CN117014034A - Pulse interference suppression method and related equipment - Google Patents
Pulse interference suppression method and related equipment Download PDFInfo
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Abstract
The invention provides a pulse interference suppression method and related equipment, and relates to the technical field of wireless communication, wherein the method comprises the following steps: performing pulse interference detection on a signal sequence to be processed based on a Myriad filter, and determining pulse interference points; carrying out local smooth restoration on the pulse interference points based on an improved local weighted regression algorithm to obtain a smooth signal sequence; the improved local weighted regression algorithm is based on the local weighted regression algorithm, and a robust repair weight diagonal matrix constructed based on a probability density function of fitting Gaussian distribution is introduced; the probability density function of the fitted gaussian distribution is determined according to the amplitude of the non-pulsed interference points in the signal sequence to be processed. According to the invention, the calculation complexity is reduced through pulse interference detection and local smooth repair, and the noise reduction efficiency is improved; meanwhile, a robust restoration weight diagonal array is introduced based on the LOESS method, so that the noise reduction effect is improved.
Description
Technical Field
The present invention relates to the field of wireless communications technologies, and in particular, to a method for suppressing impulse interference and related devices.
Background
One of the important problems faced by wireless communication systems is noise and interference during transmission, which can have a significant impact on the wireless communication system. Where the long duration and high incidence of impulse interference severely degrades the receiver signal quality, resulting in a significant degradation of the performance of the communication system. Therefore, it is necessary to apply the impulse interference suppression technique at the transmitting end and the receiving end.
In the related art, the robust local regression smoothing (Robust Locally Regression Smoothing, RLRS) algorithm not only maintains the good noise reduction effect of the local weighted regression (Locally Weighted Regression, LOESS) algorithm on gaussian background noise, but also improves the robustness on impulse interference through a robust restoration weight matrix, and has good noise reduction effect on random impulse interference and background noise.
However, the RLRS method needs to recalculate the weighted least square solution of the fitting parameters after updating the fitting weight robust repair coefficient each time, so that the matrix inversion operation process consumes a large amount of calculation resources, and the noise reduction efficiency is low.
Therefore, it is necessary to provide a pulse interference suppression method having a good noise reduction effect and high noise reduction efficiency for random pulse interference.
Disclosure of Invention
The invention provides a pulse interference suppression method and related equipment, which are used for solving the defect that the noise reduction efficiency and the noise reduction effect in the prior art cannot be simultaneously met, and realizing good noise reduction effect and high noise reduction efficiency.
The invention provides a pulse interference suppression method, which comprises the following steps:
performing pulse interference detection on a signal sequence to be processed based on a Myriad filter, and determining pulse interference points;
Carrying out local smooth restoration on the pulse interference points based on an improved local weighted regression algorithm to obtain a smooth signal sequence; the improved local weighted regression algorithm is based on a local weighted regression algorithm, and a robust repair weight diagonal array constructed based on a probability density function of fitting Gaussian distribution is introduced; the probability density function of the fitting Gaussian distribution is determined according to the amplitude of the non-pulse interference points in the signal sequence to be processed.
In some embodiments, the detecting impulse interference of the signal sequence to be processed based on the Myriad filter, determining the impulse interference point includes:
inputting the signal sequence to be processed into the Myriad filter, obtaining a loss parameter which minimizes the loss function of the Myriad filter, and taking the loss parameter as a pulse interference check threshold;
and taking signal points with the amplitude larger than the pulse interference checking threshold value in the signal sequence to be processed as the pulse interference points.
In some embodiments, the inputting the signal sequence to be processed into the Myriad filter, obtaining a loss parameter that minimizes a loss function of the Myriad filter, includes:
Initializing the loss parameters to be the amplitude of any signal point in the signal sequence to be processed, and determining a loss function value corresponding to the amplitude of each signal point;
sorting the loss function values corresponding to the amplitudes of all the signal points in the signal sequence to be processed from small to large, and determining the amplitudes of the two signal points corresponding to the two forefront loss function values;
searching between the amplitudes of the two signal points based on a dichotomy to obtain a loss parameter which minimizes the loss function.
In some embodiments, the performing local smoothing repair on the pulse interference point based on the improved local weighted regression algorithm to obtain a smoothed signal sequence includes:
traversing the positions of all pulse interference points to obtain the smooth signal sequence; at the location of each pulse interference point:
taking the position of the pulse interference point as the midpoint of the sliding window;
carrying out weighted regression on the signal points in the sliding window based on an improved local weighted regression algorithm to obtain smooth signal points; each signal point in the sliding window corresponds to one smooth signal point;
and replacing the pulse interference points in the sliding window with the corresponding smooth signal points.
In some embodiments, the weighted regression of the signal points within the sliding window based on the improved local weighted regression algorithm to obtain smoothed signal points includes:
determining a regression coefficient matrix;
and carrying out weighted regression on the signal points in the sliding window according to the regression coefficient matrix to obtain smooth signal points.
In some embodiments, the determining the regression coefficient matrix includes:
determining a position parameter matrix, a local data matrix and a fitting weight diagonal matrix according to the signal points in the sliding window;
and determining the regression coefficient matrix according to the position parameter matrix, the local data matrix, the fitting weight diagonal matrix and the robust restoration weight diagonal matrix.
