Quantitative sensing wide linear minimum error mode adaptive filter

Abstract

The invention relates to the technical field of digital filters, and discloses a quantization sensing wide linear minimum error mode adaptive filter, which performs non-uniform low-bit quantization on an input signal and an expected signal by using a quantization parameter to obtain a corresponding quantization value; constructing a linear factor based on the quantization parameter; based on the non-roundness of the input signal, constructing a normalized correlation matrix of the input signal and the linearized error signal, and constructing a compensation matrix in combination with a linear factor; obtaining input signal quantized values at a current moment and a historical moment, constructing an augmented input signal vector, calculating a product of the augmented input signal vector and the compensation matrix, and constructing a corrected input vector; performing wide linear filtering on the corrected input vector to obtain an output signal, and calculating an error signal in combination with an expected signal quantized value; and updating the weight vector at the next moment by using the corrected input vector and the error signal. According to the invention, by introducing a non-uniform low-bit quantization and deviation compensation mechanism, the cost and energy consumption of the analog-to-digital converter are remarkably reduced, and a relatively low steady-state error is kept.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of digital filter, in particular to a quantization-aware wide linear minimum error norm adaptive filter. BACKGROUND

[0002] System identification is an important branch of adaptive signal processing, and many problems such as traditional adaptive channel equalization, adaptive noise cancellation, adaptive echo cancellation, active noise control can be attributed to system identification. Wide linear adaptive filter is a complex system identification model, which has the characteristics of filtering the complex input signal vector and its conjugate vector at the same time, and can fully utilize the second-order non-circular characteristics of complex signals, overcoming the limitation of strictly linear model adaptive filter which can only process circular complex signals, and has good application prospect in stereo echo canceller.

[0003] Wide linear least mean square (WL-LMS) adaptive filter is a commonly used wide linear adaptive filter, which is based on the least mean square error criterion and has good convergence performance in a noise environment that approximately conforms to the Gaussian model. However, when there is severe non-Gaussian noise such as impulse noise, its performance will decrease sharply, and even diverge, which is not robust. In view of this situation, some robust wide linear adaptive filters have been proposed, such as wide linear maximum correlation entropy adaptive filter. These filters introduce nonlinear processing of the error signal, which can suppress the destructive effect of impulse noise, but the calculation of the nonlinear function is large, which becomes a hindrance in many low-cost and miniaturized application scenarios. Research in real adaptive filters shows that using the least absolute error criterion can achieve the effect of resisting impulse noise with less calculation.

[0004] Currently, the main adaptive filters all assume that the input and expected signals are accurate, but in real-world applications, high-precision signals cannot be obtained, especially for mobile and miniaturized devices, high-precision analog-to-digital converters (ADCs) will consume more energy resources and increase the cost of the device. When the precision is reduced, the quantization error will affect the convergence result of the adaptive filter. SUMMARY

[0005] Therefore, the technical problem to be solved by the present application is to overcome the convergence deviation problem of the wide linear least mean square filter when processing low-bit quantized signals, and the poor filtering robustness problem in the prior art.

[0006] To solve the above technical problems, the present application provides a quantization-aware wide linear minimum error norm adaptive filter, comprising: using a quantization parameter to perform non-uniform low-bit quantization on the input signal and the expected signal to obtain input signal quantization values and expected signal quantization values; Based on the non-circularity of the input signal and the preset identity matrix, a normalized correlation matrix of the input signal and a normalized correlation matrix of the linearized error signal are constructed. Based on the quantization parameters, a linear factor is constructed; based on the linear factor, the normalized correlation matrix of the input signal, and the normalized correlation matrix of the linearized error signal, a compensation matrix is ​​constructed. Get Time and Quantized values ​​of the input signal from multiple consecutive time steps prior to time step [time] are used to construct [the following]. The augmented input signal vector at time t; calculate The product of the augmented input signal vector and the compensation matrix at time step [time] is obtained. The corrected input vector at time step; calculation The product of the augmented weight vector and the corrected input vector at time step 1 is obtained. The output signal at time; calculate the quantized value of the desired signal and The difference between the output signals at time points is obtained. Error signal at time; based on The corrected input vector and error signal at time step, for Update the augmented weight vector at time step [time] to obtain [the data]. The augmented weight vector at time step.

