platform in aviation safety domain, ADS-B system performs functions involving detection, localization and alerting of external traffic. In this work, a binary phase modulation is introduced to convert the original 1090 MHz ADS-B signal waveform into a radar signal. Both the statistical and deterministic models of new waveform are developed and analyzed. The waveform characterization, optimization and its application are studied in details. An alternative way to achieve low sidelobe levels without trading o range resolution and SNR is the adaptive pulse compression - RMMSE (Reiterative Minimum Mean-Square error). Theoretically, RMMSE is able to suppress the sidelobe level down to the receiver noise floor. However, the application of RMMSE to actual radars and the related implementation issues have not been investigated before. In this work, implementation aspects of RMMSE such as waveform sensitivity, noise immunity and computational complexity are addressed. Results generated by applying RMMSE to both simulated and measured radar data are presented and analyzed. Furthermore, a two-dimensional RMMSE algorithm is derived to mitigate the sidelobe effects from both pulse compression processing and antenna radiation pattern. In addition, to achieve even better control of the sidelobe level, a joint transmit and receive optimization scheme (JTRO) is proposed, which reduces the impacts of HPA nonlinearity and receiver distortion. Experiment results obtained with a Ku-band spaceborne radar transceiver testbed are presented

Radar Technology

In this book “Radar Technology”, the chapters are divided into four main topic areas: Topic area 1: “Radar Systems” consists of chapters which treat whole radar systems, environment and target functional chain. Topic area 2: “Radar Applications” shows various applications of radar systems, including meteorological radars, ground penetrating radars and glaciology. Topic area 3: “Radar Functional Chain and Signal Processing” describes several aspects of the radar signal processing. From parameter extraction, target detection over tracking and classification technologies. Topic area 4: “Radar Subsystems and Components” consists of design technology of radar subsystem components like antenna design or waveform design

Electronic scan weather radar: scan strategy and signal processing for volume targets

2013 Fall.Includes bibliographical references.Following the success of the WSR-88D network, considerable effort has been directed toward searching for options for the next generation of weather radar technology. With its superior capability for rapidly scanning the atmosphere, electronically scanned phased array radar (PAR) is a potential candidate. A network of such radars has been recommended for consideration by the National Academies Committee on Weather Radar Technology beyond NEXRAD. While conventional weather radar uses a rotating parabolic antenna to form and direct the beam, a phased array radar superimposes outputs from an array of many similar radiating elements to yield a beam that is scanned electronically. An adaptive scan strategy and advanced signal designs and processing concepts are developed in this work to use PAR effectively for weather observation. An adaptive scan strategy for weather targets is developed based on the space-time variability of the storm under observation. Quickly evolving regions are scanned more often and spatial sampling resolution is matched to spatial scale. A model that includes the interaction between space and time is used to extract spatial and temporal scales of the medium and to define scanning regions. The temporal scale constrains the radar revisit time while the measurement accuracy controls the dwell time. These conditions are employed in a task scheduler that works on a ray-by-ray basis and is designed to balance task priority and radar resources. The scheduler algorithm also includes an optimization procedure for minimizing radar scan time. In this research, a signal model for polarimetric phased array weather radar (PAWR) is presented and analyzed. The electronic scan mechanism creates a complex coupling of horizontal and vertical polarizations that produce the bias in the polarimetric variables retrieval. Methods for bias correction for simultaneous and alternating transmission modes are proposed. It is shown that the bias can be effectively removed; however, data quality degradation occurs at far off boresight directions. The effective range for the bias correction methods is suggested by using radar simulation. The pulsing scheme used in PAWR requires a new ground clutter filtering method. The filter is designed to work with a signal covariance matrix in the time domain. The matrix size is set to match the data block size. The filter's design helps overcome limitations of spectral filtering methods and make efficient use of reducing ground clutter width in PAWR. Therefore, it works on modes with few samples. Additionally, the filter can be directly extended for staggered PRT waveforms. Filter implementation for polarimetric retrieval is also successfully developed and tested for simultaneous and alternating staggered PRT. The performance of these methods is discussed in detail. It is important to achieve high sensitivity for PAWR. The use of low-power solid state transmitters to keep costs down requires pulse compression technique. Wide-band pulse compression filters will partly reduce the system sensitivity performance. A system for sensitivity enhancement (SES) for pulse compression weather radar is developed to mitigate this issue. SES uses a dual-waveform transmission scheme and an adaptive pulse compression filter that is based on the self-consistency between signals of the two waveforms. Using SES, the system sensitivity can be improved by 8 to 10 dB

