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Staring radars use a transmitting static wide-beam antenna and a directive digital array to form multiple simultaneous beams on receive. Because beams are fixed, the radar can employ long integration times to detect slow low-RCS targets, such as drones, which present a challenge to traditional air surveillance radar. The use of multiple spatially separated receivers cooperating with the staring transmitters in a multistatic network allows multi-perspective target acquisitions that can help mitigate interference, such as signal multipath, and ultimately enhance the detection of drones and reduce target parameter estimation errors. Here, the design of a dual-channel receiver prototype for use in a multistatic cooperative network is presented. Several measurements have been taken using the prototype receiver in a bistatic configuration to test and assess its performance.

The algorithm proposed in this paper jointly selects the transmitted waveform and the control input so that a radar sensor on a moving platform can prosecute a target by minimising a predefined cost that accounts for the energy of the transmitted radar signal, the energy of a platform control input and the relative position error between the platform and the target. The cost is a function of the waveform design and control input. The algorithm extends the existing Joint Waveform Guidance and Control Optimization (JWGCO) solution to nonlinear equations to account for the dependency of the radar measurement accuracies on Signal to Noise Ratio (SNR) and, as a consequence, on the target position. The performance of the proposed solution based on statistical linearisation is assessed with a set of simulations for a pulsed Doppler radar transmitting linearly frequency modulated chirps.

The concept of a traditional monostatic radar with co-located transmit and receive antennas naturally imposes performance limits that can adversely impact applications. Using a multiplicity of transmit and receive antennas and exploiting spatial diversity provides additional degrees of design freedom that can help overcome such limitations. Further, when coupled with cognitive signal processing, such advanced systems offer significant improvement in performance over their monostatic counterparts. This will also likely lead to new applications for radar sensing. In this paper we explore the fundamentals of multistatic network radar highlighting both potential and constraints whilst identifying future research needs and applications. Initial experimental results are presented for a 2-node networked staring radar.

Understanding the decision-making process of deep learning networks is a key challenge that has rarely been investigated for synthetic aperture radar (SAR) images. In this article, a set of new analytical tools is proposed and applied to a convolutional neural network (CNN) handling automatic target recognition on two SAR datasets containing military targets. First, an analysis of the respective influence of target, shadow, and background areas on classification performance is carried out. The shadow appears to be the least used portion of the image affecting the decision process, compared to the target and clutter, respectively. Second, the location of the most influential features is determined with classification maps obtained by systematically hiding specific target parts and registering the associated classification rate relative to the images to be classified. The location of the image areas without which classification fails is target type and orientation specific. Nonetheless, a strong contribution of specific parts of the target, such as the target top and the areas facing the radar, is noticed. Finally, results show that features are increasingly activated along the CNN depth according to the target type and its orientation, even though target orientation is absent from the loss function.

Radars monitoring small targets often must increase their integration times to achieve sufficient signal-to-noise ratio (SNR) for maintaining a viable track. These longer integation times can prevent micro-Doppler signature extraction and instead result in Doppler signatures consisting of spectral lines to the radar's higher-level processing. Whether the radar operates in the micro-Doppler or spectral line regime depends on both radar parameters (e.g. waveforms, wavelengths and integration times) as well as target parameters (e.g. rotor length, rotational frequency, target reflectivity and geometry). Additionally, understanding the transition region between these regimes can further aid target recognition algorithms. This paper uses modelling, simulations and experimental data to refine the understanding of how a particular radar will observe a target Doppler signature in either of these regimes, highlighting the transition region between the two.

Staring radars use a transmitting static wide-beam antenna and a directive digital array to form multiple simultaneous beams on receive. Because beams are static, the radar can employ long integration times that facilitate the detection of slow low-RCS targets, such as drones, which present a challenge to traditional air surveillance radar. Typical low altitude trajectories employed by drones often result in low-grazing angle multipath effects which are difficult to mitigate with a monostatic radar alone. The use of multiple spatially separated receivers cooperating with the staring transmitters in a multistatic network allows multi-perspective target acquisitions that can help mitigate multipath and ultimately enhance the detection of drones. This paper investigates how varying the network geometry affects the estimation performance of a targets position and velocity in a multipath free scenario. The optimal geometry is found by minimising the trace of the Cramer-Rao Lower Bound (CRLB) of the Maximum Likelihood (ML) estimates of range and Doppler using the Coordinate Descent (CD) algorithm. The network estimation accuracy performance is verified using Monte Carlo simulations and an ML Estimator on the target parameter estimates.

