CN114036458B - Non-cooperative underwater acoustic signal time-frequency information acquisition method - Google Patents
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Abstract
The invention discloses a time-frequency information acquisition method of non-cooperative underwater acoustic signals, which comprises the steps of segmenting an acquired underwater acoustic FSK signal, windowing segmented signals, and writing all the segmented signals after windowing into a matrix form to serve as observation vectors; dividing a frequency set of the Fourier transform matrix, and establishing the Fourier transform matrix as an observation matrix; and cutting off the matrix obtained by multiplying the observation matrix and the estimated time-frequency information by the dimension of the observation vector, establishing an optimization problem by combining constraint penalty terms with inversion fitting of the observation vector, and solving the established optimization problem for the time-frequency information. According to the invention, the optimization of the time-frequency diagram can be realized without realizing accurate sampling point synchronization with an original modulation signal, the time-frequency diagram is suitable for being used under the non-cooperative condition, and even if a divided frequency set does not contain carrier frequency, the frequency resolution of the time-frequency diagram can be effectively improved through inversion fitting and sparse constraint, so that the energy of the time-frequency diagram is more concentrated.
Description
Technical Field
The invention belongs to the technical field of underwater acoustic countermeasure, and relates to a non-cooperative underwater acoustic signal time-frequency information acquisition method.
Background
With more and more sensor networks deployed in an underwater environment to execute tasks such as remote control, diving assisting operation, intelligent monitoring, marine data acquisition and the like, research on recognition technology of underwater acoustic communication signals becomes an urgent need for underwater acoustic countermeasure. The underwater sound FSK is an underwater sound communication method with strong anti-interference capability and wide application. Under the non-cooperative condition, modulation and identification of the underwater sound FSK are getting more and more attention, and on the basis of identification, accurate estimation of parameters such as bandwidth, pulse width, carrier frequency and the like can be further realized, even blind demodulation can be realized, and the method can play an important role in the military field.
Various solutions have been proposed by the relevant scholars in the modulation recognition of the underwater acoustic FSK signal, such as instantaneous information, high-order accumulation amount, cyclic accumulation amount, etc., but these methods are susceptible to the influence of the underwater acoustic channel, resulting in poor recognition performance. With the development of the deep learning theory, the modulation recognition is realized by connecting a time-frequency diagram of the underwater sound FSK signal with a deep learning network. However, in the intra-class recognition of the underwater sound FSK, since the underwater sound FSK signal contains a plurality of carrier frequencies and the pulse width of each symbol is limited, the time-frequency diagram obtained by the conventional STFT method has serious energy divergence, and cannot distinguish the carrier frequencies and cannot guarantee the recognition performance. So the time-frequency diagram of the underwater sound FSK needs to be optimized. The optimization problem of the time-frequency diagram in the underwater sound field is rarely studied, and some documents exist in the radio to provide solutions to the optimization problem of the time-frequency diagram, such as obtaining the time-frequency diagram by using methods of EMD (empirical mode decomposition), cohen, wigner Gao Jiepu and the like. However, these methods have problems such as aliasing of modes, cross-over items and the like.
Disclosure of Invention
Aiming at the prior art, the technical problem to be solved by the invention is to provide a non-cooperative underwater sound signal time-frequency information acquisition method for solving the problem of serious energy divergence of a time-frequency image obtained by a traditional STFT method, so that the energy of the time-frequency image can be more concentrated, and the recognition rate of the underwater sound FSK signal is further improved.
In order to solve the technical problems, the non-cooperative underwater acoustic signal time-frequency information acquisition method provided by the invention comprises the following steps:
S1, assuming that the acquired observation signals are Y, the observation signals are FSK signals, segmenting the Y according to a short-time Fourier transform method, wherein the number of sampling points of each segment of signals is L, the number of overlapping sampling points of adjacent signal segments is Q, and the number of segments is M; windowing each segmented signal by using a window function, and representing the segmented signals into a matrix form and taking the matrix form as an observation vector Z;
S2, uniformly dividing a frequency set into F= [ 0] according to the sampling frequency, wherein F 1,...,fL-1],fL is equal to the sampling frequency, and establishing a Fourier transform matrix
S3, assuming that the time-frequency information to be obtained isBuilding a time-frequency information estimation model Z=AG+V, wherein V is noise;
s4, increasing the dividing frequency set density of the Fourier transform matrix A to obtain Estimated time-frequency information/>The matrix dimension of AG is j×m;
s5, cutting AG according to the dimension of the observation vector Z, and then carrying out inversion fitting on the AG and the value in Z;
S6, establishing an optimization problem Wherein/>For truncated matrix,/>Is a unitary matrix,/>For zero matrix, lambda 1 and lambda 2 are coefficients of constraint penalty items, and the specific gravity of element point sparsity and line sparsity in G is controlled respectively;
s7, substituting the function with the characteristic of approximate l 0 norms into Instead of G 0 and G 2,0: let G j,m be the element in the time-frequency information G to be estimated, the function be/>When delta approaches zero, assume thatLet ζ j＝||G(j,:)||2, j=0,..j-1,/>F δ(G)≈||G||0,Bδ(G)≈||G||2,0;
S8 substituting F δ (G) and B δ (G) in S7 into Conversion of estimation of time-frequency information G into a pairIs solved;
And S9, solving the equation established in the step S8 by using a gradient descent method to obtain time-frequency information G.
Further, the observation signal is FH signal.
Further, in S4J takes a value of 2 to 5 times L.
Further, in S9, the solution of the equation established in S8 by using the gradient descent method is specifically that the time-frequency information G is obtained:
S91, assuming the gradient of the equation in S8 is Taking a set of descending sequences delta= [ delta 0,...,δi,δi+1, ] for the parameter delta, avoiding convergence to the locally optimal solution, firstly setting a normal number delta 0, setting the step size mu 0 =1 of gradient descent, and assuming i=1;
S92, calculating />
S93, calculatingCalculation/> from the results of G (i)
S94, updating mu i-1＝βμi-1, wherein beta is a proportionality coefficient, and beta epsilon (0, 1);
s95, when And returning to the step S93 when μ i-1 does not reach the set iteration termination condition; otherwise, continuing the next step;
S96, update G (i),δi＝αδi-1,μi =1, i=i+1, where α is a scale factor, α e (0, 1), and then return to step S92;
S97, obtaining estimated time-frequency information G until delta i meets the set iteration termination condition.
Compared with the prior art, the invention has the beneficial effects that:
The invention adopts a segmentation method similar to STFT (short time Fourier transform) to realize the reconstruction of time-frequency information. Firstly, segmenting acquired observation signals, forming a matrix by the segmented signals as an observation vector, taking a Fourier transformation matrix as the observation matrix, adding constraint penalty items, and realizing optimization of a time-frequency diagram by joint sparse solution. In order to further concentrate the energy of the time-frequency diagram of the underwater sound FSK, improve the grid division density of the Fourier transform matrix, fit the observation vector in an inversion form, ensure the optimized performance of the time-frequency diagram, and aim to achieve higher recognition rate on the underwater sound FSK recognition method based on the time-frequency diagram.
In the case of in-class recognition of the acoustic FSK, there is a high demand for the time-frequency pattern, and it is necessary to distinguish carrier frequencies corresponding to the symbols of the acoustic FSK. According to the invention, the observation matrix is replaced by the Fourier transform matrix, the observation signal is segmented by adopting the STFT strategy, two constraint penalty items are added, and the inversion fitting underwater sound FSK time-frequency information acquisition method is provided. The proposed method is not only applicable to the underwater sound FSK signal but also applicable to the underwater sound FH signal, and when the acquired signal contains a plurality of FSK or FH signals, only the coefficients of two constraint punishment items need to be adjusted according to actual conditions, and the method is also applicable.
The invention has the following two advantages: (1) The method can realize the optimization of the time-frequency diagram without realizing accurate sampling point synchronization with the original modulation signal, and is suitable for being used under the non-cooperative condition. (2) Even if the divided frequency set does not contain carrier frequency, the frequency resolution of the time-frequency diagram can be effectively improved through inversion fitting and sparse constraint, so that the energy of the time-frequency diagram is more concentrated.
Drawings
FIG. 1 is a flow chart of a non-cooperative underwater sound FSK time-frequency chart optimizing method provided by the invention;
FIG. 2 is a schematic illustration of a pool experiment for testing provided by the present invention;
FIG. 3 is a graph of pool channel impulse response for testing provided by the present invention;
FIG. 4 is a block diagram of time-frequency information obtained by processing an acquired FSK signal using a conventional STFT method;
fig. 5 is a time-frequency information obtained by processing the collected FSK signal according to the method provided by the present invention.
Detailed Description
The invention is further described below with reference to the drawings and specific examples.
Referring to fig. 1, the present invention includes the steps of:
S1, supposing that the acquired observation signal is Y, segmenting the Y according to a segmentation method of STFT, wherein the length of each segment of signal is L, the number of overlapping sampling points of adjacent signal segments is Q, and the number of segments is M. To reduce spectral leakage, we window each segmented signal with a window function, representing the segmented signal as a matrix and as an observation vector Z;
S2, dividing a frequency set into F= [ F 1,...,fL]T,fL ] which is equal to the sampling frequency, and establishing a Fourier transform matrix
S3, supposing that the time-frequency information to be obtained isA time-frequency information estimation model z=ag+v can be built, where V is noise.
