A Multifrequency Radar System for Detecting Humans and Characterizing Human Activities for Short-Range Through-Wall and Long-Range Foliage Penetration Applications

Narayanan, Ram M.; Smith, Sonny; Gallagher, Kyle A.

doi:10.1155/2014/958905

Cited by 18 publications

(14 citation statements)

References 19 publications

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“…Several works exploiting micro-Doppler signatures for target classification and human identification have been published [ 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 ]. In this paper, we focused on the possibility of characterizing object movement and differences between targets.…”

Section: Introductionmentioning

confidence: 99%

Time-Frequency Spectral Signature of Limb Movements and Height Estimation Using Micro-Doppler Millimeter-Wave Radar

Balal

Balal²,

Richter³

et al. 2020

Sensors

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We present a technique for the identification of human and animal movement and height using a low power millimeter-wave radar. The detection was based on the transmission of a continuous wave and heterodyning the received signal reflected from the target to obtain micro-Doppler shifts associated with the target structure and motion. The algorithm enabled the extraction of target signatures from typical gestures and differentiated between humans, animals, and other ‘still’ objects. Analytical expressions were derived using a pendulum model to characterize the micro-Doppler frequency shifts due to the periodic motion of the human and animal limbs. The algorithm was demonstrated using millimeter-wave radar operating in the W-band. We employed a time–frequency distribution to analyze the detected signal and classify the type of targets.

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Section: Introductionmentioning

confidence: 99%

Time-Frequency Spectral Signature of Limb Movements and Height Estimation Using Micro-Doppler Millimeter-Wave Radar

Balal

Balal²,

Richter³

et al. 2020

Sensors

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show abstract

“…In this context, the round-trip return time τ R for a target located at a range of R is given by [8];…”

Section: Micro-doppler Effect In Radar [14]mentioning

confidence: 99%

“…Analysis of the micro-Doppler signatures in the joint time-frequency domain can provide useful information for target detection, classification, and recognition [8], [26]. Therefore, the micro-Doppler signatures from the scene are calculated by applying a time-frequency transforms, such as the Short-Time Fourier Transform (STFT) with a Gaussian window, to the scattered fields.…”

Section: Wall Effectsmentioning

confidence: 99%

“…The experimental work for detection and recognition of the human movements behind the wall have been done with the specific parameters as in Table 1, these parameters are; The human body model has been based on the model which is presented in [7], [8], [9] as shown in Figure 2. The sampling rate is directly proportional to the maximum speed observed.…”

Section: Experimental Case Studymentioning

confidence: 99%

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Through the Wall, Recognize Moving Targets Based on Micro-Doppler Signatures

Saeed

Al-Muifraje

Hatem

2018

IJECE

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Radar is a promising device for detection and recognition of invisible moving objects, Where, the micro-Doppler frequency shift caused by moving the object's parts have been represented as an attractive feature in the recognition process. In spite of that, no thorough analysis of human movement by bicycle and no discriminate from the running one through the wall in the literature. This paper presents a mathematical model of bicycle movement, then, the recognition of moving objects through the wall. Where three human movements; walking, running and on a bicycle have been recognized through two types of wall wood and cork. The theoretical analysis and measured was given a recognition 98.7% for human walking on his feet, 99% of the passenger on a bicycle, and 98% of the person running have been achieved without walls. While, a 95.4%, 96.2%, and 95% recognition have been gained from walking, moving by bicycle and running with wooden wall and 94%, 94.8%, and 93.3% respectively with cork wall. 2.4 GHz as a detector and SVM as classifier are used.

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“…A comprehensive set of features was extracted in [10] and feature selection was used to find the most informative and distinguishing features among classes of human activities. Besides studying time and frequency features, researchers have applied other methods of feature extraction using wavelet packets [31], entropy analysis [32] and empirical mode decomposition [33,34] to monitor and classify human activities.…”

Section: Introductionmentioning

confidence: 99%

Classification of Doppler radar reflections as preprocessing for breathing rate monitoring

Nejadgholi

Sadreazami

Rajan³

et al. 2019

IET signal process.

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Classification is presented as a pre-processing step in this study. The state of the subject is classified as the unmoving state with normal breathing (normal breathing class), unmoving state with no breathing (stop breathing class) or the state when the subject is moving (erratic signal class) before breathing estimation algorithms are applied. Estimation algorithms may be applied to obtain breathing rate if normal breathing class is detected or alarms may be generated if stop breathing is detected, and fine-grained classification of activities may be pursued if the erratic signal is detected. Experiments were performed using a single-channel pulse-modulated continuous wave radar with three subjects for a total of 135 min. In each experiment, the subject was continuously monitored for 15 min and the subject performed activities that resulted in a signal that belonged to one of the three classes. Besides extracting a feature that assessed the distribution of energy of the signal in the frequency domain, a novel nonlinear time series feature extraction method based on the higher-dimensional embedding technique was applied to ascertain periodicity of the reflected signal. Bayes classifier was used to classify each 5-s segment of radar returns. A 30-fold cross validation resulted in 97% of overall classification accuracy.

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A Multifrequency Radar System for Detecting Humans and Characterizing Human Activities for Short-Range Through-Wall and Long-Range Foliage Penetration Applications

Cited by 18 publications

References 19 publications

Time-Frequency Spectral Signature of Limb Movements and Height Estimation Using Micro-Doppler Millimeter-Wave Radar

Time-Frequency Spectral Signature of Limb Movements and Height Estimation Using Micro-Doppler Millimeter-Wave Radar

Through the Wall, Recognize Moving Targets Based on Micro-Doppler Signatures

Classification of Doppler radar reflections as preprocessing for breathing rate monitoring

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