Multibeam radars (MBRs) enable multiple independent channels by simultaneously exploiting spatial and waveform diversity. Orthogonal waveforms are employed to form multiple independent antenna beams, each one providing a different function and using different dedicated radar resources. This paper investigates sidelobe levels in MBRs and presents a comparison with those of an Electronic Steerable Array (ESA) that employs a single waveform in transmission to generate multiple simultaneous beams. Simulations are carried out for a 3-channel MBR transmitting quasi-orthogonal Linear Frequency Modulated (LFM) waveforms at Ku band. The response of the MBR to an ideal point target as a function of aspect angle as well as that to multiple targets in different locations has been investigated. Results corroborate the analytical findings and show that the sidelobe levels with respect to angle, at the target range, are attenuated by the cross-ambiguity function properties between the waveforms employed. The range response to a target in low channel isolation suffers from cross-channel interference that may alter the noise floor characteristics of the radar, hence stressing the importance of suitable waveform selection.

Detection and classification of nano-targets (less than 5 cm in size) are becoming important technical challenges as nano-targets are largely invisible to conventional radar. Nano-drones, for example, may soon become a tangible threat capable of providing short-range stealthy surveillance. Similarly, insect pests are posing a significant agricultural risk by causing crop losses and subsequently reducing the yields. Frequency Modulated Continuous Wave (FMCW) radar is a technology that can provide short-range detection, with no blind range and very high resolution, at a relatively low cost. This paper presents the latest results of an ongoing project aiming at designing and developing a low-cost and bespoke 24 GHz FMCW radar prototype to enable detection of nano-targets and extract their Doppler signatures. A home-brew S-band FMCW radar prototype has been initially designed and developed, using off-the-shelf components, to demonstrate the feasibility of our proposed design solution and inform all future activities at 24 GHz. Several experiments have been carried out to test the S-band prototype and assess its performance against larger drones and cars. Results have shown targets could be successfully detected and their micro-Doppler signatures extracted using Short-Time Fourier Transform (STFT) techniques.

Synthetic aperture radar (SAR) images for automatic target classification (automatic target recognition (ATR)) have attracted significant interest as they can be acquired day and night under a wide range of weather conditions. However, SAR images can be time consuming to analyse, even for experts. ATR can alleviate this burden and deep learning is an attractive solution. A new deep learning Pose-informed architecture solution, that takes into account the impact of target orientation on the SAR image as the scatterers configuration changes, is proposed. The classification is achieved in two stages. First, the orientation of the target is determined using a Hough transform and a convolutional neural network (CNN). Then, classification is achieved with a CNN specifically trained on targets with similar orientations to the target under test. The networks are trained with translation and SAR-specific data augmentation. The proposed Pose-informed deep network architecture was successfully tested on the Military Ground Target Dataset (MGTD) and the Moving and Stationary Target Acquisition and Recognition (MSTAR) datasets. Results show the proposed solution outperformed standard AlexNets on the MGTD, MSTAR extended operating condition (EOC)1, EOC2 and standard operating condition (SOC)10 datasets with a score of 99.13% on the MSTAR SOC10.

Echolocating bats have evolved the ability to detect, resolve and discriminate targets in highly challenging environments using biological sonar. The way bats process signals in the receiving auditory system is not the same as that of radar and sonar and hence investigating differences and similarities might provide useful lessons to improve synthetic sensors. The Spectrogram Correlation And Transformation (SCAT) receiver is an existing model of the bat auditory system that takes into account the physiology and the neural organisation of bats that emit broadband signals. In this study, the authors present a baseband receiver equivalent to the SCAT that allows an analysis of target echoes at baseband. The baseband SCAT (BSCT) is used to investigate the output of the bat-auditory model for two closely spaced scatterers and to carry out an analysis of range resolution performance and a comparison with the conventional matched filter. Results firstly show that the BSCT provides improved resolution performance. It is then demonstrated that the output of the BSCT can be obtained with an equivalent matched-filter based receiver. The results are verified with a set of laboratory experiments at radio frequencies in a high signal-to-noise ratio.

