This article develops and examines techniques to enable coherent change detection (CCD) for short-range, high-resolution drone-borne synthetic aperture radar (SAR) systems operating at high frequencies. The potential of using high frequencies at short ranges for fine-resolution imagery and sensitivity to temporal change detection is highlighted, as are the challenges in terms of sensitivity to SAR motion errors. SAR system characteristics for CCD are derived, and the impact of motion errors, which leads to spatial decorrelation and co-registration errors on CCD maps, is discussed. Subsequently, a CCD algorithm able to generate change maps with a >0.75 average coherence value is presented. The validity of the approach is tested through various experimental scenarios. As a result, car tyre marks and human footprints are possible to discern with a drone-borne SAR demonstrator operating at 24 GHz.

This book explores the problem of detecting targets using multistatic passive radar (PR) under the framework of detection theory. The term "multistatic" indicates that multiple transmitter-receiver pairs are used for target detection, with the receivers being spatially separated. In this type of radar, there is no dedicated transmitter, but instead, transmitters of opportunity, such as radio or television (TV) transmitters, are used.

In this paper, the Burg algorithm is used in order to extrapolate the elements of 2-D multiple-input multiple-output (MIMO) array. The goal is to achieve higher angular resolution beyond that of the equivalent 2-D MIMO virtual aperture. It is shown that the size and number of elements of the MIMO physical array can be significantly reduced. The experimental methodology used to acquire 3-D data using a 1-D MIMO radar is described. The experiments use a commercial-off-the-shelf 1-D MIMO radar system. Using this approach, the antenna configuration used can be tested experimentally in cases where it is not possible to manufacture a 2-D MIMO antenna array or when it is desired to observe the antenna response prior to fabricating the antenna.

This paper examines whether crop fall patterns due to thunderstorm downburst-like events can provide an insight into the flow structure of a downburst. To explore this phenomenon, a novel three-dimensional analytical model for the velocity flow field is derived and coupled with a generalised plant model which is capable of modelling crop failure. Through this approach we have established the concept of the lodging front – a dimensionless variable used to quantify the spatial extent of crop failure. Crop failure is shown to result in a diverging pattern and the angles at which the crop falls has been shown to collapse onto a single curve when suitably normalised. Comparison with full-scale data suggests that the model is capable of predicting realistic crop fall patterns and could potentially be used in the future to assess the strength of downbursts.

In the research reported in this paper a methodology of optimizing the positions of the elements of sparse 2D MIMO array is proposed. This methodology is based on simulated annealing and has the goal of minimizing the sidelobe level (SSL) whilst keeping the beamwidth constant and consistent with the size of the virtual receive array. The computational benefits of pre-defined initial positions of the array are discussed and results are presented to illustrate performance potential.

Lodging is a major problem in maize (Zea mays L.) production worldwide. An analytical lodging model has previously been established. However, some of the model inputs are time consuming to obtain and require destructive plant sampling. Efficient prediction of lodging risk early in the season would be beneficial for management decision-making to reduce lodging risks and ensure high yield potential. Remote sensing technology provides an alternative method for fast and nondestructive measurements with the potential for efficient prediction of lodging risks. The objective of this study was to explore the potential of using an active canopy sensor for the early prediction of maize stem lodging risk using simple regression and multiple linear regression (MLR) models. The results indicated that the MLR models using active canopy sensor data together with weather and management factors performed better than simple regression models using only sensor data for predicting maize stem lodging indicators. Similar results were achieved either using regression models to predict the maize stem lodging risk indicators directly or using the regression models to predict lodging related plant parameters as inputs to a process-based lodging model to predict lodging risk indicators indirectly, although the latter approach using MLR models performed slightly better. A medium planting density (7.0 plants m-2) and 240 kg ha-1 N rate would be suitable in the study region, and the recommendations may be adjusted according to different weather conditions. It is concluded that maize stem lodging risks can be predicted using active canopy sensor data together with weather and management information at V8 stage, which can be used to guide in-season management decisions. Additional research is needed to evaluate the potential of using unmanned aerial vehicles and satellite remote sensing technologies in conjunction with machine learning methods to improve the prediction of lodging risks for large scale applications.

