阅读说明：本技术 基于迭代平稳离散小波变换的水声信号处理方法和装置 (Underwater acoustic signal processing method and apparatus based on the steady wavelet transform of iteration ) 是由 杨雅涵 吴国俊 郝歌扬 杭栋栋 赵龙 于 2019-08-13 设计创作，主要内容包括：本发明提供一种基于迭代平稳离散小波变换的水声信号处理方法和装置。方法包括：将水声信号的离散信号样本作为待处理的含噪信号,利用平稳离散小波变换对含噪信号进行多尺度小波分解,得到含噪信号在不同频带下的小波系数；对小波系数进行特征分析,根据小波系数在分解尺度增加过程中所产生的特征变化选择阈值系数；先根据阈值系数对小波系数进行滤波处理,再对小波系数进行重构,得到去噪信号；计算去噪信号的信噪比以及两次迭代间信号的信噪比的差额；若差额小于预先设置的信噪比差额门限,则将该次去噪信号作为去噪完成的水声信号输出,否则,继续执行小波分解与重构。该方案能够有效去除水声信号中的噪声,适用于包含复杂背景噪声的海洋环境。(The present invention provides a kind of Underwater acoustic signal processing method and apparatus based on the steady wavelet transform of iteration.Method includes: to carry out multi-scale wavelet decomposition using the discrete signal samples of underwater sound signal as signals and associated noises to be processed to signals and associated noises using steady wavelet transform, obtain wavelet coefficient of the signals and associated noises under different frequency bands；Signature analysis is carried out to wavelet coefficient, generated changing features select threshold coefficient during decomposition scale increase according to wavelet coefficient；First wavelet coefficient is filtered according to threshold coefficient, then wavelet coefficient is reconstructed, obtains denoised signal；Calculate denoised signal signal-to-noise ratio and twice between iteration the signal-to-noise ratio of signal difference；If difference is limited less than pre-set poor signal to noise forehead, the underwater sound signal which completes as denoising is exported, otherwise, continues to execute wavelet function feedback.The program can effectively remove the noise in underwater sound signal, be suitable for inclusion in the marine environment of complex background noise.)

1. a kind of Underwater acoustic signal processing method based on the steady wavelet transform of iteration characterized by comprising

S1, the underwater sound signal of acquisition is sampled, obtains the discrete signal samples of underwater sound signal, and by the discrete signal sample This deposit signal data is concentrated；

S2, the data-signal for concentrating signal data are as signals and associated noises to be processed, using steady wavelet transform to containing Noise cancellation signal carries out multi-scale wavelet decomposition, obtains wavelet coefficient of the signals and associated noises under different frequency bands；

S3, to the wavelet coefficient carry out signature analysis, according to wavelet coefficient during decomposition scale increase generated spy Sign variation selection threshold coefficient；

S4, the wavelet coefficient is filtered according to the threshold coefficient；

S5, the wavelet coefficient by filtering processing is reconstructed, obtains denoised signal；

S6, the signal-to-noise ratio for calculating the denoised signal, and it is stored in signal-to-noise ratio data concentration；

S7, the signal-to-noise ratio data that deposit signal-to-noise ratio data is concentrated at first and the signal-to-noise ratio that secondary first deposit signal-to-noise ratio data is concentrated are calculated Difference between data

S8, judgementWhether it is less than pre-set poor signal to noise forehead to limit, if it has, then the denoising obtained through S5 is believed Number underwater sound signal output completed as denoising then will obtain if it has not, then first emptying the data that signal data is concentrated in S5 To denoised signal deposit empty after signal data concentrate, then proceed to execute S2.

2. the method according to claim 1, wherein the pre-set poor signal to noise forehead described in S8 limits Value be not more than 1.

3. the method according to claim 1, wherein described in S2 using steady wavelet transform to containing Noise cancellation signal carries out multi-scale wavelet decomposition, obtains wavelet coefficient of the signals and associated noises under different frequency bands, specific algorithm are as follows:

In formula, signal f (t) ∈ L²(R), j, k ∈ z, 1≤j≤J, C_J,kIt is 2^jScale coefficient under resolution ratio,For scale letter Number；D_j,kIt is 2^jWavelet coefficient under resolution ratio, ψ (t) are wavelet function.

