Broadband noise adaptive suppression method and system

Abstract

The invention provides a broadband noise adaptive suppression method. The method comprises the following steps: S1, acquiring an original signal; s2, dividing the original signals into two classes by taking the multiple of the noise standard deviation in the original signals as a threshold value, and performing quantization transformation on the original signals according to the classes to obtain a quantization transformation value; s3, calculating the transformation margin of the quantization transformation according to the quantization transformation, and performing adaptive filtering on the obtained transformation margin value to obtain the optimal estimation value of the quantization error; and S4, integrating the optimal estimation value of the quantization error and the quantization transformation value to obtain a de-noised useful signal. The method can be suitable for broadband noise self-adaptive suppression of various signal forms including static, steady and transient useful signals, and solves the problem that existing self-adaptive filtering cannot effectively filter out broadband noise and even introduces new noise when the characteristics of the useful signals change.

Description

Technical Field

[0001] The present invention relates to the field of information communication technology, and in particular to a method and system for adaptively suppressing broadband noise. Background Art

[0002] The output signal generated by any information acquisition system inevitably contains noise. Noise comes from various noise sources inside and outside the information acquisition system. Some specific noise sources will produce noise with special characteristics and features, such as drift noise, vibration noise, burr noise, etc. However, the noise generated by all or part of the noise sources has the characteristics of white noise or quasi-white noise: it is random and has a wide bandwidth, almost occupying the entire signal bandwidth or even wider. Such broadband noise usually has a stable characteristic. For example, as long as the operating conditions and environmental conditions of the system remain unchanged, its mean and variance remain unchanged. Broadband random noise is a noise component that must be included in the output signal generated by any information acquisition system. Eliminating and suppressing broadband noise can improve the signal-to-noise ratio of the information acquisition output and significantly improve the quality of information acquisition. This is often also a necessary guarantee for subsequent information applications, such as measurement and control.

[0003] According to signal estimation theory, to obtain the best estimate of the useful signal from the signal with superimposed noise (hereinafter referred to as the "original signal"), the original signal needs to be matched filtered, also known as optimal filtering. Since the characteristics of the useful signal such as the autocorrelation function are often unknown, adaptive filtering methods are generally used in practice to implement matched filtering and obtain the best estimate of the useful signal. Adaptive filtering is to adjust the filter parameters according to a rule based on the objective function so that the adaptive filter converges to the matched filter. If the signal changes, causing the parameters of the objective function to change, the adaptive filter parameters will be readjusted to converge to the new matched filter parameters. During the adjustment period, if the adaptive filter does not converge to the optimal filter, it will not play the role of matched filtering, and due to the parameter adjustment, new interference may be introduced into the filter output. In the output of information acquisition, the useful signal is unknown. Even if the noise is stable, the change of the useful signal often causes the mean, variance, etc. of the entire original signal to change, and the objective function parameters of the adaptive filter change accordingly. Therefore, adaptive filtering to remove noise from the original signal is only suitable for the case where the mean and variance of the useful signal and noise are constant. When the useful signal changes, not only can the noise not be filtered out, but new noise will also be introduced due to the adjustment process.

[0004] The Chinese patent document with publication number CN115842568A discloses a method for communication with interference removal, including: receiving a signal to be processed, preprocessing the signal to be processed, and obtaining a digital signal corresponding to the signal to be processed; performing spectrum analysis on the digital signal to determine the signal bandwidth between the maximum signal frequency and the minimum signal frequency after the spectrum analysis; based on the determined signal bandwidth, performing broadband noise interference judgment and in-band noise interference judgment to perform interference removal processing; and outputting the signal after the interference removal processing. This patent is not applicable to situations where the spectrum information of the useful signal is unknown, which is a typical situation in the fields of sensing and measurement; in addition, this patent performs interference identification and judgment on the digital signal after spectrum analysis, and the process is complicated and inefficient.

[0005] Therefore, a method and system for adaptively suppressing broadband noise is needed. Summary of the invention

[0006] In view of the defects in the prior art, an object of the present invention is to provide a method and system for adaptively suppressing broadband noise.