Multibeam radar (MBR) systems based on waveform diversity require a set of orthogonal waveforms in order to generate multiple channels in transmission and extract them efficiently at the receiver with digital signal processing. Linear frequency modulated (LFM) signals are extensively used in radar systems due to their pulse compression properties, Doppler tolerance, and ease of generation. Here, the authors investigate the level of isolation between MBR channels based on LFM chirps with rectangular and Gaussian amplitude envelopes. The orthogonal properties and the mathematical expressions of the isolation are derived as a function of the chirp design diversity, and specifically for diverse frequency slopes and frequency offsets. The analytical expressions are validated with a set of simulations as well as with experiments at C-band using a rotating target.

The negative effects wind farm clutter has on the performance of radar systems for Air Traffic Control and Air Surveillance is well-known in the radar research community and several mitigation techniques have been proposed to address this problem. These include bistatic and multistatic radar systems providing multiple views of the area under surveillance, and hence potential additional information that can be used to improve the receiver performance. This paper presents the analysis of a set of experimental data collected simultaneously by two radar systems, one operating at S-band and one at X-band, of echoes from an operational wind farm in the UK near Oxford. This analysis presents several parameters extracted from the time domain data and the Doppler spectra, such as Doppler centroid and bandwidth of the micro-Doppler signature as well as amplitude statistics of the time domain returns. These parameters are characterised using data recorded at monostatic and bistatic nodes, as well as at different polarisation combinations.

Orthogonal and quasi-orthogonal waveforms are used in a multibeam radar (MBR) to isolate different channels and reduce cross-channel interference. Each channel provides an independent radar beam that performs a specific functionality without interfering with adjacent channels. The MBR signal model is presented that shows how spatial and waveform diversity are employed to successfully separate multiple channels. In this paper, the theoretical predictions of channel isolation are experimentally confirmed with an analysis of micro-Doppler signatures of a rotating target obtained by applying short-time Fourier transform (STFT).

This paper presents results of an experimental trial aimed at detecting a car and a flying drone, passively, by exploiting transmissions of opportunity from a staring Holographic radar. Results show that targets could be successfully detected using a low-cost passive receiver prototype. This may be considered a first step towards a radar network that could provide more comprehensive coverage, over a wider area, against a broader set of target types and without requiring additional spectral resources.

The algorithm developed in this paper jointly selects the optimal transmitted waveform and the control input so that a radar sensor on a moving platform with linear dynamics can reach a target by minimising a predefined cost. The cost proposed in this paper accounts for the energy of the transmitted radar signal, the energy of the platform control input and the relative position error between the platform and the target, which is a function of the waveform design and control input. Similarly to the Linear Quadratic Gaussian (LQG) control problem, we demonstrate that the optimal solution satisfies the separation principle between filtering and optimisation and, therefore, the optimum can be found analytically. The performance of the proposed solution is assessed with a set of simulations for a pulsed Doppler radar transmitting linearly frequency modulated chirps. Results show the effectiveness of the proposed approach for optimal waveform design and optimal guidance control.

Multibeam radars (MBR) enable multiple independent channels by simultaneously exploiting spatial and waveform diversity. The use of orthogonal waveforms is employed to form multiple independent antenna beams, each one providing a different function using diverse radar resources. In this paper, the analytical derivations of the isolation between pseudo-orthogonal rectangular and Gaussian linear frequency modulated (LFM) waveforms are presented. Experiments are conducted to verify the analytical results for chirp pairs with different slopes and various time-bandwidth products at C-band. Experimental results are presented for a direct transmission reception loop and for a moving target to account for Doppler shift contributions.

