Low-complexity unimodular waveform design with good space-time correlation for multi-target detection in MIMO radar
Author Identifier (ORCID)
Shihao Yan: https://orcid.org/0000-0002-4586-1926
Abstract
This article designs unimodular waveforms with good space-time correlation properties for multi-input–multioutput (MIMO) radar systems. Unlike the widely studied approaches that only consider auto-correlation and cross-correlation, space-time correlation is incorporated into the design to reduce the impact of false target echoes on detection performance in K-target detection scenario. Based on Riemannian geometry theory, we analyze the geometric structure of the unimodular matrix constraint and construct a customized product manifold that matches the optimization constraints, which lays the foundation for low-complexity algorithm design. On this basis, we propose low-complexity algorithms for both normal-scale and large-scale antenna cases. Specifically, for the normal-scale case, a low-complexity unimodular manifold gradient descent (UM-GD) algorithm with a customized merging strategy and its accelerated version, unimodular manifold accelerated gradient descent (UM-AGD) algorithm, are developed. However, in large-scale case, as the number of antennas M, the waveform length N, and the number of delay bins |D| increase, the merging strategy of UM-GD and UM-AGD introduces a significant storage burden, and the computational complexity remains high. To address this issue, we propose a customized unimodular manifold stochastic variance reduced gradient (UM-SVRG) algorithm. Compared with existing benchmark algorithms whose per-iteration computational complexity is approximately O(M4+3M2N+3|D|K2MN), the proposed UM-SVRG algorithm significantly reduces it to about O(|D|K2MN). Furthermore, with appropriate parameter selection, we provide theoretical convergence guarantees for the UM-GD, UM-AGD, and UM-SVRG algorithms. Finally, numerical results validate the effectiveness of the proposed UM-GD, UM-AGD, and UM-SVRG algorithms, showing that UM-GD and UM-AGD are more suitable for normal-scale case, while UM-SVRG performs better in large-scale case.
Keywords
and riemannian optimization, beampattern design, MIMO radar, space-time correlation, unimodular waveform
Document Type
Journal Article
Date of Publication
1-1-2026
Publication Title
IEEE Transactions on Aerospace and Electronic Systems
Publisher
IEEE
School
School of Science
Funding Information
The work of Xuyang Zhao and Yongchao Wang was supported by the National Science Foundation of China under Grant 62271371. The work of Jiangtao Wang was supported by the National Science Foundation of China under Grant 62201425. This work wassupported in part by Shaanxi KeyIndustrial Innovation Chain Project in Industrial Domain under Grant 2020ZDLGY15-01, and in part by Qinchuangyuan total window “Four Chain” integration project under Grant 2024PT-ZCK-11.
Copyright
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Comments
Zhao, X., Wang, J., Yan, S., & Wang, Y. (2026). Low-complexity unimodular waveform design with good space-time correlation for multi-target detection in MIMO radar. IEEE Transactions on Aerospace and Electronic Systems, 62, 10834–10850. https://doi.org/10.1109/TAES.2026.3692418