利用稀疏贝叶斯学习快速估计阵列形状和到达方向,用于操纵拖曳线阵列

IF 1.4 4区 管理学 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC
Xiang Pan, Haoran Wang, Min Li, Jie Zhou, Yuxiao Li, Weize Xu
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引用次数: 0

摘要

稀疏贝叶斯学习(SBL)算法在到达方向(DOA)估计方面已显示出其优势。然而,从测量数据迭代估计 SBL 超参数需要大量计算成本。本文的重点是利用 SBL 快速估计短拖曳线阵列(TLA)在机动过程中的阵列形状和 DOA。在 SBL 迭代过程中,利用抛物线模型来描述弯曲的 TLA,其弓形作为超参数进行估计。然后,在迭代过程中考虑基向量剪枝策略,通过忽略信号存在的不可能方向来降低计算成本。通过使用信息传递方法近似计算后验概率密度,进一步提高了联合估计算法的收敛速度。南海的实验结果验证了优化联合估计算法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Fast estimation of array shape and direction of arrival using sparse Bayesian learning for manoeuvring towed line array

Fast estimation of array shape and direction of arrival using sparse Bayesian learning for manoeuvring towed line array

The sparse Bayesian learning (SBL) algorithm has demonstrated its advantage in the direction of arrival (DOA) estimation. However, it requires a lot of computational cost to iteratively estimate the SBL hyperparameters from measurement data. This paper focuses on fast estimating the array shape and DOAs using SBL for a short towed line array (TLA) during manoeuvring. A parabolic model is utilised to describe the bent TLA whose bow as a hyperparameter is estimated in the SBL iterative process. Then, the basis vector pruning strategy is considered in the iteration to reduce computational cost by neglecting the impossible directions of signal presence. The converged speed of the joint estimation algorithm is further improved by approximately calculating the posterior probability density with the message passing approach. The effectiveness of the optimising joint estimation algorithm is verified using the experimental results from South China Sea.

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来源期刊
Iet Radar Sonar and Navigation
Iet Radar Sonar and Navigation 工程技术-电信学
CiteScore
4.10
自引率
11.80%
发文量
137
审稿时长
3.4 months
期刊介绍: IET Radar, Sonar & Navigation covers the theory and practice of systems and signals for radar, sonar, radiolocation, navigation, and surveillance purposes, in aerospace and terrestrial applications. Examples include advances in waveform design, clutter and detection, electronic warfare, adaptive array and superresolution methods, tracking algorithms, synthetic aperture, and target recognition techniques.
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