[0007] Preferably, the acquisition of quantization parameters includes: Obtain a sequence of auxiliary real Gaussian random variables with zero mean and unit variance; Using the Lloyd-max algorithm, obtain the sequence of auxiliary real Gaussian random variables at a preset quantization bit depth. Quantization parameters below; The quantization parameters include a quantization threshold sequence. With quantized label sequence .

[0008] Preferably, using quantization parameters, non-uniform low-bit quantization is performed on the input signal and the desired signal to obtain the quantized values ​​of the input signal and the desired signal, including: The real and imaginary parts of the input signal or the desired signal are quantized separately to obtain the quantized values ​​of the real part and the imaginary part of the input signal or the desired signal. The real and imaginary quantized values ​​of the input or desired signal are rewritten into complex number form to obtain the quantized value of the input signal or the quantized value of the desired signal. The quantization of the real or imaginary part is expressed as: if Then obtain the corresponding quantized value. ; Indicates the real part or the imaginary part. denotes a standard deviation of the input signal; denotes a quantization threshold in a quantization threshold sequence, denotes a quantization label in a quantization label sequence, denotes a quantization threshold in a quantization threshold sequence, denotes a quantization label in a quantization label sequence.

[0009] Preferably, the input signal normalized correlation matrix is constructed based on the non-circularity of the input signal and a preset identity matrix, comprising: the variance of the input signal and the pseudo-variance , the non-circularity of the input signal is calculated, expressed as: the input signal normalized correlation matrix is constructed based on the non-circularity of the input signal and a preset identity matrix , expressed as: ; wherein, denotes the conjugate of the non-circularity of the input signal, denotes an identity matrix of , and denotes the length of the standard input signal vector.

[0010] Preferably, the linearized error signal normalized correlation matrix is constructed based on the non-circularity of the input signal and a preset identity matrix, comprising: the variance of the input signal and the pseudo-variance , the non-circularity of the input signal is calculated, expressed as: the real part of the non-circularity of the input signal is taken to obtain , the linearized error signal normalized correlation matrix is constructed, expressed as: ; wherein, denotes taking the real part of a complex number.

[0011] Preferably, the linear factor is constructed based on the quantization parameter, expressed as: ; wherein, denotes the linear factor, denotes a quantization threshold in a quantization threshold sequence, ​​​​​​The quantization label sequence representing the quantization parameter is the first... A quantitative label, , Indicates the preset quantization bit depth. This indicates exponentiation.

[0012] Preferably, a compensation matrix is ​​constructed based on the linearity factor, the normalized correlation matrix of the input signal, and the normalized correlation matrix of the linearized error signal, as follows: ; in, This represents the normalized correlation matrix of the input signal. This represents the normalized correlation matrix of the linearized error signal. This indicates the conjugate transpose operation.

[0013] Preferably, obtain Time and Quantized values ​​of the input signal from multiple consecutive time steps prior to time step [time] are used to construct [the following]. The augmented input signal vector at time t includes: Get Quantized value of the input signal at time 1 and the preceding consecutive Quantized value of the input signal at time 1 , build Standard input signal vector at time 1 , is represented as: ; Will Standard input signal vector at time 1 Its conjugate vector splicing, constructing a length of of Augmented input signal vector at time 1 ; in, This indicates the transpose operation. This indicates the conjugate operation. This indicates the conjugate transpose operation.

[0014] Preferably, calculation The product of the augmented weight vector and the corrected input vector at time step 1 is obtained. The output signal at time t is represented as: ; in, express Output signal at time, express Augmented weight vector at time step The conjugate transpose of; express The corrected input vector at time step is expressed as follows: , Represents the compensation matrix. express The augmented input signal vector at time t.

[0015] Preferably, based on The corrected input vector and error signal at time step, for Update the augmented weight vector at time step [time] to obtain [the data]. The augmented weight vector at time step 1 is represented as: ; in, express The augmented weight vector at time step 1. Indicates the step size parameter; express The error signal at time t is expressed as: , express The expected quantized value of the signal at time; This represents the modulo operation for complex numbers. express . conjugate.

