Applications in Low-Power Phased Array Weather Radars

Low-cost X-band radars are an emerging technology that offer significant advantages over traditional systems for weather remote sensing applications. X-band radars provide enhanced angular resolution at a fraction of the aperture size compared to larger, lower frequency systems. Because of their low cost and small form factor, these radars can now be integrated into more research and commercial applications. This work presents research and development activities using a low-cost, X-band (9410 MHz) Phase-Tilt Radar. The phase-tilt design is a novel phased array architecture that allows for rapid electronic scanning in azimuth and mechanical tilting in elevation, as a compromise between cost and performance. This work focuses on field studies and experiments in three meteorological applications. The first stage of research focuses on the real-world application of phased array radars in forest fire monitoring and observation. From April to May 2013, a phase-tilt radar was deployed to South Australia and underwent a field campaign to make polarimetric observations of prescribed burns within and around the Adelaide Hills region. Measurements show the real-time evolution of the smoke plume dynamics at a spatial and temporal resolution that has never before been observed with an X-band radar. This dissertation will perform data analysis on results from this field campaign. Results are compared against existing work, theories, and approaches. In the second stage of research, field experiments are performed to assess the data quality of X-band phased array radars. Specifically, this research focuses on the measurement of and techniques to improve the variance of weather product estimators for dual-polarized systems. Variability in the radar products is a complicated relationship between the radar system specifications, scanning strategy, and the physics governing precipitation. Here, the variance of the radar product estimators is measured using standard radar scanning strategies employed in traditional mechanical antenna systems. Results are compared against adaptive scan strategies such as beam multiplexing and frequency diversity. This work investigates the improvement that complex scanning strategies offer in dual-polarized, X-band phased array radar systems. In the third stage of research, simulations and field experiments are conducted to investigate the performance benefits of adaptive scanning to optimize the data quality of radar returns. This research focuses on the development and implementation of a waveform agile and adaptive scanning strategy to improve the quality of weather product estimators. Active phased array radars allow radar systems to quickly vary both scan pointing angles and waveform parameters in response to real-time observations of the atmosphere. As an evolution of the previous research effort, this work develops techniques to adaptively change the scan pointing angles, transmit and matched filter waveform parameters to achieve a desired level of data quality. Strategies and techniques are developed to minimize the error between observed and desired data quality measures. Simulation and field experiments are performed to assess the quality of the developed strategies

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

Millimeter-wave Mobile Sensing and Environment Mapping: Models, Algorithms and Validation

Integrating efficient connectivity, positioning and sensing functionalities into 5G New Radio (NR) and beyond mobile cellular systems is one timely research paradigm, especially at mm-wave and sub-THz bands. In this article, we address the radio-based sensing and environment mapping prospect with specific emphasis on the user equipment (UE) side. We first describe an efficient l1-regularized least-squares (LS) approach to obtain sparse range--angle charts at individual measurement or sensing locations. For the subsequent environment mapping, we then introduce a novel state model for mapping diffuse and specular scattering, which allows efficient tracking of individual scatterers over time using interacting multiple model (IMM) extended Kalman filter and smoother. We provide extensive numerical indoor mapping results at the 28~GHz band deploying OFDM-based 5G NR uplink waveform with 400~MHz channel bandwidth, covering both accurate ray-tracing based as well as actual RF measurement results. The results illustrate the superiority of the dynamic tracking-based solutions, compared to static reference methods, while overall demonstrate the excellent prospects of radio-based mobile environment sensing and mapping in future mm-wave networks