4. the method according to claim 1, wherein being increased according to wavelet coefficient in decomposition scale described in S3 The selection threshold coefficient of changing features caused by during adding, comprising:

Go out the wavelet coefficient of denoised signal and interference noise generated spy during decomposition scale increase by experimental analysis Sign variation, selects threshold coefficient.

5. method according to claim 1-4, which is characterized in that further include: to pre-set poor signal to noise Forehead limit is adjusted, to adjust the number of iterations for carrying out Wavelet decomposing and recomposing.

6. a kind of Underwater acoustic signal processing device based on the steady wavelet transform of iteration characterized by comprising

Data acquisition module obtains the discrete signal samples of underwater sound signal, and will for sampling to the underwater sound signal of acquisition The discrete signal samples deposit signal data is concentrated；

Wavelet decomposition module, data-signal for concentrating signal data as signals and associated noises to be processed, using steadily from It dissipates wavelet transformation and multi-scale wavelet decomposition is carried out to signals and associated noises, obtain wavelet coefficient of the signals and associated noises under different frequency bands；

Data processing module increased according to wavelet coefficient in decomposition scale for carrying out signature analysis to the wavelet coefficient Generated changing features select threshold coefficient in journey；

The data processing module is also used to be filtered the wavelet coefficient according to the threshold coefficient；

Signal reconstruction module obtains denoised signal for the wavelet coefficient by filtering processing to be reconstructed；

Computing module for calculating the signal-to-noise ratio of the denoised signal, and is stored in signal-to-noise ratio data concentration；

The computing module is also used to calculate the signal-to-noise ratio data that deposit signal-to-noise ratio data is concentrated at first and is first stored in signal-to-noise ratio with secondary The difference between signal-to-noise ratio data in data set

Judgment module, for judgingWhether it is less than pre-set poor signal to noise forehead to limit；

Output module, for the underwater sound signal completed output will to be denoised.

7. device according to claim 6, which is characterized in that the value of the pre-set poor signal to noise forehead limit is not Greater than 1.

8. device according to claim 6, which is characterized in that wavelet decomposition module is used to utilize steady wavelet transform Multi-scale wavelet decomposition is carried out to signals and associated noises, obtains wavelet coefficient of the signals and associated noises under different frequency bands, specific algorithm are as follows:

In formula, signal f (t) ∈ L²(R), j, k ∈ z, 1≤j≤J, C_J,kIt is 2^jScale coefficient under resolution ratio,For scale letter Number；D_j,kIt is 2^jWavelet coefficient under resolution ratio, ψ (t) are wavelet function.

9. device according to claim 6, which is characterized in that data processing module is used to decompose ruler according to wavelet coefficient Degree generated changing features selection threshold coefficient during increasing, comprising:

Go out the wavelet coefficient of denoised signal and interference noise generated spy during decomposition scale increase by experimental analysis Sign variation, selects threshold coefficient.

10. according to the described in any item devices of claim 6-9, which is characterized in that the data processing module is also used to pre- The poor signal to noise forehead limit being first arranged is adjusted, to adjust the number of iterations for carrying out Wavelet decomposing and recomposing.

Technical field

The present embodiments relate to signal processing technology fields, more particularly to one kind to be based on the steady wavelet transform of iteration Underwater acoustic signal processing method and apparatus.

Background technique

The extensity of the rich and marine field of marine nature resources makes it in the resources domain and military strategy in China Field occupies consequence, as ocean is military and the rapid development of Underwater Acoustics Engineering technology, underwater sound signal Detection Techniques become It is badly in need of the technology improved in resources development and utilization and defense strategy level.Extracting from noisy complex background faint has It is the basic demand of Underwater Detection technology with signal.Only the complexity of marine environment is taken into account, novel sonar just has can The technical performance that can be optimal.Fibre optic hydrophone is as a kind of underwater sound signal detection system of good performance, in recent years in sea Bottom observation, antisubmarine, exploratory engineering of off-shore petroleum/gas reservoir, marine resources development etc. have more and more applications.Fibre optic hydrophone makes With will receive a variety of influence of noises in the process, such as internal noise, ocean complex environment noise and the towing flow noise of system External noise.The presence of these noises limits system to the detectivity and detection limit of small-signal, also will affect signal Effect on Detecting.Therefore, in the research and development application of fibre optic hydrophone, in order to reach preferable Effect on Detecting, it is necessary to make an uproar to above-mentioned Sound is performed corresponding processing and is removed.