The invention also provides a pulse interference suppression device, which comprises:
the detection module is used for carrying out pulse interference detection on the signal sequence to be processed based on the Myriad filter and determining pulse interference points;
the repairing module is used for carrying out local smooth repairing on the pulse interference points based on an improved local weighted regression algorithm to obtain a smooth signal sequence; the improved local weighted regression algorithm is based on a local weighted regression algorithm, and a robust repair weight diagonal array constructed based on a probability density function of fitting Gaussian distribution is introduced; the probability density function of the fitting Gaussian distribution is determined according to the amplitude of the non-pulse interference points in the signal sequence to be processed.
The invention also provides an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the impulse interference suppression method as described above when executing the computer program.
The present invention also provides a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements a method of impulse interference suppression as described in any of the above.
The invention also provides a computer program product comprising a computer program which when executed by a processor implements a method of impulse interference suppression as described in any one of the above.
According to the pulse interference suppression method and the related equipment, the pulse interference detection and the local smooth restoration replace the restoration process of repeatedly iterating and adjusting the weight matrix in the RLRS method, so that the calculation complexity is reduced, and the noise reduction efficiency is improved; the amplitude distribution condition of signals and noise and the influence of impulse interference on a signal sequence to be processed are considered, the amplitude distribution of signal points in the signal sequence to be processed, from which the impulse interference is removed, is fitted into Gaussian distribution, so that a robust restoration weight diagonal array is calculated, and the noise reduction effect is improved.
Drawings
In order to more clearly illustrate the invention or the technical solutions of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are some embodiments of the invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic flow chart of a method for suppressing impulse interference according to an exemplary embodiment of the present invention;
FIG. 2 is a second flow chart of a method for suppressing impulse interference according to an exemplary embodiment of the present invention;
FIG. 3 is a schematic diagram of pulse interference point detection according to the present invention;
FIG. 4 is a third flow chart of a method for suppressing impulse interference according to an exemplary embodiment of the present invention;
FIG. 5 is a flowchart of a method for suppressing impulse interference according to an exemplary embodiment of the present invention;
fig. 6 is a schematic diagram of a pulse interference time domain amplitude when the signal-to-interference ratio is-20 dB;
FIG. 7 is a graph showing the comparison of bit error rate performance of different methods when the signal-to-interference ratio is-20 dB;
fig. 8 is a schematic diagram of a pulse interference time domain amplitude when the signal-to-interference ratio is-30 dB;
FIG. 9 is a graph showing the comparison of bit error rate performance of different methods when the signal-to-interference ratio is-30 dB;
FIG. 10 is a graph showing the comparison of bit error rate performance of the method for suppressing impulse interference according to the present invention under different impulse interference conditions;
fig. 11 is a schematic structural diagram of a pulse interference suppression apparatus provided by the present invention;
fig. 12 is a schematic structural diagram of an electronic device provided by the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is apparent that the described embodiments are some embodiments of the present invention, not all embodiments. The embodiments of the present invention and the features in the embodiments may be combined with each other without collision. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention.
It is further intended that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The term "at least one" in the present invention means one or more, and "a plurality" means two or more. The terms "first," "second," "third," "fourth," and the like in this disclosure, if any, are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order.
In embodiments of the invention, words such as "exemplary" or "such as" are used to mean serving as an example, instance, or illustration. Any embodiment or design described herein as "exemplary" or "e.g." in an embodiment should not be taken as preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary" or "such as" is intended to present related concepts in a concrete fashion.
One of the important problems faced by wireless communication systems is noise and interference during transmission, which can have a significant impact on the wireless communication system. The main interference source in the long wave communication is the astronomical interference mainly caused by lightning, and the power line communication is artificially interfered by an ignition system, a heavy power line, a current switch and the like, and the interference generally has the characteristics of high amplitude, wide bandwidth, rapid appearance and the like, and can cause the deterioration of signal quality and the reduction of transmission capacity. Such disturbances do not follow gaussian noise characteristics and need to be characterized in a pulse modeling manner, and are therefore also referred to as impulse disturbances.
The long duration and high incidence of impulse interference severely degrades the receiver signal quality, resulting in a significant degradation of the performance of the communication system. Therefore, it is necessary to suppress the impulse interference by applying the impulse interference suppression technique at the transmitting end and the receiving end.
The conventional pulse interference suppression method can be divided into three types: the first is a nonlinear preprocessing method represented by clipping and blanking, which is simple to implement on hardware and software, but has limited performance, and is difficult to adjust in real time according to the change of the characteristics of the channel and noise. The second type is a filtering method represented by robust local regression smoothing, median filtering, etc., which is developed from a low-pass linear filtering method for filtering background noise, such as local weighted regression, mean filtering, etc., and has a suppression effect on impulse interference deviating from a normal data sequence, but a filtering distortion problem easily occurs when data changes rapidly. The third type is a sparse reconstruction algorithm represented by compressed sensing, and the method can achieve a good noise reduction effect by reconstructing and eliminating the impulse interference at a receiving end by utilizing the sparse characteristic of the impulse interference in a time domain, but has high requirement on the sparsity of the noise.
With the development of the technology, the LOESS algorithm is a non-parametric method for local regression analysis, and can be used for smoothing noisy data samples by fitting a curve conforming to the overall trend, thereby achieving the purpose of denoising. The LOESS algorithm can be applied to denoising of white gaussian noise in a wireless communication system, but the existence of impulse interference can cause the LOESS algorithm to generate smooth distortion at the impulse interference position so as to not recover the original data.
