Radar Challenge “Human Activity Classification with Radar”

The Radar Challenge is a new event hosted at 2020 IET International Radar Conference that enables participants to test their classification algorithms on a common, publicly available database of radar data in order to benchmark performances.

We achieved the 1st Place in the contest. The contest is described in the IEEE Journal.

S. Yang et al., “The Human Activity Radar Challenge: benchmarking based on the ‘Radar signatures of human activities’ dataset from Glasgow University,” in IEEE Journal of Biomedical and Health Informatics, doi: 10.1109/JBHI.2023.3240895.

Congratulations to the successful project and collaboration.

Congratulations!

Our paper titled “MIMO-SAR Image Antialiasing for Cascaded mmWave Radar Sensor“, has been accepted at the 2023 IEEE Radar Conference, to be held in San Antonio, Texas, USA from May 1-5, 2023.

This paper is collaborated by China Jiliang University, Andar Technologies  and Einstein E-Tech GmbH.

Authors

Abstract

This paper presents a cascaded mmWave Radar sensor system, which is able to generate MIMO SAR imaging. Two techniques are proposed in this paper, calibration and antialiasing. Since the cascaded radar has a big aperture, the data capture time is significantly reduced by MxN times, M and N are the number of Tx and Rx channels of the cascaded radar sensor, which enables real time SAR imaging acquisition. In order to generate focused SAR imaging, the calibration of cascaded radar is necessary. Furthermore, an antialiasing technique is proposed to enhance the SAR image by reducing the ghost image bands. The proposed algorithm is evaluated by both simulation and real data processing. The experimental results show that MIMO-SAR image by our cascaded Radar sensor has the potential in real time applications.

• Journal of Applied Sciences

Title: Design and Experiments of Autonomous Path Tracking based on Dead Reckoning
Authors: Songxiao Cao, Ye Jin, Toralf Trautmann, Kang Liu

https://www.mdpi.com/2076-3417/13/1/317

• IEEE Robotics and Automation Letters (RA-L)

Title: Scan Denoising and Normal Distribution Transform for Accurate Radar Odometry and Positioning
Authors: Rongxi Zhang, Yuanhui Zhang, Duo Fu, Kang Liu

accepted date: 01/06/2023

https://doi.org/10.1109/LRA.2023.3236570

Congratulations to the students and staff at Sino-German Research Center and mmwave Radar joint lab!