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Compressive Measurements Exploiting Coprime Frequencies for Direction Finding

机译:压缩测量利用方向查找的协调频率

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Sparse arrays are considered as an effective solution to reduce the system complexity for direction-of-arrival (DOA) estimation as they can resolve more sources than the number of sensors. Inspired by such techniques, the coprime frequency-based array structure was recently proposed to further reduce the number of required sensors. Multiple sensing signals with mutually coprime carrier frequencies are transmitted, and the reflected signals are used to estimate the target DOAs. The cross-correlation obtained from each pair of frequencies provides additional degrees of freedom (DOFs). On the other hand, a compression matrix can be designed to reduce the dimension of the received signal vector, thereby effectively reducing the number of front-end circuit chains. In this paper, we use the compressive measurements of the received signal vector obtained from a coprime frequency-based array structure to estimate the signal DOAs. The proposed scheme can obtain an increased number of DOFs and high estimation accuracy because of the coarray operation and the large array aperture of the coprime frequency-based array structure. Meanwhile, the system complexity is also significantly reduced. The effectiveness of the proposed scheme is validated using simulation results.
机译:稀疏阵列被认为是减少对到达方向(DOA)估计的系统复杂度的有效解决方案,因为它们可以解决比传感器的数量更多的来源。通过这种技术的启发,最近提出了基于基于协商的基于频率的阵列结构,以进一步减少所需传感器的数量。发送具有相互协商载波频率的多个感测信号,并且反射信号用于估计目标DOA。从每对频率获得的互相关提供了额外的自由度(DOF)。另一方面,可以设计压缩矩阵以减小接收信号向量的尺寸,从而有效地减少前端电路链的数量。在本文中,我们使用从基于基于基于基于频率的阵列结构获得的接收信号向量的压缩测量来估计信号DOA。该方案可以获得增加的DOF数量和高估计精度,因为COARRAY操作和基于COPRIME频率的阵列结构的大阵列孔径。同时,系统复杂性也显着降低。使用模拟结果验证了所提出的方案的有效性。

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