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

Abstract

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