Underwater channel estimation method based on convex optimization

Abstract

The invention discloses an underwater channel estimation method based on convex optimization, which comprises the following steps of: minimizing a convolution error between received data and a signal and a channel, and solving the convolution error between the received data and the signal and the channel by adopting a Lagrange method to obtain an estimated value of the channel; and searching a channel with a minimum error in the trust region, introducing a regularization coefficient changing along with time, and updating an estimated value of the channel when each frame of signal arrives. Compared with the prior art, the method has the advantages that the self-adaptive adjustment of the channel estimation algorithm can be realized in the unknown underwater acoustic environment, the accurate estimation of the underwater acoustic channel is realized with higher calculation efficiency, and the requirements of accurate channel estimation and communication application in the unknown underwater acoustic channel environment are met; and the transmitting signal passing through the underwater acoustic channel can be estimated, so that the influence of the transmitting transducer on the system can be reduced in a channel estimation mode.

Description

technical field [0001] The invention relates to the field of underwater acoustic communication, in particular to an underwater channel estimation method based on convex optimization. Background technique [0002] At present, underwater channel estimation usually adopts the method of inserting pilot frequency training. Traditional channel estimation methods usually use the least squares algorithm (LMS), because the least squares algorithm is easily interfered by noise, and inter-subcarrier interference will also reduce the performance of the LMS algorithm. ZHAO Y et al. proposed a pilot noise reduction method using a transform domain filter. Zhang Xiaoli et al. proposed an adaptive sparse underwater acoustic channel estimation algorithm based on compressed sensing, combining the compressed sensing algorithm OMP / CoSaMP with the hard threshold LMS algorithm. Combined with the estimation of the impulse response of the underwater acoustic channel; Wang Yonggang and others propos...

AI Technical Summary

Problems solved by technology

However, these algorithms need to know the sparse nature of the channel, and in the actual underwater environment, since the complex underwater space-time propagation channel usually does not satisfy the sparsity, it is not easy to use sparse algorithms

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