Novel PSD estimation algorithm based on compressed sensing and Blackman-Tukey approach

2014 ◽  
Author(s):  
Yingtao Niu ◽  
Jianzhong Chen ◽  
Binwu Li
Keyword(s):  
Compressed Sensing ◽  
Estimation Algorithm ◽  
Psd Estimation

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).

Frequency Offset Estimation Algorithm of OFDMA Wireless Communication System Based on Compressed Sensing

2015 ◽  
Vol 7 (3) ◽  
pp. 55-62
Author(s):  
Ma Qinggong ◽  
Yang Bo
Keyword(s):  
Wireless Communication ◽  
Compressed Sensing ◽  
Communication System ◽  
Estimation Algorithm ◽  
Frequency Offset ◽  
Frequency Offset Estimation ◽  
Computation Complexity ◽  
Algorithmic Approach ◽  
Wireless Communication System ◽  
Overall Performance

The frequency offset problem of OFDMA wireless communication system has been an important obstructive factor for the rapid promotion of this technology. The frequency offset estimation algorithm of OFDMA wireless communication system based on compressed sensing reconstructs the optimized mathematical model of frequency offset. Under the premise of keeping its estimated performance, from the reduction of its computation complexity and the enhancement of real-time performance of the system, it provides an optimized algorithmic approach and thus improves the practicability of algorithm. The compressed sensing algorithm has realized the fast and accurate extraction of the frequency offset parameters, so the overall performance of the system can reach the optimal state.

A novel Spectrum Estimation Algorithm Based on Compressed Sensing and Multi-taper Method

2013 ◽  
Vol 8 (4) ◽  
Author(s):  
Wang Keqing ◽  
Chen Jianzhong ◽  
Niu Yingtao ◽  
Zhu Yonggang
Keyword(s):  
Compressed Sensing ◽  
Estimation Algorithm ◽  
Spectrum Estimation

scholarly journals Compressed Sensing PARALIND Decomposition-Based Coherent Angle Estimation for Uniform Rectangular Array

2019 ◽  
Vol 2019 ◽  
pp. 1-10
Author(s):  
Wu Wei ◽  
Xu Le ◽  
Zhang Xiaofei ◽  
Li Jianfeng
Keyword(s):  
Compressed Sensing ◽  
Data Storage ◽  
Estimation Algorithm ◽  
Doa Estimation ◽  
Spatial Smoothing ◽  
Rectangular Array ◽  
Propagator Method ◽  
Coherent Sources ◽  
2D Doa Estimation ◽  
Uniform Rectangular Array

In this paper, the topic of coherent two-dimensional direction of arrival (2D-DOA) estimation is investigated. Our study jointly utilizes the compressed sensing (CS) technique and the parallel profiles with linear dependencies (PARALIND) model and presents a 2D-DOA estimation algorithm for coherent sources with the uniform rectangular array. Compared to the traditional PARALIND decomposition, the proposed algorithm owns lower computational complexity and smaller data storage capacity due to the process of compression. Besides, the proposed algorithm can obtain autopaired azimuth angles and elevation angles and can achieve the same estimation performance as the traditional PARALIND, which outperforms some familiar algorithms presented for coherent sources such as the forward backward spatial smoothing-estimating signal parameters via rotational invariance techniques (FBSS-ESPRIT) and forward backward spatial smoothing-propagator method (FBSS-PM). Extensive simulations are provided to validate the effectiveness of the proposed CS-PARALIND algorithm.

Compressed Sensing Based Blind Carrier Frequency Offset Estimation for OFDM System

2013 ◽  
Vol 756-759 ◽  
pp. 1894-1897 ◽  
Author(s):  
Gang Yang ◽  
Hua Xin Yu ◽  
Xiao Fei Zhang
Keyword(s):  
Compressed Sensing ◽  
Carrier Frequency ◽  
Orthogonal Frequency Division Multiplexing ◽  
Carrier Frequency Offset ◽  
Signal To Noise Ratio ◽  
Estimation Algorithm ◽  
Frequency Offset ◽  
Ofdm Systems ◽  
Carrier Frequency Offset Estimation ◽  
Sensing Model

In this paper, we address the problem of carrier frequency offset (CFO) estimation for Orthogonal Frequency Division Multiplexing (OFDM) systems. This paper links CFO estimation problem in OFDM systems to the compressed sensing model. Exploiting this link, it derives a compressed sensing-based CFO estimation algorithm. The proposed algorithm has better CFO estimation performance than ESPRIT method with lower signal-to-noise ratio (SNR). Simulation results illustrate performance of this algorithm.

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