基于角度间隔分离学习的相干DOA估计方法

doi:10.12305/j.issn.1001-506X.2025.10.07

Abstract

Abstract:

In face of the accuracy degradation of traditional model-driven algorithms when processing spatially close coherent source signals and the large training sample requirement of data-driven algorithms, a high-precision direction of arrival （DOA） estimation method based on angle interval separation learning （AISL） is proposed. It leverages the sparsity of signal angle intervals and employs spatial filters to estimate angle interval information using the concept of area separation. The signals are then partitioned into corresponding angle interval areas, followed by DOA estimation through deep neural network （DNN） multi-label classifiers. Additionally, sparse autoencoder （SAE） technology is introduced to compress input data and extract key features, effectively reducing computational complexity while filtering out interference. Simulation results demonstrate that compared to other data-driven algorithms, this method achieves superior estimation accuracy and generalization ability in spatially close angle domains under limited training sample conditions.

Key words: coherent direction of arrival （DOA） estimation, angular interval separation learning （AISL）, deep neural network （DNN）, sparse autoencoder （SAE）

CLC Number:

TN 911.7

Jun WANG, Zihan WU, Guangjiao ZHOU, Zhiquan ZHOU. Coherent DOA estimation method based on angle interval separation learning[J]. Systems Engineering and Electronics, 2025, 47(10): 3188-3198.

Figures/Tables 17

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参数	取值	参数	取值
阵元数M	$21$	学习率$\alpha $	$ 0.002$
波长$\lambda /{\mathrm{m}} $	$ 1$	训练样本数	32000
波束宽度/(°)	5	验证样本数	4000
阵元间距	${\lambda \mathord{\left/ {\vphantom {\lambda 2}} \right. } 2}$	测试样本数	4000
来波方向范围/$(^\circ) $	$[ - 3 ,3]$	过拟合因子dropout	0.5
量化单元$\Delta \phi/(^\circ) $	$ 0.1 $	空域（场景）数	6

模型	输入信息维度	RMSE/(°)
AISL（未稀疏降维） AISL（稀疏降维）	420 160	0.1679 0.1506

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Coherent DOA estimation method based on angle interval separation learning

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