Frequency diversity wideband digital receiver and signal processor for solid-state dual-polarimetric weather radars

2012 Summer.Includes bibliographical references.The recent spate in the use of solid-state transmitters for weather radar systems has unexceptionably revolutionized the research in meteorology. The solid-state transmitters allow transmission of low peak powers without losing the radar range resolution by allowing the use of pulse compression waveforms. In this research, a novel frequency-diversity wideband waveform is proposed and realized to extenuate the low sensitivity of solid-state radars and mitigate the blind range problem tied with the longer pulse compression waveforms. The latest developments in the computing landscape have permitted the design of wideband digital receivers which can process this novel waveform on Field Programmable Gate Array (FPGA) chips. In terms of signal processing, wideband systems are generally characterized by the fact that the bandwidth of the signal of interest is comparable to the sampled bandwidth; that is, a band of frequencies must be selected and filtered out from a comparable spectral window in which the signal might occur. The development of such a wideband digital receiver opens a window for exciting research opportunities for improved estimation of precipitation measurements for higher frequency systems such as X, Ku and Ka bands, satellite-borne radars and other solid-state ground-based radars. This research describes various unique challenges associated with the design of a multi-channel wideband receiver. The receiver consists of twelve channels which simultaneously downconvert and filter the digitized intermediate-frequency (IF) signal for radar data processing. The product processing for the multi-channel digital receiver mandates a software and network architecture which provides for generating and archiving a single meteorological product profile culled from multi-pulse profiles at an increased data date. The multi-channel digital receiver also continuously samples the transmit pulse for calibration of radar receiver gain and transmit power. The multi-channel digital receiver has been successfully deployed as a key component in the recently developed National Aeronautical and Space Administration (NASA) Global Precipitation Measurement (GPM) Dual-Frequency Dual-Polarization Doppler Radar (D3R). The D3R is the principal ground validation instrument for the precipitation measurements of the Dual Precipitation Radar (DPR) onboard the GPM Core Observatory satellite scheduled for launch in 2014. The D3R system employs two broadly separated frequencies at Ku- and Ka-bands that together make measurements for precipitation types which need higher sensitivity such as light rain, drizzle and snow. This research describes unique design space to configure the digital receiver for D3R at several processing levels. At length, this research presents analysis and results obtained by employing the multi-carrier waveforms for D3R during the 2012 GPM Cold-Season Precipitation Experiment (GCPEx) campaign in Canada

Breaking the Practical Performance Barriers of Polarimetric Phased Array Weather Radars

Phased array radars (PAR) are being proposed as an alternative to replacing the Next Generation Weather Radar (NEXRAD) network, which has been in service for more than 30 years, reaching the end of its life cycle. The PAR can improve the temporal resolution of weather coverage compared to reflector antennas (currently implemented on NEXRAD). Temporal resolution is crucial for severe weather detection and surveillance, especially rapid-evolving phenomena such as tornadoes and hail storms. An all-digital PAR design is presently being explored based on their performance and flexibility improvement. Nevertheless, even all-digital PARs are not free from limitations. This work proposes two signal processing solutions to mitigate two significant limitations observed in those radar systems, i.e., blind range resulted from pulse compression technique and cross-polar contamination inherent in the patch antenna implementation, which is currently the only viable solution to an all-digital PAR system. The mitigation techniques to these two limitations are called Progressive Pulse Compression and Cross-Polar Canceler, respectively. The Progressive Pulse Compression (PPC) technique is proposed to mitigate the blind range problem observed in radars using a frequency modulated waveform and pulse compression. The blind range is caused by the strong leak-through coupled into the receive chain during the transmission cycle. The PPC technique is based on partial decoding. It uses a portion of the uncontaminated received signal in conjunction with pulse compression to estimate the target characteristics from the incomplete signal. The technique does not require using a fill pulse or any hardware modifications. The PPC technique can be divided into three steps. First is to apply a smooth taper to discard all the contaminated samples in the received signal that corresponds to the transmission cycle. The second step is to perform pulse compression using the so called matched filter. Finally, the third step is to calculate and apply a calibration factor to compensate for the progressively changing return signal (affected by the tapering) to recover the proper reflectivity values. This technique is implemented on the PX-1000 radar. In the near future, PPC will be implemented on the Horus phased array radar system. The PX-1000 and Horus radar systems have been designed by the Advanced Radar Research Center (ARRC) at the University of Oklahoma (OU). Nevertheless, PPC has some limitations caused by the different frequency content between the modified (tapered) return signal and the matched filter used for compression. This difference causes a shift in the mainlobe peak and an asymmetrical increase in the sidelobe levels producing a “shoulder” effect. This work proposes improving PPC by compressing the modified return signal with amplitude-modulated versions (range dependent) of the original matched filter. The improved PPC is termed PPC+ and is planned as a software update from PPC. The PPC+ has been tested using data from the PX-1000 and will be presented in this dissertation. The Cross-Polar Canceler (XPC) technique is proposed to mitigate the cross-polar contamination observed on phased array radars. The cross-polar contamination is especially problematic when steering the beam away from the broadside. It is defined as a leakage from the intended polarization observed in the perpendicular one. In the XPC technique, the elements on the array are divided into two groups: main elements and canceler elements. The main elements transmit without any modification. However, the canceler elements transmit a modulated version of the inverse (i.e., the mathematical negative) of the original waveform in the perpendicular polarization. After integration, the field radiated by the canceler elements cancels the cross-polar contamination produced by the main ones. The XPC technique involves calculating the correct number of canceler elements, their location in the array, and the complex scaling factor that better mitigates the cross-polar contamination. This technique has been designed for polarimetric radars transmitting in simultaneous transmission and simultaneous reception of H/V polarization (STSR). The XPC technique will be implemented on the Horus radar system, currently under development. For polarimetric radars, the difference in the element patterns on each polarization produces an angular mismatch between the peaks on the H and V array patterns. This angular mismatch affects the maximum performance achievable with the XPC. Calibration is included as part of XPC to mitigate this effect. Iterative calibration is necessary in the XPC technique. Additionally, calibration is performed before and after XPC is implemented on an operational PAR system. This enhanced version of XPC (including calibration) is termed improved XPC. Like the XPC, the improved XPC is intended to be implemented on the Horus radar system

