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Advances in Multichannel Radar Systems

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Radar Sensors".

Deadline for manuscript submissions: 31 December 2025 | Viewed by 802

Special Issue Editor


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Guest Editor
School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
Interests: forward-looking radar; new system radar signal processing; inverse problem
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Multichannel radar refers to a radar with multiple transmitting or receiving channels, which can exist in various forms such as single-input multi-output (SIMO), multi-input single-output (SIMO) and multi-input multi-output (MIMO). Due to its higher degree of freedom, multichannel radar has significant advantages in fields such as object detection and imaging, and it has received widespread attention in recent years.

For example, multichannel radar can perform instantaneous single snapshot processing with high processing efficiency, but often faces the problems of poor angular resolution performance caused by small aperture size and grating lobe suppression introduced by sparse channel arrangement. At the same time, multichannel radar can also be installed on motion platforms for coherent processing, such as coherent accumulation detection, synthetic aperture radar (SAR) imaging, etc., to obtain higher processing gains. Moreover, it inevitably introduces multichannel error estimation and compensation problems. In addition, multichannel radar often faces coupling problems between channels.

This Special Issue aims to gather the latest research results and highlight the advances of multichannel radar, with topics including the following: imaging algorithms, parameter estimation, motion compensation, super-resolution, anti-jamming, direction of arrival (DOA) estimation, array calibration and other signal processing technologies of multichannel radar or multichannel SAR.

Dr. Wenchao Li
Guest Editor

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Keywords

  • multichannel radar
  • direction of arrival (DOA) estimation
  • radar signal processing
  • multiple-input multiple-output (MIMO) radar
  • synthetic aperture radar (SAR)

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Published Papers (1 paper)

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Research

22 pages, 4598 KB  
Article
A ST-ConvLSTM Network for 3D Human Keypoint Localization Using MmWave Radar
by Siyuan Wei, Huadong Wang, Yi Mo and Dongping Du
Sensors 2025, 25(18), 5857; https://doi.org/10.3390/s25185857 - 19 Sep 2025
Viewed by 462
Abstract
Accurate human keypoint localization in complex environments demands robust sensing and advanced modeling. In this article, we construct a ST-ConvLSTM network for 3D human keypoint estimation via millimeter-wave radar point clouds. The ST-ConvLSTM network processes multi-channel radar image inputs, generated from multi-frame fused [...] Read more.
Accurate human keypoint localization in complex environments demands robust sensing and advanced modeling. In this article, we construct a ST-ConvLSTM network for 3D human keypoint estimation via millimeter-wave radar point clouds. The ST-ConvLSTM network processes multi-channel radar image inputs, generated from multi-frame fused point clouds through parallel pathways. These pathways are engineered to extract rich spatiotemporal features from the sequential radar data. The extracted features are then fused and fed into fully connected layers for direct regression of 3D human keypoint coordinates. In order to achieve better network performance, a mmWave radar 3D human keypoint dataset (MRHKD) is built with a hybrid human motion annotation system (HMAS), in which a binocular camera is used to measure the human keypoint coordinates and a 60 GHz 4T4R radar is used to generate radar point clouds. Experimental results demonstrate that the proposed ST-ConvLSTM, leveraging its unique ability to model temporal dependencies and spatial patterns in radar imagery, achieves MAEs of 0.1075 m, 0.0633 m, and 0.1180 m in the horizontal, vertical, and depth directions. This significant improvement underscores the model’s enhanced posture recognition accuracy and keypoint localization capability in challenging conditions. Full article
(This article belongs to the Special Issue Advances in Multichannel Radar Systems)
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