Robust DOA Estimation of Coherent Sources in Mixed Non-Gaussian Noise Environments

WANG Yize; LIU Lei

doi:10.11999/JEIT251383

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WANG Yize, LIU Lei. Robust DOA Estimation of Coherent Sources in Mixed Non-Gaussian Noise Environments[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251383

Citation:

WANG Yize, LIU Lei. Robust DOA Estimation of Coherent Sources in Mixed Non-Gaussian Noise Environments[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251383

WANG Yize, LIU Lei. Robust DOA Estimation of Coherent Sources in Mixed Non-Gaussian Noise Environments[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251383

Citation:

WANG Yize, LIU Lei. Robust DOA Estimation of Coherent Sources in Mixed Non-Gaussian Noise Environments[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251383

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Robust DOA Estimation of Coherent Sources in Mixed Non-Gaussian Noise Environments

doi: 10.11999/JEIT251383 cstr: 32379.14.JEIT251383

WANG Yize^{1, 2, 3
,},
LIU Lei^{1, 2, 3
,
,}

1.
School of Computer Science and Technology, Xinjiang University, Urumgi 830046, China
2.
Xinjiang Key Laboratory of Signal Detection and Processing, Urumgi 830046, China
3.
Joint International Research Laboratory of Silk Road Multilingual Cognitive Computing, Urumgi 830046, China

Funds: Xinjiang Uygur Autonomous Region Natural Science Foundation General Program ( 2023D01C18), The second batch of Tianchi Talents (Leading Talents) projectin Xinjiang Uygur Autonomous Region

Received Date: 2025-12-30
Accepted Date: 2026-03-16
Rev Recd Date: 2026-03-16

Available Online: 2026-06-25

Abstract

Abstract

Objective Direction-of-Arrival (DOA) estimation often fails under mixed non-Gaussian noise, particularly for coherent sources. To address this problem, a Multi-Channel Feature Fusion Network (MCFCF) based on the Maximum Correntropy Criterion (MCC) is proposed. By integrating Multi-Scale Feature Extraction (MSFE) and Spatial-Frequency Attention Fusion (SFAF), the framework performs joint spatial-frequency calibration. MCC is used instead of Mean Squared Error (MSE) to improve robustness against impulsive outliers. Experiments show that MCFCF outperforms Multiple Signal Classification (MUSIC), Estimation of Signal Parameters via Rotational Invariance Techniques (ESPRIT), and state-of-the-art deep networks in accuracy and super-resolution performance, especially under low signal-to-noise ratio (SNR) and coherent-source conditions. Methods The proposed MCFCF framework is designed for DOA estimation in mixed non-Gaussian noise and coherent-source environments. Forward-Backward Spatial Smoothing (FBSS) is used for rank recovery. MSFE and SFAF are then combined for physical manifold calibration through multi-scale feature extraction and spatial-frequency attention. The MCC loss function is adopted to form a statistically robust optimization objective against impulsive outliers. Results and Discussions Experimental analysis verifies the performance of MCFCF in complex environments (Figs. 5-8). Under mixed noise with strong impulsive and heavy-tailed components, traditional subspace methods fail because of subspace swapping, with root mean square error (RMSE) values exceeding 10°. By contrast, MCFCF maintains an RMSE of approximately $ {1.5}^{\circ } $ at –15 dB and significantly outperforms state-of-the-art models (Fig. 5). Adaptability tests across heterogeneous noise distributions show strong distribution invariance (Fig. 6). In the white-noise benchmark, MCFCF compensates for FBSS-induced aperture loss and finite-sample variance, outperforming MUSIC in the low-SNR region. Mechanism analysis indicates that the multi-scale receptive fields of MSFE improve stability, while the amplitude-phase decoupling of SFAF improves estimation precision (Figs. 7-8). In addition, the MCC-based soft-truncation mechanism suppresses heavy-tailed impulsive interference at the optimization level, thereby improving global robustness. Conclusions An MCC-based MCFCF framework is proposed to address DOA estimation failures in mixed non-Gaussian noise and coherent-source environments. FBSS is used for rank recovery, and MSFE and SFAF are integrated for manifold calibration. The MCC loss further suppresses impulsive outliers. The results confirm that the proposed method achieves higher accuracy and stronger distribution invariance at –15 dB SNR than MUSIC, ESPRIT, and state-of-the-art deep learning baselines. These findings demonstrate its robustness in hostile electromagnetic environments.
- DOA estimation,
- Deep learning,
- Multi-scale features,
- Spatial-frequency attention fusion

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References(22)

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