[0016] Compared with the prior art, the above-described technical solution of the present invention has the following advantages:

[0017] The quantization-sensing wide-linear minimum error modulus adaptive filter described in this invention introduces the minimum error modulus criterion into a wide-linear adaptive filter and uses deviation compensation to reduce the convergence deviation caused by low-bit quantization, achieving robust adaptive filtering of the output signal of the system to be estimated. This invention utilizes a wide-linear model to design an augmented complex adaptive filter, enabling dual-channel echo cancellation with a single filter. Simultaneously, by selecting a cost function based on the minimum error modulus, this invention achieves robust filtering results with relatively low computational complexity even when the desired signal is superimposed with impulse interference. Furthermore, the deviation compensation mechanism significantly reduces the convergence deviation caused by quantization errors, allowing the filter to effectively filter low-bit quantized signals, thereby reducing the cost of the analog-to-digital converter and system power consumption, and effectively improving the robustness of the adaptive filter in impulse noise environments. Attached Figure Description

[0018] To make the content of this invention easier to understand, the invention will be further described in detail below with reference to specific embodiments and accompanying drawings, wherein: Figure 1 This is a flowchart of the steps of the quantization-sensing wide linear minimum error mode adaptive filter provided by the present invention; Figure 2 is a structural block diagram of a coarse quantization system identification model; Figure 3 is a normalized mean square error curve comparison diagram of an adaptive filter system in a system identification scenario. DETAILED DESCRIPTION

[0019] The present application will be further described below in conjunction with the accompanying drawings and specific embodiments, so that those skilled in the art can better understand the present application and implement it.

[0020] Referring to Figure 1 , the step flow chart of the quantized perceptual wide linear minimum error mode adaptive filter provided by the present application, the specific steps include: S101: using a quantization parameter, performing non-uniform low-bit quantization on an input signal and an expected signal to obtain input signal quantization values and expected signal quantization values; S102: based on the non-circularity of the input signal and a preset unit matrix, constructing an input signal normalized correlation matrix and a linearized error signal normalized correlation matrix, including: S102-1: based on the variance and the pseudo-variance of the input signal, calculating the non-circularity of the input signal, expressed as: ; S102-2: based on the non-circularity of the input signal and the preset unit matrix , constructing the input signal normalized correlation matrix , expressed as: ; S102-3: performing a real part operation on the non-circularity of the input signal to obtain , constructing the linearized error signal normalized correlation matrix , expressed as: ; wherein, represents the conjugate of the non-circularity of the input signal, represents the unit matrix of , and represents the length of the standard input signal vector; represents a real part operation on a complex number; S103: based on the quantization parameter, constructing a linear factor; based on the linear factor, the input signal normalized correlation matrix, and the linearized error signal normalized correlation matrix, constructing a compensation matrix; the linear factor , expressed as: ; The quantization threshold sequence representing the quantization parameter is the first... One quantization threshold, The quantization label sequence representing the quantization parameter is the first... A quantitative label, , Indicates the preset quantization bit depth. Indicates exponentiation; Compensation matrix , is represented as: ; This represents the normalized correlation matrix of the input signal. This represents the normalized correlation matrix of the linearized error signal. This represents the conjugate transpose operation; S104: Acquisition Time and Quantized values ​​of the input signal from multiple consecutive time steps prior to time step [time] are used to construct [the following]. The augmented input signal vector at time t includes: S104-1: Obtain Quantized value of the input signal at time 1 and the preceding consecutive Quantized value of the input signal at time 1 , build Standard input signal vector at time 1 , is represented as: ; S104-2: Will Standard input signal vector at time 1 Its conjugate vector splicing, constructing a length of of Augmented input signal vector at time 1 ; in, This indicates the transpose operation. This indicates the conjugate operation. This represents the conjugate transpose operation; S105: Calculation The product of the augmented input signal vector and the compensation matrix at time step [time] is obtained. The corrected input vector at time step; calculation The product of the augmented weight vector and the corrected input vector at time step 1 is obtained. The output signal at time; calculate the quantized value of the desired signal and The difference between the output signals at time points is obtained. Error signal at time; S106: Based on The corrected input vector and error signal at time step, for The augmented weight vector at time t is updated as The augmented weight vector at time t is denoted as ; wherein denotes a step size parameter; denotes the error signal at time t, expressed as , denotes the expected signal quantization value at time t; denotes the modulo operation of a complex number, denotes the conjugate of .

