Sparse underwater acoustic channel estimation method based on generalized approximate message passing-sparse Bayesian learning

A sparse Bayesian and approximate message technology, applied in the field of underwater acoustic signal processing, can solve the problems of increased computational complexity, unfavorable real-time data processing, etc., and achieve the effect of reducing computational complexity

Active Publication Date: 2020-12-18
CNOOC TIANJIN BRANCH +1
View PDF10 Cites 1 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, when the dimension of the problem becomes larger, the computational complexity of

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Sparse underwater acoustic channel estimation method based on generalized approximate message passing-sparse Bayesian learning
  • Sparse underwater acoustic channel estimation method based on generalized approximate message passing-sparse Bayesian learning
  • Sparse underwater acoustic channel estimation method based on generalized approximate message passing-sparse Bayesian learning

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0038] The present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0039] The present invention is achieved like this:

[0040] (1) The receiving end demodulates the received passband signal into a frequency domain baseband complex signal;

[0041] (2) Using GAMP to calculate the channel impulse response under the framework of SBL;

[0042] (3) Update noise variance and channel hyperparameters;

[0043] (4) If the iteration termination condition is met, output the channel estimation result.

[0044] That is to say, the present invention combines SBL and GAMP to realize sparse underwater acoustic channel estimation; when solving channel impulse response, vector operation is converted into scalar operation. The combination of SBL and GAMP is realized by using GAMP to estimate channel impulse response under the framework of SBL. When solving the channel impulse response, converting the vector operat...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention provides a sparse underwater acoustic channel estimation method based on generalized approximate message passing sparse Bayesian learning, and belongs to the field of underwater acousticsignal processing. The invention relates to a channel estimation method combining generalized approximate message passing (GAMP) and sparse Bayesian learning (SBL). The method comprises the followingsteps: (1) inputting a frequency domain baseband receiving signal, a dictionary matrix, an iteration termination condition and a related parameter initial value; (2) calculating a channel impulse response by utilizing GAMP under the framework of the SBL; (3) updating a noise variance and a channel hyper-parameter; and (4) judging an iteration termination condition, and outputting a channel estimation result if the iteration termination condition is met. The method has the advantages that the underwater acoustic channel impulse response is calculated in an approximate message passing mode under the framework of the SBL, the calculation complexity of the SBL is reduced under the condition of basically no performance loss, and the operation time of the algorithm is shortened.

Description

technical field [0001] The invention relates to an underwater acoustic signal processing method, in particular to a sparse underwater acoustic channel estimation method based on generalized approximate message passing (GAMP)-sparse Bayesian learning (SBL). Background technique [0002] In underwater acoustic communication, the complex and changeable underwater acoustic channel has a significant impact on underwater acoustic communication. In order to improve the quality of communication, it is necessary to estimate the state of the channel. The underwater acoustic channel has significant sparsity. Sparse Bayesian learning has better reconstruction performance for sparse signals, and it is still relatively stable in some scenes with poor conditions, which has attracted more and more attention. However, when the dimension of the problem becomes larger, the computational complexity of SBL will increase accordingly, which is not conducive to the real-time processing of data. Ge...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
IPC IPC(8): H04L25/02H04B13/02
CPCH04L25/0202H04L25/024H04B13/02Y02D30/70
Inventor 王尔钧孟文波赵启彬殷敬伟任冠龙蒋东雷董钊张崇唐咸弟韩笑
Owner CNOOC TIANJIN BRANCH
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap