| 摘要: | 方向到達（DOA）估算在無線通信、雷達監測和聲學定位等應用中至關重要。然而，傳統方法（如 MUSIC 和 ESPRIT）在面對陣列缺陷（例如增益/相位誤差、感測器互耦和位置偏移）以及低訊雜比（SNR）環境時，效能顯著下降。本文提出了一種基於 Inception 編碼器（IE）的 DOA 估算框架，並進一步優化為 IE+，通過結合多尺度特徵提取和注意力機制，實現了在雜訊和陣列不完善條件下的卓越性能。
IE 架構通過多尺度分析提取局部與全域特徵，能夠有效捕捉信號中的空間和時間相關性，同時濾除雜訊。與傳統卷積編碼器（CE）相比，IE 在面對複雜失真時展現出更高的準確性與穩健性，且其模組化設計和深度可分離卷積顯著降低了計算需求。IE+ 則通過加入注意力機制進一步提升了模型的適應能力，能動態關注關鍵特徵，有效緩解陣列缺陷的影響。
實驗結果表明，IE 和 IE+ 在不同場景中均優於 CE 和傳統方法。在不完善條件下，IE+ 的估算偏差明顯低於其他方法；在 SNR 降至 -15 dB 的極端情境下，IE+ 仍保持穩定的準確性。此外，IE+ 在大規模 MIMO 系統中展現了出色的可擴展性，其優化設計支持低延遲推理，適用於實時應用。
總之，基於 Inception 的編碼器為 DOA 估算在陣列缺陷下的應用提供了一個高效、穩健且可擴展的解決方案。該方法在處理現實世界中的失真和雜訊環境方面樹立了新的基準，並為現代通信和感測系統提供了可靠的技術支撐。;Direction of Arrival (DOA) estimation is crucial in applications such as wireless communications, radar monitoring, and acoustic localization. However, traditional methods (e.g., MUSIC and ESPRIT) suffer significant performance degradation in the presence of array imperfections, such as gain/phase errors, mutual coupling, and sensor position deviations, as well as in low signal-to-noise ratio (SNR) environments. This paper proposes a DOA estimation framework based on the Inception Encoder (IE), further optimized as IE+, which integrates multi-scale feature extraction and attention mechanisms to achieve superior performance under noisy and imperfect array conditions. The IE architecture leverages multi-scale analysis to extract both local and global features, effectively capturing the spatial and temporal correlations within signals while filtering out noise. Compared to conventional convolutional encoders (CE), IE demonstrates higher accuracy and robustness against complex distortions, while its modular design and depthwise separable convolutions significantly reduce computational demands. IE+ further enhances model adaptability by incorporating attention mechanisms, dynamically focusing on key features to mitigate the impact of array imperfections. Experimental results show that both IE and IE+ outperform CE and traditional methods across various scenarios. Under imperfect conditions, IE+ exhibits significantly lower estimation bias than other approaches, and even in extreme cases where SNR drops to-15 dB, it maintains stable accuracy. Furthermore, IE+ demonstrates excellent scalability in large-scale MIMO systems, with an optimized design that supports low-latency inference, making it suitable for real-time applications. In summary, the Inception-based encoder provides an efficient, robust, and scalable solution for DOA estimation in the presence of array imperfections. This approach establishes a new benchmark for handling real-world distortions and noisy environments, offering reliable technological support for modern communication and sensing systems. |