Missile seekers are becoming increasingly more capable of using Doppler Beam Sharpening (DBS) modes as part of the homing cycle, which requires new countermeasures against this mode. One type of countermeasure, is to create false targets within the seeker DBS image. This paper proposes a jamming technique to generate false targets at a precise location within a seeker DBS image, by both delaying and adding a Doppler shift to received waveforms. The effects of tracking errors on the position of the false target are analysed, both analytically and with simulations and used to assess the practical implementation of the jamming scheme. An experimental DBS system was built to test the effectiveness of the jamming scheme against a platform moving in steps and assess errors caused by incorrectly estimating the seeker trajectory.

Published in IET Radar, Sonar and Navigation. Online first 21/06/2016., The potential for using micro-Doppler signatures as a basis for distinguishing between aided and unaided gaits is considered in this study for the purpose of characterising normal elderly gait and assessment of patient recovery. In particular, five different classes of mobility are considered: normal unaided walking, walking with a limp, walking using a cane or tripod, walking with a walker, and using a wheelchair. This presents a challenging classification problem as the differences in micro-Doppler for these activities can be quite slight. Within this context, the performance of four different radar and sonar systems – a 40 kHz sonar, a 5.8 GHz wireless pulsed Doppler radar mote, a 10 GHz X-band continuous wave (CW) radar, and a 24 GHz CW radar – is evaluated using a broad range of features. Performance improvements using feature selection is addressed as well as the impact on performance of sensor placement and potential occlusion due to household objects. Results show that nearly 80% correct classification can be achieved with 10 s observations from the 24 GHz CW radar, whereas 86% performance can be achieved with 5 s observations of sonar.

Aveillant Ltd has developed a staring L-band radar that deploys a static quasi-monostatic antenna in transmission and a static digital phased array on receive capable of generating multiple simultaneous beams. Because the antenna is not rotating, the radar can stare at targets and select long dwell times with no effect on the scan rate. High Doppler resolution can be achieved and used to detect small targets, such as drones, even in heavy clutter. Despite the staring array, targets moving with a variable radial velocity generate echoes with a time-varying Doppler frequency shift that limits the integration gain achievable with standard Fourier Transform based techniques. As a result, the number of pulses can be integrated remains limited to the effective coherent processing interval with a consequent suboptimal Signal to Noise Ratio (SNR). This paper presents the results of a set of simulations aimed at studying the integration gain efficiency of a staring radar of the type of the Aveillant Holographic radar for targets moving with a constant and non-constant radial velocity. The case of a target flying horizontally with respect to the radar boresight is investigated to show that compensation techniques can be potentially employed to maximise coherence on the target and the resulting integration gain.

In this study, a feature extraction algorithm is presented which automatically generates a set of shape spectrum features based on the cadence velocity diagram of the human micro-Doppler signature. Recognition performance between humans undertaking the same activity is assessed on a set of experimental data collected with a continuous wave radar operating at X-band using a Naive Bayesian classifier and a shape-similarity-spectrum classifier. Recognition performance is analysed as a function of key parameters, such as the dwell time on the target and the size of the training set, to investigate the level of robustness of the proposed features. Results show that high level recognition performance can be achieved for both the walking and running activities.

Echolocating bats have evolved an impressive ability to detect and discriminate targets in highly challenging environments. It is believed that over 50 million years of evolution have contributed to optimize their echolocation system so that highlevel performance could be achieved within their operating environment. The way bats interrogate the surroundings present differences, as well as similarities, with respect to typical signal processing techniques used in synthetic sensors. In identifying and investigating these differences, useful lessons can be made available to engineers that can potentially be used to improve radar and sonar systems. In this chapter, we review some of the strategies that bats are believed to employ to detect and classify moving and static targets and present a comparison with the radar and sonar counterparts. We introduce a baseband receiver based on an existing model of the bat auditory system and apply it to baseband synthetic ultrasound signals to investigate target detection and resolution performance.

In this chapter, we take inspiration from the bat and develop an algorithm that guides an airborne radar interceptor towards a target by jointly developing an optimal guidance and automatically adapting and optimising the transmitted waveform on a pulse-to-pulse basis. The algorithm uses a Kalman filter to predict the relative position and speed of the interceptor with respect to the target.

