Purpose

The existing dimensionality reduction algorithms suffer serious performance degradation under low signal-to-noise ratio (SNR) owing to the presence of noise. To address these problems, an enhanced spatial smoothing scheme is proposed that exploits the subarray time-space correlation matrices to reconstruct the data matrix to overcome this weakness. This method uses the strong correlation of signal and the weak correlation of noise in time and space domains, which improves the noise suppression ability.

Design/methodology/approach

In this paper, an enhanced spatial smoothing method is proposed. By using the strong correlation of signal and the weak correlation of noise, the time-space smoothed array covariance matrix based on the subarray time-space correlation matrices is constructed to improve the noise suppression ability. Compared with the existing Toeplitz matrix reconstruction and spatial smoothing methods, the proposed method improves the DOA estimation performance at low SNR.

Findings

Theoretical analysis and simulation results show that compared with the existing dimensionality reduction processing algorithms, the proposed method improves the DOA estimation performance in cases with a low SNR. Furthermore, in cases where the DOAs between the coherent sources are closely spaced and the snapshot number is low, our proposed method significantly improves the performance of the DOA estimation performance.

Originality/value

The proposed method improves the DOA estimation performance at low SNR. In particular, for the cases with a low SNR, the proposed method provides a better RMSE. The convergence of the proposed method is also faster than other methods for the low number of snapshots. Our analysis also confirms that in cases where the DOAs between the coherent sources are closely spaced, the proposed method achieves a much higher angular resolution than that of the other methods.

中文翻译：

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相干信号DOA估计的增强空间平滑算法

目的

由于噪声的存在，现有的降维算法在低信噪比（SNR）下性能严重下降。为了解决这些问题，提出了一种增强的空间平滑方案，该方案利用子阵列时空相关矩阵来重构数据矩阵，以克服这一弱点。该方法利用信号的强相关性和噪声在时空域的弱相关性，提高了噪声抑制能力。

设计/方法/方法

在本文中，提出了一种增强的空间平滑方法。利用信号的强相关性和噪声的弱相关性，构建基于子阵列时空相关矩阵的时空平滑阵列协方差矩阵，以提高噪声抑制能力。与现有的 Toeplitz 矩阵重构和空间平滑方法相比，该方法提高了低信噪比下的 DOA 估计性能。

发现

理论分析和仿真结果表明，与现有的降维处理算法相比，该方法提高了低信噪比情况下的DOA估计性能。此外，在相干源之间的 DOA 间隔很近且快照数较低的情况下，我们提出的方法显着提高了 DOA 估计性能的性能。

原创性/价值

所提出的方法提高了低信噪比下的 DOA 估计性能。特别是，对于低信噪比的情况，所提出的方法提供了更好的 RMSE。对于少量快照，所提出的方法的收敛速度也比其他方法快。我们的分析还证实，在相干源之间的 DOA 间隔很近的情况下，所提出的方法比其他方法实现了更高的角分辨率。