[0007] A method for adaptively suppressing broadband noise provided by the present invention comprises:

[0008] Step S1: collecting original signals;

[0009] Step S2: using the multiple of the noise standard deviation in the original signal as a threshold, dividing the original signal into two categories, and performing quantization transformation on the original signal according to the category to obtain a quantization transformation value;

[0010] Step S3: Calculate the transformation residual of the quantization transformation according to the quantization transformation, and perform adaptive filtering on the obtained transformation residual value to obtain the optimal estimation value of the quantization error;

[0011] Step S4: Integrate the optimal estimate of the quantization error and the quantization transformation value to obtain a useful signal after denoising.

[0012] Preferably, the step S1 comprises:

[0013] The collected original signal x(n) is:

[0014] x(n)=s(n)+w(n)

[0015] s(n) is the useful signal, w(n) is the broadband random noise, where the standard deviation of the noise is σ.

[0016] Preferably, the step S2 comprises:

[0017] Step S2.1: Calculate the difference value Δ(n) of the original signal x(n):

[0018] Δ(n)=x(n)-x(n-1)

[0019] Step S2.2: Use the multiple of the standard deviation as the threshold to divide the original signal into two categories; specifically, use 6 times the variance as the threshold 6σ, and compare the absolute value of the difference value |Δ(n)| with the threshold 6σ. When |Δ(n)|≥6σ, the original signal belongs to the first category, and when |Δ(n)|<6σ, the original signal belongs to the second category.

[0020] Step S2.3: For different types of original signals, the original signal is quantized differently with resolution R as the quantization scale to obtain the quantized transformation value x Q (n),

[0021] Specific:

[0022]

[0023] Among them, the resolution R is the set value.

[0024] Preferably, step S3 comprises:

[0025] Step S3.1: Calculate the transformation residual Q(n) of the quantization transformation of the original signal x(n):

[0026] Q(n)=x(n)-x Q (n)

[0027] Step S3.2: Quantization error ε s (n) is the error between the quantized transform of the original signal x(n) and the useful signal s(n), which is

[0028] ε s (n) = s(n) - x Q (n) = Q(n) - w(n);

[0029] Step S3.3: Adaptively filter the transform residue Q(n) to obtain an optimal estimate of the quantization error;

[0030] Specifically, Q(n) is used as the reference signal, Q(nM) is used as the input signal of the adaptive filter, and ε is obtained through the adaptive filter. s The best estimate of (n)

[0031] M is a preset integer parameter, which is a value greater than the autocorrelation length of the noise w(n) and less than the autocorrelation length of the useful signal s(n).

[0032] Preferably, step S4 comprises:

[0033] According to the optimal estimate of the transform residual Q(n) and the quantization error Get the useful signal after denoising:

[0034]

[0035] A system for adaptively suppressing broadband noise provided by the present invention comprises:

[0036] Module M1: collect original signal;

[0037] Module M2: Using the multiple of the noise standard deviation in the original signal as the threshold, the original signal is divided into two categories, and the original signal is quantized according to the category to obtain the quantized transformation value;

[0038] Module M3: Calculate the transformation residual of the quantization transformation according to the quantization transformation, perform adaptive filtering on the obtained transformation residual value, and obtain the optimal estimation value of the quantization error;

[0039] Module M4: Integrate the optimal estimate of the quantization error and the quantization transformation value to obtain the useful signal after denoising.

[0040] Preferably, the module M1 comprises:

[0041] The collected original signal x(n) is:

[0042] x(n)=s(n)+w(n)

[0043] s(n) is the useful signal, w(n) is the broadband random noise, where the standard deviation of the noise is σ.

[0044] Preferably, the module M2 comprises:

[0045] Module M2.1: Calculate the difference value Δ(n) of the original signal x(n):

[0046] Δ(n)=x(n)-x(n-1)

[0047] Module M2.2: Use multiples of the standard deviation as the threshold to divide the original signal into two categories; specifically, use 6 times the variance as the threshold 6σ, and compare the absolute value of the difference value |Δ(n)| with the threshold 6σ. When |Δ(n)|≥6σ, the original signal belongs to the first category, and when |Δ(n)|<6σ, the original signal belongs to the second category.