Echolocating bats show a unique ability to detect, resolve and discriminate targets. The Spectrogram Correlation and Transformation (SCAT) receiver is a model of the Eptesicus fuscus auditory system that presents key signal processing differences compared to radar which may offer useful lessons for improvement. A baseband version of the SCAT is used to investigate advantages and disadvantages of bat-like signal processing against the task of target resolution. The baseband receiver is applied to RF experimental data and results show higher range resolution than the reciprocal of the transmitted bandwidth can be achieved for two closely spaced scatterers.

We present an algorithm that allows an interceptor aircraft equipped with an airborne radar to meet another air target (the intercepted) by developing a guidance law and automatically adapting and optimising the transmitted waveform on a pulse to pulse basis. The algorithm uses a Kalman filter to predict the relative position and speed of the interceptor with respect to the target. The transmitted waveform is automatically selected based on its ambiguity function and accuracy properties along the approaching path. For each pulse, the interceptor predicts its position and velocity with respect to the target, takes a measurement of range and radial velocity and, with the Kalman filter, refines the relative range and range rate estimates. These are fed into a Linear Quadratic Gaussian (LQG) controller that ensures the interceptor reaches the target automatically and successfully with minimum error and with the minimum guidance energy consumption.

This Letter presents preliminary results of the analysis of amplitude statistics of wind turbine clutter as extracted from multistatic radar data. It is shown that the T-location-scale distribution provides good fitting of the experimental data, and that there are combinations of bistatic angle and polarisations where the bistatic clutter has more favourable statistics for target detection than the simultaneous monostatic clutter.

The authors present an algorithm that allows an interceptor aircraft equipped with an airborne radar to meet another air target (the intercepted) by developing a guidance law and automatically adapting and optimising the transmitted waveform on a pulse-to-pulse basis. The algorithm uses a Kalman filter to predict the relative position and speed of the interceptor with respect to the target. The transmitted waveform is automatically selected based on its ambiguity function and accuracy properties along the approaching path. For each pulse, the interceptor predicts its position and velocity with respect to the target, takes a measurement of range and radial velocity and, with the Kalman filter, refines the relative range and range rate estimates. These are fed into a linear quadratic Gaussian controller that ensures the interceptor reaches the target automatically and successfully with minimum error and with the minimum guidance energy consumption.

The authors study the radar signature of a new type of wind turbine, named the Wind Lens. This design includes a flanged shroud around the turbine which concentrates the wind flow past the turbine blades and hence improves the efficiency. The design also offers improved safety and reduces acoustic noise. Furthermore, it may offer a significantly lower radar signature, which may make the design much more attractive for use in situations where conventional wind turbine designs may disturb the operation of radars. The authors present the results of an experimental trial, carried out in the UK, to measure the radar cross section (RCS) of a 5 kW Wind Lens turbine prototype and provide a reference database that can be used for comparing the Wind Lens RCS with that of conventional turbines. The authors also investigate the methods to further reduce the Wind Lens RCS and present the results of a time-varying Doppler analysis. The results show that the addition of a metallic mesh around the shroud obscures the rotating blades, and hence mitigates the RCS by 15 dB, at angles where the radar interference is highest.

Echolocating bats have evolved an excellent ability to detect and discriminate targets in highly challenging environments. They have had more than 50 million years of evolution to optimise their echolocation system with respect to their surrounding environment. Behavioural experiments have shown their exceptional ability to detect and classify targets even in highly cluttered surroundings. The way bats process signals is not exactly the same as in radar and hence it can be useful to investigate the differences. The Spectrogram Correlation And Transformation receiver (SCAT) is an existing model of the bat auditory system that takes into account the physiology and underlying neural organisation in bats which emit chirped signals. In this paper, we propose a baseband receiver equivalent to the SCAT. This will allow biologically inspired signal processing to be applied to radar baseband signals. It will also enable further theoretical analysis of the key concepts, advantages and limitations of the "bat signal processing" for the purpose of target detection, localisation and resolution. The equivalence is demonstrated by comparing the output of the original SCAT to that of our proposed baseband version using both simulated and experimental target echoes. Results show that the baseband receiver provides compatible frequency interference pattern for two closely located scatterers.