Traditional digital signal processing method has the methods of adaptive-filtering, pattern-recognition, Fast Fourier Transform (FFT).These Classical signals processing method mainly has many advantages, such as that analysis bandwidth, operand is few, real-time is high and applied widely.Adaptively Filtering is suitable for inhibiting periodic noise interference, but the convergence of the method is poor, and when occurring simultaneously in signal, a variety of noises are dry Disturb, and periodic narrowband interference frequency range it is wider when, be difficult to restrain, therefore be not suitable for ring complicated in marine field The removal of border noise.Mode identification method utilizes the phase characteristic of signal, needs to accumulate a large amount of priori knowledge.Fast Fourier Filtering method is suitable for eliminating periodic narrow noise jamming, and filter result is larger for the dependence of filtering cycle threshold value, The selection of filtering cycle threshold value is relatively difficult in practical applications, and haves the shortcomings that frequency resolution is low.To underwater sound signal When being monitored and handling, there are ambient sea noise interference problems, especially the nearby radiated noise interference problem of ship, and two Person partly overlaps in frequency domain bandwidth, is then random signal on temporal signatures.Therefore, although the above method is signal processing In common method, but the marine environment of complex background noise can not be suitable for inclusion in well.On the other hand, with the modern times The operand of the development of submarine signal detection technique, detected signal is significantly increased, and the requirement to frequency resolution is also increasingly Height, traditional signal detection technique can not adapt to new detection demand.

Summary of the invention

In view of the above drawbacks of the prior art, the embodiment of the present invention provides a kind of based on the steady wavelet transform of iteration Underwater acoustic signal processing method and apparatus.

In a first aspect, the embodiment of the present invention provides a kind of Underwater acoustic signal processing side based on the steady wavelet transform of iteration Method, comprising:

S1, the underwater sound signal of acquisition is sampled, obtains the discrete signal samples of underwater sound signal, and by the discrete letter Number sample deposit signal data is concentrated；

S2, the data-signal for concentrating signal data utilize steady wavelet transform as signals and associated noises to be processed Multi-scale wavelet decomposition is carried out to signals and associated noises, obtains wavelet coefficient of the signals and associated noises under different frequency bands；

S3, signature analysis is carried out to the wavelet coefficient, it is produced during decomposition scale increase according to wavelet coefficient Changing features select threshold coefficient；

S4, the wavelet coefficient is filtered according to the threshold coefficient；

S5, the wavelet coefficient by filtering processing is reconstructed, obtains denoised signal；

S6, the signal-to-noise ratio for calculating the denoised signal, and it is stored in signal-to-noise ratio data concentration；

S7, the signal-to-noise ratio data that deposit signal-to-noise ratio data is concentrated at first and the letter that secondary first deposit signal-to-noise ratio data is concentrated are calculated It makes an uproar than the difference between data

S8, judgementWhether it is less than pre-set poor signal to noise forehead to limit, if it has, then going what is obtained through S5 The underwater sound signal output that noise cancellation signal is completed as denoising, if it has not, then first emptying the data that signal data is concentrated, then will be in S5 Obtained in denoised signal deposit empty after signal data concentrate, then proceed to execute S2.

In method as described above, the value of the pre-set poor signal to noise forehead limit described in S8 is not more than 1.

In method as described above, being carried out using steady wavelet transform to signals and associated noises described in S2 is multiple dimensioned Wavelet decomposition obtains wavelet coefficient of the signals and associated noises under different frequency bands, specific algorithm are as follows:

In formula, signal f (t) ∈ L²(R), j, k ∈ z, 1≤j≤J, C_J,kIt is 2^jScale coefficient under resolution ratio,For ruler Spend function；D_j,kIt is 2^jWavelet coefficient under resolution ratio, ψ (t) are wavelet function.