[0048] Module M2.3: For different types of original signals, the original signal is quantized differently with resolution R as the quantization scale to obtain the quantization transformation value x Q (n),

[0049] Specific:

[0050]

[0051] Among them, the resolution R is the set value.

[0052] Preferably, the module M3 includes:

[0053] Module M3.1: Calculate the transformation residual Q(n) of the quantized transformation of the original signal x(n):

[0054] Q(n)=x(n)-x Q (n)

[0055] Module M3.2: Quantization error ε s (n) is the error between the quantized transform of the original signal x(n) and the useful signal s(n), which is

[0056] ε s (n) = Q(n) - w(n);

[0057] Module M3.3: Adaptively filter the transform residue Q(n) to obtain the optimal estimate of the quantization error;

[0058] Specifically, Q(n) is used as the reference signal, Q(nM) is used as the input signal of the adaptive filter, and ε is obtained through the adaptive filter. s The best estimate of (n)

[0059] M is a preset integer parameter, which is a value greater than the autocorrelation length of the noise w(n) and less than the autocorrelation length of the useful signal s(n).

[0060] Preferably, the module M4 includes:

[0061] According to the optimal estimate of the transform residual Q(n) and the quantization error Get the useful signal after denoising:

[0062]

[0063] Compared with the prior art, the present invention has the following beneficial effects:

[0064] 1. The present invention can significantly suppress broadband random noise and improve the quality of signals through a combination of signal differentiation, quantization transformation and adaptive filtering. It can adaptively suppress broadband noise in useful signals in various signal forms, including static, steady-state, transient, etc., and solve the problem that the existing adaptive filtering cannot effectively filter out broadband noise and even introduces new noise when the characteristics of the useful signal change.

[0065] 2. The present invention can also estimate the variance of the noise in the signal and the resolution of the useful signal, and can still suppress the noise when the noise or the resolution is unknown. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments made with reference to the following drawings:

[0067] Figure 1 A flowchart of a method for adaptively suppressing broadband noise according to the present invention;

[0068] Figure 2 This is the signal noise suppression processing flow of the first embodiment of the present invention;

[0069] Figure 3 This is a block diagram of an adaptive filter according to Embodiment 2 of the present invention. DETAILED DESCRIPTION

[0070] The present invention is described in detail below in conjunction with specific embodiments. The following embodiments will help those skilled in the art to further understand the present invention, but are not intended to limit the present invention in any form. It should be noted that, for those of ordinary skill in the art, several changes and improvements can also be made without departing from the concept of the present invention. These all belong to the protection scope of the present invention.

[0071] The present invention provides a universal broadband noise adaptive suppression method, which is applicable to broadband noise adaptive suppression of various signal forms including static, steady-state, and transient useful signals, and solves the problem that the existing adaptive filtering cannot effectively filter out broadband noise and even introduces new noise when the characteristics of the useful signal change. The specific technical ideas and implementation steps of the present invention are: checking the difference of the signal, performing quantization transformation according to the difference, and adaptively filtering the residual of the quantization transformation to obtain the optimal estimate of the useful signal and the quantization transformation error. The sum of the optimal estimate and the quantization transformation is the signal with suppressed noise.

[0072] The present invention provides a method for adaptively suppressing broadband noise, comprising:

[0073] Step S1: collecting original signals;

[0074] Step S2: using the multiple of the noise standard deviation in the original signal as a threshold, dividing the original signal into two categories, and performing quantization transformation on the original signal according to the category to obtain a quantization transformation value;

[0075] Step S3: Calculate the transformation residual of the quantization transformation according to the quantization transformation, and perform adaptive filtering on the obtained transformation residual value to obtain the optimal estimation value of the quantization error;

[0076] Step S4: Integrate the optimal estimate of the quantization error and the quantization transformation value to obtain a useful signal after denoising.

[0077] Specifically, the step S1 includes:

[0078] The collected original signal x(n) is:

[0079] x(n)=s(n)+w(n)

[0080] s(n) is the useful signal, w(n) is the broadband random noise, where the standard deviation of the noise is σ.

