scholarly journals A Source Number Estimation Algorithm Based on Data Local Density and Fuzzy C-Means Clustering

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Na Wu ◽  
Ke Wang ◽  
Liangtian Wan ◽  
Ning Liu
Keyword(s):  
Local Density ◽  
Signal To Noise Ratio ◽  
Estimation Algorithm ◽  
One Dimensional ◽  
Source Number Estimation ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering ◽  
The One ◽  
Correlation Noise ◽  
Source Number

An advanced source number estimation (SNE) algorithm based on both fuzzy C-means clustering (FCM) and data local density (DLD) is proposed in this paper. The DLD of an eigenvalue refers to the number of eigenvalues within a specific neighborhood of this eigenvalue belonging to the data covariance matrix. This local density essentially as the one-dimensional sample feature of the FCM is extracted into the SNE algorithm based on FCM and can enable to improve the probability of correct detection (PCD) of the SNE algorithm based on the FCM especially for low signal-to-noise ratio (SNR) environment. Comparison experiment results demonstrate that compared to the SNE algorithm based on the FCM and other similar algorithms, our proposed algorithm can achieve highest PCD of the incident source number in both cases of spatial white noise and spatial correlation noise.

scholarly journals Joint Angles and Mutual Coupling Estimation Algorithm for Bistatic MIMO Radar

2012 ◽  
Vol 2012 ◽  
pp. 1-8 ◽  
Author(s):  
Jianfeng Li ◽  
Xiaofei Zhang
Keyword(s):  
Mutual Coupling ◽  
Multiple Input Multiple Output ◽  
Computational Cost ◽  
Estimation Algorithm ◽  
Mimo Radar ◽  
Coupling Coefficients ◽  
Angle Estimation ◽  
One Dimensional ◽  
Input Multiple Output ◽  
The One

We study the problem of angle estimation for a bistatic multiple-input multiple-output (MIMO) radar with unknown mutual coupling and proposed a joint algorithm for angles and mutual coupling estimation with the characteristics of uniform linear arrays and subspaces exploitation. We primarily obtain an initial estimate of DOA and DOD, then employ the local one-dimensional searching to estimate exactly DOA and DOD, and finally evaluate the parameters of mutual coupling coefficients via the estimated angles. Exploiting twice of the one-dimensional local searching, our method has much lower computational cost than the algorithm in (Liu and Liao (2012)), and automatically obtains the paired two-dimensional angle estimation. Slightly better performance for angle estimation has been achieved via our scheme in contrast to (Liu and Liao (2012)), while the two methods indicate very close performance of mutual coupling estimation. The simulation results verify the algorithmic effectiveness of our scheme.

Detection of double defects for plate-like structures based on a fuzzy c-means clustering algorithm

2018 ◽  
Vol 18 (3) ◽  
pp. 757-766 ◽  
Author(s):  
Shaojie Chen ◽  
Shaoping Zhou ◽  
Chaofeng Chen ◽  
Yong Li ◽  
Shuangmiao Zhai
Keyword(s):  
Clustering Algorithm ◽  
Signal To Noise Ratio ◽  
Circular Array ◽  
Location Error ◽  
Direct Wave ◽  
Fuzzy C Means ◽  
Single Defect ◽  
Fuzzy C Means Clustering ◽  
Signal Processing Algorithms ◽  
Novel Method

A variety of signal processing algorithms have been proposed to detect and locate defects in plate-like structures. However, the signal-to-noise ratio in these algorithms is too small especially in the reflection wave from the boundary, which further degrades the accuracy of localization of defects. A novel method for localization of defects is proposed in this article, based on the direct wave and fuzzy c-means clustering algorithm. To verify its effectiveness, experiments using the parallel linear and circular array are conducted, respectively. The experimental results show that the proposed method not only accurately locates single defect but also locates double defects in plate-like structures, and by comparing with the current discrete elliptic imaging algorithm, its location error of single defect is reduced from 20–25 mm to 0–3 mm and double defects is also reduced from 60–90 mm to 0–3 mm.

Sign in / Sign up

Export Citation Format

Share Document