With the deployment of radar in versatile scenarios and a wide variety of potential targets, demand for automatic classification of various targets is increasing. The wide variety of radar signatures from physically smaller targets due to lower velocity / radar cross-section thresholds and the increased deployment of radar-based sensors do crowd the radar screen with misinterpreted targets. Micro-Doppler signatures have been widely employed by researchers for the recognition of those targets that exhibit micro-motions. This review article presents the evolution and recent advances in radar micro-Doppler based signature analysis and feature extraction. A review of the micro-Doppler-based target classification techniques along with their applications in defense and commercial sectors, has
Wireless Body Area Network (WBAN) refers to a short-range, wireless communications in the vicinity of, or inside a human body. WBAN is emerging solution to cater the needs of local and remote health care related facility. Medical and nonmedical applications have been revolutionarily under consideration for providing a healthy and gratify service to humanity. Being very critical in communication of the data from body, it faces many challenges, which are to be tackled for the safety of life and benefit of the user. There is variety of challenges faced by WBAN. WBAN is favorite playground for attackers due to its usability in various applications. This article provides systematic overview of challenges in WBAN in communication and security perspectives.
Significant research efforts have focused on techniques for alleviating the nuisance alarm rate (NAR) in the field of φ-OTDR pattern recognition systems. Unfortunately, ephemeral events were mostly neglected in previous research, and algorithms meant for improving classification accuracy were emphasized at the cost of acquiring a very large number of traces. This problem engendered an additional source of NAR in a specific class of events. The proposed solution uses a novel correlation based wrapper on top of differential signals that aims to filter out the effect of unnecessary phases in direct detected φ-OTDR systems. This technique avoids the use of irrelevant data in these differential signals by exploiting a better use of these unnecessary phases and provides a better intensity translation with fewer acquired traces as compared with contemporary techniques of extracting features.
Nuisance Alarm Rate (NAR) is critical in φ-OTDR perturbation detection systems. We present in this letter a novel matched filtering-based feature extractor which aims to noise reduction so that the detection system gets improved performance. This feature extractor requires a small number of data vectors to be acquired which is combined with a random forestbased machine learning strategy to significantly reduce the NAR. In addition, since the number of data vectors is small, this system can also be useful for time-sensitive detection applications.
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