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E-STNet: A Non-Ideal Array DOA Estimation Method Based on Enhanced Spatio-Temporal Features
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DOI：10.3390/electronics15061270.png)
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Abstract
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To address the challenge of degraded DOA estimation performance under array errors and low signal-to-noise ratio conditions, this paper proposes an Enhanced Spatio-Temporal Network (E-STNet). This network adopts a dual-source input architecture. By integrating multi-scale pooling and a hybrid Long Short-Term Memory-Transformer (LSTM-Transformer) encoder, the network jointly refines spatial feature representations and captures multi-granularity temporal dependencies. Simulation results demonstrate that, under challenging scenarios such as array errors, low Signal-to-Noise Ratio (SNR), and closely spaced sources, E-STNet achieves higher estimation accuracy and stronger robustness than conventional algorithms and existing deep learning methods, providing an effective solution for DOA estimation in complex environments.
Keywords:
array error
spatio-temporal features
multi-scale pooling
DOA estimation
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