[0021] Specifically, in step S101, the input signal quantization value and the expected signal quantization value are obtained, including: S101-1: obtaining the quantization parameter, including: obtaining a sequence of auxiliary real Gaussian random variables with zero mean and unit variance; obtaining the quantization parameter of the sequence of auxiliary real Gaussian random variables at a preset quantization bit number by using the Lloyd-max algorithm; The quantization parameter includes a sequence of quantization threshold values and a sequence of quantization labels ; S101-2: non-uniform low-bit quantization, including: quantizing the real part and the imaginary part of the input signal or the expected signal respectively to obtain the real part quantization value and the imaginary part quantization value of the input signal or the expected signal; rewriting the real part quantization value and the imaginary part quantization value of the input signal or the expected signal into a complex number form to obtain the input signal quantization value or the expected signal quantization value; wherein quantizing the real part or the imaginary part is denoted as: if , the corresponding quantization value is obtained; denotes the real part or the imaginary part, denotes the standard deviation of denotes the quantization threshold value in the sequence of quantization threshold values, ; denotes the quantization label in the sequence of quantization labels, .

[0022] The application introduces quantization parameter to quantize input signal and expected signal, so that the adaptive filter can process the low-bit quantized input signal and expected signal with second-order non-circular characteristics, thereby reducing the cost of analog-to-digital converter and system energy consumption.

[0023] Specifically, in step S105, comprising: S105-1: calculating the product of the augmented input signal vector at the moment and the compensation matrix, obtaining the modified input vector at the moment , denoted as: , denotes the compensation matrix, denotes the augmented input signal vector at the moment; S105-2: calculating the product of the augmented weight vector at the moment and the modified input vector, obtaining the output signal at the moment , denoted as: ; denotes the conjugate transpose of the augmented weight vector at the moment ; S105-3: calculating the difference between the quantized value of the expected signal and the output signal at the moment , obtaining the error signal at the moment , denoted as: .

[0024] The quantized perceptual wide linear minimum error module adaptive filter provided by the application introduces the minimum error module criterion into the wide linear adaptive filter, and uses the bias compensation to reduce the convergence bias caused by low-bit quantization, so as to realize the robust adaptive filtering of the system output signal to be estimated. The wide linear model is used to design the augmented complex adaptive filter, so that the double-channel echo cancellation function can be realized by a single filter. Meanwhile, the cost function based on the minimum error module is selected, so that the robust filtering result can be realized with small calculation complexity in the case that the expected signal is superimposed with pulse interference. The bias compensation mechanism is used to significantly reduce the convergence bias caused by the quantization error, so that the filter can effectively filter the low-bit quantized signal, thereby reducing the cost of analog-to-digital converter and system energy consumption, and effectively improving the robustness of the adaptive filter in the pulse noise environment.

[0025] Based on the above embodiment, in the embodiment of the application, the quantized perceptual wide linear minimum error module adaptive filter provided by the application is used for filtering, and the specific steps include:

[0026] S201: Calculate the optimal quantization parameters using the Lloyd-Max algorithm for the input signal. With expected signal Perform non-uniform low-bit quantization to obtain the quantized value of the input signal. and the expected signal quantization value ,include: S201-1: Generate a sequence of auxiliary real Gaussian random variables with zero mean and unit variance, and calculate its quantization bit depth using the Lloyd-max algorithm. The optimal quantization parameters, including the quantization threshold. and quantification labels ; S201-2: For the input signal and expected signal Quantize the real and imaginary parts separately: set up express or The standard deviation of the real or imaginary part is . ,if Then its quantized value is obtained. ; The quantized real and imaginary parts are then rewritten as complex numbers to obtain the quantized value of the input signal. and the expected signal quantization value ;

