Yang, F., Guo, J., Zhu, R., Le Kernec, J. , Liu, Q. and Zeng, T. (2022) Ground clutter mitigation for slow-time MIMO radar using independent component analysis. Remote Sensing, 14(23), 6098. (doi: 10.3390/rs14236098)
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Abstract
The detection of low, slow and small (LSS) targets, such as small drones, is a developing area of research in radar, wherein the presence of ground clutter can be quite challenging. LSS targets, because of their unusual flying mode, can be easily shadowed by ground clutter, leading to poor radar detection performance. In this study, we investigated the feasibility and performance of a ground clutter mitigation method combining slow-time multiple-input multiple-output (st-MIMO) waveforms and independent component analysis (ICA) in a ground-based MIMO radar focusing on LSS target detection. The modeling of ground clutter under the framework of st-MIMO was first defined. Combining the spatial and temporal steering vector of st-MIMO, a universal signal model including the target, ground clutter, and noise was established. The compliance of the signal model for conducting ICA to separate the target was analyzed. Based on this, a st-MIMO-ICA processing scheme was proposed to mitigate ground clutter. The effectiveness of the proposed method was verified with simulation and experimental data collected from an S-band st-MIMO radar system with a desirable target output signal-to-clutter-plus-noise ratio (SCNR). This work can shed light on the use of ground clutter mitigation techniques for MIMO radar to tackle LSS targets.
Item Type: | Articles |
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Additional Information: | Funding: This research was funded the National Key R&D Program of China (Grant No. 2018YFE0202101 and Grant No. 2018YFE0202103), the Natural Science Foundation of Chongqing, China (Grant No. 2020ZX3100039), and the National Natural Science Foundation of China (Grant No. 62201048). |
Status: | Published |
Refereed: | Yes |
Glasgow Author(s) Enlighten ID: | Le Kernec, Dr Julien |
Authors: | Yang, F., Guo, J., Zhu, R., Le Kernec, J., Liu, Q., and Zeng, T. |
College/School: | College of Science and Engineering > School of Engineering > Autonomous Systems and Connectivity |
Journal Name: | Remote Sensing |
Publisher: | MDPI |
ISSN: | 2072-4292 |
ISSN (Online): | 2072-4292 |
Published Online: | 01 December 2022 |
Copyright Holders: | Copyright © 2022 The Authors |
First Published: | First published in Remote Sensing 14(23): 6098 |
Publisher Policy: | Reproduced under a Creative Commons License |
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