scholarly journals Identification of Human Motion Using Radar Sensor in an Indoor Environment

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2305
Author(s):  
Sung-wook Kang ◽  
Min-ho Jang ◽  
Seongwook Lee
Keyword(s):  
Signal Processing ◽  
Indoor Environment ◽  
Human Motion ◽  
Sensor Data ◽  
Statistical Characteristics ◽  
Radar Signal ◽  
Radar Sensor ◽  
Wave Radar ◽  
The Moment ◽  
Human Motions

In this paper, we propose a method of identifying human motions, such as standing, walking, running, and crawling, using a millimeter wave radar sensor. In our method, two signal processing is performed in parallel to identify the human motions. First, the moment at which a person’s motion changes is determined based on the statistical characteristics of the radar signal. Second, a deep learning-based classification algorithm is applied to determine what actions a person is taking. In each of the two signal processing, radar spectrograms containing the characteristics of the distance change over time are used as input. Finally, we evaluate the performance of the proposed method with radar sensor data acquired in an indoor environment. The proposed method can find the moment when the motion changes with an error rate of 3%, and also can classify the action that a person is taking with more than 95% accuracy.

scholarly journals Simultaneous Target Classification and Moving Direction Estimation in Millimeter-Wave Radar System

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5228
Author(s):  
Jin-Cheol Kim ◽  
Hwi-Gu Jeong ◽  
Seongwook Lee
Keyword(s):  
Millimeter Wave ◽  
Radar System ◽  
Sensor Data ◽  
Test Field ◽  
Target Classification ◽  
Radar Sensor ◽  
Millimeter Wave Radar ◽  
Direction Estimation ◽  
Wave Radar ◽  
Moving Direction

In this study, we propose a method to identify the type of target and simultaneously determine its moving direction in a millimeter-wave radar system. First, using a frequency-modulated continuous wave (FMCW) radar sensor with the center frequency of 62 GHz, radar sensor data for a pedestrian, a cyclist, and a car are obtained in the test field. Then, a You Only Look Once (YOLO)-based network is trained with the sensor data to perform simultaneous target classification and moving direction estimation. To generate input data suitable for the deep learning-based classifier, a method of converting the radar detection result into an image form is also proposed. With the proposed method, we can identify the type of each target and its direction of movement with an accuracy of over 95%. Moreover, the pre-trained classifier shows an identification accuracy of 85% even for newly acquired data that have not been used for training.

Automotive Anti-Collision Radar Signal Processing System Design Based on DSP

2014 ◽  
Vol 1044-1045 ◽  
pp. 727-730
Author(s):  
Xin Yu Zhang
Keyword(s):  
Signal Processing ◽  
Power Supply ◽  
Continuous Wave ◽  
Processing System ◽  
Radar Signal ◽  
Radar Signal Processing ◽  
Signal Processing System ◽  
Positive Role ◽  
Wave Radar ◽  
Circuit Power

The principle of the frequency modulated continuous wave radar for measuring distance and velocity and signal processing method are described in this paper; To solve the signal processing problem for FMCW automotive anti-collision radar system ,the radar signal processing circuit is researched and designed, including the design of corresponding gain control amplifier circuit, power supply and filter circuit, external memory circuit ,power supply circuit ,signal interface circuit, and analyzed the results of the measurement for system. Experiments showed that the system achieved good accuracy design effect and higher measurement precision and has certain positive role to improve vehicle safety.

Face identification using millimetre-wave radar sensor data

2020 ◽  
Vol 56 (20) ◽  
pp. 1077-1079
Author(s):  
J. Kim ◽  
J.-E. Lee ◽  
H.-S. Lim ◽  
S. Lee
Keyword(s):  
Sensor Data ◽  
Face Identification ◽  
Radar Sensor ◽  
Millimetre Wave ◽  
Wave Radar

scholarly journals Waveform Optimization of Compressed Sensing Radar without Signal Recovery

Information ◽  
2019 ◽  
Vol 10 (9) ◽  
pp. 271
Author(s):  
Quanhui Wang ◽  
Ying Sun
Keyword(s):  
Signal Processing ◽  
Compressed Sensing ◽  
Optimization Method ◽  
Statistical Characteristics ◽  
Iteration Step ◽  
Radar Signal ◽  
Signal Recovery ◽  
Waveform Optimization ◽  
Inference Problems ◽  
Output Sinr

Radar signal processing mainly focuses on target detection, classification, estimation, filtering, and so on. Compressed sensing radar (CSR) technology can potentially provide additional tools to simultaneously reduce computational complexity and effectively solve inference problems. CSR allows direct compressive signal processing without the need to reconstruct the signal. This study aimed to solve the problem of CSR detection without signal recovery by optimizing the transmit waveform. Therefore, a waveform optimization method was introduced to improve the output signal-to-interference-plus-noise ratio (SINR) in the case where the target signal is corrupted by colored interference and noise having known statistical characteristics. Two different target models are discussed: deterministic and random. In the case of a deterministic target, the optimum transmit waveform is derived by maximizing the SINR and a suboptimum solution is also presented. In the case of random target, an iterative waveform optimization method is proposed to maximize the output SINR. This approach ensures that SINR performance is improved in each iteration step. The performance of these methods is illustrated by computer simulation.

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