In order to strengthen the robustness of the LOESS algorithm to impulse interference, a robust repair weight matrix related to smooth residual errors is introduced on the basis of the LOESS algorithm to develop an RLRS algorithm. The RLRS algorithm is a filtering method based on outlier data detection and iterative weighted correction, and the robustness to random impulse noise is improved through iterative adjustment of a weight matrix, so that the whole filter is of nonlinear filtering characteristics.
The RLRS algorithm not only keeps the good noise reduction effect of the LOESS algorithm on Gaussian background noise, but also improves the robustness on impulse interference through a robust restoration weight matrix, and has good noise reduction effect on random impulse interference and background noise. However, the RLRS method needs to recalculate the weighted least square solution of the fitting parameters after updating the fitting weight robust repair coefficient each time, so that the matrix inversion operation process consumes a large amount of calculation resources, and the noise reduction efficiency is low.
Therefore, it is necessary to provide a pulse interference suppression method having a good noise reduction effect and high noise reduction efficiency for random pulse interference.
Fig. 1 is a schematic flow chart of a method for suppressing impulse interference according to an exemplary embodiment of the present invention, as shown in fig. 1, the method includes:
step 110, pulse interference detection is performed on the signal sequence to be processed based on the Myriad filter, and a pulse interference point is determined.
Specifically, the signal sequence to be processed is a signal sequence containing background noise and impulse interference received by the receiving end. The signal sequence to be processed is marked as Y= { Y l L=1, 2, … L }, where y l And (3) representing any signal point in the signal sequence to be processed, wherein L is the signal length of the signal sequence to be processed, namely the number of signal points contained in the signal sequence to be processed.
The Myriad filter is a robust nonlinear filter developed on the basis of an average filter and a median filter, is very sensitive to abnormal values, and can effectively inhibit impulse interference. And using the Myriad filter for pulse interference detection by utilizing the robustness of the Myriad filter to pulse interference, identifying pulse interference points in the signal sequence Y to be processed, and counting the total number of the pulse interference points as H.
Step 120, carrying out local smoothing restoration on the pulse interference points based on an improved local weighted regression algorithm to obtain a smooth signal sequence; the improved local weighted regression algorithm is based on the local weighted regression algorithm, and a robust repair weight diagonal matrix constructed based on a probability density function of fitting Gaussian distribution is introduced; the probability density function of the fitted gaussian distribution is determined according to the amplitude of the non-pulsed interference points in the signal sequence to be processed.
Specifically, after identifying the pulse interference points in the signal sequence Y to be processed, the pulse interference points are removed from the signal sequence Y to be processed, so as to obtain a signal sequence without the pulse interference pointsBecause the number of the pulse interference points is H, the signal length of the signal sequence Y to be processed is L, and the signal sequence is +.>Representing the signal sequence +.>Any one of the signal points.
Due to signal sequencesDoes not contain impulse interference points, thus signal sequence +.>The amplitude distribution of the L-H signal points in the middle can be fitted with a gaussian distribution. For signal sequences->And (3) calculating the mean value and variance of the amplitude values of the L-H signal points, and determining the parameters of the probability density function of the fit Gaussian distribution, thereby obtaining the probability density function of the fit Gaussian distribution.
The probability density function is expressed as follows:
wherein f PDF () Representing probability density functions, σ representing signal sequencesVariance of amplitude correspondence of signal points in (a), μ represents signal sequence +.>The average value corresponding to the amplitude of the signal point in the middle, and x represents the input quantity of the probability density function.
The diagonal line elements in the diagonal matrix of the robust repair weights are the robust repair weights delta corresponding to the signal points n I.e. fitting a probability density function f of gaussian distribution PDF Probability density values at the signal points.
Robust repair weight delta n The expression of (2) is as follows:
in delta n Represented at |y n Probability density value at y n I represents the absolute value of the amplitude of the signal point and σ represents the signal sequenceVariance of amplitude correspondence of signal points in (a), μ represents signal sequence +.>The average value corresponding to the amplitude of the medium signal point.
The improved LOESS algorithm is to introduce a robust repair weight diagonal matrix based on the LOESS method. After the pulse interference points are determined, the pulse interference points are locally and smoothly repaired based on an improved LOESS algorithm, and a smooth signal sequence is obtained.
According to the pulse interference suppression method provided by the embodiment, the repeated iterative adjustment weight matrix restoration process in the RLRS method is replaced by pulse interference detection and local smooth restoration, so that the calculation complexity is reduced, and the noise reduction efficiency is improved; the amplitude distribution condition of signals and noise and the influence of impulse interference on a signal sequence to be processed are considered, the amplitude distribution of signal points in the signal sequence to be processed, from which the impulse interference is removed, is fitted into Gaussian distribution, so that a robust restoration weight diagonal array is calculated, and the noise reduction effect is improved.
Referring to fig. 2, fig. 2 is a second flowchart of a method for suppressing impulse interference according to an exemplary embodiment of the invention. This example is a further illustration of the foregoing examples, and is mainly illustrative: and carrying out pulse interference detection on the signal sequence to be processed based on the Myriad filter, and determining a specific process of a pulse interference point. As shown in fig. 2, the method for suppressing impulse interference provided in this embodiment includes:
step 210, inputting the signal sequence to be processed into the Myriad filter, obtaining a loss parameter minimizing the loss function of the Myriad filter, and taking the loss parameter as a pulse interference check threshold.
Specifically, the signal sequence to be processed is denoted as y= { Y l L=1, 2, … L }, where y l And (3) representing any signal point in the signal sequence to be processed, wherein L is the signal length of the signal sequence to be processed.
