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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.

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.

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.

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.

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.

International audience; Automatic Target Recognition (ATR) is the task of automatically detecting and classifying targets. Recognition using Synthetic Aperture Radar (SAR) images is interesting because SAR images can be acquired at night and under any weather conditions, whereas optical sensors operating in the visible band do not have this capability. Existing SAR ATR algorithms have mostly been evaluated using the MSTAR dataset. 1 The problem with the MSTAR is that some of the proposed ATR methods have shown good classification performance even when targets were hidden, 2 suggesting the presence of a bias in the dataset. Evaluations of SAR ATR techniques are currently challenging due to the lack of publicly available data in the SAR domain. In this paper, we presenta high resolution SAR dataset consisting of images of a set of ground military target models taken at various aspect angles, The dataset can be used for a fair evaluation and comparison of SAR ATR algorithms. We applied the Inverse Synthetic Aperture Radar (ISAR) technique to echoes from targets rotating on a turntable and illuminated with a stepped frequency waveform. The targets in the database consist of four variants of two 1.7m-long models of T-64 and T-72 tanks. The gun, the turret position and the depression angle are varied to form 26 different sequences of images. The emitted signal spanned the frequency range from 13 GHz to 18 GHz to achieve a bandwidth of 5 GHz sampled with 4001 frequency points. The resolution obtained with respect to the size of the model targets is comparable to typical values obtained using SAR airborne systems. Single polarized images (Horizontal-Horizontal) are generated using the backprojection algorithm. 3 A total of 1480 images are produced using a 20 ° integration angle. The images in the dataset are organized in a suggested training and testing set to facilitate a standard evaluation of SAR ATR algorithm

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.

A 3-D staring radar operates by using a wide beam transmitter to illuminate the entire surveillance region and generates multiple receive beams using a 2-D static array that can be digitised at element level. The sensor achieves permanent search in all directions and harnesses the spatial, temporal and spectral domains to improve detection and discrimination of low observable, highly manoeuvrable targets in congested air space against strong non-stationary clutter. While the susceptibility of traditional scanning radars to jammers has been well researched, very little work has been carried out to assess the performance of 3-D staring radars in the presence of an interference source. In this paper, the response of a staring array radar to a jammer is modelled. Results are presented showing that by exploiting the persistent dwell time of the staring array, it is possible to achieve effective jammer suppression using null steering or similar techniques.

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.

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.

Multiple-Beam Radar Systems (MBRS) 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 using digital signal processing. Chirp signals are extensively used in radar systems due to their pulse compression properties, Doppler tolerance and ease of generation. In this paper, we investigate the level of isolation between two linear frequency modulated (LFM) chirps as a function of the frequency slope and of the chirp starting frequency. Results are derived analytically and verified with a set of measurements at S-band.

In this paper, we derive the Ambiguity Function (AF) of a narrowband and a wideband hyperbolic chirp. We calculate the second derivatives of the squared amplitude of the narrowband Complex Ambiguity Function (CAF) and use them to calculate the Fisher Information Matrix (FIM) of the estimators of the target range and velocity. The FIM is then used to calculate the Cramer-Rao Lower Bounds (CRLB) of the variance of the estimators and to ´ carry out an analysis of estimation performance and a comparison with the case of a liner chirp with a rectangular and a Gaussian amplitude modulation. The analysis and the calculations of the CRLB are also extended to a train of hyperbolic chirps. Results corroborate that at narrowband the hyperbolic chirp is less Doppler tolerant than the linear chirp and show that the hyperbolic chirp provides a comparable measurement accuracy to the linear chirp. Results at wideband corroborate the superior Doppler tolerance of the hyperbolic chirp with respect to that of the linear chirp.

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.

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].

Landmine clearance is a severe and unresolved humanitarian issue. The development of low-cost, smaller, faster and lighter Ground Penetrating Radars (GPR), which can be mounted on unmanned platforms, will allow faster and safer 24/7 operations. This technology will make it possible to survey affected areas with more flexible trajectories and these will provide measurements of landmine signatures from many different aspect angles. As a result, multi-perspective information over wide angular windows and the behaviour of the signature as a function of the angle of illumination can be exploited. Landmine signatures are expected to present features that are less sensitive to the angle of illumination with respect to those of common cluttered objects, and this can lead to an improvement in detection and discrimination performance. In this paper, we present the results of an experimental trial carried out to collect the High Range Resolution Profiles (HRRPs) of two landmines, the SB-33 and the VS-50, off the ground. An analysis of the auto-correlation function of each range bin as a function of the aspect angle is presented together with that of the cross-correlation between profiles collected from different aspect angles.

In this paper, the concept of using Waveform Diversity (WD) with the Doppler Beam Sharpening (DBS) technique for airborne radar systems is investigated with a set of preliminary simulations. WD provides the ability to produce simultaneous multiple beams pointing in various directions without reliance on phase shifters. By using the DBS technique which exploits the Doppler shift of targets within each beam, multiple targets can be resolved and detected within the same range-azimuth cell. The combination of WD and DBS is unique and can provide airborne radar with faster and enhanced coverage, resolution and imaging. This study shows how two orthogonal linear chirps can be used to transmit two simultaneous beams pointing in two different directions with a linear antenna array. It also demonstrates how targets in the same range-azimuth cells can be resolved inside each beam with the combined use of DBS. Insights on critical requirements and directions for future work are also discussed.

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.

Nature provides a number of examples where acoustic echolocation is the primary sensing modality, the most well known of these being the bat, whale, and dolphin. All demonstrate a remarkable ability to “see with sound.” Using echolocation, they navigate, locate, and capture prey. As species, they have not only survived but have thrived in all their individual environments, often solely reliant on echolocation. All of these creatures are inherently cognitive. They all maintain a perception of their environment through the nervous system that allows them to take actions. In this paper, we focus on the bat as an example of a cognitive system exploiting a memory-driven perception-action cycle, enabling it to navigate and interact with its environment. The key conceptual components of cognition and how it could be applied to man-made echoic sensors is introduced. This is followed by a description of how echoic flow fields, a bio-inspired technique that bats have been shown to use, fit guidance, and control problems. We then go on to explain how bats are able to reliably distinguish between different targets. A combination of the theory and examples is used to demonstrate the vast potential for advancing the capability of made in man-made systems by adopting aspects of natural echolocating cognitive dynamic systems.

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.

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.

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.

Presented is a statistical analysis of vertically-polarised bistatic sea clutter data, with bistatic angles ranging from 30° to 90°, compared with simultaneously-gathered monostatic clutter data. It is found that there is no significant change in the bistatic reflectivity compared to the monostatic except close to bistatic angles of 90°. It is found that the shape parameter of the K-distribution fitted to the clutter is significantly higher for the bistatic clutter than for the equivalent monostatic clutter.

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.