[0027] S202: Obtain the continuous sequence at time n and before time n. The low-bit quantized values ​​of the input signal at each time step are used to construct the augmented input signal vector. ,include: S202-1: Obtain Time and preceding consecutive Quantized value of the input signal at time 1 Construct the standard input signal vector ; S202-2: Convert the standard input signal vector Its conjugate vector splicing, constructing a length of augmented input signal vector ; in, This indicates the transpose operation. This indicates the conjugate operation. This represents the conjugate transpose operation;

[0028] S203: Non-circularity based on input signal Construct the normalized correlation matrix of the input signal. and a normalized correlation matrix of the linearized error signal , respectively, are denoted as ; ; wherein denotes a real part operation on a complex number, denotes a non-circularity of the input signal , and and denote a variance and a pseudo-variance of , respectively, denotes an identity matrix of ;

[0029] S204: calculating a linear factor based on the quantization parameter, and constructing a compensation matrix , comprising: S204-1: constructing a linear factor based on the quantization threshold and the quantization label, denoted as ; S204-2: constructing a compensation matrix based on the linear factor, the normalized correlation matrix of the input signal, and the normalized correlation matrix of the linearized error signal, denoted as ; wherein denotes an exponential operation;

[0030] S205: constructing a modified input vector based on a product of the compensation matrix and an augmented input signal vector , denoted as ;

[0031] S206: performing a wide linear filtering on the modified input vector to obtain an output signal; that is, calculating the output signal based on an inner product of the augmented weight vector and the modified input vector, denoted as ; wherein denotes an augmented weight vector of the wide linear adaptive filter at the time ;

[0032] S207: calculating an error signal using a difference between the expected signal quantization value after coarse quantization and the output signal, denoted as ;

[0033] S208: updating the weight vector at the next time using the modified input vector and the error signal ​ , represented as: ; in, Indicates the step size parameter. Represents the modulo operation for complex numbers.

[0034] The quantization-sensing wide-linear minimum error modulus adaptive filter of this invention enables the adaptive filter to process low-bit quantized input signals and desired signals with second-order non-circular characteristics through deviation compensation, thereby reducing the power consumption and hardware cost of the device. In addition, the weight update formula derived based on the minimum error modulus criterion achieves the adaptive filter's ability to resist impulse noise interference with low computational complexity.

[0035] To demonstrate the effectiveness of this invention, this embodiment employs computer experiments to verify the performance of the Quantization-Aware Wide Linear Minimum Error Mode (QA-WL-LEM) adaptive filter provided by this invention. The experiment targets a system identification application scenario, estimating an unknown system in an environment containing impulse noise interference, and comparing the results with those of the Quantization-Aware Wide Linear Minimum Mean Square (QA-WL-LMS) adaptive filter. The Quantization-Aware Wide Linear Minimum Mean Square (QA-WL-LMS) adaptive filter is a result of extending the existing Quantization-Aware Minimum Mean Square (QA-LMS) adaptive filter method to a wide linear scenario.

[0036] Reference Figure 2 The diagram shown is a structural block diagram of the coarse-quantization system identification model. In this embodiment, the noise signal is Gaussian noise plus impulse noise; the normalized mean square deviation (NMSD) is used as the performance measure in the system identification scene experiment. The unit is dB, where Indicates taking the logarithm. These are the weights of the actual system.

[0037] In the experiment, the weight vector of the unknown system was randomly generated, and the input signal used had a variance of 1 and a non-circularity of [value missing]. The zero-mean Gaussian white signal, using the noise signal It contains a mean of zero and a variance of . Gaussian white noise and a pulse noise ,Right now The noise-free desired signal and Gaussian noise are preserved. The signal-to-noise ratio in dB remains consistent with the impulse noise. Signal-to-noise ratio in dB. Impulse noise. Produced by Bernoulli Gaussian processes, i.e. ,in is a Bernoulli process with probability 0.9 of taking 0 and probability 0.1 of taking 1, is a zero-mean Gaussian white noise.

