Fast iteration adaptive filtering method

Abstract

The invention discloses a fast iteration adaptive filtering method. The method is implemented by adopting the steps of 1, performing data collection on dual-channel digital signals; 2, performing filtering and noise reduction processing on the collected data by adopting improved LMS (Least Mean Square) adaptive iteration filtering; and 3, adjusting a step length factor until optimal target digitalsignals are obtained. The fast iteration adaptive filtering method eliminates an operation of using a residual scalar e(n) in a weight factor iteration formula to serve as a multiplication factor, and controls the adjustment direction by a symbol of the residual scalar e(n), thereby enabling the convergence speed to be greatly improved, and simplifying the computational complexity. The fast iteration adaptive filtering method is applicable to any LMS operating environment and algorithm, and particularly applicable to operating in real time on various 16-bit MCUs.

Description

technical field [0001] The invention belongs to the field of self-adaptive filtering and noise reduction, and in particular relates to an self-adaptive filtering method with fast iteration, simplified calculation complexity and enhanced error tracking ability. Background technique [0002] Adaptive filtering technology can be used to detect stationary and non-stationary signals, has strong self-learning and tracking capabilities, and the algorithm is simple and easy to implement. It has been widely used in the fields of noise interference cancellation, linear predictive coding, echo cancellation, communication equalization, and adaptive parameter identification of unknown systems. [0003] The adaptive filtering rule is to use the filtering parameters captured at the previous moment to automatically adjust the filter coefficients at the current moment to adapt to the unknown statistical characteristics of the signal and noise, so as to achieve optimal filtering. According t...

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Problems solved by technology

[0017] The present invention solves the problems of difficult selection of μ existing in the original LMS algorithm and the increase of computational complexity or the decrease of computational speed in the commonly used improved algorithm. It not only greatly improves the convergence speed, but also simplifies the computational complexity and enhances the Error tracking ability, realizing the essential improvement of tracking ability

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