[0081] Specifically, step S2 includes:

[0082] Step S2.1: Calculate the difference value Δ(n) of the original signal x(n):

[0083] Δ(n)=x(n)-x(n-1)

[0084] Step S2.2: Use the multiple of the standard deviation as the threshold to divide the original signal into two categories; specifically, use 6 times the variance as the threshold 6σ, and compare the absolute value of the difference value |Δ(n)| with the threshold 6σ. When |Δ(n)|≥6σ, the original signal belongs to the first category, and when |Δ(n)|<6σ, the original signal belongs to the second category.

[0085] Step S2.3: For different types of original signals, the original signal is quantized differently with resolution R as the quantization scale to obtain the quantized transformation value x Q (n),

[0086] Specific:

[0087]

[0088] Among them, the resolution R is the set value.

[0089] Specifically, step S3 includes:

[0090] Step S3.1: Calculate the transformation residual Q(n) of the quantization transformation of the original signal x(n):

[0091] Q(n)=x(n)-x Q (n)

[0092] Step S3.2: Quantization error ε s (n) is the error between the quantized transform of the original signal x(n) and the useful signal s(n), which is

[0093] ε s (n) = s(n) - x Q (n) = Q(n) - w(n);

[0094] Step S3.3: Adaptively filter the transform residue Q(n) to obtain an optimal estimate of the quantization error;

[0095] Specifically, Q(n) is used as the reference signal, Q(nM) is used as the input signal of the adaptive filter, and ε is obtained through the adaptive filter. s The best estimate of (n)

[0096] M is a preset integer parameter, which is a value greater than the autocorrelation length of the noise w(n) and less than the autocorrelation length of the useful signal s(n).

[0097] Specifically, step S4 includes:

[0098] According to the optimal estimate of the transform residual Q(n) and the quantization error Get the useful signal after denoising:

[0099]

[0100] Embodiment 1

[0101] Assume that the original signal of information acquisition is x(n), x(n)=s(n)+w(n), s(n) is the useful signal, w(n) is the broadband random noise. Assume that the mean of the noise is zero and the variance is σ 2 (standard deviation σ), and the noise is considered to have the characteristics of "white noise": that is, the noise is independent of the useful signal s(n), and due to the wide bandwidth of the noise, the autocorrelation length of the noise is shorter than the autocorrelation length of the useful signal. The resolution of the information acquisition system is an important parameter for system design and performance description. It is set to R, which has the same dimension as the signal x(n). R is determined by the standard deviation σ of the noise, and R=mσ, usually 3≥m≥1. Its physical meaning is: the change in the value of the useful signal s(n) must not be less than R to detect the change of s(n) from x(n). Based on this, we propose a broadband noise suppression method based on signal differential adaptive filtering: 1) Determine the state of the useful signal in the signal x(n) based on the differential - change or no change, 2) Perform different quantization transformations on the signal x(n) according to the two states, with resolution R as the quantization level, 3) Adaptively filter the residual of the quantization transformation of x(n) to obtain the optimal estimate of the residual of the quantization transformation of the useful signal, 4) The sum of the quantization transformation of x(n) and the optimal estimate of the residual of the quantization transformation is the optimal estimate of x(n) after removing the broadband noise. The specific analysis is as follows.

[0102] Among them, when |Δ(n)|≥6σ, it indicates that the useful signal has a detectable change, that is, the useful signal has a change greater than the resolution R. In order to eliminate the change in the statistical characteristics of the original signal caused by the change of the useful signal, the current value of the original signal is quantized with the resolution R as the quantization scale to obtain the quantized transformation value x Q(n); when |Δ(n)|<6σ, it indicates that there is no detectable change in the useful signal, then the original signal is quantized with the resolution R as the quantization scale at the previous moment to obtain the quantized transformation value x Q (n);

[0103] Assume that the difference of signal x(n) is Δ(n), Δ(n) = x(n) - x(n-1), and the quantization transformation of x(n) is x Q (n), definition

[0104]

[0105] The residual of the quantization transformation of the signal x(n) is

[0106] Q(n)=x(n)-x Q (n) = ε(n)(2)

[0107] Obviously, when |Δ(n)|≥6σ, Q(n) is the error ε(n) of quantizing x(n) with R as the quantization level. The quantization noise ε(n) is distributed in The mean is zero. Since the mean of w(n) is also zero, the mean of the residual Q(n) of the quantization transformation is always zero.