Automatic target classification under all conditions is a key challenge for modern radar and sonar systems. Echolocating nectar feeding bats are able to detect and select flowers of bat-pollinated plants even in highly cluttered environments. It is thought that these flowers have evolved to ease classification by bats, and that their echo-acoustic signatures contain critical information that aids the bat in choosing the most suitable flowers. In investigating the features of these flowers that aid the bats search for nectar, the strategy underpinning the task of classification of static targets by bats may be understood and this may additionally offer lessons for radar and sonar systems. Here, we analyse a real set of data containing high range resolution profiles of unpollinated corollas of Cobaea scandens, which is a flower of the type that is pollinated by bats. These were collected by transmitting a synthetic wideband linear chirp with an acoustic radar capable of very high range resolution. Classification performance of a k-NN classifier and a Naive Bayesian classifier is assessed using information available in both the time and frequency domains. This facilitates quantification of the differences in these echoes because of the flower wilting process and lack of physical parts.

The results of recent measurements taken with two radar systems to measure the simultaneous monostatic and bistatic signature of wind turbines, at S-band and X-band are presented. Coherent monostatic and bistatic data was collected with the University College London (UCL) NetRAD 2.4 GHz radar, and the Cranfield University CW radar operating at X-band. Initial analysis shows the bistatic Doppler signature of wind turbines and informs on the key differences seen at modest bistatic angles. Polarimetric variations are also analysed via data gathered from using co-polarised VV and HH and cross-polarised VH components.

A set of experimental trials was conducted with a 10 GHz continuous wave radar to collect micro-Doppler signatures of a large single, double and triple bladed rotating fin and a sized miniature helicopter. We analysed the target micro-Doppler signatures and decomposed them using the Empirical Mode Decomposition (EMD) method in order to extract a series of Intrinsic Mode Functions (IMFs) which admit only an instantaneous frequency. The aim of this paper is to investigate what information is available in the target IMFs to help identify key features that can be used for improving target classification and identification. The experimental testing was complimented with a set of simulations to assist in the understanding of the results.

In the last century both radar and sonar active technology developed from inception to the point where high resolution images can be obtained from long ranges. The available technology can exploit Doppler effects, structural resonances, nonlinear scattering, synthetic aperture platforms, and sediment-penetrating modalities. Active sonar and radar share many common approaches, in part because of the similarities of the problems they address, both in the military and commercial research areas. Moreover, in the last two decades, research into both radar and sonar has explored biomimetic and bioinspired solutions [1-5], in recognition of the fact that whilst man-made systems have access to power and bandwidth far beyond that available to any living organism [6], some biological solutions have benefitted from millions of years of natural optimisation to evolve sensing capabilities and strategies and meet the challenges of survival (finding food and mates, avoiding predators, sensing and navigating to and within appropriate habitats, etc.) [7]. Problem solving for survival goes beyond the particular radiation used in sensing, covering also signal processing, detection and classification of targets, use of platforms, and strategies for deploying sensors and interpreting data [8-10]. Bio-inspired approaches follow logically when the problems facing manufactured technology resemble those addressed in nature. For example, in the last 15 years the arena for sonar challenge has changed from the passive detection of large quiet nuclear submarines in the deep, relatively quiet and uncluttered waters, to minehunting by active sonar in shallow coastal waters [11], a problem far closer to that faced by dolphins and bats in their natural environments [12,13].

In this chapter we introduce the theory behind sonar micro-Doppler signatures and highlight advantages and disadvantages with respect to their radar counterpart. We present the results of two experimental trials carried out at (1) the U.S. Army Research Laboratory and (2) University College London in which sonar micro-Doppler signatures of personnel targets were collected at 40 and 80 kHz, respectively. This chapter shows that ultrasound sensors could represent a cost-effective alternative to radar systems for applications that do not require long-range detection. They certainly demonstrate that target detection and possibly classification by ultrasound micro-Doppler signatures are achievable and they might potentially open up a new research avenue looking at the exploitation of these techniques in future systems.