The expression for the loss function of the Myriad filter is as follows:
wherein D is sum Represents the loss function, beta represents the loss parameter, K represents the scale parameter, and is the maximum value of the signal amplitude in the signal sequence Y to be processed, namely K=max|Y|, |y i The I represents the amplitude of any signal point in the signal sequence Y to be processed, and the L represents the signal length of the signal sequence Y to be processed.
Inputting the signal sequence Y to be processed into Myriad filter, namely substituting signal points in the signal sequence Y to be processed into a loss function to determine a loss parameter beta * Loss parameter beta * Is the loss parameter that minimizes the loss function. Will lose the parameter beta * As a pulse disturbance check threshold τ.
Loss parameter beta * The expression of (2) is as follows:
wherein beta is * Represents a loss parameter that minimizes the loss function, β represents a loss parameter, K represents a scale parameter, |y i The I represents the amplitude of any signal point in the signal sequence Y to be processed, and the L represents the signal length of the signal sequence Y to be processed.
And 220, taking signal points with the amplitude larger than the pulse interference checking threshold value in the signal sequence to be processed as pulse interference points.
Specifically, the amplitude of a signal point in the signal sequence Y to be processed is compared with a pulse interference checking threshold tau, and whether the amplitude of the signal point in the signal sequence Y to be processed is larger than the pulse interference checking threshold tau is judged.
Fig. 3 is a schematic diagram of pulse interference point detection provided by the present invention, as shown in fig. 3, when the amplitude of a signal point in a signal sequence Y to be processed is less than or equal to a pulse interference check threshold τ, the signal point is used as a non-pulse interference point. And under the condition that the amplitude of a signal point in the signal sequence Y to be processed is larger than the pulse interference checking threshold tau, taking the signal point as a pulse interference point and marking the position of the pulse interference point.
The set of the positions of the pulse interference points is recorded as U= { U h |h=1, 2, … H }, where U represents the set of locations where all impulse interference points are located, U h Indicating the position of the H pulse interference point, and H indicates the total number of the pulse interference points.
According to the pulse interference suppression method, the loss parameter with the minimum loss function is used as the pulse interference detection threshold, and the signal point with the amplitude larger than the pulse interference detection threshold in the signal sequence to be processed is used as the pulse interference point, so that the correct detection probability is improved, and the false alarm probability is kept at a lower level.
Referring to fig. 4, fig. 4 is a third flowchart of a method for suppressing impulse interference according to an exemplary embodiment of the invention. This example is a further illustration of the foregoing examples, and is mainly illustrative: inputting the signal sequence to be processed into the Myriad filter, and obtaining a loss parameter which minimizes the loss function of the Myriad filter. As shown in fig. 4, the method for suppressing impulse interference provided in this embodiment includes:
step 410, initializing the loss parameter to be the amplitude of any signal point in the signal sequence to be processed, and determining the loss function value corresponding to the amplitude of each signal point.
Step 420, sorting the loss function values corresponding to the magnitudes of all the signal points in the signal sequence to be processed from small to large, and determining the magnitudes of the two signal points corresponding to the two forefront loss function values.
Step 430, searching between the magnitudes of the two signal points based on the dichotomy, obtains a loss parameter that minimizes the loss function.
Specifically, it willThe loss parameter is initialized to the amplitude of any signal point in the signal sequence to be processed, namely the loss parameter beta= |y l |,y l E Y, l=1, 2, …, L, the loss function becomes:
wherein D is sum Represents a loss function, beta represents a loss parameter, K represents a scale parameter, and is the maximum value of signal amplitude in a signal sequence Y to be processed, |y i The I represents the amplitude of any signal point in the signal sequence Y to be processed, and the I Y is as follows l The I represents the amplitude of any signal point in the signal sequence Y to be processed, and the L represents the signal length of the signal sequence Y to be processed.
Calculating the amplitude y of each signal point l Loss function value D corresponding to I sum . The loss function value D corresponding to the amplitude of all signal points in the signal sequence Y to be processed sum Determining the amplitudes of two signal points corresponding to the forefront two loss function values according to the sequence from small to large, and marking the amplitudes of the two signal points as beta m1 And beta m2 . At beta m1 And beta m2 The two-way search is used to make the loss function value D sum Minimum beta * 。
According to the pulse interference suppression method provided by the embodiment of the invention, the range of the loss parameters is locked at the amplitude of the signal points in the signal sequence to be processed, the range of the loss parameters is reduced to be between the amplitudes of the two signal points through the loss function value corresponding to the amplitude of each signal point, and finally the loss parameters which minimize the loss function are searched between the amplitudes of the two signal points by a dichotomy, so that the accurate acquisition of the loss parameters beta is realized in a mode of gradually reducing the range of the loss parameters.
Referring to fig. 5, fig. 5 is a flowchart illustrating a method for suppressing impulse interference according to an exemplary embodiment of the present invention. This example is a further illustration of the foregoing examples, and is mainly illustrative: and carrying out local smoothing restoration on the pulse interference points based on an improved local weighted regression algorithm to obtain a smooth signal sequence. As shown in fig. 5, the method for suppressing impulse interference provided in this embodiment includes:
step 510, taking the position of the pulse interference point as the middle point of the sliding window.
Specifically, the sliding window includes N signal points, and the midpoint of the sliding window is the position u where the pulse interference point is located h . Optionally, N is an odd number, so that the number of signal points on the left and right sides of the pulse interference point is the same.
