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arXiv 2103.03031eess.SP
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认知驱动的MIMO雷达波束形成稀疏阵列收发机优化

Cognitive-Driven Optimization of Sparse Array Transceiver for MIMO Radar Beamforming

Weitong Zhai, Xiangrong Wang, Syed A. Hamza, Moeness G. Amin

摘要:

认知多输入多输出(MIMO)雷达能够在复杂的动态环境中通过感知和学习自适应地调整系统参数。MIMO雷达的波束形成性能受波束形成权系数和收发机配置的影响。我们提出了一种认知驱动的MIMO阵列设计,其中波束形成权重和收发器配置都是自适应的,并在不同的环境条件下同时优化。感知-动作循环包括全虚拟阵列数据采集、协方差重构和天线选择联合设计收发阵列,通过迭代最小化加权混合L21范数提高二维组稀疏度,实现收发阵列的最优设计,并对收发天线间距施加约束以保证收发隔离度。仿真结果表明,该认知稀疏MIMO阵列在动态目标和干扰环境下具有良好的“感知-动作”能力,能够有效地增强波束形成和抗干扰能力。

英文摘要:

Cognitive multiple-input multiple-output (MIMO) radar is capable of adjusting system parameters adaptively by sensing and learning in complex dynamic environment. Beamforming performance of MIMO radar is guided by both beamforming weight coefficients and the transceiver configuration. We propose a cognitive-driven MIMO array design where both the beamforming weights and the transceiver configuration are adaptively and concurrently optimized under different environmental conditions. The perception-action cycle involves data collection of full virtual array, covariance reconstruction and joint design of the transmit and receive arrays by antenna selection.The optimal transceiver array design is realized by promoting two-dimensional group sparsity via iteratively minimizing reweighted mixed L21-norm, with constraints imposed on transceiver antenna spacing for proper transmit/receive isolation. Simulations are provided to demonstrate the "perception-action" capability of the proposed cognitive sparse MIMO array in achieving enhanced beamforming and anti-jamming in dynamic target and interference environment.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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