The purpose of this paper is to present an experimental trial carried out at the Defence Academy of the United Kingdom to measure simultaneous multistatic and multimodal micro-Doppler signatures of various targets, including humans and flying UAVs. ewline Signatures were gathered using a network of sensors consisting of a CW monostatic radar operating at 10 GHz (X-band) and an ultrasound radar with a monostatic and a bistatic channel operating at 45 kHz and 35 kHz, respectively. A preliminary analysis of automatic target classification performance and a comparison with the radar monostatic case is also presented.

In this chapter we aim to exploit experience of the natural world in which echolocating mammals are able to detect and classify objects with apparent ease. These observations suggest that waveform diversity and orientation strategies play an important role. It is this hypothesis that we test and show to be valid, as confirmed through real-world radar experiments. Specifically, there is additional information contained in different perspectives of a target that can help classify it, thus boosting performance. There is also a law of diminishing returns, in that the opportunity to extract additional new information reduces as the number of perspectives increases.

We study the radar signature of a new type of wind turbine, named the Wind Lens. This design includes a flanged shroud around the turbine which concentrates the wind flow past the turbine blades and hence improves the efficiency. The design also offers improved safety, and reduces acoustic noise. Furthermore, it may offer a significantly lower radar signature, which may make the design much more attractive for use in situations where conventional wind turbine designs may disturb the operation of radars. We present the results of an experimental trial, carried out in the UK, to measure the Radar Cross Section (RCS) of a 5 kW Wind Lens turbine prototype and we provide a reference database that can be used for comparing the Wind Lens RCS with that of conventional turbines. We investigate methods to further reduce the Wind Lens RCS and present the results of a time-varying Doppler analysis. Results show that the addition of a metallic mesh around the shroud obscures the rotating blades, and hence mitigates the RCS by 15 dBm2, at angles where the radar interference is highest. (5 pages)

Target motions, other than the main bulk translation of the target, induce Doppler modulations around the main Doppler shift that form what is commonly called a target micro-Doppler signature. Radar micro-Doppler signatures are generally both target and action specific and hence can be used to classify and recognise targets as well as to identify possible threats. In recent years, research into the use of micro-Doppler signatures for target classification to address many defence and security challenges has been of increasing interest. In this article, we present a review of the work published in the last 10 years on emerging applications of radar target analysis using micro-Doppler signatures. Specifically we review micro-Doppler target signatures in bistatic SAR and ISAR, through-the-wall radar and ultrasound radar. This article has been compiled to provide radar practitioners with a unique reference source covering the latest developments in micro-Doppler analysis, extraction and mitigation techniques. The article shows that this research area is highly active and fast moving and demonstrates that micro-Doppler techniques can provide important solutions to many radar target classification challenges.

This paper describes a multistatic, sea clutter and vessel database, assembled using the three node, S Band radar, known as NetRAD (for, “Netted Radar”). This data was collected in the United Kingdom and South Africa during 2009-201. It is the intention of the investigators and sponsors of this work to make the raw data available to individuals and organisations interested in processing this multistatic data in support of the development of the understanding of sea clutter and vessel properties in the multistatic configuration.

Echolocating mammals such as bats, whales and dolphins are able to detect, select and attack prey even in dense cluttered and often hostile environments. They have developed echolocation for over 50 million years and rely on exceptional performance for their survival. Although the frequencies and waveform parameters used by radar sensors and by echolocating mammals are not the same, there remain close parallels that suggest lessons can be learnt from nature. In this chapter, we investigate the behaviour and performance of echolocating bats in terms of detecting, locating, tracking and capturing prey. We show how echolocation calls are diversified in a dynamic and intelligent manner according to the task performed and relate the results to typical flight trajectories. We discuss how echoes may be transformed into a meaningful perception of the target and finally we explore how this information can be used to develop a new architecture for radar automatic target recognition (ATR).

We present the results of measurements of the radar signature of a new type of wind turbine, named the Wind Lens. This design includes a shroud around the turbine which concentrates the wind flow past the turbine blades and hence improves the efficiency. The design also offers improved safety, and reduces acoustic noise. Furthermore, it may offer a significantly lower radar signature, which may make the design much more attractive for use in situations where conventional wind turbine designs may disturb the operation of radars in an unacceptable manner.