[0038] Referring to Figure 3 Fig. 2 shows a comparison of normalized mean square deviation (NMSD) curves of adaptive filtering systems in a system identification scenario, where the NMSD curves are averaged over 100 Monte Carlo experiments; the step size parameters of the methods are QA-WL-LMS( ), QA-WL-LEM( ). It can be seen from Figure 3 that the QA-WL-LEM adaptive filtering system of the embodiments of the present application can maintain a lower steady-state error, and has good anti-impulse interference capability and bias compensation effect.

[0039] The quantized perceptual wide-linear least-mean error module adaptive filter disclosed by the present application introduces the least-mean error module criterion into the wide-linear adaptive filter, and uses bias compensation to reduce the convergence bias caused by low-bit quantization, thereby realizing robust adaptive filtering of the output signal of the system to be estimated. By introducing the low-bit non-uniform quantization and bias compensation mechanism, the present application can significantly reduce the cost of an analog-to-digital converter and the energy consumption of a system, maintain a low steady-state error, effectively improve the robustness of the adaptive filter in a pulse noise environment, and be suitable for parameter estimation scenarios of the Internet of Things and other scenarios with high requirements for energy efficiency and anti-interference performance. The present application minimizes the cost function based on the error module, thereby improving the robustness of the adaptive filter in an environment containing pulse noise with a small calculation complexity. At the same time, the present application is based on a wide-linear framework, can fully utilize the second-order non-circular characteristics of a complex signal to improve the filtering capability, and realize better filtering effect.

[0040] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can adopt a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer usable storage media containing computer usable program code (including but not limited to disk storage, CD-ROM, optical storage, etc.).

[0041] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 one or more flow or blocks Figure 1 one or more flow or blocks

[0042] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 one or more flow or blocks Figure 1 one or more flow or blocks

[0043] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 one or more flow or blocks Figure 1 one or more flow or blocks

[0044] Obviously, the above-described embodiments are only examples for clarity of description and are not limiting on the embodiments. Based on the above description, one of ordinary skill in the art can further make other different forms of changes or modifications. Here, all the embodiments are not required to be enumerated, and the obvious changes or modifications derived therefrom are still within the protection scope of the present application.

Claims

1. A quantized perceptually wide linear minimum error modeling adaptive filter characterized by, The method comprises the following steps: performing non-uniform low-bit quantization on the input signal and the expected signal by using a quantization parameter to obtain input signal quantization values and expected signal quantization values; constructing an input signal normalized correlation matrix and a linearized error signal normalized correlation matrix based on the non-circularity of the input signal and a preset unit matrix; constructing a linear factor based on the quantization parameter; constructing a compensation matrix based on the linear factor, the input signal normalized correlation matrix and the linearized error signal normalized correlation matrix; Get Time and Quantized values ​​of the input signal from multiple consecutive time steps prior to time step [time] are used to construct [the following]. The augmented input signal vector at time t; Computing the product of the augmented input signal vector at the time instant and the compensation matrix, obtaining the modified input vector at the time instant; computing the product of the augmented weight vector at the time instant and the modified input vector, obtaining the output signal at the time instant; computing the difference between the desired signal quantization value and the output signal at the time instant, obtaining the error signal at the time instant; Based on the correction input vector and the error signal, the augmented weight vector at the time instant is updated to obtain the augmented weight vector at the time instant the time instant is updated to obtain the augmented weight vector at the time instant the time instant is updated to obtain the augmented weight vector at the time instant 2. The quantized perceptual wide linear minimum error modeling adaptive filter according to claim 1, characterized in that, the acquisition of the quantization parameter comprises: obtaining an auxiliary real Gaussian random variable sequence with zero mean and unit variance; The Lloyd-max algorithm is used to obtain quantization parameters of the auxiliary real Gaussian random variable sequence under a preset quantization bit number . The quantization parameter includes a quantization threshold sequence With the quantization label sequence .

