scholarly journals Validation of hydrometeor occurrence predicted by the ECMWF Model using millimeter wave radar data

1998 ◽  
Vol 25 (10) ◽  
pp. 1645-1648 ◽  
Author(s):  
Gerald G. Mace ◽  
Christian Jakob ◽  
Kenneth P. Moran
Keyword(s):  
Millimeter Wave ◽  
Radar Data ◽  
Millimeter Wave Radar ◽  
Wave Radar ◽  
Ecmwf Model

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.

scholarly journals The Millimeter-Wave Radar SLAM Assisted by the RCS Feature of the Target and IMU

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5421
Author(s):  
Yang Li ◽  
Yutong Liu ◽  
Yanping Wang ◽  
Yun Lin ◽  
Wenjie Shen
Keyword(s):  
Millimeter Wave ◽  
Point Cloud ◽  
Radar Data ◽  
Measurement Unit ◽  
Small Data ◽  
Millimeter Wave Radar ◽  
Localization And Mapping ◽  
Wave Radar ◽  
Data Volume ◽  
The Face

Compared with the commonly used lidar and visual sensors, the millimeter-wave radar has all-day and all-weather performance advantages and more stable performance in the face of different scenarios. However, using the millimeter-wave radar as the Simultaneous Localization and Mapping (SLAM) sensor is also associated with other problems, such as small data volume, more outliers, and low precision, which reduce the accuracy of SLAM localization and mapping. This paper proposes a millimeter-wave radar SLAM assisted by the Radar Cross Section (RCS) feature of the target and Inertial Measurement Unit (IMU). Using IMU to combine continuous radar scanning point clouds into “Multi-scan,” the problem of small data volume is solved. The Density-based Spatial Clustering of Applications with Noise (DBSCAN) clustering algorithm is used to filter outliers from radar data. In the clustering, the RCS feature of the target is considered, and the Mahalanobis distance is used to measure the similarity of the radar data. At the same time, in order to alleviate the problem of the lower accuracy of SLAM positioning due to the low precision of millimeter-wave radar data, an improved Correlative Scan Matching (CSM) method is proposed in this paper, which matches the radar point cloud with the local submap of the global grid map. It is a “Scan to Map” point cloud matching method, which achieves the tight coupling of localization and mapping. In this paper, three groups of actual data are collected to verify the proposed method in part and in general. Based on the comparison of the experimental results, it is proved that the proposed millimeter-wave radar SLAM assisted by the RCS feature of the target and IMU has better accuracy and robustness in the face of different scenarios.

scholarly journals Millimeter wave radar data of people walking

Data in Brief ◽  
2020 ◽  
Vol 31 ◽  
pp. 105996
Author(s):  
Ennio Gambi ◽  
Gianluca Ciattaglia ◽  
Adelmo De Santis ◽  
Linda Senigagliesi
Keyword(s):  
Millimeter Wave ◽  
Radar Data ◽  
Millimeter Wave Radar ◽  
Wave Radar

scholarly journals Traffic Flow Parameters Collection under Variable Illumination Based on Data Fusion

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Shaojie Jin ◽  
Ying Gao ◽  
Shoucai Jing ◽  
Fei Hui ◽  
Xiangmo Zhao ◽  
...  
Keyword(s):  
Traffic Flow ◽  
Millimeter Wave ◽  
Flow Characteristics ◽  
Vehicle Detection ◽  
Radar Data ◽  
Urban Traffic ◽  
Millimeter Wave Radar ◽  
Flow Parameters ◽  
Wave Radar ◽  
Traffic Flow Characteristics

Accurate traffic flow parameters are the supporting data for analyzing traffic flow characteristics. Vehicle detection using traffic surveillance pictures is a typical method for gathering traffic flow characteristics in urban traffic scenes. In complicated lighting conditions at night, however, neither classical nor deep-learning-based image processing algorithms can provide adequate detection results. This study proposes a fusion technique combining millimeter-wave radar data with image data to compensate for the lack of image-based vehicle detection under complicated lighting to complete all-day parameters collection. The proposed method is based on an object detector named CenterNet. Taking this network as the cornerstone, we fused millimeter-wave radar data into it to improve the robustness of vehicle detection and reduce the time-consuming postcalculation of traffic flow parameters collection. We collected a new dataset to train the proposed method, which consists of 1000 natural daytime images and 1000 simulated nighttime images with a total of 23094 vehicles counted, where the simulated nighttime images are generated by a style translator named CycleGAN to reduce labeling workload. Another four datasets of 2400 images containing 20161 vehicles were collected to test the proposed method. The experimental results show that the method proposed has good adaptability and robustness at natural daytime and nighttime scenes.

The Large Millimeter Wave Radar Phased Antenna Array

2000 ◽  
Vol 54 (10) ◽  
pp. 101-111
Author(s):  
Aleksey Alekseevich Tolkachev ◽  
Vasiliy Andreevich Makota ◽  
Mariya Petrovna Pavlova ◽  
Anatoliy Moiseevich Nikolaev ◽  
Vladimir Victorovich Denisenko ◽  
...  
Keyword(s):  
Antenna Array ◽  
Millimeter Wave ◽  
Millimeter Wave Radar ◽  
Wave Radar ◽  
Phased Antenna Array

Antenna for Navigational Shipborne Millimeter-Wave Radar

2006 ◽  
Vol 65 (16) ◽  
pp. 1453-1462
Author(s):  
A. N. Nechiporenko ◽  
L. D. Fesenko
Keyword(s):  
Millimeter Wave ◽  
Millimeter Wave Radar ◽  
Wave Radar
Sign in / Sign up

Export Citation Format

Share Document