An Analysis of the Unmanned Aerial Systems-to-Ground Channel and Joint Sensing and Communications Systems Using Software Defined Radio

abstract: Software-defined radio provides users with a low-cost and flexible platform for implementing and studying advanced communications and remote sensing applications. Two such applications include unmanned aerial system-to-ground communications channel and joint sensing and communication systems. In this work, these applications are studied. In the first part, unmanned aerial system-to-ground communications channel models are derived from empirical data collected from software-defined radio transceivers in residential and mountainous desert environments using a small (< 20 kg) unmanned aerial system during low-altitude flight (< 130 m). The Kullback-Leibler divergence measure was employed to characterize model mismatch from the empirical data. Using this measure the derived models accurately describe the underlying data. In the second part, an experimental joint sensing and communications system is implemented using a network of software-defined radio transceivers. A novel co-design receiver architecture is presented and demonstrated within a three-node joint multiple access system topology consisting of an independent radar and communications transmitter along with a joint radar and communications receiver. The receiver tracks an emulated target moving along a predefined path and simultaneously decodes a communications message. Experimental system performance bounds are characterized jointly using the communications channel capacity and novel estimation information rate.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201

Multistatic radar optimization for radar sensor network applications

The design of radar sensor networks (RSN) has undergone great advancements in recent years. In fact, this kind of system is characterized by a high degree of design flexibility due to the multiplicity of radar nodes and data fusion approaches. This thesis focuses on the development and analysis of RSN architectures to optimize target detection and positioning performances. A special focus is placed upon distributed (statistical) multiple-input multipleoutput (MIMO) RSN systems, where spatial diversity could be leveraged to enhance radar target detection capabilities. In the first part of this thesis, the spatial diversity is leveraged in conjunction with cognitive waveform selection and design techniques to quickly adapt to target scene variations in real time. In the second part, we investigate the impact of RSN geometry, particularly the placement of multistatic radar receivers, on target positioning accuracy. We develop a framework based on cognitive waveform selection in conjunction with adaptive receiver placement strategy to cope with time-varying target scattering characteristics and clutter distribution parameters in the dynamic radar scene. The proposed approach yields better target detection performance and positioning accuracy as compared with conventional methods based on static transmission or stationary multistatic radar topology. The third part of this thesis examines joint radar and communication systems coexistence and operation via two possible architectures. In the first one, several communication nodes in a network operate separately in frequency. Each node leverages the multi-look diversity of the distributed system by activating radar processing on multiple received bistatic streams at each node level in addition to the pre-existing monostatic processing. This architecture is based on the fact that the communication signal, such as the Orthogonal Frequency Division Multiplexing (OFDM) waveform, could be well-suited for radar tasks if the proper waveform parameters are chosen so as to simultaneously perform communication and radar tasks. The advantage of using a joint waveform for both applications is a permanent availability of radar and communication functions via a better use of the occupied spectrum inside the same joint hardware platform. We then examine the second main architecture, which is more complex and deals with separate radar and communication entities with a partial or total spectrum sharing constraint. We investigate the optimum placement of radar receivers for better target positioning accuracy while reducing the radar measurement errors by minimizing the interference caused by simultaneous operation of the communication system. Better performance in terms of communication interference handling and suppression at the radar level, were obtained with the proposed placement approach of radar receivers compared to the geometric dilution of precision (GDOP)-only minimization metric

An evaluation of selected estimation methods for the processing of differential absorption lidar data

This work examines the application of selected estimation methods to path integrated direct detection CO₂ lidar data, with the objective of improving the precision in the estimates of the log power, and log power ratios. Particular emphasis is given to the optimal estimation techniques of Kalman filtering theory, and to the consequent requirements for system and measurement model identification. A dual wavelength system was designed and constructed, employing two hybridised TEA lasers, a co-axial transceiver, and direct detection.Over a period of several months, a database of differential absorption measurements was accumulated, each consisting of 10,000 dual wavelength lidar returns. Various wavelength pairs were used, including those recommended for the monitoring of H₂O, CO₂, NH₃ and C₂H₄. A subset of this database is used to evaluate the above mentioned estimation methods. The results are compared with simulated data files in which it was possible to control precisely process models which are believed to form an approximation to the real processes latent in the actual lidar data

