Current Research in Micro‐Doppler: Editorial for the Special Issue on Micro‐Doppler

The content of this Special Issue deals with progress in the development of micro-Doppler techniques and approaches for extracting actionable information from vast amounts of sensor data. Using micro-Doppler techniques can produce identifying signatures for vehicles, machinery, animals, and human activities. The small micro-motions of a parts of a subject are observable through the micro-Doppler signature it creates in response to an active emitter in the radar, laser, sonar, or acoustic domain. These micro-Doppler signatures can be used to extract the salient features of the subject's motion and ultimately identify the subject. The rapidly declining cost of micro-Doppler-capable sensors and their improving capabilities provide significant motivation in developing micro-Doppler techniques that can improve the exploitation of these sensors. Many of the fundamentals of micro-Doppler have been discussed in detail [1], including some practical applications [2] and even compressive sensing approaches [3], [4]. This special issue includes an updated general review of the field [5] as well as multiple research papers on particular topics within micro-Doppler research. The goal of this Special Issue is to provide the current progress, challenges, and perspectives on micro-Doppler research and provide a consistent venue for micro-Doppler research work. The motivation for publishing this Special Issue for the IET Radar, Sonar and Navigation is the fact that this journal is recognised by the radar community as one of the top journals where cutting-edge research results are presented and sought, but also that it can encompass the many domains in which micro-Doppler is used. Current work in micro-Doppler signatures can effectively utilise these signatures within a classification system for multiple types of subjects. A classifier was demonstrated for distinguishing micro-Doppler signatures of pedestrians, skaters, and cyclists [6] and another classifier for human activities [7], as well as recognising individuals walking with a cane [8]. A classifier using micro-Doppler Shape Spectrum features was also shown to be effective [9], as were approaches for discriminating humans while sensing from a moving aircraft [10]. Evaluation of micro-Doppler features for classification [11] as well as selection for human activity classification and human versus animal classification has been studied across various systems as well as algorithms for optimising classification performance [12]. Textural features were also explored for the classification of humans and vehicles [13]. Classification based on micro-Doppler signatures has been rapidly maturing through the incorporation of image and video-based classification techniques used to separate the micro-Doppler signatures. The use of micro-range micro-Doppler signatures may significantly improve the basis for mapping signatures to motions [14-18] by moving from an image like a spectrogram into a range-Doppler video [17], which can also be viewed as a range-Doppler surface [19]. Multistatic signatures have also been shown to improve the classification of armed versus unarmed humans [20], [21] and have been used with helicopters [22]. The micro-range micro-Doppler signatures add a significant new dimension to the separability of the micro-Doppler signals which should improve the classification capabilities, while multistatic approaches have been proven to add significant value to the signature for classification. Though the subjects in micro-Doppler are important, understanding the subjects within their clutter and multipath environment is also important. Sea clutter is significantly variable [23], [24] and sea surface subjects have their micro-motions influenced by the sea state [25-27], which can be a challenge but can also be exploited or penetrated [28]. The effects on micro-Doppler signature classification in a free-space versus a through-the-wall environment are explored [29], [30]. Removing the micro-Doppler of windfarms from radar data has been an emerging area of research [31], [32]. The environment also provides signals that can be utilised to make passive radars which can detect helicopters through their unique micro-Doppler signature [22]. Although micro-Doppler features provide additional information for target classification, recognition, and identification, they also can contaminate the signal when the phase is used for imaging such as inverse synthetic aperture radar (ISAR). The theory of extracting rigid body micro-Doppler has been well described in this special issue [33], as has its statistical analysis [34]. The general signal decomposition for micro-Doppler has also been well described [35]. The detection and elimination of the micro-Doppler signals from radar imaging may significantly improve the quality of the resulting imagery and the resulting classifications, but the micro-Doppler can be used in tandem with the imagery to provide more information on the subject. This Special Issue covers many specific domains within micro-Doppler research including classification from micro-Doppler signatures, multistatic micro-Doppler signatures, the decomposition and analysis of micro-Doppler signatures, modeling and simulation of micro-Doppler signatures with experimental validation, and through-barrier radar micro-Doppler signatures. David Tahmoush is the corresponding guest editor for this special issue and is the US Army subject matter expert on micro-Doppler as well as a Principal Investigator