Technological developments in Connected and Autonomous Vehicles (AVs) have created opportunities to allow groups of vehicles to travel in close proximity, through methods known as platooning. There are potential benefits from platooning in terms of fuel consumption, through a reduction in aerodynamic drag for trailing vehicles in the platoon; however, it is still not understood whether these benefits remain when the platoon is subject to crosswind. For the first time, this study examines the flow structure and aerodynamic response of a platoon of eight closely spaced lorry type vehicles subjected to a crosswind with a 30o yaw angle. The numerical study is conducted using a Delayed Detached Eddy Simulation. It is observed that there is an increase in the overall drag when compared to a similar simulation with no crosswinds. Streamline illustrations indicate that a recirculation region is formed on the leeside of the lorries, which with the chosen vehicle spacing does not exhibit any interactions with the consecutive lorry, resulting in a diminished drag reduction. High pressure on the windward side of the lorries and a low pressure region on the top of the lorry boxes results in high lift, side force and rolling moment coefficients, but relatively minor pitching and yawing moments.

We present a quantum enabled networked radar system that incorporates an optical atomic clock to provide the radar systems with both an ultra-low noise oscillator and time synchronization. Characterization of the photonic microwave generator against traditional oscillators currently used in the radar systems is presented. Near-future developments of the radar network are discussed.

In order to be effective, radar drone surveillance systems need to be able to discriminate between birds and drones. In this work, convolutional neural networks (CNNs) are used to distinguish between bird and drone spectrograms, where the classifier is tested on real, low signal to background ratio (SBR) data obtained using an L-band staring radar. This allows for a better understanding of the classifier's ability to generalise against new models of drone and new clutter environments. This work highlights the importance of SBR for drone surveillance, placing limits on the size of drone that can be reliably classified, as well as range from the radar.

Professor Nadav Levanon, one of the world's foremost authorities on radar, with a lifetime's experience and expertise of both teaching and researching, has published a new two-volume introductory e-book on radar, Radar—Concise Course, Volume 1 [1] and Radar—Concise Course, Volume 2 [2]. The radar community is fortunate to already have a number of wonderfully written texts introducing this complex and varied subject. Therefore, any new comer, covering this well-trodden territory, must successfully rise to the challenging trick of offering material that both enhances and enriches existing books, while simultaneously offering genuinely new perspectives that help to deepen the reader's overall understanding.

This work consists of evaluating the impact of the most relevant characteristics of railway vehicles, namely geometric, mechanical and aerodynamic properties, in their runnability in the presence of strong winds, more precisely in the risk of derailment. Such objective is achieved by performing several dynamic with a non-linear vehicle-structure interaction model developed by the authors [1,2] and used in other works in this field [3,4], which allows the evaluation of the wheel-rail contact forces and, consequently, the unloading index, as suggested by the European Norm EN 14067-6 [5]. The calculations are carried out for several scenarios characterized by different train and wind speeds. The wind is simulated with a stochastic model that allows the generation of turbulent wind time-histories based on power spectral density functions. The reference vehicle adopted in this work corresponds to the European InterCity Express 3 (ICE-3) train, whose original properties were parameterized to evaluate their influence in the vehicle’s stability. This parametric study focuses on the variation of several properties of the vehicle that may influence its dynamic behavior against crosswinds, namely the carbody mass, height of the gravity center, aerodynamic coefficients, and stiffness and damping of the suspensions. Apart from the suspensions’ properties, which prove to have a negligible influence in the vehicle’s stability, the remaining parameters have a significant impact in the running safety against crosswinds.

This article develops and examines methods for the production of real-world, very high-resolution imagery using a high-frequency drone-borne synthetic aperture radar (SAR) operating at short ranges. The significance of motion errors which lead to space-invariant/variant phase errors is discussed. Subsequently, an imaging algorithm capable of handling these errors is proposed and presented. The validity of the approach is tested through both simulation and experiment. We present novel short-range, fine-resolution imagery (less than 2 cm in cross-range) of an extended target area generated using a low-cost, drone-borne vehicular frequency-modulated continuous-wave (FMCW) radar operating at 77 GHz, without using a dedicated inertial navigation system (INS) or Global Positioning System (GPS).

The aim of the present study consists of evaluating the influence of the most relevant geometric, mechanical and aerodynamic vehicle properties in the risk of derailment caused by crosswinds. To achieve this objective, a vehicle-structure interaction model is used to carry out non-linear dynamic analyses to assess the train-track coupling behaviour in the presence of winds. By computing the wheel-rail contact forces, the derailment risk is evaluated based on the unloading criterion, as suggested by the European Norm EN 14067–6 (2016), for several scenarios with different train and wind speeds. The wind is simulated with a stochastic model that allows the generation of turbulent wind time-histories based on power spectral density functions. The reference vehicle adopted in this work corresponds to the European InterCity Express 3 (ICE-3) train, whose original properties were parameterized in order to evaluate their influence in the vehicle's stability. The parametric study focused on several properties of the vehicle, namely the carbody mass, height of the gravity centre, aerodynamic coefficients, and stiffness and damping of the suspensions. Apart from the suspensions' properties, which prove to have a negligible influence in the vehicle's stability, the remaining parameters have a significant impact in the running safety against crosswinds.

