Radar signature characterization from wind turbine scattering

textThe growth in the number of wind farms has raised significant concerns in the radar community due to their potential interference on radar systems. The motion of the turbine blades creates unwanted Doppler clutter that can interfere in the tracking of moving targets. Large turbine structures can also produce electromagnetic shadows that may make observing objects behind a wind farm difficult. Detailed characterization of the clutter is the first step towards effective mitigation techniques. The goal of this dissertation research is to gain a better understanding of the dynamic radar signatures resulting from scattering by wind turbines. First, the scattering characteristics of turbines in the presence of ground surface are studied. Image theory in conjunction with a shooting-and-bouncing ray code, Ahilo, is used to carry out the dynamic signature simulation. The observed features in the simulation are corroborated with laboratory model measurements. Second, the effects of higher order motions of a turbine undergoing rotation on the radar signatures are investigated and characterized. Mathematical models for the motions are proposed and used to simulate the joint time-frequency and inverse synthetic aperture radar characteristics of the turbine undergoing these motions. The motions are studied for an isolated turbine as well as for a turbine rotating above a ground. Selected motions are corroborated by laboratory model measurements. Next, a method to remove the dynamic clutter produced by wind turbines is presented. A physics-based basis is constructed to model the radar backscattering from a wind turbine. This basis is used in conjunction with the matching pursuit algorithm to iteratively remove the Doppler clutter due to wind turbines. The algorithm is tested using radar return generated using Ahilo. Finally, radar features of wind turbines are simulated and studied in the HF (high frequency) band. The features are presented in the range-Doppler plane for single as well as arrays of turbines. Doppler aliasing due to the limited pulse repetition frequency of HF radars is examined. Shadowing characteristics of arrays of turbines are simulated and analyzed. Electromagnetic modeling details including effects of thin-wire modeling, non-conducting turbine components, and the presence of a conducting ground surface are discussed.Electrical and Computer Engineerin

Radar Detection, Tracking and Identification for UAV Sense and Avoid Applications

Advances in Unmanned Aerial Vehicle (UAV) technology have enabled wider access for the general public leading to more stringent flight regulations, such as the line of sight restriction, for hobbyists and commercial applications. Improving sensor technology for Sense And Avoid (SAA) systems is currently a major research area in the unmanned vehicle community. This thesis overviews efforts made to advance intelligent algorithms used to detect, track, and identify commercial UAV targets by enabling rapid prototyping of novel radar techniques such as micro-Doppler radar target identification or cognitive radar. To enable empirical radar signal processing evaluations, an S-Band and X-Band frequency modulated, software-defined radar testbed is designed, implemented, and evaluated with field measurements. The final evaluations provide proof of functionality, performance measurements, and limitations of this testbed and future software-defined radars. The testbed is comprised of open-source software and hardware meant to accelerate the development of a reliable, repeatable, and scalable SAA system for the wide range of new and existing UAVs

Human activity classification using micro-Doppler signatures and ranging techniques