Signal sequences within a sliding window are noted asThe position corresponding to the signal point in the sliding window is marked as +.>
And step 520, performing weighted regression on the signal points in the sliding window based on the improved LOESS algorithm to obtain smooth signal points.
Specifically, weighting regression is performed on signal points in the sliding window based on an improved LOESS algorithm, namely different regression coefficients are added to the signal points in the sliding window so as to reduce the amplitude of pulse interference points in the sliding window, increase the amplitude of non-pulse interference points in the sliding window, obtain smooth signal points and realize smooth restoration of the signal points in the sliding window. Each signal point in the sliding window corresponds to a smooth signal point.
In step 530, the impulse interference points in the sliding window are replaced by corresponding smoothed signal points.
Specifically, after obtaining the smoothed signal points, the pulse interference points in the sliding window are replaced by the corresponding smoothed signal points, namely, the signal sequence in the sliding windowIs the midpoint y of (2) (N+1)/2 Sequence Y consisting of smooth signal points GLOESS Is defined by a central point of the lens.
Step 540, determine whether to traverse all locations where the pulse interference points are located.
Specifically, whether all the positions of the pulse interference points are traversed is judged, namely whether the sequence number H of the current pulse interference point is equal to H is judged. If the sequence number H of the current pulse interference point is not equal to H, determining that all the pulse interference points are not traversed, and executing step 550; if the sequence number H of the current pulse interference point is equal to H, it is determined that all the positions of the pulse interference points have been traversed, and step 560 is performed.
At step 550, the sliding window slides to the position where the next impulse interference point is located.
Specifically, after the sliding window slides to the position of the next pulse interference point, steps 510 to 540 are repeatedly performed.
Step 560, a smoothed signal sequence is obtained.
Specifically, after local smoothing repair is performed on all pulse interference points, that is, after the signal points corresponding to the positions of all pulse interference points are replaced, the replaced signal sequence is used as a smoothed signal sequence, and the smoothed signal sequence is recorded as
The method for suppressing impulse interference provided in this embodiment is implemented at the location of each impulse interference point by traversing the locations of all the impulse interference points: carrying out weighted regression on the signal points in the sliding window based on an improved local weighted regression algorithm to obtain smooth signal points; the pulse interference points in the sliding window are replaced by the corresponding smooth signal points, so that the signal sequence to be processed is converted into the smooth signal sequence, and good pulse interference suppression is further realized.
Optionally, this embodiment is a further description of the foregoing embodiment, mainly illustrating: and carrying out weighted regression on the signal points in the sliding window based on the improved LOESS algorithm to obtain a specific process of smoothing the signal points. The pulse interference suppression method provided by the embodiment comprises the following steps:
Determining a regression coefficient matrix;
and carrying out weighted regression on the signal points in the sliding window according to the regression coefficient matrix to obtain smooth signal points.
Specifically, the regression coefficient alpha is determined based on a weighted least square method GLOESS . After obtaining the regression coefficient matrix alpha GLOESS Then, the position parameter matrix X is multiplied by alpha GLOESS I.e. according to regression coefficient matrix alpha GLOESS Weighting regression is carried out on the signal points in the sliding window to obtain a matrix of N rows and 1 columns, and the matrix of N rows and 1 columns is used as a local smoothing sequence Y GLOESS ∈R N×1 I.e. locally smoothed sequence Y GLOESS The interior contains N smooth signal points.
Local smoothing sequence Y GLOESS The expression of (2) is as follows:
Y GLOESS ＝Xα GLPESS
wherein Y is GLPESS Representing a local smoothing sequence, X representing a position parameter matrix, alpha GLOESS Representing a regression coefficient matrix.
According to the pulse interference suppression method provided by the embodiment, weighted regression of signal points in the sliding window is realized through the regression coefficient matrix, and good pulse interference suppression is further realized.
Optionally, this embodiment is a further description of the foregoing embodiment, mainly illustrating: and determining a specific process of the regression coefficient matrix. The pulse interference suppression method provided by the embodiment comprises the following steps:
determining a position parameter matrix, a local data matrix and a fitting weight diagonal matrix according to signal points in the sliding window;
Determining a regression coefficient matrix according to the position parameter matrix, the local data matrix, the fitting weight diagonal matrix and the robust restoration weight diagonal matrix;
and obtaining smooth signal points according to the regression coefficient matrix and the position parameter matrix.
Specifically, the position parameter matrix X is a matrix of N rows and M+1 columns, namely X εR N×(M+1) M is the polynomial order, and the value of M is selected according to the compromise of the simulation error code performance and the calculation complexity. The nth row and mth column elements in the position parameter matrix X are the positions X of the nth signal point in the sliding window n To the power of m-1, i.e
The local data matrix Y 'is a matrix of N rows and 1 columns, i.e., Y' ∈R N×1 Each row element in the local data matrix Y' is a corresponding signal point in the sliding window, namely, the nth row element is the nth signal point Y n 。
The fitting weight diagonal matrix W is a matrix of N rows and N columns, namely W epsilon R N×N The diagonal line element of the fitting weight diagonal matrix W is the fitting weight omega corresponding to the position of the nth signal point in the sliding window n 。
Fitting weight omega n The expression of (2) is as follows:
wherein omega is n Representing the fitting weight, x corresponding to the position of the nth signal point s Representing the filtering position, i.e. the position u of the impulse interference point in the sliding window h ，d(x s ) Represents x s Furthest from other signal points within the sliding window, i.e. d (x s )＝max{|x s -x i |}，，x i Indicating where other signal points than the impulse interference point are located within the sliding window.