It is generated during decomposition scale increase according to wavelet coefficient described in S3 in method as described above Changing features select threshold coefficient, comprising:

The wavelet coefficient for going out denoised signal and interference noise by experimental analysis is produced during decomposition scale increase Changing features, select threshold coefficient.

Method as described above, further includes: pre-set poor signal to noise forehead limit is adjusted, is carried out with adjustment small The number of iterations of Wave Decomposition reconstruct.

Second aspect, the embodiment of the present invention provide a kind of Underwater acoustic signal processing dress based on the steady wavelet transform of iteration It sets, comprising:

Data acquisition module obtains the discrete signal samples of underwater sound signal for sampling to the underwater sound signal of acquisition, And discrete signal samples deposit signal data is concentrated；

Wavelet decomposition module, data-signal for concentrating signal data is as signals and associated noises to be processed, using flat Steady wavelet transform carries out multi-scale wavelet decomposition to signals and associated noises, obtains wavelet systems of the signals and associated noises under different frequency bands Number；

Data processing module increases according to wavelet coefficient in decomposition scale for carrying out signature analysis to the wavelet coefficient The selection threshold coefficient of changing features caused by during adding；

The data processing module is also used to be filtered the wavelet coefficient according to the threshold coefficient；

Signal reconstruction module obtains denoised signal for the wavelet coefficient by filtering processing to be reconstructed；

Computing module for calculating the signal-to-noise ratio of the denoised signal, and is stored in signal-to-noise ratio data concentration；

The computing module is also used to calculate the signal-to-noise ratio data that deposit signal-to-noise ratio data is concentrated at first and time first deposit is believed It makes an uproar than the difference between the signal-to-noise ratio data in data set

Judgment module, for judgingWhether it is less than pre-set poor signal to noise forehead to limit；

Output module, for the underwater sound signal completed output will to be denoised.

Device as described above, wherein the value of the pre-set poor signal to noise forehead limit is not more than 1.

Device as described above, wherein wavelet decomposition module be used for using steady wavelet transform to signals and associated noises into Row multi-scale wavelet decomposition obtains wavelet coefficient of the signals and associated noises under different frequency bands, specific algorithm are as follows:

In formula, signal f (t) ∈ L²(R), j, k ∈ z, 1≤j≤J, C_J,kIt is 2^jScale coefficient under resolution ratio,For ruler Spend function；D_j,kIt is 2^jWavelet coefficient under resolution ratio, ψ (t) are wavelet function.

Device as described above, wherein data processing module is used for according to wavelet coefficient during decomposition scale increase Generated changing features select threshold coefficient, comprising:

The wavelet coefficient for going out denoised signal and interference noise by experimental analysis is produced during decomposition scale increase Changing features, select threshold coefficient.

Device as described above, wherein the data processing module is also used to limit pre-set poor signal to noise forehead It is adjusted, to adjust the number of iterations for carrying out Wavelet decomposing and recomposing.

Technical solution provided in an embodiment of the present invention, using the discrete signal samples of underwater sound signal as noisy letter to be processed Number, multi-scale wavelet decomposition is carried out to signals and associated noises using steady wavelet transform, obtains signals and associated noises under different frequency bands Wavelet coefficient；To wavelet coefficient carry out signature analysis, according to wavelet coefficient during decomposition scale increase generated spy Sign variation selection threshold coefficient；Wavelet coefficient is filtered according to threshold coefficient；To the wavelet systems by filtering processing Number is reconstructed, and obtains denoised signal；Calculate denoised signal signal-to-noise ratio and twice between iteration the signal-to-noise ratio of signal difference； It is if difference is limited less than pre-set poor signal to noise forehead, the secondary denoised signal is defeated as the underwater sound signal of denoising completion Out, otherwise, wavelet function feedback is continued to execute, until obtaining the underwater sound signal that denoising is completed.The program can effectively remove Noise in underwater sound signal, especially suitable for the marine environment comprising complex background noise, and treatment effeciency is high.