A 449 MHz Modular Wind Profiler Radar System

This thesis presents the design of a 449 MHz radar for wind profiling, with a focus on modularity, antenna sidelobe reduction, and solid-state transmitter design. It is one of the first wind profiler radars to use low-cost LDMOS power amplifiers combined with spaced antennas. The system is portable and designed for 2-3 month deployments. The transmitter power amplifier consists of multiple 1-kW peak power modules which feed 54 antenna elements arranged in a hexagonal array, scalable directly to 126 elements. The power amplifier is operated in pulsed mode with a 10% duty cycle at 63% drain efficiency. The antenna array is designed to have low sidelobes, confirmed by measurements. The radar was operated in Boulder, Colorado and Salt Lake City, Utah. Atmospheric wind vertical and horizontal components at altitudes between 200m and 4km were calculated from the collected atmospheric return signals. Sidelobe reduction of the antenna array pattern is explored to reduce the effects of ground or sea clutter. Simulations are performed for various shapes of compact clutter fences for the 915-MHz beam-steering Doppler radar and the 449-MHz spaced antenna interferometric radar. It is shown that minimal low-cost hardware modifications to existing compact ground planes of 915-MHz beam-steering radar allow for reduction of sidelobes of up to 5dB. The results obtained on a single beam-steering array are extended to the 449 MHz triple hexagonal array spaced antenna interferometric radar. Cross-correlation, transmit beamwidth, and sidelobe levels are evaluated for various clutter fence configurations and array spacings. The resulting sidelobes are as much as 10 dB below those without a clutter fence and can be incorporated into existing and future 915 and 449 MHz wind profiler systems with minimal hardware modifications

X-band Dual Polarization Phased-Array Radar for Meteorological Applications

This dissertation details the development and operation of a novel dual-polarized Phase-Tilt Weather Radar (PTWR) designed for meteorological applications. The use of radar has a well-documented history in detection and classification of weather phenomena, but due to the limited mechanical scanning speed, its usage for severe weather observations remains far from ideal. The PTWR utilizes phased-array technology and provides unique capabilities such as smart scanning, fast scan update, and tracking. This technology is considered a candidate for a replacement and consolidation of the current US weather and surveillance radar networks. The dissertation can be divided into three parts. First, the hardware design of the radar is presented. Methods of an element and array calibration are discussed. The measured sidelobe level and pattern match exhibit satisfactory performance. The algorithms for signal processing in alternate transmit alternate receive mode of operation are described in detail. The PTWR weather detection capability is validated by an inter-comparison with a collocated X-band high-power radar. These tests showed correlation exceeding 90% for measurements of reflectivity in a convective storm system. The results support the hypothesis that phased-array technology poses an attractive solution for weather remote sensing. The second part addresses the radar waveform considerations. The sensitivity of the radar can be improved by several decibels by means of pulse compression techniques. This is necessary, since the PTWR utilizes low-power solid-state transmitters. The work discusses the trade-offs in waveform design and introduces a novel compression filter, which outperforms traditional window-based solutions. The pulse compression performance is validated using clutter data collected by the PTWR, proving that a deep sidelobe reduction in excess of 40dB can be achieved at the minimal penalty in signal-to-noise level (below 0.5dB). Finally, the third part focuses on the scanning geometry of a 1-D phase-tilt ar- chitecture. It is shown that as the elevation angle is increased, the measurements are affected by a self-induced apparent canting angle. The methods of polarization rotation correction are presented. The biases in typical weather radar products such as reflectivity, differential reflectivity, correlation coefficient, and specific propagation phase, are investigated. The analysis shows that for elevation angles below 15deg , the retrievals errors are acceptable