[0108]

[0109] When |Δ(n)|<6σ, the residual of the quantization transform

[0110]

[0111] So when |Δ(n)|<6σ,

[0112]

[0113] Let w(n) obey Gaussian distribution, then its odd moment is zero, so

[0114] E[w(n-1)ε(n-1)]=σ 2 (6)

[0115] So when |Δ(n)|<6σ, we still have

[0116]

[0117] Therefore, the residual Q(n) of the quantization transformation is stable, and its mean and variance do not change with the change of the sensor quantity, that is, the useful signal s(n).

[0118] On the other hand, the residual of the quantization transform

[0119]

[0120] Let ε s (n) = Q(n) - w(n), we have

[0121]

[0122] Obviously s (n) is the error between the quantized transform of the original signal x(n) and the useful signal s(n).

[0123]

[0124] So we only need to estimate ε from Q(n) s (n), we can estimate the useful signal s(n). According to the signal estimation theory, by matching filter Q(n), we can get ε s The optimal estimate of (n) Therefore, adaptive filtering can be used, with Q(n) as the reference signal and Q(nM) as the input signal of the adaptive filter. When the adaptive filter converges, the filter output is ε s (n). The selection principle of parameter M is that it is greater than the autocorrelation length of noise w(n) and less than the autocorrelation length of useful signal s(n). Since the residual Q(n) of quantization transformation is stable, once the adaptive filter converges to a steady state, it will not need to be readjusted to reach a new steady state due to changes in the useful signal s(n). This fundamentally solves the problem that adaptive filtering cannot effectively filter out broadband noise in non-stationary useful signals. Because the useful signal is non-stationary, its changes may cause the adaptive filter to adjust, and the output of the filter being adjusted is definitely not the optimal filtering result. In this way, the filter output being adjusted not only cannot remove the noise in the signal, but may also add new interference. According to the above analysis, no matter how the useful signal changes, it will not cause the mean and variance of the quantization change residual Q(n) to change, so the change of the useful signal will not cause the adaptive filter to adjust.

[0125] According to the above principle analysis, the implementation steps of the present invention are determined: 1) Calculate the difference of the original signal; 2) According to the comparison result of the absolute value of the difference and the resolution of the useful signal, quantize the original signal - a) When the absolute value of the difference is greater than or equal to 6 times the standard deviation of the noise, the quantization transformation of the original signal is the quantization result with the resolution as the quantization scale; b) When the absolute value of the difference is less than 6 times the standard deviation of the noise, the quantization transformation of the original signal is the quantization result of the signal at the previous moment; 3) Adaptively filter the signal and the residue (difference) of its quantization transformation - the time delay of the residue is used as the input of the adaptive filter, and the residue itself is the reference signal; 4) Obtain the adaptive filtering output, which is the optimal estimate of the error between the useful signal and the quantization transformation in the original signal; 5) The sum of the adaptive filter output and the quantization transformation is the optimal estimate of the original signal, that is, the output with suppressed broadband random noise. See Figure 1 .

[0126] From the above analysis, we can also see that the variance of the residual of the quantization transformation of the original signal is determined by the noise variance and the quantization level, and the quantization level is the resolution of the useful signal. In fact, the residual (error) of signal quantization has this characteristic: the variance of the quantization error is determined by the noise variance and the quantization level. Therefore, the present invention can also provide noise variance and resolution estimation of the original signal. In other words, when the present invention is used to filter the broadband noise of the original signal, it is not necessary to predict the variance and resolution of the noise. The noise variance can be obtained by analyzing the variance of the signal quantization residual. The specific implementation steps are: take the useful signal resolution as the quantization level, quantize the original signal, calculate the variance of the quantization error, and subtract the quantization variance from the variance, that is, It is the variance of the noise. On the other hand, in an actual information acquisition system, the resolution of the useful signal is often determined by the standard deviation of the noise. Generally, 1-3 times the standard deviation of the noise is taken as the resolution of the useful signal. Therefore, by setting the relationship between the resolution and the standard deviation of the noise, the actual resolution can also be calculated from the variance of the noise.