Lodging is a major constraint to increasing the global productivity of maize (Zea Maize L.). The objectives of this paper are to: i) describe a model for stem and root lodging in maize, ii) calibrate the anchorage strength component of the model, iii) evaluate the model’s applicability by assessing its capacity to explain effects of crop husbandry on lodging risk and iv) investigate the potential to further develop the lodging model to predict lodging risk at an early enough growth stage for tactical agronomic action to minimise lodging risk. The study involved a multidisciplinary collaboration between crop scientists, wind engineers and geospatial scientists in the UK and China. Three field experiments with plant population density and nitrogen (N) fertiliser rate treatments were conducted in the UK and China to develop and test the lodging model. Plant characteristics associated with lodging were measured in the experiments after flowering. An existing model of cereal anchorage strength that uses the spread of the root plate as its primary input was demonstrated to be applicable for maize and calibrated for this crop species. The lodging model’s predictions of the effects of plant population and N fertiliser on lodging risk were consistent with published observations. The lodging model calculated that increasing the plant population significantly reduced the anchorage and stem failure wind speeds in all experiments, thus increasing the risk of lodging. This effect was primarily due to increased plant population reducing the spread of the root plate and the stem strength. Changes in N fertiliser had a smaller effect on the lodging associated plant characters. A sensitivity analysis showed that stem failure wind speed was influenced most by variation in stem strength and root failure wind speed was influenced most by variation in the spread of the root plate. This study has shown that the leaf area index measured at leaf 4, 6 or 8 stages is a good indicator of a crop’s future risk of lodging, which demonstrates the potential to develop the model into a practical tool for predicting lodging risk in time for tactical agronomic decisions to be made during the crop’s growing period.

This study describes a cognitive radar architecture with application to real-time obstacle avoidance in mobile robotic platforms. The concept of a world memory map is introduced as a means of providing an enhanced perception of the environment around the robotic platform. This is combined with a specially designed obstacle avoidance algorithm, Nearest Steering Vector Searching, all capable of operating in real-time. The study analytically derives the radar signal processing algorithm, starting from range-angle maps, so that a collision free course to a set destination point can be robustly navigated. Finally, the performance of this cognitive approach is examined through a number of proof-of-concept experiments using a commercial off-the-shelf radar mounted on a mobile ground robotic platform.

This paper shows the early results from an investigation into the potential gain in detection performance achievable by optimizing the parameterization of a CA-CFAR detector to local clutter behaviour. The effect of varying CFAR window length is studied in particular for range correlated time independent samples, and the CFAR Loss (measured by increasing the RCS of a non-fluctuating target simulated at each range cell) of an optimal “fixed” CFAR is compared to a varying window length that minimizes CFAR Loss for each range cell, showing potential performance increase.

The paper introduces a radar signal processing method for goal-oriented, collision-free navigation in mobile robotic platforms. The derived algorithm creates an enhanced perception of the area in front of the sensor through accumulating a sequence of radar pulses that is constantly updated, and uses previously obtained perception to inform future robot steering actions on the fly, thus creating a form of working memory. The algorithm is analytically described, and experimentally confirmed in laboratory conditions with a ground mobile robot operating in real-time.

This paper examines the impact that climate change may have on the lodging of oats in the Republic of Ireland and the UK. Through the consideration of a novel lodging model representing the motion of an oat plant due to the interaction of wind and rain and integrating future predictions of wind and rainfall due to climate change, appropriate conclusions have been made. In order to provide meteorological data for the lodging model, wind and rainfall inputs are analysed using 30 years’ time series corresponding to peak lodging months (June and July) from 38 meteorological stations in the United Kingdom and the Irish Republic, which enables the relevant probability density functions (PDFs) to be established. Moreover, climate data for the next six decades in the British Isles produced by UK climate change projections (UKCP18) are analysed, and future wind and rainfall PDFs are obtained. It is observed that the predicted changes likely to occur during the key growing period (June to July) in the next 30 years are in keeping with variations, which can occur due to different husbandry treatments/plant varieties. In addition, the utility of a double exponential function for representing the rainfall probability has been observed with appropriate values for the constants given.