Cognitive radar network design and applications

PhD ThesisIn recent years, several emerging technologies in modern radar system design are attracting the attention of radar researchers and practitioners alike, noteworthy among which are multiple-input multiple-output (MIMO), ultra wideband (UWB) and joint communication-radar technologies. This thesis, in particular focuses upon a cognitive approach to design these modern radars. In the existing literature, these technologies have been implemented on a traditional platform in which the transmitter and receiver subsystems are discrete and do not exchange vital radar scene information. Although such radar architectures benefit from these mentioned technological advances, their performance remains sub-optimal due to the lack of exchange of dynamic radar scene information between the subsystems. Consequently, such systems are not capable to adapt their operational parameters “on the fly”, which is in accordance with the dynamic radar environment. This thesis explores the research gap of evaluating cognitive mechanisms, which could enable modern radars to adapt their operational parameters like waveform, power and spectrum by continually learning about the radar scene through constant interactions with the environment and exchanging this information between the radar transmitter and receiver. The cognitive feedback between the receiver and transmitter subsystems is the facilitator of intelligence for this type of architecture. In this thesis, the cognitive architecture is fused together with modern radar systems like MIMO, UWB and joint communication-radar designs to achieve significant performance improvement in terms of target parameter extraction. Specifically, in the context of MIMO radar, a novel cognitive waveform optimization approach has been developed which facilitates enhanced target signature extraction. In terms of UWB radar system design, a novel cognitive illumination and target tracking algorithm for target parameter extraction in indoor scenarios has been developed. A cognitive system architecture and waveform design algorithm has been proposed for joint communication-radar systems. This thesis also explores the development of cognitive dynamic systems that allows the fusion of cognitive radar and cognitive radio paradigms for optimal resources allocation in wireless networks. In summary, the thesis provides a theoretical framework for implementing cognitive mechanisms in modern radar system design. Through such a novel approach, intelligent illumination strategies could be devised, which enable the adaptation of radar operational modes in accordance with the target scene variations in real time. This leads to the development of radar systems which are better aware of their surroundings and are able to quickly adapt to the target scene variations in real time.Newcastle University, Newcastle upon Tyne: University of Greenwich

Adaptive OFDM Radar for Target Detection and Tracking

We develop algorithms to detect and track targets by employing a wideband orthogonal frequency division multiplexing: OFDM) radar signal. The frequency diversity of the OFDM signal improves the sensing performance since the scattering centers of a target resonate variably at different frequencies. In addition, being a wideband signal, OFDM improves the range resolution and provides spectral efficiency. We first design the spectrum of the OFDM signal to improve the radar\u27s wideband ambiguity function. Our designed waveform enhances the range resolution and motivates us to use adaptive OFDM waveform in specific problems, such as the detection and tracking of targets. We develop methods for detecting a moving target in the presence of multipath, which exist, for example, in urban environments. We exploit the multipath reflections by utilizing different Doppler shifts. We analytically evaluate the asymptotic performance of the detector and adaptively design the OFDM waveform, by maximizing the noncentrality-parameter expression, to further improve the detection performance. Next, we transform the detection problem into the task of a sparse-signal estimation by making use of the sparsity of multiple paths. We propose an efficient sparse-recovery algorithm by employing a collection of multiple small Dantzig selectors, and analytically compute the reconstruction performance in terms of the $ell_1$-constrained minimal singular value. We solve a constrained multi-objective optimization algorithm to design the OFDM waveform and infer that the resultant signal-energy distribution is in proportion to the distribution of the target energy across different subcarriers. Then, we develop tracking methods for both a single and multiple targets. We propose an tracking method for a low-grazing angle target by realistically modeling different physical and statistical effects, such as the meteorological conditions in the troposphere, curved surface of the earth, and roughness of the sea-surface. To further enhance the tracking performance, we integrate a maximum mutual information based waveform design technique into the tracker. To track multiple targets, we exploit the inherent sparsity on the delay-Doppler plane to develop an computationally efficient procedure. For computational efficiency, we use more prior information to dynamically partition a small portion of the delay-Doppler plane. We utilize the block-sparsity property to propose a block version of the CoSaMP algorithm in the tracking filter