[0127] Embodiment 2

[0128] The noise variance σ is known 2 The block diagram of the adaptive filter is shown in Figure 3 Where Q(n) is the quantization transformation residual of the original signal x(n),

[0129] Q(n)=x(n)-x Q (n) (15)

[0130] Quantization transformation x Q (n) is defined as

[0131]

[0132] The order of the adaptive filter is L, and the filter vector H(n) consists of the filter coefficients at time n:

[0133] H(n) = [h(0,n), h(1,n), … h(L,n)] T (17)

[0134] The corresponding input signal vector is

[0135] Q(n) = [Q(n), Q(n - 1), … Q(n - L)] T (18)

[0136] In this embodiment, the LMS adaptive filter is adopted. The adaptive filter updated according to the LMS adjustment rule is

[0137] H(n + 1) = H(n) + 2μe(n)Q(n - M) (19)

[0138] μ is the iteration step size, which can take σ 2 is the noise variance, R is the resolution of the useful signal, and 0 < k < 1. The order L of the adaptive filter is comprehensively selected according to parameters such as the processing speed and the frequency resolution of the signal. The higher the order, the higher the frequency resolution and the slower the processing speed. Generally, it is selected between 5 and 100. The delay time M takes any value greater than the autocorrelation length of the noise w(n) and less than the autocorrelation length of the useful signal s(n), or M can also be selected only according to the characteristics of the noise. For example, for white noise, M = 1 can be taken.

[0139] According to Figure 2 the process programming, the adaptive filtering of the original signal x(n) is realized to obtain the output y(n) with the noise suppressed, which is also the optimal estimate of the useful signal s(n). In the processing flow. The starting point A of the adaptive filtering can be determined artificially, and it is necessary to ensure that A ≥ L + M.

[0140] Embodiment Three

[0141] The variance of the noise in the original signal x(n) to be processed is unknown. It is necessary to first collect x(n) that does not contain the useful signal and only contains noise, calculate its mean and variance, and obtain the variance σ 2 After that, the noise suppression processing is performed on the original signal according to Embodiment One.

[0142] Embodiment Four

[0143] The variance of the noise in the original signal x(n) to be processed is unknown, and the resolution R of the useful signal is known. First, x(n) (which may contain the useful signal) is quantized, and the quantization scale is the resolution of the useful signal, and the variance σ of the quantization error is calculated Q 2 According to

[0144]

[0145] Calculate the noise variance σ 2 If the result is negative or 0, it means that the actual noise level of the original signal has exceeded the noise intensity that satisfies the useful signal resolution R. R is not the true resolution of the useful signal, and the noise variance calculation is invalid. It is necessary to re-analyze the resolution R of the useful signal and calculate the noise variance again through formula (20) until the correct result is obtained. If the result is positive, the correct variance σ is obtained. 2 , according to the first embodiment, the original signal is subjected to noise suppression processing.

[0146] Embodiment 5

[0147] The variance of the noise and the resolution of the useful signal in the original signal x(n) being processed are unknown. Quantize x(n) (which may contain the useful signal) with the quantization scale being the resolution of the useful signal, and calculate the variance of the quantization error σ Q 2 The relationship between resolution and noise variance (standard deviation) is set as R = mσ, where 3 ≥ m ≥ 1. The noise variance σ is calculated according to formula (20): 2 And the resolution R of the useful signal. According to the first embodiment, the original signal is subjected to noise suppression processing.

[0148] The present invention also provides a system for broadband noise adaptive suppression, which can be implemented by executing the process steps of the method for broadband noise adaptive suppression, that is, those skilled in the art can understand the method for broadband noise adaptive suppression as a preferred implementation of the system for broadband noise adaptive suppression.

[0149] A system for adaptively suppressing broadband noise provided by the present invention comprises: a module M1 for collecting original signals; a module M2 for dividing the original signals into two categories by taking the multiple of the standard deviation of the noise in the original signals as the threshold, and subjecting the original signals to quantization transformation according to the categories to obtain quantization transformation values; a module M3 for calculating the transformation residue of the quantization transformation according to the quantization transformation, and performing adaptive filtering on the obtained transformation residue value to obtain the optimal estimation value of the quantization error; and a module M4 for integrating the optimal estimation value of the quantization error and the quantization transformation value to obtain the useful signal after denoising.

