Human Monitoring Using MIMO Radar

Honma, Naoki; Sasakawa, Dai; Shiraki, Nobuyuki; Nakayama, Tomohiro; Iizuka, Satoshi

doi:10.1109/iwem.2018.8536630

Cited by 10 publications

(4 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Constraint (10) denotes the relationship between measurement vector y i and space reflection vector of the i-th posture η i . Constraints (11) and (12) indicate that the time duration for all the states is positive and sum up to δ in each frame.…”

Section: A Problem Formulationmentioning

confidence: 99%

“…However, due to the complicated and unpredictable wireless environments, the accuracy and flexibility of posture recognition are greatly affected by unwanted multi-path fading [11] and the limited number of independent channels from the transmitters to the receivers in the conventional RF sensing systems. Recently, the reconfigurable intelligent surface (RIS) technique has been developed as a promising technology to actively customize the propagation channels to create favorable propagation environment [12].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Reconfigurable Intelligent Surfaces based RF Sensing: Design, Optimization, and Implementation

Hu¹,

Zhang²,

Di³

et al. 2019

Preprint

View full text Add to dashboard Cite

Using radio-frequency (RF) sensing techniques for human posture recognition has attracted growing interest due to its advantages of pervasiveness, contact-free observation, and privacy protection. Conventional RF sensing techniques are constrained by their radio environments, which limit the number of transmission channels to carry multi-dimensional information about human postures. Instead of passively adapting to the environment, in this paper, we design an RF sensing system for posture recognition based on reconfigurable intelligent surfaces (RISs). The proposed system can actively customize the environments to provide the desirable propagation properties and diverse transmission channels. However, achieving high recognition accuracy requires the optimization of RIS configuration, which is a challenging problem. To tackle this challenge, we formulate the optimization problem, decompose it into two subproblems and propose algorithms to solve them. Based on the developed algorithms, we implement the system and carry out practical experiments. Both simulation and experimental results verify the effectiveness of the designed algorithms and system. Compared to the random configuration and non-configurable environment cases, the designed system can greatly improve the recognition accuracy.

show abstract

Section: A Problem Formulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Reconfigurable Intelligent Surfaces based RF Sensing: Design, Optimization, and Implementation

Hu¹,

Zhang²,

Di³

et al. 2019

Preprint

View full text Add to dashboard Cite

show abstract

“…However, human and object recognition by using RF signals usually employs a passive signal collection and analysis process without changing the radio environment. Hence, due to the complicated and unpredictable radio environments, the accuracy and flexibility of recognition can be affected significantly by undesirable multi-path fading [10][11], and/or the limited number of independent channels from the transmitters to the receivers in the conventional RF-sensing systems.…”

Section: Introductionmentioning

confidence: 99%

MetaSketch: Wireless Semantic Segmentation by Metamaterial Surfaces

Hu¹,

Zhang²,

Bian³

et al. 2021

Preprint

View full text Add to dashboard Cite

Semantic segmentation is a process of partitioning an image into multiple segments for recognizing humans and objects, which can be widely applied in scenarios such as healthcare and safety monitoring.To avoid privacy violation, using RF signals instead of an image for human and object recognition has gained increasing attention. However, human and object recognition by using RF signals is usually a passive signal collection and analysis process without changing the radio environment, and the recognition accuracy is restricted significantly by unwanted multi-path fading, and/or the limited number of independent channels between RF transceivers in uncontrollable radio environments. This paper introduces MetaSketch, a novel RF-sensing system that performs semantic recognition and segmentation for humans and objects by making the radio environment reconfigurable. A metamaterial surface is incorporated into MetaSketch and diversifies the information carried by RF signals. Using compressive sensing techniques, MetaSketch reconstructs a point cloud consisting of the reflection coefficients of humans and objects at different spatial points, and recognizes the semantic meaning of the points by using symmetric multilayer perceptron groups. Our evaluation results show that MetaSketch is capable of generating favorable radio environments and extracting exact point clouds, and labeling the semantic meaning of the points with an average error rate of less than 1% in an indoor space.

show abstract

“…The radio environment is unpredictable and usually unfavorable, and thus the sensing accuracy of conventional RF sensing methods is usually affected by unwanted multi-path fading [13][14],…”

Section: Introductionmentioning

confidence: 99%

MetaSensing: Intelligent Metasurface Assisted RF 3D Sensing by Deep Reinforcement Learning

Zhang

Bian

et al. 2020

Preprint

View full text Add to dashboard Cite

Using RF signals for wireless sensing has gained increasing attention. However, due to the unwanted multi-path fading in uncontrollable radio environments, the accuracy of RF sensing is limited. Instead of passively adapting to the environment, in this paper, we consider the scenario where an intelligent metasurface is deployed for sensing the existence and locations of 3D objects. By programming its beamformer patterns, the metasurface can provide desirable propagation properties. However, achieving a high sensing accuracy is challenging, since it requires the joint optimization of the beamformer patterns and mapping of the received signals to the sensed outcome. To tackle this challenge, we formulate an optimization problem for minimizing the cross-entropy loss of the sensing outcome, and propose a deep reinforcement learning algorithm to jointly compute the optimal beamformer patterns and the mapping of the received signals. Simulation results verify the effectiveness of the proposed algorithm and show how the sizes of the metasurface and the target space influence the sensing accuracy.

show abstract

Human Monitoring Using MIMO Radar

Cited by 10 publications

References 5 publications

Reconfigurable Intelligent Surfaces based RF Sensing: Design, Optimization, and Implementation

Reconfigurable Intelligent Surfaces based RF Sensing: Design, Optimization, and Implementation

MetaSketch: Wireless Semantic Segmentation by Metamaterial Surfaces

MetaSensing: Intelligent Metasurface Assisted RF 3D Sensing by Deep Reinforcement Learning

Contact Info

Product

Resources

About