Millimeter wave radar stationary-target classification using a high-order neural network

1991 ◽  
Author(s):  
James H. Hughen ◽  
Kenneth R. Hollon
Keyword(s):  
Neural Network ◽  
Millimeter Wave ◽  
High Order ◽  
Target Classification ◽  
Stationary Target ◽  
Millimeter Wave Radar ◽  
Wave Radar ◽  
High Order Neural Network

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.

scholarly journals On the Effect of Training Convolution Neural Network for Millimeter-Wave Radar-Based Hand Gesture Recognition

Sensors ◽  
2021 ◽  
Vol 21 (1) ◽  
pp. 259
Author(s):  
Kang Zhang ◽  
Shengchang Lan ◽  
Guiyuan Zhang
Keyword(s):  
Neural Network ◽  
Millimeter Wave ◽  
Gesture Recognition ◽  
Radar Data ◽  
Hand Gesture Recognition ◽  
Convolution Neural Network ◽  
Hand Gesture ◽  
Millimeter Wave Radar ◽  
Average Accuracy ◽  
Wave Radar

The purpose of this paper was to investigate the effect of a training state-of-the-art convolution neural network (CNN) for millimeter-wave radar-based hand gesture recognition (MR-HGR). Focusing on the small training dataset problem in MR-HGR, this paper first proposed to transfer the knowledge with the CNN models in computer vision to MR-HGR by fine-tuning the models with radar data samples. Meanwhile, for the different data modality in MR-HGR, a parameterized representation of temporal space-velocity (TSV) spectrogram was proposed as an integrated data modality of the time-evolving hand gesture features in the radar echo signals. The TSV spectrograms representing six common gestures in human–computer interaction (HCI) from nine volunteers were used as the data samples in the experiment. The evaluated models included ResNet with 50, 101, and 152 layers, DenseNet with 121, 161 and 169 layers, as well as light-weight MobileNet V2 and ShuffleNet V2, mostly proposed by many latest publications. In the experiment, not only self-testing (ST), but also more persuasive cross-testing (CT), were implemented to evaluate whether the fine-tuned models generalize to the radar data samples. The CT results show that the best fine-tuned models can reach to an average accuracy higher than 93% with a comparable ST average accuracy almost 100%. Moreover, in order to alleviate the problem caused by private gesture habits, an auxiliary test was performed by augmenting four shots of the gestures with the heaviest misclassifications into the training set. This enriching test is similar with the scenario that a tablet reacts to a new user. The results of two different volunteer in the enriching test shows that the average accuracy of the enriched gesture can be improved from 55.59% and 65.58% to 90.66% and 95.95% respectively. Compared with some baseline work in MR-HGR, the investigation by this paper can be beneficial in promoting MR-HGR in future industry applications and consumer electronic design.

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