Direction-of-arrival method based on randomize-then-optimize approach

Q1 Engineering

Journal of Electronic Science and Technology Pub Date : 2022-12-01 DOI:10.1016/j.jnlest.2022.100182

Cai-Yi Tang , Sheng Peng , Zhi-Qin Zhao , Bo Jiang

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引用次数: 0

Abstract

The direction-of-arrival (DOA) estimation problem can be solved by the methods based on sparse Bayesian learning (SBL). To assure the accuracy, SBL needs massive amounts of snapshots which may lead to a huge computational workload. In order to reduce the snapshot number and computational complexity, a randomize-then-optimize (RTO) algorithm based DOA estimation method is proposed. The “learning” process for updating hyperparameters in SBL can be avoided by using the optimization and Metropolis-Hastings process in the RTO algorithm. To apply the RTO algorithm for a Laplace prior, a prior transformation technique is induced. To demonstrate the effectiveness of the proposed method, several simulations are proceeded, which verifies that the proposed method has better accuracy with 1 snapshot and shorter processing time than conventional compressive sensing (CS) based DOA methods.

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基于随机优化方法的到达方向法

为了保证准确性，SBL需要大量的快照，这可能导致巨大的计算工作量。为了减少快照个数和计算复杂度，提出了一种基于随机优化(RTO)算法的DOA估计方法。在RTO算法中使用最优化和Metropolis-Hastings过程可以避免SBL中更新超参数的“学习”过程。为了将RTO算法应用于拉普拉斯先验，导出了一种先验变换技术。为了验证该方法的有效性，仿真结果表明，与传统的基于压缩感知(CS)的DOA方法相比，该方法具有单快照精度高、处理时间短的优点。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Electronic Science and Technology Engineering-Electrical and Electronic Engineering

CiteScore

4.30

自引率

0.00%

发文量

1362

审稿时长

99 days

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