Subspace-based methods suffer from the rank loss of the noise free data covariance matrix in the context of direction of arrival (DOA) estimation of coherent sources. The well-known spatial smoothing techniques are then widely employed to create a rank restored data covariance matrix. However, conventional spatial smoothing techniques, such as the spatial smoothing pre-processing (SSP), modified spatial smoothing pre-processing (MSSP), and improved spatial smoothing (ISS), do not make full use of the available information in the data covariance matrix. In this paper, an enhanced spatial smoothing (ESS) technique is proposed to exploit both the covariance matrices of individual subarrays and the cross-covariance matrices of different subarrays. Besides, the proposed method can work directly on the signal subspace (ESS-SS), since the signal subspace contains all the information of the DOAs of incoming signals. After de-correlation, the subspace method ESPRIT is adopted to estimate the DOAs. Compared with conventional approaches, the proposed method is more powerful to de-correlate the correlation between signals, and also more robust to the noise impact. The proposed method is tested on numerical data in coherent scenarios, and compared with conventional approaches. Simulation results show that the proposed method has an enhanced resolving capability and a lower signal-to-noise ratio threshold.

中文翻译：

---

基于ESPRIT算法的增强空间平滑技术用于相干场景中的到达方向估计

在相干源的到达方向 (DOA) 估计上下文中，基于子空间的方法会遭受无噪声数据协方差矩阵的秩损失。然后广泛采用众所周知的空间平滑技术来创建秩恢复数据协方差矩阵。然而，传统的空间平滑技术，如空间平滑预处理（SSP）、改进的空间平滑预处理（MSSP）和改进的空间平滑（ISS），没有充分利用数据协方差中的可用信息。矩阵。在本文中，提出了一种增强空间平滑 (ESS) 技术，以利用单个子阵列的协方差矩阵和不同子阵列的交叉协方差矩阵。此外，所提出的方法可以直接在信号子空间（ESS-SS）上工作，因为信号子空间包含输入信号的 DOA 的所有信息。去相关后，采用子空间方法ESPRIT估计DOA。与传统方法相比，所提出的方法在去相关信号之间的相关性方面更强大，并且对噪声影响也更稳健。所提出的方法在相干场景中的数值数据上进行了测试，并与传统方法进行了比较。仿真结果表明，该方法具有增强的分辨能力和较低的信噪比阈值。并且对噪声影响也更加稳健。所提出的方法在相干场景中的数值数据上进行了测试，并与传统方法进行了比较。仿真结果表明，该方法具有增强的分辨能力和较低的信噪比阈值。并且对噪声影响也更加稳健。所提出的方法在相干场景中的数值数据上进行了测试，并与传统方法进行了比较。仿真结果表明，该方法具有增强的分辨能力和较低的信噪比阈值。