in Robotic Perception as well as Text and Video Analytics. He works at the US Army Research Laboratory in Maryland. David's latest book titled “Radar Micro-Doppler Signatures - Processing and Applications” was published in 2014 by IET. David is a NATO (North Atlantic Treaty Organisation) SET (Sensors and Electronics Technology) panel member. He created and heads the Tri-Service Workshop on Dismount Detection and Classification as well as the Special Joint Sessions on Micro-Doppler Signatures at SPIE (the International Society for Optical Engineering). He is a member of the Advanced Instrumentation Systems Technology (AIST) board and recognised subject matter expert by SENSIAC (Military Sensing Information Analysis Center). He has taught “Radar Micro-Doppler Applications” for the IEEE Radar Conference and “Radar Micro-Doppler: Principles and Applications” for SPIE Defense. Hao Ling has been on the faculty of the University of Texas at Austin since 1986 and is currently a Professor of electrical and computer engineering and holder of the L. B. Meader Professorship in Engineering. His principal areas of research are in radar signature prediction and radar feature extraction. He has actively contributed to the development and validation of numerical and asymptotic methods for characterising the radar signatures from complex targets. His recent research interests also include radar signal processing, radar sensing of humans, miniaturised and broadband antenna design, and propagation channel modeling in non-line-of-sight environments. He has published 200 journal papers and 220 conference papers to date and co-authored a book on radar imaging. He was a guest editor of a 2003 Special Issue on time-frequency analysis for synthetic aperture radar and feature extraction for the IEE Proceedings on Radar, Sonar, and Navigation. Ljubiša Stanković has been on the faculty at the University of Montenegro since 1992, where he has been a full professor since 1995. His current interests are in signal processing. He has published about 350 technical papers, more than 120 of them in the leading journals, mainly the IEEE editions. He received the highest state award of Montenegro in 1997 for scientific achievements. He was a member of the IEEE SPS Technical Committee on Theory and Methods, an Associate Editor of the IEEE Transactions on Image Processing, the IEEE Signal Processing Letters, and numerous Special Issues of journals. He is an Associate Editor of the IEEE Transactions on Signal Processing. He has been member of the National Academy of Science and Arts of Montenegro (CANU) since 1996 and a member of the European Academy of Sciences and Arts. Thayananthan Thayaparan received the B.Sc. (Hons.) degree in physics from the University of Jaffna, Jaffna, Sri Lanka, the M.Sc. degree in physics from the University of Oslo, Oslo, Norway, in 1991, and the Ph.D. degree in atmospheric physics from the University of Western Ontario, London, ON, Canada, in 1996. From 1996 to 1997, he was employed as a Postdoctoral Fellow at the University of Western Ontario. In 1997, he joined the Defence Research and Development Canada-Ottawa, Department of National Defence, Canada, as a Defence Scientist. He is currently an Adjunct Professor at McMaster University, Ontario, ON, Canada. His research interests include advanced radar signal and image processing methodologies and techniques against SAR/ inverse synthetic aperture radar and high-frequency surface wave radar problems such as detection, classification, recognition, and identification. His current research includes synthetic aperture radar imaging algorithms, time–frequency analysis for radar imaging and signal analysis, radar micro-Doppler analysis, and noise radar technology. He has authored or coauthored over 210 publications in journals, proceedings, and internal distribution reports. Dr. Thayaparan is a Fellow of the Institute of Engineering and Technology (IET). He is currently serving in the Editorial Board of IET Signal Processing. He was the recipient of the IET Premium Award for Signal Processing for the best paper published in 2009–2010. As a principal writer, he wrote four editorials for the international journals IET Signal Processing and IET Radar, Sonar and Navigation. He co-authored a text book entitled, ‘Time-Frequency Signal Analysis with Applications’. Ram Narayanan serves as a tenured Professor of Electrical Engineering at the Pennsylvania State University. His current research interests lie in the design and development of high-resolution radar systems, tomographic imaging, advanced waveform design and analysis, human detection through barriers, and remote sensing for disaster relief. He has coauthored 120 refereed journal papers and over 300 conference publications. A topic he is actively working on currently is the analysis of micro-Doppler signals arising out of human activity and the use of such signals for remote detection of such activity for possible intent recognition. He pioneered the application of Hilbert-Huang transform in micro-Doppler analysis to process nonlinear and non-stationary signals, and has published several papers on this topic. He was elected Fellow of the IEEE in 2001, Fellow of the SPIE in 2004, and Fellow of the IETE in 2010. Currently, he serves as Member of IEEE Ultrawideband Radar (UWBR) Standards Committee (2004-present), Associate Editor of the IEEE Transactions on Aerospace and Electronic Systems (2008-present), and Associate Editor of the IETE Journal of Education (2012–present).

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