Advanced Signal Processing for Vital Sign Extraction With Applications in UWB Radar Detection of Trapped Victims in Complex Environments

Li, Jing; Liu, Lanbo; Zeng, Zhaofa; Liu, Fengshan

doi:10.1109/jstars.2013.2259801

Cited by 138 publications

(63 citation statements)

References 16 publications

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“…Feasibility of such sensors has been demonstrated through patient fall detection [3], health monitoring using through-the-wall UWB radars for heart and breathing rates [72], and even the identification of abnormalities in the detected heart and breathing rates such as heart rate variability (HRV) and respiratory sinus arrhythmia (RSA) [73]. The nature of UWB and noise radars paired with these types of measurements additionally enables many sensors in the same local environment to cooperate in the search for survivors in disaster relief scenarios [74]. Considering these motivations, the RF validation of many solvable systems utilized for wireless sensing is an important and exciting direction.…”

Section: Discussionmentioning

confidence: 99%

Generating and Detecting Solvable Chaos at Radio Frequencies with Consideration to Multi-User Ranging

Beal

Cohen

Syed

2020

Sensors

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High entropy waveforms exhibit desirable correlation properties in radar and sonar applications when multiple systems are used in close proximity. Unfortunately, the information content of these signals can impose high sampling requirements for digital detection techniques. Solvable chaotic oscillators have been proposed to address such issues due to their simple, matched filters, where hardware has been demonstrated with a bandwidth of 10–20 kHz. To extend applications of these systems, we present theory, design, and experimental verification of solvable chaos at 1 MHz using simple off-the-shelf components. The waveforms produced by this system were propagated over a 2.45 GHz RF link and detected with an RLC-based, purely analog matched filter. Further, we show that properties of this special class of chaotic systems can be exploited to yield RF noise sources that are generally advantageous for multi-user ranging applications when compared to conventional techniques. The result is a simple, low-cost, and potentially low-power RF ranging system that requires very little digital signal processing.

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Section: Discussionmentioning

confidence: 99%

Generating and Detecting Solvable Chaos at Radio Frequencies with Consideration to Multi-User Ranging

Beal

Cohen

Syed

2020

Sensors

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“…Superposed multipaths show stronger textures and can be obviously observed with the curvelet transform. In addition, the curvelet transform provides a multi-scale and multi-orientation decomposition for the 2-D radar matrix to adequately represent texture and edge information with curve-like features [12], providing information on signal strength and moving direction of people. The definition of discrete Curvelet transform is given as follows,…”

Section: Hybrid Feature Extraction Methods a Curvelet Transform mentioning

confidence: 99%

Dense People Counting Using IR-UWB Radar With a Hybrid Feature Extraction Method

Yang

Yin

et al. 2019

IEEE Geosci. Remote Sensing Lett.

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People counting is one of the hottest issues in sensing applications. The impulse radio ultra-wideband (IR-UWB) radar has been extensively applied to count people, providing a device-free solution without illumination and privacy concerns. However, performance of current solutions is limited in congested environments due to the superposition and obstruction of signals. In this letter, a hybrid feature extraction method based on curvelet transform and distance bin is proposed. 2-D radar matrix features are extracted in multiple scales and multiple angles by applying the curvelet transform. Furthermore, the distance bin is introduced by dividing each row of the matrix into several bins along the propagating distance to select features. The radar signal dataset in three dense scenarios is constructed, including people randomly walking in the constrained area with densities of 3 and 4 persons per square meter, and queueing with an average distance of 10 centimeters. The number of people is up to 20 in the dataset. Four classifiers including decision tree, AdaBoost, random forest and neural network are compared to validate the hybrid features, and random forest performs the highest accuracies of all above 97% in three dense scenarios. Moreover, to ensure the reliability of the hybrid features, three other features including cluster features, activity features and CNN features are compared. The experimental results reveal that the proposed hybrid feature extraction method exhibits stable performance with significantly superior effectiveness.

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“…In [27] they present a model of the respiratory signal resulting from an ultra-wideband radar, which allows the detection of this signal through obstacles. In [28] they perform mathematical methods to separate the signals given by the ultra-wideband radar, allowing the identification of a person's vital signs. In [29] they present an app which uses the mobile phones of the rescue teams, allowing to record the sounds and estimate the victim's location through the use of a central processing system.…”

Section: Related Workmentioning

confidence: 99%

Robot Swarm Navigation and Victim Detection Using Rendezvous Consensus in Search and Rescue Operations

Cardona

Calderón

2019

Applied Sciences

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Cooperative behaviors in multi-robot systems emerge as an excellent alternative for collaboration in search and rescue tasks to accelerate the finding survivors process and avoid risking additional lives. Although there are still several challenges to be solved, such as communication between agents, power autonomy, navigation strategies, and detection and classification of survivors, among others. The research work presented by this paper focuses on the navigation of the robot swarm and the consensus of the agents applied to the victims detection. The navigation strategy is based on the application of particle swarm theory, where the robots are the agents of the swarm. The attraction and repulsion forces that are typical in swarm particle systems are used by the multi-robot system to avoid obstacles, keep group compact and navigate to a target location. The victims are detected by each agent separately, however, once the agents agree on the existence of a possible victim, these agents separate from the general swarm by creating a sub-swarm. The sub-swarm agents use a modified rendezvous consensus algorithm to perform a formation control around the possible victims and then carry out a consensus of the information acquired by the sensors with the aim to determine the victim existence. Several experiments were conducted to test navigation, obstacle avoidance, and search for victims. Additionally, different situations were simulated with the consensus algorithm. The results show how swarm theory allows the multi-robot system navigates avoiding obstacles, finding possible victims, and settling down their possible use in search and rescue operations.

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Advanced Signal Processing for Vital Sign Extraction With Applications in UWB Radar Detection of Trapped Victims in Complex Environments

Cited by 138 publications

References 16 publications

Generating and Detecting Solvable Chaos at Radio Frequencies with Consideration to Multi-User Ranging

Generating and Detecting Solvable Chaos at Radio Frequencies with Consideration to Multi-User Ranging

Dense People Counting Using IR-UWB Radar With a Hybrid Feature Extraction Method

Robot Swarm Navigation and Victim Detection Using Rendezvous Consensus in Search and Rescue Operations

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