[0150] Specifically, the module M1 includes: the collected original signal x(n) is:

[0151] x(n)=s(n)+w(n)

[0152] s(n) is the useful signal, w(n) is the broadband random noise, where the standard deviation of the noise is σ.

[0153] Specifically, the module M2 includes: Module M2.1: Calculate the difference value Δ(n) of the original signal x(n):

[0154] Δ(n)=x(n)-x(n-1)

[0155] Module M2.2: Use the multiple of the standard deviation as the threshold to divide the original signal into two categories; specifically, use 6 times the variance as the threshold 6σ, and compare the absolute value of the difference value |Δ(n)| with the threshold 6σ. When |Δ(n)|≥6σ, the original signal belongs to the first category, and when |Δ(n)|<6σ, the original signal belongs to the second category; Module M2.3: For different categories of original signals, the original signal is quantized differently with the resolution R as the quantization scale to obtain the quantization transformation value x Q (n), specifically:

[0156]

[0157] Among them, the resolution R is the set value.

[0158] Specifically, the module M3 includes: Module M3.1: Calculate the transformation residual Q(n) of the quantization transformation of the original signal x(n):

[0159] Q(n)=x(n)-x Q (n)

[0160] Module M3.2: Quantization error ε s (n) is the error between the quantized transform of the original signal x(n) and the useful signal s(n), which is

[0161] ε s (n) = s(n) - x Q (n) = Q(n) - w(n);

[0162] Module M3.3: Adaptively filter the transform residue Q(n) to obtain the optimal estimate of the quantization error; specifically, Q(n) is used as the reference signal, Q(nM) is used as the input signal of the adaptive filter, and ε is obtained through the adaptive filter. s The best estimate of (n) M is a preset integer parameter, which is a value greater than the autocorrelation length of the noise w(n) and less than the autocorrelation length of the useful signal s(n).

[0163] Specifically, the module M4 includes: according to the optimal estimate of the transformation residue Q(n) and the quantization error Get the useful signal after denoising:

[0164] Those skilled in the art know that, in addition to realizing the system and its various devices, modules, and units provided by the present invention in a purely computer-readable program code, it is entirely possible to realize the same functions in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers by logically programming the method steps. Therefore, the system and its various devices, modules, and units provided by the present invention can be considered as a hardware component, and the devices, modules, and units included therein for realizing various functions can also be regarded as structures within the hardware component; the devices, modules, and units for realizing various functions can also be regarded as both software modules for realizing the method and structures within the hardware component.

[0165] The above describes the specific embodiments of the present invention. It should be understood that the present invention is not limited to the above specific embodiments, and those skilled in the art can make various changes or modifications within the scope of the claims, which does not affect the essence of the present invention. In the absence of conflict, the embodiments of the present application and the features in the embodiments can be combined with each other arbitrarily.

Claims

1. A method for adaptively suppressing broadband noise, characterized in that: include: Step S1: collecting original signals; Step S2: using the multiple of the noise standard deviation in the original signal as a threshold, dividing the original signal into two categories, and performing quantization transformation on the original signal according to the category to obtain a quantization transformation value; Step S3: Calculate the transformation residual of the quantization transformation according to the quantization transformation, and perform adaptive filtering on the obtained transformation residual value to obtain the optimal estimation value of the quantization error; Step S4: Integrate the optimal estimate of the quantization error and the quantization transformation value to obtain a useful signal after denoising.

2. The method for adaptively suppressing broadband noise according to claim 1, characterized in that: The step S1 comprises: The collected original signal x(n) is: x(n)=s(n)+w(n) s(n) is the useful signal, w(n) is the broadband random noise, where the standard deviation of the noise is σ.