Detailed description of the invention

In order to illustrate the technical solution of the embodiments of the present invention more clearly, below will be to needed in the embodiment attached Figure is briefly described, it should be apparent that, the accompanying drawings in the following description is only some embodiments of the present invention, general for this field For logical technical staff, without creative efforts, it is also possible to obtain other drawings based on these drawings.

Fig. 1 is the Underwater acoustic signal processing method flow provided in an embodiment of the present invention based on the steady wavelet transform of iteration Figure；

Fig. 2 is the frequency domain of the anechoic tank, underwater sound signal of 1000Hz in Application Example provided by the invention before treatment Figure；

Fig. 3 is the frequency domain of the anechoic tank, underwater sound signal of 1000Hz in Application Example provided by the invention after treatment Figure；

Fig. 4 is the Underwater acoustic signal processing apparatus structure provided in an embodiment of the present invention based on the steady wavelet transform of iteration Schematic diagram；

Fig. 5 is that the Underwater acoustic signal processing equipment provided in an embodiment of the present invention based on the steady wavelet transform of iteration is implemented The structural schematic diagram of example.

Specific embodiment

To make the object, technical solutions and advantages of the present invention clearer, below in conjunction with attached drawing of the invention, to this hair Technical solution in bright is clearly and completely described, it is clear that and described embodiments are some of the embodiments of the present invention, and The embodiment being not all of.Based on the embodiments of the present invention, those of ordinary skill in the art are not making creative work Under the premise of every other embodiment obtained, shall fall within the protection scope of the present invention.

Usually contained in underwater sound signal in marine environment source multiplicity, Unknown Distribution noise, a variety of noise informations with Echo signal frequency aliasing.Using wavelet analysis as the modern signal detection technique of representative, because it is in terms of nonstationary random response With unique algorithm advantage, it is widely used in terms of noise remove.Due to more resolution features of wavelet transformation With time-frequency local characteristics, the time and frequency domain analysis of multiresolution, frequency point can be carried out to any details for focusing on signal Resolution is high, and therefore, wavelet transformation is applied in Underwater acoustic signal processing by the present invention.Fig. 1 is provided in an embodiment of the present invention is based on The Underwater acoustic signal processing method flow diagram of the steady wavelet transform of iteration, as shown in Figure 1, the method for the present embodiment may include S1~S8.

S1, the underwater sound signal of acquisition is sampled, obtains the discrete signal samples of underwater sound signal, and by the discrete letter Number sample deposit signal data is concentrated.

S2, the data-signal for concentrating signal data utilize steady wavelet transform as signals and associated noises to be processed Multi-scale wavelet decomposition is carried out to signals and associated noises, obtains wavelet coefficient of the signals and associated noises under different frequency bands.

Multi-scale wavelet decomposition is carried out to signals and associated noises using steady wavelet transform described in the step, is contained Wavelet coefficient of the noise cancellation signal under different frequency bands, specific algorithm can be with are as follows:

In formula, signal f (t) ∈ L²(R), j, k ∈ z, 1≤j≤J, C_J,kIt is 2^jScale coefficient under resolution ratio,For ruler Spend function；D_j,kIt is 2^jWavelet coefficient under resolution ratio, ψ (t) are wavelet function.

S3, signature analysis is carried out to the wavelet coefficient, it is produced during decomposition scale increase according to wavelet coefficient Changing features select threshold coefficient.

According to wavelet coefficient, generated changing features select threshold during decomposition scale increase described in the step Value coefficient is usually decomposing ruler by the wavelet coefficient that experimental analysis goes out denoised signal and interference noise in a particular application Degree generated changing features during increasing, select threshold coefficient.For example, the selection of filtering threshold coefficient, passes through experiment point Analysis it is found that with decomposition scale increase, the information dispersion of signal and interference noise is in different wavelet coefficients, if selecting High filtering threshold is easy to weaken signal strength, if the filtering threshold that selection is too low, excessive interference noise information can be protected It stays, it is therefore desirable to according to the Wavelet Coefficients Characteristic of signal and interference noise with the variation of decomposition scale, select threshold coefficient.

S4, the wavelet coefficient is filtered according to the threshold coefficient.

S5, the wavelet coefficient by filtering processing is reconstructed, obtains denoised signal.

S6, the signal-to-noise ratio for calculating the denoised signal, and it is stored in signal-to-noise ratio data concentration.

S7, the signal-to-noise ratio data that deposit signal-to-noise ratio data is concentrated at first and the letter that secondary first deposit signal-to-noise ratio data is concentrated are calculated It makes an uproar than the difference between data

S8, judgementWhether it is less than pre-set poor signal to noise forehead to limit, if it has, then going what is obtained through S5 The underwater sound signal output that noise cancellation signal is completed as denoising, if it has not, then first emptying the data that signal data is concentrated, then will be in S5 Obtained in denoised signal deposit empty after signal data concentrate, then proceed to execute S2.

For example, the threshold coefficient according to selection is filtered wavelet coefficient, and the wavelet systems that will be obtained after filtering Number reconstruct, obtains filtered underwater sound signal, calculates the Signal to Noise Ratio (SNR) of filtered underwater sound signal_i(i indicates wavelet transformation weight The number of iterations of structure).After repeating S2 to S6, the difference of the signal-to-noise ratio of signal between iteration twice is calculatedWhen its is small When poor signal to noise forehead limits σ, terminate this iterative process.The value of pre-set poor signal to noise forehead limit described in the step Usually more than 1.In a particular application, pre-set poor signal to noise forehead can be limited and is adjusted, carried out with adjustment small The number of iterations of Wave Decomposition reconstruct.

The technical solution that the present invention implements to provide is based on Multiscale Wavelet Decomposition, threshold denoising and wavelet reconstruction, to water Acoustical signal carries out the iterative processing of Wavelet decomposing and recomposing, realizes the noise remove of underwater sound signal.The Multiscale Wavelet Decomposition Threshold denoising processing, when threshold denoising, threshold are carried out using steady wavelet transform, and to the wavelet coefficient after decomposition Value coefficient is adaptively adjusted according to the coefficient of wavelet decomposition feature of signals and associated noises, is controlled with poor signal to noise forehead limit value small The number of iterations of Wave Decomposition reconstruct.The program can effectively remove the noise in underwater sound signal, be suitable for inclusion in complex background and make an uproar The marine environment of sound.

Given below is an Application Example of technical solution provided in an embodiment of the present invention, in conjunction with specific formula pair The implementation process of technical solution of the present invention is described further:

If signal f (t) ∈ L²(R), the Mallat fast algorithm of wavelet transformation is carried out to signal, wherein scaling function isWavelet function is ψ (t):

Wherein

Then have

F (t)=A_jF (t)=A_j+1f(t)+D_j+1f(t) (2)

WhereinD_j+1F (t)=∑ kD_j+1,kψ_j+1,k(t)

Iterative calculation can obtain

D_j,kComponent (1≤j≤J) for f (t) k-th of period in j-th of frequency range is high frequency section (discrete details system Number), C_J,kFor the component of k-th period of the f (t) in the J+1 frequency range, i.e. low frequency part (discrete approximation coefficient).

The wavelet coefficient of signal and interference noise ingredient is analyzed with the variation of decomposition scale, obtains the distribution characteristics of signal. According to the distribution characteristics of signal, suitable filtering threshold is chosen in generated variation during decomposition scale increases, after decomposition Detail coefficients D_j,kWith approximation coefficient C_J,kIt is handled, then to treated, wavelet coefficient carries out wavelet inverse transformation, that is, reconstructs, Filtered underwater sound signal is obtained, the Signal to Noise Ratio (SNR) of filtered underwater sound signal is calculated_i(i indicates the iteration of wavelet transformation reconstruct Number).Due to the complexity of underwater sound signal structure, the wavelet function feedback of single, be not enough to by ambient noise information with have With unpack, for the selection of filtering threshold, the decomposition scale of effect is very few, be not enough to by ambient noise information with have A part of useful information can be also removed when removing noise information in small echo if the decomposition scale of effect is excessive with Signal separator Contribution in coefficient causes signal weak.Therefore it changes to the step of above-mentioned wavelet decomposition, threshold filter, wavelet reconstruction Generation operation calculates the difference of the signal-to-noise ratio of signal between iteration twiceWhen it is less than poor signal to noise forehead limit σ, terminate to change For process.Iterative process is terminated as threshold value using poor signal to noise forehead limit σ, σ value cannot be greater than 1, and the excessive reservation of too big meeting is made an uproar Acoustic intelligence.Can not be too small, if too small will increase the number of iterations, and it may cause inspection and do not measure signal.