3. The quantized perceptual wide linear minimum error modeling adaptive filter according to claim 2, wherein, performing non-uniform low-bit quantization on the input signal and the expected signal by using a quantization parameter to obtain input signal quantization values and expected signal quantization values, comprising: quantizing the real part and the imaginary part of the input signal or the expected signal respectively to obtain real part quantization values and imaginary part quantization values of the input signal or the expected signal; rewriting the real part quantization values and the imaginary part quantization values of the input signal or the expected signal into complex numbers to obtain input signal quantization values or expected signal quantization values; wherein quantizing the real or imaginary part is represented as: if , then obtaining the corresponding quantized value ; denotes the real or imaginary part, denotes the standard deviation of denotes the quantization threshold in the sequence of quantization thresholds, ; denotes the quantization label in the sequence of quantization labels, .

4. The quantized perceptual wide linear minimum error modeling adaptive filter according to claim 1, wherein, constructing an input signal normalized correlation matrix based on the non-circularity of the input signal and a preset unit matrix, comprising: Based on the variance of the input signal With the pseudo-variance , the non-circularity of the input signal is calculated , is represented as: ; Non-circularity based on input signal and preset unit matrix , constructing an input signal normalized correlation matrix , is expressed as: ； wherein denotes the conjugate of the non-circularity of the input signal, denotes the identity matrix, denotes the length of the standard input signal vector.

5. The quantized perceptual wide linear minimum error modeling adaptive filter according to claim 1, wherein, constructing a linearized error signal normalized correlation matrix based on the non-circularity of the input signal and a preset unit matrix, comprising: Based on the variance of the input signal With the pseudo-variance , the non-circularity of the input signal is calculated , is expressed as: ; Non-circularity of an input signal performing a real part operation to obtain , a linearized error signal normalized correlation matrix is expressed as: ； wherein represents taking the real part of a complex number.

6. The quantized perceptual wide-linear minimum error modeling adaptive filter according to claim 1, wherein, constructing a linear factor based on the quantization parameter, expressed as: ； wherein, denotes a linear factor, denotes the i-th quantization threshold of a quantization threshold sequence of a quantization parameter, denotes the i-th quantization threshold of a quantization threshold sequence of a quantization parameter, denotes the i-th quantization label of a quantization label sequence of a quantization parameter, denotes the i-th quantization label of a quantization label sequence of a quantization parameter, , denotes a preset quantization bit number, denotes an exponential operation.

7. The quantized perceptual wide linear minimum error modeling adaptive filter according to claim 6, characterized in that, constructing a compensation matrix based on the linear factor, the input signal normalized correlation matrix and the linearized error signal normalized correlation matrix, expressed as: ； wherein denotes the input signal normalized correlation matrix, denotes the linearized error signal normalized correlation matrix, denotes the conjugate transpose operation.

8. The quantized perceptual wide linear minimum error modeling adaptive filter according to claim 1, wherein, Get Time and Quantized values ​​of the input signal from multiple consecutive time steps prior to time step [time] are used to construct [the following]. The augmented input signal vector at time t includes: acquiring quantized values of the input signal at the time , and the preceding quantized values of the input signal at the time , a standard input signal vector at the time is constructed , denoted as: ; The standard input signal vector at time and its conjugate vector concatenated, resulting in a length of the augmented input signal vector at time ; wherein, denotes a transpose operation, denotes a conjugate operation, denotes a conjugate transpose operation.

9. The quantized perceptual wide linear minimum error modeling adaptive filter according to claim 1, wherein, Computing the augmented weight vector at the time instant and the modified input vector, obtaining the output signal at the time instant, denoted as: ； wherein denotes the output signal at time instant denotes the augmented weight vector at time instant the conjugate transpose of denotes the modified input vector at time instant , denotes the compensation matrix denotes the augmented input signal vector at time instant 10. The quantized perceptual wide linear minimum error modeling adaptive filter according to claim 9, characterized in that, Based on The modified input vector at the time instant and the error signal are used to update the augmented weight vector at the time instant The augmented weight vector at the time instant is updated, and is expressed as: The augmented weight vector at the time instant is updated, and is expressed as: ； wherein denotes augmented weight vector at time instant denotes step size parameter; denotes error signal at time instant , denotes expected signal quantized value at time instant denotes modulo operation on complex numbers, denotes conjugate of

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