Underwater acoustic channel identification method based on regularized minimum mean square error variable step size algorithm

Abstract

The present invention relates to an underwater acoustic channel identification method based on the regularized minimum mean square error variable step size algorithm. Firstly, the underwater acoustic channel is modeled in order to obtain the expression framework of the impulse response function of the underwater acoustic channel. On this basis . Using the periodic training mode, a regularized minimum mean square error variable step size algorithm is designed to iteratively optimize the time-domain underwater acoustic channel impulse response function, that is, the time-domain underwater acoustic channel information is obtained through identification. The method of the invention can estimate the impulse response function of the time-varying underwater acoustic channel. Because the method of the invention skillfully uses the change strategy of the moving window control step size, it can obtain more accurate channel estimation than the traditional algorithm in the iterative process, which will help to improve the estimation accuracy based on the output of the channel estimation equalizer.

Description

technical field [0001] The invention belongs to the fields of underwater acoustic communication, underwater acoustic signal processing, etc., and relates to an underwater acoustic channel identification method based on a regularized minimum mean square error variable step size algorithm, and relates to time-domain underwater acoustic identification of received signals and training sequences under impulse interference A channel identification method, which will improve the identification performance of underwater acoustic channels under impulse interference. Background technique [0002] Problems such as underwater acoustic channel identification and underwater acoustic communication can be attributed to the identification and optimization of the impulse response function, which is used to express and identify the underwater acoustic channel based on the training sequence and the received signal. At present, the identification methods for underwater acoustic channels include ...

AI Technical Summary

Problems solved by technology

However, the fixed step size of the adaptive identification method cannot guarantee the convergence speed and convergence accuracy of the algorithm at the same time. Therefore, designing a new step size update strategy can not only ensure the convergence speed, but also improve the identification accuracy.

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