PhD ThesisHuman activity recognition is emerging as a very import research area due to its potential applications in surveillance, assisted living, and military operations. Various sensors including accelerometers, RFID, and cameras, have been applied to achieve automatic human activity recognition. Wearable sensor-based techniques have been well explored. However, some studies have shown that many users are more disinclined to use wearable sensors and also may forget to carry them. Consequently, research in this area started to apply contactless sensing techniques to achieve human activity recognition unobtrusively. In this research, two methods were investigated for human activity recognition, one method is radar-based and the other is using LiDAR (Light Detection and Ranging). Compared to other techniques, Doppler radar and LiDAR have several advantages including all-weather and all-day capabilities, non-contact and nonintrusive features. Doppler radar also has strong penetration to walls, clothes, trees, etc. LiDAR can capture accurate (centimetre-level) locations of targets in real-time. These characteristics make methods based on Doppler radar and LiDAR superior to other techniques. Firstly, this research measured micro-Doppler signatures of different human activities indoors and outdoors using Doppler radars. Micro-Doppler signatures are presented in the frequency domain to reflect different frequency shifts resulted from different components of a moving target. One of the major differences of this research in relation to other relevant research is that a simple pulsed radar system of very low-power was used. The outdoor experiments were performed in places of heavy clutter (grass, trees, uneven terrains), and confusers including animals and drones, were also considered in the experiments. Novel usages of machine learning techniques were implemented to perform subject classification, human activity classification, people counting, and coarse-grained localisation by classifying the micro-Doppler signatures. For the feature extraction of the micro-Doppler signatures, this research proposed the use of a two-directional twodimensional principal component analysis (2D2PCA). The results show that by applying 2D2PCA, the accuracy results of Support Vector Machine (SVM) and k-Nearest Neighbour (kNN) classifiers were greatly improved. A Convolutional Neural Network (CNN) was built for the target classifications of type, number, activity, and coarse localisation. The CNN model obtained very high classification accuracies (97% to 100%) for the outdoor experiments, which were superior to the results obtained by SVM and kNN. The indoor experiments measured several daily activities with the focus on dietary activities (eating and drinking). An overall classification rate of 92.8% was obtained in activity recognition in a kitchen scenario using the CNN. Most importantly, in nearly real-time, the proposed approach successfully recognized human activities in more than 89% of the time. This research also investigated the effects on the classification performance of the frame length of the sliding window, the angle of the direction of movement, and the number of radars used; providing valuable guidelines for machine learning modeling and experimental setup of micro-Doppler based research and applications. Secondly, this research used a two dimensional (2D) LiDAR to perform human activity detection indoors. LiDAR is a popular surveying method that has been widely used in localisation, navigation, and mapping. This research proposed the use of a 2D LiDAR to perform multiple people activity recognition by classifying their trajectories. Points collected by the LiDAR were clustered and classified into human and non-human classes. For the human class, the Kalman filter was used to track their trajectories, and the trajectories were further segmented and labelled with their corresponding activities. Spatial transformation was used for trajectory augmentation in order to overcome the problem of unbalanced classes and boost the performance of human activity recognition. Finally, a Long Short-term Memory (LSTM) network and a (Temporal Convolutional Network) TCN was built to classify the trajectory samples into fifteen activity classes. The TCN achieved the best result of 99.49% overall accuracy. In comparison, the proposed TCN slightly outperforms the LSTM. Both of them outperform hidden Markov Model (HMM), dynamic time warping (DTW), and SVM with a wide margin

Investigation of non-cooperative target recognition of small and slow moving air targets in modern air defence surveillance radar

This thesis covers research in the field of non-cooperative target recognition given the limitations of modern air defence surveillance radars. The potential presence of low observable manned or unmanned targets within the vast surveillance volume demand highly sensitive systems. This may again introduce unwanted detections of single birds of comparable radar cross section, previously avoided by use of wide clutter rejection filters and sensitivity time control. The demand for methods effectively separating between birds and slow moving manmade targets is evident. The research questions addressed are connected to identification of characteristic features of birds and manmade targets of comparable size. Ultimately the goal has been to find methods that can utilize such features to effectively distinguish between the classes. In contrast to the vast majority of non-cooperative target recognition publications, this thesis includes non-rigid targets covering a range of dielectric properties and targets falling in the resonant and Rayleigh scattering regions. These factors combined with insufficient spatial resolution for classification require alternative approaches such as utilization of periodic RCS modulation, micro-Doppler- and polarimetric signatures. Signatures of birds and UAVs are investigated through electromagnetic prediction and radar measurements. A flexible and fully polarimetric radar capable of simultaneous operation in both L- and S-band is developed for collection of relevant signatures. Inspired by the use of polarimetric radar for classification of precipitation covered in the weather radar literature, focus has been on using similar methods to recognize signatures of rotors, propellers and bird wings. Novel micro-Doppler signatures combining polarimetric information from this sensor is found to hold information about the orientation of such target parts. This information combined with several other features is evaluated for classification. The benefit from involving polarimetric measurements is especially investigated, and is found to be highly valuable when information provided by other methods is limited