S4, in order to further concentrate the energy of the time-frequency diagram, firstly, the dividing frequency set density of the Fourier transform matrix A is increased to obtainEstimated time-frequency information/>The matrix dimension of AG is j×m.
S5, cutting off AG according to the dimension of the observation vector Z, and then carrying out inversion fitting on the AG and the value in Z. In order to ensure the performance of inversion fitting, J can be set to 2-5 times L in general.
S6, because the time-frequency information G of the underwater sound FSK has the characteristics of point sparsity and line sparsity, the optimization problem can be established according to S5Wherein/>In order to truncate the matrix,Is a unitary matrix,/>For zero matrix, λ 1 and λ 2 control the specific gravity of element point sparsity and line sparsity in G, respectively.
S7, substituting a function with the characteristic of approximate l 0 norms such as Gaussian or arctangent function into the optimization problem established in S6 to replace the optimization problem with the characteristic of G 0 and G 2,0. Assuming G j,m as an element in the time-frequency information G to be estimated, the function of the approximation l 0 norm isWhen δ approaches zero, it is assumed/>Let ζ j＝||G(j,:)||2, j=0,..j-1,/>F δ(G)≈||G||0,Bδ(G)≈||G||2,0.
S8, substituting the correlation function in S7 into the optimization problem established in S6, and estimating the time-frequency information G can be converted into a pairIs a solution to (c).
S9, solving the equation established in the S8 by using a gradient descent method, and finally obtaining time-frequency information G:
s91, assuming that the gradient of the equation in the S8 obtained is Taking a set of decreasing sequences δ= [ δ 0,...,δi,δi+1, ] for the parameter δ avoids converging on a locally optimal solution. First, a large positive constant δ 0 is set, and the step size μ 0 =1 of the gradient descent is set, assuming i=1.
S92, calculating/>
S93, calculatingCalculation/> from the results of G (i)
S94, updating mu i-1＝βμi-1, wherein beta is a proportional coefficient beta epsilon (0, 1)
S95, whenAnd mu i-1 returns to step S93 when the iteration termination condition is not reached. And continuing to the next step when the condition is not satisfied.
S96, update G (i),δi＝αδi-1,μi =1, i=i+1, where α is a proportional coefficient α e (0, 1), and then return to step S92.
S97, obtaining estimated time-frequency information G until delta i meets the iteration termination condition.
Examples are given below in connection with specific parameters:
With reference to fig. 1, assume that the transmission signal is an 8FSK signal with carrier frequencies {12.25kHz,12.75kHz,13.25kHz,13.75kHz,11.75kHz,11.25kHz,10.75kHz,10.25kHz }, and a system sampling rate of 48kHz, each symbol corresponds to 192 sampling points. The experimental schematic is shown in fig. 2, the channel impulse response diagram in the experiment is shown in fig. 3, and the time-frequency information obtained by using the classical STFT method is shown in fig. 4. The specific implementation of the invention comprises the following steps:
And S1, randomly collecting signals of N=1000 sampling points as observation signals Y. Setting the number L=45 of sampling points of segmented signals, and the number Q=40 of overlapping sampling points of adjacent segmented signals, wherein each segment of signals is windowed by using a Blackman window, and the segmented signals are expressed in a matrix form and are used as observation vectors Z;
S2, dividing the frequency set to obtain a Fourier transform matrix Cutting AG by the dimension of an observation vector Z, then carrying out inversion fitting on the AG and the value in Z, and establishing an optimization problem/>Wherein/>For truncated matrix,/>Is a unitary matrix,/>For zero matrix, λ 1 and λ 2 control the specific gravity of element point sparsity and line sparsity in G, respectively.
S3, substituting the Gaussian function into the optimization problem established in the S2 to replace the G 0 and the G 2,0. Assuming G j,m as an element in the time-frequency information G to be estimated, the function of the approximation l 0 norm isWhen δ approaches zero, it is assumed/>Let ζ j＝||G(j,:)||2, j=0,..j-1,/>F δ(G)≈||G||0,Bδ(G)≈||G||2,0.
S4, substituting the correlation function in S3 into the optimization problem established in S2, and estimating the time-frequency information G can be converted into a pairIs a solution to (c).
And S5, solving the equation established in the step S4 by using a gradient descent method, and finally obtaining the time-frequency information G.
S5 comprises the following steps:
S51, assuming that the gradient of the equation in the S4 is obtained as Taking a set of decreasing sequences δ= [ δ 0,...,δi,δi+1, ] for the parameter δ avoids converging on a locally optimal solution. First, a large positive constant δ 0 =1 is set, and the step size μ 0 =1 of the gradient descent is set, assuming i=1.
