A 2-D DOA estimation method based on blind source separation

2009 ◽  
Author(s):  
Fulai Liu ◽  
Jinkuan Wang ◽  
Ruiyan Du ◽  
Bin Wang
Keyword(s):  
Blind Source Separation ◽  
Source Separation ◽  
Estimation Method ◽  
Doa Estimation

scholarly journals A Novel Underdetermined Blind Source Separation Method and Its Application to Source Contribution Quantitative Estimation

Sensors ◽  
2019 ◽  
Vol 19 (6) ◽  
pp. 1413 ◽  
Author(s):  
Jiantao Lu ◽  
Wei Cheng ◽  
Yanyang Zi
Keyword(s):  
Blind Source Separation ◽  
Quantitative Estimation ◽  
Source Separation ◽  
Estimation Method ◽  
Least Square ◽  
Underdetermined Blind Source Separation ◽  
Source Contribution ◽  
Time Frequency ◽  
Source Contributions ◽  
Source Signals

To identify the major vibration and radiation noise, a source contribution quantitative estimation method is proposed based on underdetermined blind source separation. First, the single source points (SSPs) are identified by directly searching the identical normalized time-frequency vectors of mixed signals, which can improve the efficiency and accuracy in identifying SSPs. Then, the mixing matrix is obtained by hierarchical clustering, and source signals can also be recovered by the least square method. Second, the optimal combination coefficients between source signals and mixed signals can be calculated based on minimum redundant error energy. Therefore, mixed signals can be optimally linearly combined by source signals via the coefficients. Third, the energy elimination method is used to quantitatively estimate source contributions. Finally, the effectiveness of the proposed method is verified via numerical case studies and experiments with a cylindrical structure, and the results show that source signals can be effectively recovered, and source contributions can be quantitatively estimated by the proposed method.

scholarly journals A Novel Underdetermined Blind Source Separation Method Based on OPTICS and Subspace Projection

Symmetry ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 1677
Author(s):  
Qingyi Wang ◽  
Yiqiong Zhang ◽  
Shuai Yin ◽  
Yuduo Wang ◽  
Genping Wu
Keyword(s):  
Blind Source Separation ◽  
Source Separation ◽  
Estimation Method ◽  
Signal Recovery ◽  
Subspace Projection ◽  
Underdetermined Blind Source Separation ◽  
Matrix Estimation ◽  
Time Frequency ◽  
Source Signal ◽  
Mixing Matrix

In recent years, the problem of underdetermined blind source separation (UBSS) has become a research hotspot due to its practical potential. This paper presents a novel method to solve the problem of UBSS, which mainly includes the following three steps: Single source points (SSPs) are first screened out using the principal component analysis (PCA) approach, which is based on the statistical features of signal time-frequency (TF) points. Second, a mixing matrix estimation method is proposed that combines Ordering Points To Identify the Clustering Structure (OPTICS) with an improved potential function to directly detect the number of source signals, remove noise points, and accurately calculate the mixing matrix vector; it is independent of the input parameters and offers great accuracy and robustness. Finally, an improved subspace projection method is used for source signal recovery, and the upper limit for the number of active sources at each mixed signal is increased from m−1 to m. The unmixing process of the proposed algorithm is symmetrical to the actual signal mixing process, allowing it to accurately estimate the mixing matrix and perform well in noisy environments. When compared to previous methods, the source signal recovery accuracy is improved. The method’s effectiveness is demonstrated by both theoretical and experimental results.

scholarly journals New Tool Wear Estimation Method of the Milling Process Based on Multisensor Blind Source Separation

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Chen Gao ◽  
Sun Bintao ◽  
Heng Wu ◽  
Mengjuan Peng ◽  
Yuqing Zhou
Keyword(s):  
Tool Wear ◽  
Blind Source Separation ◽  
Source Separation ◽  
Estimation Method ◽  
Milling Process ◽  
Small Samples ◽  
Support Vector ◽  
Dimensionless Time ◽  
Time Frequency ◽  
Tool Wear Estimation

Timely and effective identification and monitoring of tool wear is important for the milling process. However, traditional methods of tool wear estimation have run into difficulties due to under small samples with less prior knowledge. This article addresses this issue by employing a multisensor tool wear estimation method based on blind source separation technology. Stationary subspace analysis (SSA) technology is applied to transform multisensor signals to stationary and nonstationary sources without prior information of signals. Ten dimensionless time-frequency indices of the nonstationary signal are extracted to train least squares support vector regression (LS-SVR) to obtain a tool wear estimation model for small samples. The analysis and comparison of one benchmark tool wear dataset and tool wear experiments verify the feasibility and effectiveness of the proposed method and outperform other two current methods.

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