Codesign of Transmit Waveform and Receive Filter with Similarity Constraints for FDA-MIMO Radar

Codesign of Transmit Waveform and Receive Filter with Similarity Constraints for FDA-MIMO Radar

The codesign of the receive filter and transmit waveform under similarity constraints is one of the key technologies in frequency diverse array multiple-input multiple-output (FDA-MIMO) radar systems. This paper discusses the design of constant modulus waveforms and filters aimed at maximizing the s...

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Bibliographic Details
Main Authors: Qiping Zhang, Jinfeng Hu, Xin Tai, Yongfeng Zuo, Huiyong Li, Kai Zhong, Chaohai Li
Format: Article
Language:English
Published: MDPI AG 2025-05-01
Series:Remote Sensing
Subjects:
frequency diverse array–multiple input multiple output (FDA-MIMO) radar
similarity constraint
manifold optimization
Riemannian quasi-Newton algorithm
waveform design
Online Access:https://www.mdpi.com/2072-4292/17/10/1800
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Summary:The codesign of the receive filter and transmit waveform under similarity constraints is one of the key technologies in frequency diverse array multiple-input multiple-output (FDA-MIMO) radar systems. This paper discusses the design of constant modulus waveforms and filters aimed at maximizing the signal-to-interference-and-noise ratio (SINR). The problem’s non-convexity renders it challenging to solve. Existing studies have typically employed relaxation-based methods, which inevitably introduce relaxation errors that degrade system performance. To address these issues, we propose an optimization framework based on the joint complex circle manifold–complex sphere manifold space (JCCM-CSMS). Firstly, the similarity constraint is converted into the penalty term in the objective function using an adaptive penalty strategy. Then, JCCM-CSMS is constructed to satisfy the waveform constant modulus constraint and filter norm constraint. The problem is projected into it and transformed into an unconstrained optimization problem. Finally, the Riemannian limited-memory Broyden–Fletcher–Goldfarb–Shanno (RL-BFGS) algorithm is employed to optimize the variables in parallel. Simulation results demonstrate that our method achieves a 0.6 dB improvement in SINR compared to existing methods while maintaining competitive computational efficiency. Additionally, waveform similarity was also analyzed.
ISSN:2072-4292

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