And obtaining a regression coefficient matrix according to the position parameter matrix X, the local data matrix Y', the fitting weight diagonal matrix W and the robust restoration weight diagonal matrix delta. The expression of the regression coefficient matrix is as follows:
α GLOESS ＝(X T WΔX) -1 X T WΔY′
wherein alpha is GLOESS Represents a regression coefficient matrix, X represents a position parameter matrix, W represents a fitting weight diagonal matrix, delta represents a robust repair weight diagonal matrix, delta epsilon R N×N The superscript T denotes a transpose operation, and Y' denotes a local data matrix.
According to the impulse interference suppression method provided by the embodiment of the invention, the position parameter matrix, the local data matrix and the fitting weight diagonal matrix are determined according to the signal points in the sliding window; and a robust repair weight diagonal matrix is added on the basis of the position parameter matrix, the local data matrix and the fitting weight diagonal matrix to determine a regression coefficient matrix, so that the accurate determination of the regression coefficient matrix is realized, and the subsequent local smooth repair is further facilitated.
The technical effects of the present invention are described below by data comparison of one example.
The simulation uses an input signal, for example, a minimum shift keying (Minimum Shift Keying, MSK) signal, and the MSK signal is generated by using a quadrature modulation method. The pulse interference signal is modeled by using a Bernoulli Gaussian model, and the signal received by the receiving end is the sum of an MSK signal, the pulse interference signal and a Gaussian white noise signal. The pulse interference suppression method provided by the invention processes the received signal, and then calculates the error rate after MSK demodulation.
Fig. 6 is a schematic diagram of pulse interference time domain amplitude when the signal-to-interference ratio provided by the invention is-20 dB, fig. 7 is a comparative diagram of error rate performance of different methods when the signal-to-interference ratio provided by the invention is-20 dB, the method (Myriad-GLOESS) provided by the invention is compared with the error rate performance of other methods (RLRS, clipping algorithm and no pulse interference) when the pulse interference condition is p=0.05 and sinr= -20dB, and the schematic diagram of the pulse interference used in simulation is shown in fig. 6. Where p represents the probability of impulse interference occurrence, SINR represents the power ratio of the useful signal to impulse interference, i.e. the signal-to-interference ratio, E b /N 0 Representing the power ratio of the useful signal to white gaussian noise, i.e. the signal to noise ratio.
As shown in fig. 7, the error rate performance of the proposed method is superior to RLRS algorithm and conventional clipping algorithm. When the signal-to-noise ratio is 14dB, the bit error rate of the method provided by the invention can reach 10 -7 . When the error rate is 10 -5 When the method provided by the invention is used, the signal to noise ratio is only 1dB different from the interference-free condition.
Fig. 8 is a schematic diagram of pulse interference time domain amplitude when the signal-to-interference ratio provided by the invention is-30 dB, fig. 9 is a comparative diagram of error rate performance of different methods when the signal-to-interference ratio provided by the invention is-30 dB, the error rate performance of the method (Myriad-GLOESS) provided by the invention and other comparative methods when the pulse interference condition is p=0.05 and sinr= -30dB is compared, and the schematic diagram of pulse interference used in simulation is shown in fig. 8.
As shown in fig. 9, when the error rate is 10 -5 When the method provided by the invention is used, the signal to noise ratio is only 0.5dB different from the interference-free condition. Since the RLRS algorithm improves robustness to impulse interference through an iteration process, performance of the RLRS algorithm is greatly affected by the number of iterations, and when the number of iterations is insufficient, an error code platform appears, so that system performance is deteriorated, as shown in fig. 9. The method provided by the invention introduces a fitting Gao Silu bar repair matrix without repairing through iteration, so that the method is more robust to pulse interference.
Fig. 10 is a graph showing the comparison of bit error rate performance of the pulse interference suppression method (Myriad-GLOESS) provided by the present invention under different pulse interference conditions. As can be seen from fig. 10, the method provided by the present invention has better bit error rate performance under the condition that the probability p of pulse interference occurrence and the signal-to-interference ratio SINR are lower, when the probability p of pulse interference occurrence is lower, the number of interfered signal samples is smaller, and when the signal-to-interference ratio is lower, the difference between the pulse interference and the signal in amplitude is larger, and the pulse interference is easier to detect in the first step of pulse noise inspection. When p=0.01 and SINR= -30dB, the error code curve of the method provided by the invention gradually coincides with the error code curve under the condition of no interference, which proves that the method effectively inhibits impulse interference.
The simulation result shows that the error code performance of the method provided by the invention is obviously improved relative to the RLRS algorithm and the amplitude limiting algorithm, and the method is more robust to pulse interference.
From the aspect of time complexity analysis, the RLRS algorithm needs to carry out regression smoothing on each signal sample point, improves the robustness to pulse interference through iterative operation, and consumes a great amount of time. The method provided by the invention detects the pulse interference position by using the Myriad algorithm sensitive to the pulse interference, and only carries out smooth restoration at the pulse interference position, thereby greatly reducing the operation amount, improving the robustness to the pulse interference by introducing a new robust restoration weight diagonal array, and not needing iterative operation, so that the operation time is greatly saved, and the calculation complexity is greatly reduced.