Middle Atmosphere Program. Handbook for MAP, volume 28

Extended abstracts from the fourth workshop on the technical and scientific aspects of MST (mesosphere stratosphere troposphere) radar are presented. Individual sessions addressed the following topics: meteorological applications of MST and ST radars, networks, and campaigns; dynamics of the equatorial middle atmosphere; interpretation of radar returns from clear air; techniques for studying gravity waves and turbulence; intercomparison and calibration of wind and wave measurements at various frequencies; progress in existing and planned MST and ST radars; hardware design for MST and ST radars and boundary layer/lower troposphere profilers; signal processing; and data management

Detecting and locating electronic devices using their unintended electromagnetic emissions

Electronically-initiated explosives can have unintended electromagnetic emissions which propagate through walls and sealed containers. These emissions, if properly characterized, enable the prompt and accurate detection of explosive threats. The following dissertation develops and evaluates techniques for detecting and locating common electronic initiators. The unintended emissions of radio receivers and microcontrollers are analyzed. These emissions are low-power radio signals that result from the device\u27s normal operation. In the first section, it is demonstrated that arbitrary signals can be injected into a radio receiver\u27s unintended emissions using a relatively weak stimulation signal. This effect is called stimulated emissions. The performance of stimulated emissions is compared to passive detection techniques. The novel technique offers a 5 to 10 dB sensitivity improvement over passive methods for detecting radio receivers. The second section develops a radar-like technique for accurately locating radio receivers. The radar utilizes the stimulated emissions technique with wideband signals. A radar-like system is designed and implemented in hardware. Its accuracy tested in a noisy, multipath-rich, indoor environment. The proposed radar can locate superheterodyne radio receivers with a root mean square position error less than 5 meters when the SNR is 15 dB or above. In the third section, an analytic model is developed for the unintended emissions of microcontrollers. It is demonstrated that these emissions consist of a periodic train of impulses. Measurements of an 8051 microcontroller validate this model. The model is used to evaluate the noise performance of several existing algorithms. Results indicate that the pitch estimation techniques have a 4 dB sensitivity improvement over epoch folding algorithms --Abstract, page iii

Target shadow profile reconstruction in forward scatter radar

This thesis is dedicated to the matter of imaging (further explained as profile reconstruction) in Forward Scatter Radars (FSR). Firstly, an introduction to radar systems, including forward scatter radar, is made, then an introduction to the scalar theory of diffraction and principles of holography follows. The application of holographic imaging principles in the microwave domain is studied. The practical modelling of forward scatter radar target signals is made, based on the theoretical expectations and approximations outlined. Theoretical background of the imaging in FSR is made, based on previously published work. A novel approach for profile reconstruction is introduced based on the practices of holographic imaging, together with simulated results. Experimental set-ups used in the feasibility proof are described and experimental results are presented for 8 different targets in both a single-node and multistatic configurations. Preliminary accuracy analysis of these reconstructed target profiles is done, outlining practical application issues and domain of accuracy. Quantitative measures of the accuracy of the reconstructed images are defined

Development and application of radar reflectometer using micro to infrared waves

Progress in microwave and millimeter-wave technologies has made possible advanced diagnostics for application to various fields, including radio astronomy, alien substance detection, plasma diagnostics, airborne and space-borne imaging radars called as synthetic aperture radars, and living body measurements. Transmission, reflection, scattering, and radiation processes of electromagnetic waves are utilized as diagnostic principles. The diagnostics are classified as active and passive systems. Specifically, active radar reflectometry has become of importance in various applications due to the possibility of high localization and accessibility of the measurements as well as the non-invasive nature of the systems. In this paper, recent development and application of radar reflectometers are described. The key words are profile reflectometry, fluctuation reflectometry, imaging radar (optics imaging and synthetic aperture imaging), and radio-optics fusion technology in order to improve the spatial resolution

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