Method and device for updating coefficient vector of finite impulse response filter

A device for updating a coefficient vector of a finite impulse response (FIR) filter, comprising a memory, a processor, and a computer program stored in the memory and executable by the processor, wherein the processor is configured to, when executing the computer program, perform the following steps:

• obtaining an updated step-size diagonal matrix for a coefficient vector of the FIR filter;
• and
• obtaining, based on the updated step-size diagonal matrix, an updated coefficient vector of the FIR filter;
• wherein the processor is further configured to, when executing the computer program, perform the following steps:
• updating, based on an end moment of pre-learning and a pre-defined updating period, a step-size diagonal matrix used to update the coefficient vector of the FIR filter, to obtain the updated step-size diagonal matrix;
• wherein the processor is further configured to, when executing the computer program, perform the following steps:
• obtaining, a coefficient vector of the FIR filter at an updating moment of the step-size diagonal matrix;
• obtaining, based on the coefficient vector of the FIR filter at the updating moment of the step-size diagonal matrix, an estimated value of an attenuation factor of a step-size diagonal matrix of an exponentially weighted step-size normalized least mean square (ES-NLMS) algorithm;
• and
• obtaining, based on the estimated value of the attenuation factor, an updated step-size diagonal matrix of the ES-NLMS algorithm at the updating moment of the step-size diagonal matrix.

A non-transitory computer-readable storage medium, having a computer program stored thereon, wherein the computer program, when being executed by a processor, performs the following steps:

• obtaining an updated step-size diagonal matrix for a coefficient vector of the FIR filter;
• and
• obtaining, based on the updated step-size diagonal matrix, an updated coefficient vector of the FIR filter;
• wherein the obtaining the updated step-size diagonal matrix for the coefficient vector of the FIR filter comprises:
• updating, based on an end moment of pre-learning and a pre-defined updating period, a step-size diagonal matrix used to update the coefficient vector of the FIR filter, to obtain the updated step-size diagonal matrix;
• wherein the updating, based on the end moment of the pre-learning and the pre-defined updating period, the step-size diagonal matrix used to update the coefficient vector of the FIR filter, to obtain the updated step-size diagonal matrix, comprises:
• obtaining, a coefficient vector of the FIR filter at an updating moment of the step-size diagonal matrix;
• obtaining, based on the coefficient vector of the FIR filter at the updating moment of the step-size diagonal matrix, an estimated value of an attenuation factor of a step-size diagonal matrix of an exponentially weighted step-size normalized least mean square (ES-NLMS) algorithm;
• and
• obtaining, based on the estimated value of the attenuation factor, an updated step-size diagonal matrix of the ES-NLMS algorithm at the updating moment of the step-size diagonal matrix.