Both algorithms are based on a least square method, the least square operation times of the RLRS algorithm is L.I, wherein L is the length of a signal sample point, I is the iteration times, the least square operation times of the method provided by the invention is H, wherein H is the number of pulse interference detected by a Myriad module, and the H is less than L because of the time domain sparsity of common pulse interference. Therefore, the method provided by the invention effectively reduces the time complexity and improves the noise reduction efficiency.
The following describes the apparatus for suppressing impulse interference provided by the present invention, and the apparatus for suppressing impulse interference described below and the method for suppressing impulse interference described above can be referred to correspondingly.
Fig. 11 is a schematic structural diagram of a pulse interference suppression device provided by the present invention, and as shown in fig. 11, the pulse interference suppression device provided by the present invention includes: a detection module 1110 and a repair module 1120. Wherein:
the detection module 1110 is configured to perform pulse interference detection on a signal sequence to be processed based on a Myriad filter, and determine a pulse interference point;
the repair module 1120 is configured to perform local smoothing repair on the pulse interference points based on an improved local weighted regression algorithm, so as to obtain a smoothed signal sequence; the improved local weighted regression algorithm is based on a local weighted regression algorithm, and a robust repair weight diagonal array constructed based on a probability density function of fitting Gaussian distribution is introduced; the probability density function of the fitting Gaussian distribution is determined according to the amplitude of the non-pulse interference points in the signal sequence to be processed.
In some embodiments, the detection module 1110 includes: the device comprises a first acquisition sub-module and a second acquisition sub-module; wherein:
The first acquisition submodule is used for inputting the signal sequence to be processed into the Myriad filter, acquiring a loss parameter which minimizes a loss function of the Myriad filter, and taking the loss parameter as a pulse interference check threshold;
and the second acquisition submodule is used for taking signal points with the amplitude larger than the pulse interference check threshold value in the signal sequence to be processed as the pulse interference points.
In some embodiments, the first acquisition submodule includes: the device comprises a first determining unit, a second determining unit and an obtaining unit; wherein:
a first determining unit, configured to initialize the loss parameter to an amplitude value of any signal point in the signal sequence to be processed, and determine a loss function value corresponding to the amplitude value of each signal point;
the second determining unit is used for sorting the loss function values corresponding to the amplitudes of all the signal points in the signal sequence to be processed from small to large, and determining the amplitudes of the two signal points corresponding to the two forefront loss function values;
and the acquisition unit is used for searching between the amplitudes of the two signal points based on a dichotomy and acquiring a loss parameter which minimizes the loss function.
In some embodiments, the repair module 1120 includes a third acquisition sub-module; the third acquisition submodule is used for traversing the positions of all pulse interference points and acquiring the smooth signal sequence; the third acquisition submodule includes: a sliding unit, a weighting unit and a replacing unit; wherein:
The sliding unit is used for taking the position of the pulse interference point as the midpoint of the sliding window;
the weighting unit is used for carrying out weighted regression on the signal points in the sliding window based on an improved local weighted regression algorithm to obtain smooth signal points; each signal point in the sliding window corresponds to one smooth signal point;
and the replacing unit is used for replacing the pulse interference points in the sliding window with the corresponding smooth signal points.
In some embodiments, the weighting unit comprises: determining a subunit and acquiring the subunit; wherein:
a determining subunit configured to determine a regression coefficient matrix;
and the acquisition subunit is used for carrying out weighted regression on the signal points in the sliding window according to the regression coefficient matrix to acquire smooth signal points.
In some embodiments, the determining subunit is specifically configured to:
determining a position parameter matrix, a local data matrix and a fitting weight diagonal matrix according to the signal points in the sliding window;
and determining the regression coefficient matrix according to the position parameter matrix, the local data matrix, the fitting weight diagonal matrix and the robust restoration weight diagonal matrix.
It should be noted that, the pulse interference suppression device provided by the present invention can implement all the method steps implemented by the method embodiment and achieve the same technical effects, and the same parts and beneficial effects as those of the method embodiment in the present embodiment are not described in detail herein.
Fig. 12 is a schematic structural diagram of an electronic device according to the present invention, and as shown in fig. 12, the electronic device may include: processor 1210, communication interface (Communications Interface), 1220, memory 1230 and communication bus 1240, wherein processor 1210, communication interface 1220 and memory 1230 communicate with each other via communication bus 1240. Processor 1210 may invoke logic instructions in memory 1230 to perform a glitch suppression method comprising: performing pulse interference detection on a signal sequence to be processed based on a Myriad filter, and determining pulse interference points; carrying out local smooth restoration on the pulse interference points based on an improved local weighted regression algorithm to obtain a smooth signal sequence; the improved local weighted regression algorithm is based on a local weighted regression algorithm, and a robust repair weight diagonal array constructed based on a probability density function of fitting Gaussian distribution is introduced; the probability density function of the fitting Gaussian distribution is determined according to the amplitude of the non-pulse interference points in the signal sequence to be processed.
In addition, the logic instructions in the memory 1230 described above may be implemented in the form of software functional units and sold or used as a stand-alone product, stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
In another aspect, the present invention also provides a computer program product, the computer program product comprising a computer program, the computer program being storable on a non-transitory computer readable storage medium, the computer program, when executed by a processor, being capable of performing the method of impulse interference suppression provided by the methods described above, the method comprising: performing pulse interference detection on a signal sequence to be processed based on a Myriad filter, and determining pulse interference points; carrying out local smooth restoration on the pulse interference points based on an improved local weighted regression algorithm to obtain a smooth signal sequence; the improved local weighted regression algorithm is based on a local weighted regression algorithm, and a robust repair weight diagonal array constructed based on a probability density function of fitting Gaussian distribution is introduced; the probability density function of the fitting Gaussian distribution is determined according to the amplitude of the non-pulse interference points in the signal sequence to be processed.
