Doppler-based detection and tracking of humans in indoor environments

Ram, Shobha Sundar; Li, Yang; Lin, Anna L.; Ling, Hao

doi:10.1016/j.jfranklin.2008.04.001

Cited by 102 publications

(30 citation statements)

References 37 publications

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“…Thus, it has potential applicability in wireless communication covertness in cluttered environments [38,39] with selective beams automatically tracking respective moving users. It can also be integrated with micro-Doppler processing [40] to further analyze the motion characteristics of each moving target. We present the mathematical formulation of the algorithm and study factors affecting its tracking sensitivity.…”

Section: Introductionmentioning

confidence: 99%

Imaging and tracking of targets in clutter using differential time-reversal techniques

Fouda

Teixeira

2012

Waves in Random and Complex Media

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Two time-reversal algorithms for identifying, imaging, and tracking moving targets in clutter are introduced. The first algorithm classifies existing scatterers into stationary vs. moving targets. Multistatic data matrices (MDMs) corresponding to successive radar acquisitions (snapshots) of the scene are recorded. Singular value decomposition of the (time-)averaged MDM provides information on stationary targets, whereas singular value decomposition of the differential MDM provides information on moving targets. The second algorithm yields real-time selective tracking of each moving target by means of differential time-reversal. It requires minimal processing and memory resources, and exploits distinctive features of timereversal such as statistical stability and superresolution. Numerical simulations are used to illustrate the capabilities of the proposed algorithms in different scenarios involving clutter from discrete secondary scatterers and from inhomogeneous random medium backgrounds.

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Section: Introductionmentioning

confidence: 99%

Imaging and tracking of targets in clutter using differential time-reversal techniques

Fouda

Teixeira

2012

Waves in Random and Complex Media

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“…The S-method is proposed in [11] for microDoppler based characterization. Reassigned joint time-frequency transforms are proposed in [12] for analysis. Existing systems for human Doppler detection mostly deal with gross movement of the human torso.…”

Section: Introductionmentioning

confidence: 99%

Through-the-Wall Detection of Stationary Human Targets Using Doppler Radar

Narayanan¹,

Shastry²,

Chen³

et al. 2010

PIER B

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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 specifically 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 identified 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 effectiveness of our approach to extract Doppler signatures corresponding to human activity through walls using a 750-MHz Doppler radar system.

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“…For the various random human motion signals, the optimal a priori base function is difficult to determine, and it cannot adaptively change with the signal characteristics; thus, it usually cannot achieve the best analysis performance owing to various types of restrictions. Owing to these shortcomings, although these T-F transform methods have made some contribution to the analysis and feature extraction of extremely sketchy and general MDS of human motion, the T-F resolution, detail resolution, and anti-interference capability are very poor; hence, more detailed MDS of different limbs and torsos cannot be separated and displayed clearly in the 2D T-F spectrum [5,28]. In addition, as our previous manuscript [7] and other studies [5,13] demonstrated, the MDS resulting from the common methods will attenuate sharply or become too weak and blurred under the through-wall or remote detection conditions.…”

Section: Introductionmentioning

confidence: 99%

MHHT-Based Method for Analysis of Micro-Doppler Signatures for Human Finer-Grained Activity Using Through-Wall SFCW Radar

Liang

et al. 2017

Remote Sensing

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Ultra-wideband radar-based penetrating detection and recognition of human activities has become a focus on remote sensing in various military applications in recent years, such as urban warfare, hostage rescue, and earthquake post-disaster rescue. However, an excellent micro-Doppler signature (MDS) extracting method of human motion with high time-frequency resolution, outstanding anti-interference ability, and extensive adaptability, which aims to provide favorable and more detailed features for human activity recognition and classification, especially in the non-free space detection environment, is in great urgency. To cope with the issue, a multiple Hilbert-Huang transform (MHHT) method is proposed for high-resolution time-frequency analysis of finer-grained human activity MDS hidden in ultra-wideband (UWB) radar echoes during the through-wall detection environment. Based on the improved HHT with effective intrinsic mode function (IMF) selection according to the cosine similarity (CS) principle, the improved HHT is applied to each channel signal in the effective channel scope of the UWB radar signal and then integrated along the range direction. The activities of swinging one or two arms while standing at a spot 3 m from a wall were used to validate the abilities of the proposed method for extracting and separating the MDS of different moving body structures with a high time-frequency resolution. Simultaneously, the corresponding relationship between the frequency components in MHHT-based spectra and structures of the moving human body was demonstrated according to the radar Doppler principle combined with the principle of human body kinematics. Moreover, six common finer-grained human activities and a piaffe at different ranges under the through-wall detection environment were exploited to confirm the adaptability of the novel method for different activities and pre-eminent anti-interference ability under a low signal-noise-clutter ratio (SNCR) environment, which is critical for remote sensing in various military application, such as urban warfare, hostage rescue, earthquake post-disaster rescue.

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Doppler-based detection and tracking of humans in indoor environments

Cited by 102 publications

References 37 publications

Imaging and tracking of targets in clutter using differential time-reversal techniques

Imaging and tracking of targets in clutter using differential time-reversal techniques

Through-the-Wall Detection of Stationary Human Targets Using Doppler Radar

MHHT-Based Method for Analysis of Micro-Doppler Signatures for Human Finer-Grained Activity Using Through-Wall SFCW Radar

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