Variable step size VLMP filtering algorithm based on sigmoid function and application thereof

Abstract

The invention belongs to the technical field of digital signal processing, and specifically relates to a variable step size VLMP (adaptive Volterra least mean P norm) algorithm and an application thereof in noise reduction of engines. The variable step size VLMP algorithm provided by the invention comprises the following steps: firstly, updating a step size factor by using a sigmoid function, and then performing iterative update on a first-order weight coefficient and a second-order weight coefficient, therefore a relatively small steady state error can be acquired; and constructing a nonlinear filter by employing the variable step size VLMP algorithm, using engine noise as a noise signal, identifying a kernel of a Volterra filter, and performing noise reduction processing on the engine noise while identifying and optimizing the kernel so as to achieve the objectives of filtering Gaussian noise and pulse noise contained in the engine noise and to maximally reduce the engine noise. The variable step size VLMP algorithm has the advantages of high instantaneity and good tracking performance.

Description

[0001] The invention belongs to the technical field of digital signal processing, and in particular relates to a variable step size VLMP (Volterra minimum average P norm) filtering algorithm based on a sigmoid function and an application thereof. Background technique [0002] Compared with the traditional linear wiener filter, the nonlinear Volterra filter has both linear and nonlinear characteristics, so the use of the Volterra filter can express most nonlinear systems and all linear systems, and has a better effect on the impulse noise in the signal The filtering effect has been widely used in system identification, adaptive channel equalization, noise cancellation, etc. [0003] The most widely used nonlinear filter is the Volterra Minimum Average P-norm (VLMP) algorithm, which can effectively filter out pulse interference. Due to its computational simplicity, the VLMP algorithm and others related to it have been widely used in various applications of nonlinear filtering. ...

AI Technical Summary

Problems solved by technology

[0004] In most filtering algorithms in the prior art, the value of the step factor is fixed, resulting in a fixed adjustment value of the weight of each iteration, so that the steady-state error and the convergence speed of the system cannot be optimal at the same time

Existing studies have acknowledged and used the variable step size optimization algorithm structure, which can solve the problems existing in the fixed step size algorithm, but for the engine noise background, the filter noise reduction effect of most filtering algorithms is not very ideal

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