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

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...

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Problems solved by technology

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.

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