Source number estimation algorithm based on wide-band compressed sensing

2016 ◽  
Author(s):  
Guangyu Zhang ◽  
Hong Chen
Keyword(s):  
Compressed Sensing ◽  
Wide Band ◽  
Estimation Algorithm ◽  
Source Number Estimation ◽  
Source Number

Design of Digital Channelizer Based on Source Number Estimation

2015 ◽  
Vol 25 (02) ◽  
pp. 1650008
Author(s):  
Junpeng Hu ◽  
Zhiping Huang ◽  
Chunwu Liu ◽  
Shaojing Su ◽  
Jing Zhou
Keyword(s):  
Filter Banks ◽  
Estimation Algorithm ◽  
Energy Detection ◽  
Intermediate Frequency ◽  
Perfect Reconstruction ◽  
Source Number Estimation ◽  
Band Signal ◽  
Band Number ◽  
Unknown Signal ◽  
Source Number

Digital channelizer is used to separate the sub-band signals contained within a wideband intermediate frequency (IF) received signal. This paper presents a structure of digital channelizer which is built upon estimation of the sub-band signal number. The proposed structure is designed by employing nonuniform filter banks (NUFBs), which are implemented by merging the adjacent sub-branches of the perfect reconstruction (PR) cosine modulated filter banks (CMFBs). Using source number estimation algorithm, the estimated value of sub-band number can be obtained. By energy detection, the energy characteristic of the analysis filter banks (AFBs) can be acquired and another value of sub-band number is calculated. Comparing the estimated value and the calculated one, we can evaluate whether the design of the AFB is suitable for the received signal. The structure of digital channelizer proposed by this paper provides a potential way to channelize the unknown signal and suggests a solution for the dynamic channelizaiton. A design example is also presented to demonstrate the feasibility and simplicity of this method.

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.

A Sparsity Enhanced Channel Estimation Algorithm Based on Compressed Sensing in MIMO-OFDM Systems

2014 ◽  
Vol 35 (3) ◽  
pp. 665-670 ◽  
Author(s):  
Zhi-bin Xie ◽  
Tong-si Xue ◽  
Yu-bo Tian ◽  
Wei-chen Zou ◽  
Qing-hua Liu ◽  
...  
Keyword(s):  
Channel Estimation ◽  
Compressed Sensing ◽  
Estimation Algorithm ◽  
Ofdm Systems ◽  
Mimo Ofdm

scholarly journals A Direction-of-Arrival Estimation Algorithm Based on Compressed Sensing and Density-Based Spatial Clustering and Its Application in Signal Processing of MEMS Vector Hydrophone

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2191
Author(s):  
Huichao Yan ◽  
Ting Chen ◽  
Peng Wang ◽  
Linmei Zhang ◽  
Rong Cheng ◽  
...  
Keyword(s):  
Compressed Sensing ◽  
Spatial Clustering ◽  
Direction Of Arrival ◽  
Estimation Algorithm ◽  
Doa Estimation ◽  
Cluster Center ◽  
Direction Of Arrival Estimation ◽  
Vector Hydrophone ◽  
Regularization Parameters ◽  
Selection Of

Direction of arrival (DOA) estimation has always been a hot topic for researchers. The complex and changeable environment makes it very challenging to estimate the DOA in a small snapshot and strong noise environment. The direction-of-arrival estimation method based on compressed sensing (CS) is a new method proposed in recent years. It has received widespread attention because it can realize the direction-of-arrival estimation under small snapshots. However, this method will cause serious distortion in a strong noise environment. To solve this problem, this paper proposes a DOA estimation algorithm based on the principle of CS and density-based spatial clustering (DBSCAN). First of all, in order to make the estimation accuracy higher, this paper selects a signal reconstruction strategy based on the basis pursuit de-noising (BPDN). In response to the challenge of the selection of regularization parameters in this strategy, the power spectrum entropy is proposed to characterize the noise intensity of the signal, so as to provide reasonable suggestions for the selection of regularization parameters; Then, this paper finds out that the DOA estimation based on the principle of CS will get a denser estimation near the real angle under the condition of small snapshots through analysis, so it is proposed to use a DBSCAN method to process the above data to obtain the final DOA estimate; Finally, calculate the cluster center value of each cluster, the number of clusters is the number of signal sources, and the cluster center value is the final DOA estimate. The proposed method is applied to the simulation experiment and the micro electro mechanical system (MEMS) vector hydrophone lake test experiment, and they are proved that the proposed method can obtain good results of DOA estimation under the conditions of small snapshots and low signal-to-noise ratio (SNR).

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