Sustainability, transport and design: reviewing the prospects for safely encouraging eco-driving

Private vehicle use contributes a disproportionately large amount to the degradation of the environment we inhabit. Technological advancement is of course critical to the mitigation of climate change, however alone it will not suffice; we must also see behavioural change. This paper will argue for the application of Ergonomics to the design of private vehicles, particularly low-carbon vehicles (e.g. hybrid and electric), to encourage this behavioural change. A brief review of literature is offered concerning the effect of the design of a technological object on behaviour, the inter-related nature of goals and feedback in guiding performance, the effect on fuel economy of different driving styles, and the various challenges brought by hybrid and electric vehicles, including range anxiety, workload and distraction, complexity, and novelty. This is followed by a discussion on the potential applicability of a particular design framework, namely Ecological Interface Design, to the design of in-vehicle interfaces that encourage energy-conserving driving behaviours whilst minimising distraction and workload, thus ensuring safety

Multiple drone classification using millimeter-wave CW radar micro-Doppler data

Funding: Army Research Laboratory under Cooperative Agreement Number: W911NF-19-2-0075.This paper investigates the prospect of classifying different types of rotary wing drones using radar. The proposed method is based on the hypothesis that the rotor blades of different sizes and shapes will exhibit distinct Doppler features. When sampled unambiguously, these features can be properly extracted and then can be used for classification. We investigate various continuous wave (CW) spectrogram features of different drones obtained with a low phase noise, coherent radar operating at 94 GHz. Two quadcopters of different sizes (DJI Phantom Standard 3 and Joyance JT5L-404) and a hexacopter (DJI S900) have been used during the experimental trial for data collection. For classification training, we first show the limitation of the feature extraction based method. We then propose a convolutional neural network (CNN) based approach in which the classification training is done by using micro-Doppler spectrogram images. We have created an extensive dataset of spectrogram images for classification training, which have been fed to the existing GoogLeNet model. The trained model then has been tested with unseen and unlabelled data for performance verification. Validation accuracy of above 99% is achieved along with very accurate testing results, demonstrating the potential of using neural networks for multiple drone classification.Postprin

Topological Isomorphisms of Human Brain and Financial Market Networks

Although metaphorical and conceptual connections between the human brain and the financial markets have often been drawn, rigorous physical or mathematical underpinnings of this analogy remain largely unexplored. Here, we apply a statistical and graph theoretic approach to the study of two datasets – the time series of 90 stocks from the New York stock exchange over a 3-year period, and the fMRI-derived time series acquired from 90 brain regions over the course of a 10-min-long functional MRI scan of resting brain function in healthy volunteers. Despite the many obvious substantive differences between these two datasets, graphical analysis demonstrated striking commonalities in terms of global network topological properties. Both the human brain and the market networks were non-random, small-world, modular, hierarchical systems with fat-tailed degree distributions indicating the presence of highly connected hubs. These properties could not be trivially explained by the univariate time series statistics of stock price returns. This degree of topological isomorphism suggests that brains and markets can be regarded broadly as members of the same family of networks. The two systems, however, were not topologically identical. The financial market was more efficient and more modular – more highly optimized for information processing – than the brain networks; but also less robust to systemic disintegration as a result of hub deletion. We conclude that the conceptual connections between brains and markets are not merely metaphorical; rather these two information processing systems can be rigorously compared in the same mathematical language and turn out often to share important topological properties in common to some degree. There will be interesting scientific arbitrage opportunities in further work at the graph-theoretically mediated interface between systems neuroscience and the statistical physics of financial markets

Study of radar signatures of drones equipped with threat payloads

The authors acknowledge the funding received by the Army Research Laboratory under Cooperative Agreement Number: W911NF-19-2-0075.Commercial or customised drones with the ability to carry payloads have the potential to cause security threats so the need to accurately detect and identify them with suitable sensors has increased in recent times. Radar sensors are well capable of detecting and classifying a drone by using the unique signatures produced from both the stationary and rotating parts of the target. In this study we have examined the radar signatures of drones carrying different types of payloads which simulate the following three hazardous scenarios: 1) liquid spray, 2) Inertial forces simulating a gun recoil effect, and 3) heavy payloads. The main objective was to model the radar signatures of these scenarios and analyse the characteristic signatures. Two radars, operating at 24 GHz and 94 GHz, have been used to collect data to validate the modelling. The results of the study demonstrate that the payloads produce unique radar return signals, mainly in the Doppler domain, which can be used for robust classification.Publisher PD

Amplitude characteristics of littoral sea clutter data at K-band and W-band

Funding: UK Engineering and Physical Sciences Research Council under grant EP/S032851/1.Sea clutter data at millimeter wave frequencies are quite limited in the literature. Recent advancements in millimeter wave radar technology have created a potential for its use in maritime surveillance and autonomy. Hence, collecting data at this frequency range is of great interest to both academia and industry. This study reports on a field trial conducted at St Andrews in winter 2020 to collect littoral sea clutter data using K-band (24 GHz) and W-band (94 GHz) radar systems. Extensive data collection was done during the trial, where this work specifically concentrates on analysis of the amplitude characteristics of the sea clutter returns. Analysis of the dataset shows that the radar backscatter was heavily dominated by sea-spikes. The modal normalized radar cross section (NRCS) values for Bragg, burst and whitecap scattering are measured to be -47, -30 and -17 dB respectively at 24 GHz in horizontal polarization and -48, -26 and -12 dB respectively at 94 GHz in circular polarization, measured at grazing angles of 1-3°. The backscatter from the smooth surface is found to be below the noise floor equivalent NRCS (-65 dB). Also, the power spectrum analysis of range-time intensity plots is discussed, revealing information on the sea surface dynamics.Postprin