Fig. 2 is the frequency domain of the anechoic tank, underwater sound signal of 1000Hz in Application Example provided by the invention before treatment Figure, Fig. 3 are the frequency domain figure of the anechoic tank, underwater sound signal of 1000Hz in Application Example provided by the invention after treatment.By scheming 3 as can be seen that the interference noise of low frequency and high band in Fig. 2 is filtered out substantially.

In conclusion technical solution provided by the invention is by carrying out steady wavelet transform for signals and associated noises, being based on The threshold coefficient of Wavelet Coefficients Characteristic variation is chosen, wavelet coefficient threshold filters and the iterative processing of the processes such as reconstruct, realizes The processing and extraction of underwater sound signal.Solve asking for a variety of noise informations and echo signal frequency aliasing under the complex environment of ocean Topic.Technical solution provided by the invention can also be adjusted in time according to the singularity and wavelet scale resolution characteristic of signal and noise The number of iterations of whole relevant treatment scale, correction threshold filter factor and small echo processing, has preferable adaptivity.

Fig. 4 is the Underwater acoustic signal processing apparatus structure provided in an embodiment of the present invention based on the steady wavelet transform of iteration Schematic diagram.As shown in figure 4, the device of the present embodiment may include: data acquisition module 401, wavelet decomposition module 402, data Processing module 403, signal reconstruction module 404, computing module 405, judgment module 406 and output module 407.Wherein, data are adopted Collection module 401 obtains the discrete signal samples of underwater sound signal, and will be described discrete for sampling to the underwater sound signal of acquisition Sample of signal is stored in signal data and concentrates；The data-signal that wavelet decomposition module 402 is used to concentrate signal data is as wait locate The signals and associated noises of reason carry out multi-scale wavelet decomposition to signals and associated noises using steady wavelet transform, obtain signals and associated noises and exist Wavelet coefficient under different frequency bands；Data processing module 403 is used to carry out signature analysis to the wavelet coefficient, according to wavelet systems Number generated changing features during decomposition scale increase select threshold coefficient；Data processing module 403 is also used to basis The threshold coefficient is filtered the wavelet coefficient；Signal reconstruction module 404 is used for by the small of filtering processing Wave system number is reconstructed, and obtains denoised signal；Computing module 405 is used to calculate the signal-to-noise ratio of the denoised signal, and is stored in letter It makes an uproar than in data set；Computing module 405 is also used to calculate the signal-to-noise ratio data that deposit signal-to-noise ratio data is concentrated at first and first deposits with secondary Enter the difference between the signal-to-noise ratio data of signal-to-noise ratio data concentrationJudgment module 406 is for judgingWhether it is less than Pre-set poor signal to noise forehead limit；Output module 407 is used to denoise the underwater sound signal output completed.

Device as described above, wherein the value of the pre-set poor signal to noise forehead limit is not more than 1；Small wavelength-division It solves module 402 to be used to carry out multi-scale wavelet decomposition to signals and associated noises using steady wavelet transform, obtains signals and associated noises and exist Wavelet coefficient under different frequency bands, specific algorithm are as follows:

In formula, signal f (t) ∈ L²(R), j, k ∈ z, 1≤j≤J, C_J,kIt is 2^jScale coefficient under resolution ratio,For Scaling function；D_j,kIt is 2^jWavelet coefficient under resolution ratio, ψ (t) are wavelet function.

