FDA-MIMO雷达恒模波形和接收滤波器的协同设计

IF 3 3区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC
Qiping Zhang , Xin Tai , Yongfeng Zuo , Hua Wang , Jinfeng Hu
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引用次数: 0

摘要

波形与接收滤波器的联合设计是目前多频阵列多输入多输出(FDA-MIMO)雷达研究的重要技术之一。本文研究的问题模型是在波形的恒模约束和滤波器的范数约束下,使系统的信噪比(SINR)最大化。该问题是非凸的,这给其求解带来了挑战。现有方法采用基于松弛的方法来解决这一问题,但这不可避免地会引入松弛误差。为了解决上述问题,我们注意到复圆球流形空间(CCSMS)可以自然地满足常模约束和范数约束。基于这一特征,该问题成为CCSMS流形上的无约束优化问题,消除了对松弛的需要。然后可以直接应用黎曼共轭梯度算法求解波形并并行接收滤波器。通过仿真对比发现:1)信噪比提高了4dB;2)与现有方法相比,降低了计算复杂度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Codesign of constant modulus waveform and receive filters for FDA-MIMO radar
The joint design of waveform and receive filter is one of the important technologies currently being studied in Frequency diverse array multi-input multi-output (FDA-MIMO) radar. The problem model studied in this paper is to maximize the signal-to-interference-noise ratio (SINR) of the system under the constant modulus constraint of the waveform and the norm constraint of the filter. The problem is non-convex, which brings challenges to its solution. Existing methods use relaxation-based methods to solve this problem, but this will inevitably introduce relaxation errors. To solve the above problems, we notice that the complex circle-sphere manifold space (CCSMS) can naturally satisfy the constant modulus constraint and norm constraint. Based on this feature, the problem becomes an unconstrained optimization problem on the CCSMS manifold, eliminating the need for relaxation. The Riemannian conjugate gradient algorithm can then be directly applied to solve the waveform and receive filter in parallel. We compared it with the existing methods through simulation: 1) SINR was improved by 4dB; 2) the computational complexity was reduced compared with existing methods.
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来源期刊
Digital Signal Processing
Digital Signal Processing 工程技术-工程:电子与电气
CiteScore
5.30
自引率
17.20%
发文量
435
审稿时长
66 days
期刊介绍: Digital Signal Processing: A Review Journal is one of the oldest and most established journals in the field of signal processing yet it aims to be the most innovative. The Journal invites top quality research articles at the frontiers of research in all aspects of signal processing. Our objective is to provide a platform for the publication of ground-breaking research in signal processing with both academic and industrial appeal. The journal has a special emphasis on statistical signal processing methodology such as Bayesian signal processing, and encourages articles on emerging applications of signal processing such as: • big data• machine learning• internet of things• information security• systems biology and computational biology,• financial time series analysis,• autonomous vehicles,• quantum computing,• neuromorphic engineering,• human-computer interaction and intelligent user interfaces,• environmental signal processing,• geophysical signal processing including seismic signal processing,• chemioinformatics and bioinformatics,• audio, visual and performance arts,• disaster management and prevention,• renewable energy,
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