3. The method for adaptively suppressing broadband noise according to claim 1, characterized in that: The step S2 comprises: Step S2.1: Calculate the difference value Δ(n) of the original signal x(n): Δ(n)=x(n)-x(n-1) Step S2.2: Use the multiple of the standard deviation as the threshold to divide the original signal into two categories; specifically, use 6 times the variance as the threshold 6σ, and compare the absolute value of the difference value |Δ(n)| with the threshold 6σ. When |Δ(n)|≥6σ, the original signal belongs to the first category, and when |Δ(n)|<6σ, the original signal belongs to the second category. Step S2.3: For different types of original signals, the original signal is quantized differently with resolution R as the quantization scale to obtain the quantized transformation value x Q (n), Specific: Among them, the resolution R is the set value.

4. The method for adaptively suppressing broadband noise according to claim 1, characterized in that: The step S3 comprises: Step S3.1: Calculate the transformation residual Q(n) of the quantization transformation of the original signal x(n): Q(n)=x(n)-x Q (n) Step S3.2: Quantization error ε s (n) is the error between the quantized transform of the original signal x(n) and the useful signal s(n), which is ε s (n) = s(n) - x Q (n) = Q(n) - w(n); Step S3.3: Adaptively filter the transform residue Q(n) to obtain an optimal estimate of the quantization error; Specifically, Q(n) is used as the reference signal, Q(nM) is used as the input signal of the adaptive filter, and ε is obtained through the adaptive filter. s The best estimate of (n) M is a preset integer parameter, which is a value greater than the autocorrelation length of the noise w(n) and less than the autocorrelation length of the useful signal s(n).

5. The method for adaptively suppressing broadband noise according to claim 1, characterized in that: The step S4 comprises: According to the optimal estimate of the transform residual Q(n) and the quantization error Get the useful signal after denoising:

6. A system for adaptively suppressing broadband noise, characterized in that: include: Module M1: collect original signal; Module M2: Using the multiple of the noise standard deviation in the original signal as the threshold, the original signal is divided into two categories, and the original signal is quantized according to the category to obtain the quantized transformation value; Module M3: Calculate the transformation residual of the quantization transformation according to the quantization transformation, perform adaptive filtering on the obtained transformation residual value, and obtain the optimal estimation value of the quantization error; Module M4: Integrate the optimal estimate of the quantization error and the quantization transformation value to obtain the useful signal after denoising.

7. The broadband noise adaptive suppression system according to claim 6, characterized in that: The module M1 comprises: The collected original signal x(n) is: x(n)=s(n)+w(n) s(n) is the useful signal, w(n) is the broadband random noise, where the standard deviation of the noise is σ.

8. The broadband noise adaptive suppression system according to claim 6, characterized in that: The module M2 comprises: Module M2.1: Calculate the difference value Δ(n) of the original signal x(n): Δ(n)=x(n)-x(n-1) Module M2.2: Use multiples of the standard deviation as the threshold to divide the original signal into two categories; specifically, use 6 times the variance as the threshold 6σ, and compare the absolute value of the difference value |Δ(n)| with the threshold 6σ. When |Δ(n)|≥6σ, the original signal belongs to the first category, and when |Δ(n)|<6σ, the original signal belongs to the second category. Module M2.3: For different types of original signals, the original signal is quantized differently with resolution R as the quantization scale to obtain the quantization transformation value x Q (n), Specific: Among them, the resolution R is the set value.

9. The broadband noise adaptive suppression system according to claim 6, characterized in that: The module M3 comprises: Module M3.1: Calculate the transformation residual Q(n) of the quantized transformation of the original signal x(n): Q(n)=x(n)-x Q (n) Module M3.2: Quantization error ε s (n) is the error between the quantized transform of the original signal x(n) and the useful signal s(n), which is ε s (n) = s(n) - x Q (n) = Q(n) - w(n); Module M3.3: Adaptively filter the transform residue Q(n) to obtain the optimal estimate of the quantization error; Specifically, Q(n) is used as the reference signal, Q(nM) is used as the input signal of the adaptive filter, and ε is obtained through the adaptive filter. s The best estimate of (n) M is a preset integer parameter, which is a value greater than the autocorrelation length of the noise w(n) and less than the autocorrelation length of the useful signal s(n).

10. The broadband noise adaptive suppression system according to claim 6, characterized in that: The module M4 comprises: According to the optimal estimate of the transform residual Q(n) and the quantization error Get the useful signal after denoising:

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