Device as described above, wherein data processing module 403 is used to increase process in decomposition scale according to wavelet coefficient In generated changing features select threshold coefficient, comprising:

The wavelet coefficient for going out denoised signal and interference noise by experimental analysis is produced during decomposition scale increase Changing features, select threshold coefficient.

Device as described above, wherein data processing module 403 be also used to limit pre-set poor signal to noise forehead into Row adjustment, to adjust the number of iterations for carrying out Wavelet decomposing and recomposing.

The Underwater acoustic signal processing device based on the steady wavelet transform of iteration of the present embodiment can be used for executing Fig. 1 institute Show the method for embodiment of the method, realization principle is similar with technical effect to be achieved, and details are not described herein.

Fig. 5 is that the Underwater acoustic signal processing equipment provided in an embodiment of the present invention based on the steady wavelet transform of iteration is implemented The structural schematic diagram of example.As shown in figure 5, should include at least based on the Underwater acoustic signal processing equipment of the steady wavelet transform of iteration One processor 501 (such as CPU), memory 503 and at least one communication bus 504, for realizing the connection between device Communication.Processor 501 is for executing the executable module stored in memory 503, such as computer program.Memory 503 can Can include high-speed random access memory (RAM:Random Access Memory), it is also possible to further include non-labile storage Device (non-volatile memory), for example, at least a magnetic disk storage.

In some embodiments, memory 503 stores program 505, and program 505 can be executed with device 501 processed, this A program include execute a kind of Underwater acoustic signal processing method based on the steady wavelet transform of iteration, this method comprises:

S1, the underwater sound signal of acquisition is sampled, obtains the discrete signal samples of underwater sound signal, and by the discrete letter Number sample deposit signal data is concentrated；

S2, the data-signal for concentrating signal data utilize steady wavelet transform as signals and associated noises to be processed Multi-scale wavelet decomposition is carried out to signals and associated noises, obtains wavelet coefficient of the signals and associated noises under different frequency bands；

S3, signature analysis is carried out to the wavelet coefficient, it is produced during decomposition scale increase according to wavelet coefficient Changing features select threshold coefficient；

S4, the wavelet coefficient is filtered according to the threshold coefficient；

S5, the wavelet coefficient by filtering processing is reconstructed, obtains denoised signal；

S6, the signal-to-noise ratio for calculating the denoised signal, and it is stored in signal-to-noise ratio data concentration；

S7, the signal-to-noise ratio data that deposit signal-to-noise ratio data is concentrated at first and the letter that secondary first deposit signal-to-noise ratio data is concentrated are calculated It makes an uproar than the difference between data

S8, judgementWhether it is less than pre-set poor signal to noise forehead to limit, if it has, then going what is obtained through S5 The underwater sound signal output that noise cancellation signal is completed as denoising, if it has not, then first emptying the data that signal data is concentrated, then will be in S5 Obtained in denoised signal deposit empty after signal data concentrate, then proceed to execute S2.

Preferably, the value of the pre-set poor signal to noise forehead limit described in S8 is not more than 1, to pre-set letter It makes an uproar and is adjusted than difference thresholding, to adjust the number of iterations for carrying out Wavelet decomposing and recomposing.

Multi-scale wavelet decomposition is carried out to signals and associated noises using steady wavelet transform described in S2, is obtained noisy Wavelet coefficient of the signal under different frequency bands, specific algorithm are as follows:

In formula, signal f (t) ∈ L²(R), j, k ∈ z, 1≤j≤J, C_J,kIt is 2^jScale coefficient under resolution ratio,For Scaling function；D_j,kIt is 2^jWavelet coefficient under resolution ratio, ψ (t) are wavelet function.

Described in S3 according to wavelet coefficient, generated changing features select threshold value during decomposition scale increase Coefficient, comprising:

The wavelet coefficient for going out denoised signal and interference noise by experimental analysis is produced during decomposition scale increase Changing features, select threshold coefficient.

Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention., rather than its limitations；To the greatest extent Pipe present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that: its according to So be possible to modify the technical solutions described in the foregoing embodiments, or to some or all of the technical features into Row equivalent replacement；And these are modified or replaceed, various embodiments of the present invention technology that it does not separate the essence of the corresponding technical solution The range of scheme.