Remote Sensing of Heartbeat based on Space Diversity Using MIMO FMCW Radar

Yamamoto, Kohei; Endo, Koji; Ohtsuki, Tomoaki

doi:10.1109/globecom46510.2021.9685033

Cited by 6 publications

(3 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The MIMO FMCW radar is an FMCW radar that has N Tx transmit antennas and N Rx receive antennas [9], [10], [11], [12], [13], [14], [15], [16], [17], [18], [28]. The radar transmits orthogonal chirp signals from N Tx transmit antennas and receives each signal at N Rx receive antennas.…”

Section: Principle Of Mimo Fmcw Radarmentioning

confidence: 99%

See 1 more Smart Citation

Multi-Person Position Estimation Based on Correlation Between Received Signals Using MIMO FMCW Radar

et al. 2023

Self Cite

View full text Add to dashboard Cite

By using Multiple-Input Multiple-Output (MIMO) Frequency Modulated Continuous Wave (FMCW) radar, a range-angle map can be obtained that displays the signal strength in two dimensions with respect to distance and angular directions. In this process, a threshold algorithm such as Constant False Alarm Rate (CFAR) is used to detect positions where the signal strength exceeds a certain threshold value, enabling position estimation of targets. Furthermore, a method has been proposed to use the curve length of the trajectory of the I/Q signal to emphasize the signal of the person's position on the range angle map. However, the conventional CFAR may cause false alarms when noise and clutter signals are strong. In this paper, we propose a new method for human location estimation using the MIMO FMCW radar. Comparing the reflected signals from people and clutter, we see that I/Q signal variations associated with human motion have little correlation with clutter from stationary objects or noise, and I/Q signal variations associated with clutter often have high correlation with signals at other positions. We focus on the fact that there are many positions where the clutter component signal has a high correlation, compared to the signal caused by a person. Using the correlation map that expresses the correlation between the received I/Q signals on the range-angle plane, we evaluated the characteristics of noise and clutter components from walls on the correlation map by standard deviation. As a result, we can remove strong noise and clutter components caused from near walls, thus improving the accuracy of object location estimation.

show abstract

Section: Principle Of Mimo Fmcw Radarmentioning

confidence: 99%

“…In addition to DOA, MIMO FMCW radar can estimate the distance between the radar and the object. These position information can be used for object identification [9], [10], activity recognition [11], [12], [13], and vital sign detection [14], [15], [16], [17], [18].…”

Section: Introductionmentioning

confidence: 99%

Multi-Person Position Estimation Based on Correlation Between Received Signals Using MIMO FMCW Radar

et al. 2023

Self Cite

View full text Add to dashboard Cite

show abstract

“…In addition to the DOA, the MIMO FMCW radar can estimate the range between a radar and an object, which means that this radar can estimate object locations. The object location information is essential for applications with the MIMO FMCW radar, e.g., object identification [ 1 , 2 ], activity recognition [ 3 , 4 , 5 ] and vital sign detection [ 6 , 7 , 8 , 9 , 10 ]. Thus, there is the need to estimate object locations with high accuracy [ 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 ].…”

Section: Introductionmentioning

confidence: 99%

A Denoising Method Using Deep Image Prior to Human-Target Detection Using MIMO FMCW Radar

Endo

Yamamoto

Ohtsuki

2022

Sensors

Self Cite

View full text Add to dashboard Cite

A Multiple-Input Multiple-Output (MIMO) Frequency-Modulated Continuous Wave (FMCW) radar can provide a range-angle map that expresses the signal power against each range and angle. It is possible to estimate object locations by detecting the signal power that exceeds a threshold using an algorithm, such as Constant False Alarm Rate (CFAR). However, noise and multipath components often exist over the range-angle map, which could produce false alarms for an undesired location depending on the threshold setting. In other words, the threshold setting is sensitive in noisy range-angle maps. Therefore, if the noise is reduced, the threshold can be easily set to reduce the number of false alarms. In this paper, we propose a method that improves the CFAR threshold tolerance by denoising a range-angle map using Deep Image Prior (DIP). DIP is an unsupervised deep-learning technique that enables image denoising. In the proposed method, DIP is applied to the range-angle map calculated by the Curve-Length (CL) method, and then the object location is detected over the denoised range-angle map based on Cell-Averaging CFAR (CA-CFAR), which is a typical threshold setting algorithm. Through the experiments to estimate human locations in indoor environments, we confirmed that the proposed method with DIP reduced the number of false alarms and estimated the human location accurately while improving the tolerance of the threshold setting, compared to the method without DIP.

show abstract

mmGAT: Pose Estimation by Graph Attention with Mutual Features from mmWave Radar Point Cloud

Al,

Shi,

Mondher

et al. 2024

ICC 2024 - IEEE International Conference on Communications

View full text Add to dashboard Cite

Remote Sensing of Heartbeat based on Space Diversity Using MIMO FMCW Radar

Cited by 6 publications

References 18 publications

Multi-Person Position Estimation Based on Correlation Between Received Signals Using MIMO FMCW Radar

Multi-Person Position Estimation Based on Correlation Between Received Signals Using MIMO FMCW Radar

A Denoising Method Using Deep Image Prior to Human-Target Detection Using MIMO FMCW Radar

mmGAT: Pose Estimation by Graph Attention with Mutual Features from mmWave Radar Point Cloud

Contact Info

Product

Resources

About