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

Advanced Techniques for Ground Penetrating Radar Imaging

Ground penetrating radar (GPR) has become one of the key technologies in subsurface sensing and, in general, in non-destructive testing (NDT), since it is able to detect both metallic and nonmetallic targets. GPR for NDT has been successfully introduced in a wide range of sectors, such as mining and geology, glaciology, civil engineering and civil works, archaeology, and security and defense. In recent decades, improvements in georeferencing and positioning systems have enabled the introduction of synthetic aperture radar (SAR) techniques in GPR systems, yielding GPR–SAR systems capable of providing high-resolution microwave images. In parallel, the radiofrequency front-end of GPR systems has been optimized in terms of compactness (e.g., smaller Tx/Rx antennas) and cost. These advances, combined with improvements in autonomous platforms, such as unmanned terrestrial and aerial vehicles, have fostered new fields of application for GPR, where fast and reliable detection capabilities are demanded. In addition, processing techniques have been improved, taking advantage of the research conducted in related fields like inverse scattering and imaging. As a result, novel and robust algorithms have been developed for clutter reduction, automatic target recognition, and efficient processing of large sets of measurements to enable real-time imaging, among others. This Special Issue provides an overview of the state of the art in GPR imaging, focusing on the latest advances from both hardware and software perspectives

Advanced Aviation Weather Radar Data Processing and Real-Time Implementations

The objectives of this dissertation work are developing an enhanced intelligent radar signal and data processing framework for aviation hazard detection, classification and monitoring, and real-time implementation on massive parallel platforms. Variety of radar sensor platforms are used to prove the concept including airborne precipitation radar and different ground weather radars. As a focused example of the proposed approach, this research applies evolutionary machine learning technology to turbulence level classification for civil aviation. An artificial neural network (ANN) machine learning approach based on radar observation is developed for classifying the cubed root of the Eddy Dissipation Rate (EDR), a widely-accepted measure of turbulence intensity. The approach is validated using typhoon weather data collected by Hong Kong Observatory’s (HKO) Terminal Doppler Weather Radar (TDWR) located near Hong Kong International Airport (HKIA) and comparing HKO-TDWR EDR$^{1/3}$ detections and predictions with in situ EDR$^{1/3}$ measured by commercial aircrafts. The testing results verified that machine learning approach performs reasonably well for both detecting and predicting tasks. As the preliminary step to explore the possibility of acceleration by integrating General Purpose Graphic Processing Unit (GPGPU), this research introduces a practical approach to implement real-time processing algorithms for general surveillance radar based on NVIDIA graphical processing units (GPUs). The pulse compression algorithms are implemented using compute unified device architecture (CUDA) libraries such as CUDA basic linear algebra subroutines and CUDA fast Fourier transform library, which are adopted from open source libraries and optimized for the NVIDIA GPUs. For more advanced, adaptive processing algorithms such as adaptive pulse compression, customized kernel optimization is investigated. A statistical optimization approach is developed for this purpose without needing much knowledge of the physical configurations of the kernels. It was found that the kernel optimization approach can significantly improve the performance. Benchmark performance is compared with the CPU performance in terms of processing accelerations. The proposed implementation framework can be used in various radar systems including ground-based phased array radar, airborne sense and avoid radar, and aerospace surveillance radar. After the investigation of the GPGPU on radar signal processing chain, the benchmark of applying machine learning approach on embedded GPU platform was performed. According to the performance, real-time requirement of the machine learning method of turbulence detection developed in this research could be met as well as Size, Weight and Power (SWaP) restrictions on embedded GPGPU platforms