A new simulation methodology for generating accurate drone micro-Doppler with experimental validation

Authors acknowledge the financial support of the Engineering and Physical Sciences Council and QinetiQ who are funding MM's PhD.Unmanned Aerial Vehicles, or drones, pose a significant threat to privacy and security. To understand and assess this threat, classification between different drone models and types is required. One way in which this has been demonstrated experimentally is through this use of micro-Doppler information from radars. Classifiers capable of exploiting differences in micro-Doppler spectra will require large amounts of data but obtaining such data experimentally is expensive and time consuming. The authors present the methodology and results of a drone micro-Doppler simulation framework which uses accurate 3D models of drone components to yield detailed and realistic synthetic micro-Doppler signatures. This is followed by the description of a purpose-built validation radar that has been developed specifically to gather high-fidelity experimental drone micro-Doppler data with which is used to validate the simulation. Detailed comparisons between the experimental and simulated micro-Doppler spectra from three models of drones with differently shaped propellers are given, showing very good agreement. The aim is to introduce the simulation methodology. Validation using single propeller micro-Doppler is provided, although the simulation can be extended to multiple propellers. The simulation framework offers the potential to generate large quantities of realistic drone micro-Doppler signatures for training classification algorithms.Publisher PDFPeer reviewe

Enabling technologies for high performance millimetre and aub-millimetre wave radar

The successful realisation of high performance millimetre and sub-millimetre wave radars requires key enabling technologies, many of which are not yet commercially available. This paper illustrates some of the key enabling technologies developed to address radar system requirements including chirp generation, feedhorns, duplexing and non-mechanical beam steering. The type of high performance radar system which can be achieved using these technologies is illustrated with the examples of the ‘T-220’ 94 GHz FMCW Doppler radar used for high sensitivity target and clutter phenomenology studies and the ‘CONSORTIS’ 340 GHz 3D imaging radar developed for concealed object detection as required for next generation aviation security screening.Postprin

Multiple drone type classification using machine learning techniques based on FMCW radar micro-Doppler data

Systems designed to detect the threat posed by drones should be able to both locate a drone and ideally determine its type in order to better estimate the level of threat. Previously, drone types have been discriminated using millimeter-wave Continuous Wave (CW) radar, which produces high quality micro-Doppler signatures of the drone propeller blades with fully sampled Doppler spectra. However, this method is unable to locate the target as it cannot measure range. By contrast, Frequency Modulated Continuous Wave (FMCW) data typically undersamples the micro-Doppler signatures of the blades but can be used to locate the target. In this paper we investigate FMCW features of four drones and if they can be used to discriminate the models using machine learning techniques, enabling both the location and classification of the drone. Millimeter-wave radar data are used for better Doppler sensitivity and shorter integration time. Experimentally collected data from Ttree quadcopters (DJI Phantom Standard 3, DJI Inspire 1, and Joyance JT5L-404) and a hexacopter (DJI S900) have been. For classification, feature extraction based machine learning was used. Several algorithms were developed for automated extraction of micro-Doppler strength, bulk Doppler to micro-Doppler ratio, and HERM line spacing from spectrograms. These feature values were fed to classifiers for training. The four models were classified with 85.1% accuracy. Higher accuracies greater than 95% were achieved for training using fewer drone models. The results are promising, establishing the potential for using FMCW radar to discriminate drone types.Publisher PD

Fast classification of drones and birds with an LSTM network applied to 1D phase data

Funding: Science and Technology Facilities Council under grant ST/N006569/1.This study investigates a new type of drone classifier based on Long Short-Term Memory (LSTM) networks. As a real-time surveillance system, the classification time of a drone detection radar is crucial. The motivation for this work is to develop a classification framework which has low latency in terms of data processing for the algorithm input. Theoretical modeling of a rotary wing drone and a bird wing flapping returns were done first to exhibit the difference in the patterns of the respective phase progressions. Then, 94 GHzexperimental trial data containing 4800 sequences of drones, birds, noise and clutter were used to create a diverse training dataset of 1D phase data for supervised learning. A stackedLSTM network with tuned hyperparameters was generated to reduce the possible overfitting from a simple LSTM model. Validation accuracy of 98.1% was achieved for 2-class classification of drone and non-drone. Further performance assessment was then done with 30 unseen test data, where the network was able to correctly classify all the sequences. It is ascertained that this method can be ~10 times faster than a spectrogram based classification model, which requires additional Fast Fourier Transform (FFT) operations

Doppler characteristics of sea clutter at K-band and W-band : results from the St Andrews and Coniston water trials

Funding: UK Engineering and Physical Sciences Research Council under grant EP/S032851/1.This study reports on the experimental results from two field trials conducted by the University of St Andrews, focusing exclusively here on Doppler data. The first trial was at the Bruce Embankment in St Andrews, UK (winter 2020) and the second one was at Coniston Water in the Lake District, UK (autumn 2022). A 24 GHz K-band radar and a 94 GHz W-band radar were used in both trials to collect sea clutter data for phenomenology studies. As very few sea clutter data and analysis of these are available in the literature at these high frequencies, the results are expected to be of general interest within this field of study. The data collection at both trials was done for low grazing angles in the littoral zone. The datasets are quite varied in terms of wave direction, polarization and wind speed. The Doppler signatures and corresponding statistical parameters for these various conditions are reported here. The spectral analysis of different wave types (burst, whitecap, rough surface scattering) along with the combined spectra are also discussed. It is anticipated that these empirical results will be the precursor for improving upon the frequency ranges of existing sea clutter Doppler models