Bistatic radar is a technique of considerable potential importance and interest. Despite this, current understanding of the properties of bistatic clutter, and in particular, bistatic sea clutter, is limited at best. We present some results of a recent trials campaign to gather bistatic sea clutter data, and to analyse the data in order to develop models to represent bistatic radar sea clutter. The results indicate that the shape parameter of the compound K distribution model fitted to the data tends to be higher for the bistatic clutter than for the equivalent monostatic clutter. This suggests that a bistatic geometry may allow a lower detection threshold for a given probability of false alarm, and hence give improved detection performance of weak targets against a clutter background compared to the conventional monostatic geometry.

Classification of targets by micro-Doppler signatures has attracted a growing interest in recent years. The main bulk translation of a target and additional target motions, such as vibrations and rotations, generate Doppler modulations in the echo that contain unique target features and thus can be used to perform target recognition. Although, target classification by micro-Doppler signatures has been exploited in the RF regime for radar systems, the frequency spectrum is becoming increasingly congested and expensive to use, so that it is desirable to identify and exploit other types which have similar capabilities. In this paper a frequency-agile non-coherent ultrasound radar developed to gather micro-Doppler signatures is presented. This was used in an experimental trial to gather micro-Doppler signatures of personnel targets whilst undertaking various types of motion. Classification performance by these same micro-Doppler signatures is then assessed and results discussed.

Bats achieve remarkable target detection and selection performance in the most challenging environments. These activities are carried out mainly by echolocation, i.e. by transmitting pulses at ultrasound frequencies and processing the echoes from targets. Because they have relied on high level performance for survival, as a consequence of natural selection, it is believed that they have evolved in order to optimise these capabilities. Echolocation calls are very sophisticated, diverse, and commonly composed of a number of harmonic components. If evolution has resulted in multi-harmonic waveforms there might be advantages deriving by doing so. In this paper we simulate multi-harmonics waveforms and exploit advantages or disadvantages by a range analysis of their ambiguity functions. Results are discussed in relation to the radar and sonar case.

In recent years, with the development of high range resolution radars, the desire to be able to identify targets under all weather and clutter conditions has become of great importance. This is an activity carried out with great success by echolocating mammals such as nectar feeding bats that are able to detect and select flowers of bat-pollinated plants even in a dense clutter environment. In this paper data consisting of acoustically generated high range resolution profiles of four bat pollinated flower heads are analysed. Multi perspective classification performance is assessed and compared with the radar case. There are close parallels that suggest lessons can be learnt from nature.

In this work we deal with the problem of analyzing and modeling marine (lake) surface scattering as seen by high resolution radars, with the aim of highlighting, where possible, the differences due to changes in the range resolution and polarization

Classification of targets using their micro-Doppler signatures has attracted a growing interest in recent years. In addition to their main bulk translation, targets may exhibit additional motions, such as vibrations and rotations, which generate Doppler modulations in the echo that contain unique target features and thus can be used to perform target recognition. Although target classification by micro-Doppler signatures has been exploited in the radio frequency regime for radar systems, much less work has been done in acoustic. In this work, an ultrasound radar operating at 80 kHz has been developed to gather micro-Doppler signatures of personnel targets performing various actions. The performance of a range of classifiers and feature extraction algorithms in distinguishing between these micro-Doppler signatures is presented.

Microwave induced plasma torches find wide applications in material and chemical analysis. Investigation of a coaxial electrode microwave induced plasma (CE-MIP) torch is conducted in this study, making it available for glass surface modification and polishing. A dedicated nozzle is designed to inject secondary gases into the main plasma jet. This study details the adaptation of a characterisation process for CE-MIP technology. Microwave spectrum analysis is used to create a polar plot of the microwave energy being emitted from the coaxial electrode, where the microwave energy couples with the gas to generate the plasma jet. Optical emission spectroscopy analysis is also employed to create spatial maps of the photonic intensity distribution within the plasma jet when different additional gases are injected into it. The CE-MIP torch is experimentally tested for surface energy modification on glass where it creates a super-hydrophilic surface.