In yet another aspect, the present invention also provides a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, is implemented to perform the method of impulse interference suppression provided by the above methods, the method comprising: performing pulse interference detection on a signal sequence to be processed based on a Myriad filter, and determining pulse interference points; carrying out local smooth restoration on the pulse interference points based on an improved local weighted regression algorithm to obtain a smooth signal sequence; the improved local weighted regression algorithm is based on a local weighted regression algorithm, and a robust repair weight diagonal array constructed based on a probability density function of fitting Gaussian distribution is introduced; the probability density function of the fitting Gaussian distribution is determined according to the amplitude of the non-pulse interference points in the signal sequence to be processed.
The apparatus embodiments described above are merely illustrative, wherein the elements illustrated as separate elements may or may not be physically separate, and the elements shown as elements may or may not be physical elements, may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art will understand and implement the present invention without undue burden.
From the above description of the embodiments, it will be apparent to those skilled in the art that the embodiments may be implemented by means of software plus necessary general hardware platforms, or of course may be implemented by means of hardware. Based on this understanding, the foregoing technical solution may be embodied essentially or in a part contributing to the prior art in the form of a software product, which may be stored in a computer readable storage medium, such as ROM/RAM, a magnetic disk, an optical disk, etc., including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method described in the respective embodiments or some parts of the embodiments.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (10)
1. A method of impulse interference suppression, comprising:
performing pulse interference detection on a signal sequence to be processed based on a Myriad filter, and determining pulse interference points;
carrying out local smooth restoration on the pulse interference points based on an improved local weighted regression algorithm to obtain a smooth signal sequence; the improved local weighted regression algorithm is based on a local weighted regression algorithm, and a robust repair weight diagonal array constructed based on a probability density function of fitting Gaussian distribution is introduced; the probability density function of the fitting Gaussian distribution is determined according to the amplitude of the non-pulse interference points in the signal sequence to be processed.
2. The method of claim 1, wherein the performing pulse interference detection on the signal sequence to be processed based on the Myriad filter to determine the pulse interference point comprises:
inputting the signal sequence to be processed into the Myriad filter, obtaining a loss parameter which minimizes the loss function of the Myriad filter, and taking the loss parameter as a pulse interference check threshold;
and taking signal points with the amplitude larger than the pulse interference checking threshold value in the signal sequence to be processed as the pulse interference points.
3. The method of impulse interference suppression according to claim 2, wherein inputting the signal sequence to be processed into the Myriad filter, obtaining a loss parameter that minimizes a loss function of the Myriad filter, comprises:
initializing the loss parameters to be the amplitude of any signal point in the signal sequence to be processed, and determining a loss function value corresponding to the amplitude of each signal point;
sorting the loss function values corresponding to the amplitudes of all the signal points in the signal sequence to be processed from small to large, and determining the amplitudes of the two signal points corresponding to the two forefront loss function values;
Searching between the amplitudes of the two signal points based on a dichotomy to obtain a loss parameter which minimizes the loss function.
4. The method of claim 1, wherein the performing local smoothing repair on the impulse interference points based on the improved local weighted regression algorithm to obtain a smoothed signal sequence comprises:
traversing the positions of all pulse interference points to obtain the smooth signal sequence; at the location of each pulse interference point:
taking the position of the pulse interference point as the midpoint of the sliding window;
carrying out weighted regression on the signal points in the sliding window based on an improved local weighted regression algorithm to obtain smooth signal points; each signal point in the sliding window corresponds to one smooth signal point;
and replacing the pulse interference points in the sliding window with the corresponding smooth signal points.
5. The method of impulse interference suppression according to claim 4, wherein said weighting regression is performed on signal points within the sliding window based on an improved local weighting regression algorithm to obtain smoothed signal points, comprising:
determining a regression coefficient matrix;
and carrying out weighted regression on the signal points in the sliding window according to the regression coefficient matrix to obtain smooth signal points.
6. The method of impulse interference suppression according to claim 5, wherein said determining a regression coefficient matrix comprises:
determining a position parameter matrix, a local data matrix and a fitting weight diagonal matrix according to the signal points in the sliding window;
and determining the regression coefficient matrix according to the position parameter matrix, the local data matrix, the fitting weight diagonal matrix and the robust restoration weight diagonal matrix.
7. A pulse interference suppression apparatus, comprising:
the detection module is used for carrying out pulse interference detection on the signal sequence to be processed based on the Myriad filter and determining pulse interference points;
the repairing module is used for carrying out local smooth repairing on the pulse interference points based on an improved local weighted regression algorithm to obtain a smooth signal sequence; the improved local weighted regression algorithm is based on a local weighted regression algorithm, and a robust repair weight diagonal array constructed based on a probability density function of fitting Gaussian distribution is introduced; the probability density function of the fitting Gaussian distribution is determined according to the amplitude of the non-pulse interference points in the signal sequence to be processed.
8. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the impulse interference suppression method according to any one of claims 1 to 6 when executing the computer program.
9. A non-transitory computer readable storage medium having stored thereon a computer program, wherein the computer program when executed by a processor implements the impulse interference suppression method according to any one of claims 1 to 6.
10. A computer program product comprising a computer program, characterized in that the computer program, when executed by a processor, implements the impulse interference suppression method according to any one of claims 1 to 6.
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