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Mark Levi

Bio: Mark Levi is an academic researcher. The author has contributed to research in topics: Doppler effect & Doppler radar. The author has an hindex of 1, co-authored 1 publications receiving 57 citations.

Papers
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Journal ArticleDOI
TL;DR: This work proposes a technique to detect and characterize activity associated with a stationary human in through-the-wall scenarios using a Doppler radar system, using bio-mechanical human arm movement models and the empirical mode decomposition (EMD) algorithm for doppler feature extraction.
Abstract: In homeland security and law enforcement situations, it is often required to remotely detect human targets obscured by walls and barriers. In particular, we are speciflcally interested in scenarios that involve a human whose torso is stationary. We propose a technique to detect and characterize activity associated with a stationary human in through-the-wall scenarios using a Doppler radar system. The presence of stationary humans is identifled by detecting Doppler signatures resulting from breathing, and movement of the human arm and wrist. The irregular, transient, non-uniform, and non-stationary nature of human activity presents a number of challenges in extracting and classifying Doppler signatures from the signal. These are addressed using bio-mechanical human arm movement models and the empirical mode decomposition (EMD) algorithm for Doppler feature extraction. Experimental results demonstrate the efiectiveness of our approach to extract Doppler signatures corresponding to human activity through walls using a 750-MHz Doppler radar system.

57 citations


Cited by
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Journal ArticleDOI
TL;DR: In this article, the authors used the empirical mode decomposition to produce a unique feature vector from the human micro-Doppler signals following which a support vector machine was used to classify human motions.
Abstract: The ability to identify human movements can serve as an important tool in many different applications such as surveillance, military combat situations, search and rescue operations and patient monitoring in hospitals. This information can provide soldiers, security personnel and search and rescue workers with critical knowledge that can be used to potentially save lives and/or avoid dangerous situations. Most research involving human activity recognition employs the short-time Fourier transform (STFT) as a method of analysing human micro-Doppler signatures. However, the STFT has time-frequency resolution limitations and Fourier transform-based methods are not well-suited for use with non-stationary and non-linear signals. The authors approach uses the empirical mode decomposition to produce a unique feature vector from the human micro-Doppler signals following which a support vector machine is used to classify human motions. This study presents simulations of simple human motions, which are subsequently validated using experimental data obtained from both an S-band radar and a W-band millimetre wave (mm-wave) radar. Very good classification accuracies are obtained at distances of up to 90 m between the human and the radar.

142 citations

Journal ArticleDOI
TL;DR: A time and frequency analysis method based on the complete ensemble empirical mode decomposition (CEEMD) method in ground-penetrating radar (GPR) signal processing demonstrates that CEEMD promises higher spectral-spatial resolution than the other two EMD methods in GPR signal denoising and target extraction.
Abstract: In this letter, we apply a time and frequency analysis method based on the complete ensemble empirical mode decomposition (CEEMD) method in ground-penetrating radar (GPR) signal processing. It decomposes the GPR signal into a sum of oscillatory components, with guaranteed positive and smoothly varying instantaneous frequencies. The key idea of this method relies on averaging the modes obtained by empirical mode decomposition (EMD) applied to several realizations of Gaussian white noise added to the original signal. It can solve the mode-mixing problem in the EMD method and improve the resolution of ensemble EMD (EEMD) when the signal has a low signal-to-noise ratio. First, we analyze the difference between the basic theory of EMD, EEMD, and CEEMD. Then, we compare the time and frequency analysis with Hilbert–Huang transform to test the results of different methods. The synthetic and real GPR data demonstrate that CEEMD promises higher spectral–spatial resolution than the other two EMD methods in GPR signal denoising and target extraction. Its decomposition is complete, with a numerically negligible error.

78 citations

Journal ArticleDOI
TL;DR: A portable, digital, real-time random noise radar system operating in the ultrahigh frequency range for through-the-wall detection and imaging and its capability to image target scenes and characterize human activity from different stand-off distances is demonstrated.
Abstract: We present the design and implementation of a portable, digital, real-time random noise radar system operating in the ultrahigh frequency range for through-the-wall detection and imaging. Noise radar technology is combined with modern digital signal processing approaches to architect a system to covertly perform range imaging of obscured stationary and moving targets as well as to detect the presence of humans via micro-Doppler detection combined with empirical mode decomposition. We model the propagation and sampling nonidealities in the system and propose techniques to overcome the effect of these nonidealities. Experimental results demonstrate the system's capability to image target scenes and characterize human activity from different stand-off distances.

71 citations

Journal ArticleDOI
TL;DR: A technique for classifying stationary targets based on the high-range resolution profile (HRRP) extracted from 3-D TWRIs using a naive Bayesian classifier supported by principal component analysis is presented.
Abstract: A through-the-wall radar image (TWRI) bears little resemblance to the equivalent optical image, making it difficult to interpret. To maximize the intelligence that may be obtained, it is desirable to automate the classification of targets in the image to support human operators. This paper presents a technique for classifying stationary targets based on the high-range resolution profile (HRRP) extracted from 3-D TWRIs. The dependence of the image on the target location is discussed using a system point spread function (PSF) approach. It is shown that the position dependence will cause a classifier to fail, unless the image to be classified is aligned to a classifier-training location. A target image alignment technique based on deconvolution of the image with the system PSF is proposed. Comparison of the aligned target images with measured images shows the alignment process introducing normalized mean squared error (NMSE) ≤ 9%. The HRRP extracted from aligned target images are classified using a naive Bayesian classifier supported by principal component analysis. The classifier is tested using a real TWRI of canonical targets behind a concrete wall and shown to obtain correct classification rates ≥97%.

50 citations

ReportDOI
01 Jan 2015
TL;DR: In this paper, the influence of EMC was studied over four batches of 15 specimens each, conditioned for 6-8 weeks before testing at a temperature of 20 ± 2oC and at four different relative humidities (50, 65, 85, and 95%).
Abstract: The materials used in the study consisted of 720 small clear specimens of nominal dimensions 20 x 20 x 60 mm, of Turkish Red Pine (Pinus brutia) Lebanon cedar (Cedrus libani), Oriental beech (Fagus oriantalis), and English oak (Quercus robur ) from Turkey. The specimens were grouped into 4 batches of 15 specimens each and were tested in the ETH laboratories in Zurich, Switzerland. The influence of EMC was studied over four batches of 15 specimens each, conditioned for 6-8 weeks before testing at a temperature of 20 ± 2oC and at four different relative humidities (50%, 65%, 85%, and 95%). Time of flight value was measured with an ultrasonic commercial device Steinkamp BP-V. Measurements were made end to end directions (L, R, T) on each specimen, with a constant sensor coupling pressure. According to the time results of ultrasound devices, the wave velocities (length/time) and Edyn were calculated. Samples were also tested in uniaxial compression in order to determine E values in three orthotropic directions using a Zwick Z 100 universal testing machine. A load cell with 100-kN maximum capacity was used for compression tests performed in all directions. The feed rate was defined in such a way that the failure of the specimen should be reached in 90 (±30) s. The strains were evaluated using the digital image correlation DIC technique. Wood MC was determined by the oven-drying method. The R values between E and Edyn ranged from 0.79 to 0.96 for the species tested. Moisture content seems to be an influencing factor on sound velocities.

44 citations