S52, calculating/>
S53, calculatingCalculation/> from the results of G (i)
S54, updating mu i-1＝βμi-1, wherein beta is a proportional coefficient beta epsilon (0, 1), and setting beta=0.8
S55, whenAnd mu i-1 returns to step S93 when the iteration termination condition mu i-1＞10-10 is not reached. And continuing to the next step when the condition is not satisfied.
S56, update G (i),δi＝αδi-1,μi =1, i=i+1, where α is a proportional coefficient α e (0, 1), set α=0.9, and then return to step S92.
S57, until delta i meets the iteration termination condition delta i＞10-10, estimated time-frequency information G is obtained, as shown in fig. 5.
The method can find that the time-frequency information acquired by the STFT has serious energy divergence and is difficult to distinguish the frequencies, and the time-frequency information acquired by the method has more concentrated energy and effectively distinguishes the frequencies.
Claims (4)
1. The non-cooperative underwater acoustic signal time-frequency information acquisition method is characterized by comprising the following steps of:
S1, assuming that the acquired observation signals are Y, the observation signals are FSK signals, segmenting the Y according to a short-time Fourier transform method, wherein the number of sampling points of each segment of signals is L, the number of overlapping sampling points of adjacent signal segments is Q, and the number of segments is M; windowing each segmented signal by using a window function, and representing the segmented signals into a matrix form and taking the matrix form as an observation vector Z;
S2, uniformly dividing a frequency set into F= [ 0] according to the sampling frequency, wherein F 1,...,fL-1],fL is equal to the sampling frequency, and establishing a Fourier transform matrix
S3, assuming that the time-frequency information to be obtained isBuilding a time-frequency information estimation model Z=AG+V, wherein V is noise;
s4, increasing the dividing frequency set density of the Fourier transform matrix A to obtain Estimated time-frequency informationThe matrix dimension of AG is j×m;
s5, cutting AG according to the dimension of the observation vector Z, and then carrying out inversion fitting on the AG and the value in Z;
S6, establishing an optimization problem Wherein,For truncated matrix,/>Is a unitary matrix,/>For zero matrix, lambda 1 and lambda 2 are coefficients of constraint penalty items, and the specific gravity of element point sparsity and line sparsity in G is controlled respectively;
s7, substituting the function with the characteristic of approximate l 0 norms into Instead of G 0 and G 2,0: let G j,m be the element in the time-frequency information G to be estimated, the function be/>When delta approaches zero, assume thatLet ζ j＝||G(j,:)||2, j=0,..j-1,/>F δ(G)≈||G||0,Bδ(G)≈||G||2,0;
S8 substituting F δ (G) and B δ (G) in S7 into Conversion of estimation of time-frequency information G into a pairIs solved;
And S9, solving the equation established in the step S8 by using a gradient descent method to obtain time-frequency information G.
2. The method for acquiring time-frequency information of a non-cooperative underwater acoustic signal according to claim 1, wherein: the observed signal is the FH signal.
3. The method for acquiring time-frequency information of a non-cooperative underwater acoustic signal according to claim 1, wherein: and in S4, J is 2 to 5 times of L.
4. The method for acquiring time-frequency information of a non-cooperative underwater acoustic signal according to claim 1, wherein: and S9, solving the equation established in the S8 by using a gradient descent method, wherein the obtained time-frequency information G specifically comprises the following steps:
S91, assuming the gradient of the equation in S8 is Taking a set of descending sequences delta= [ delta 0,...,δi,δi+1, ] for the parameter delta, avoiding convergence to the locally optimal solution, firstly setting a normal number delta 0, setting the step size mu 0 =1 of gradient descent, and assuming i=1;
S92, calculating />
S93, calculatingCalculation/> from the results of G (i)
S94, updating mu i-1＝βμi-1, wherein beta is a proportionality coefficient, and beta epsilon (0, 1);
s95, when And returning to the step S93 when μ i-1 does not reach the set iteration termination condition; otherwise, continuing the next step;
S96, update G (i),δi＝αδi-1,μi =1, i=i+1, where α is a scale factor, α e (0, 1), and then return to step S92;
S97, obtaining estimated time-frequency information G until delta i meets the set iteration termination condition.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106772224A (en) * | 2016-12-28 | 2017-05-31 | 武汉大学 | A kind of L-type array estimating two-dimensional direction-of-arrival algorithm of use time frequency analysis |
| CN107301434A (en) * | 2017-07-28 | 2017-10-27 | 西安交通大学 | Blind separation hybrid matrix method of estimation based on synchronous compression Short Time Fourier Transform |
| CN109120562A (en) * | 2018-08-06 | 2019-01-01 | 电子科技大学 | One kind is added up matched MFSK signal frequency estimation method based on frequency spectrum |
| CN111025257A (en) * | 2019-12-31 | 2020-04-17 | 西安电子科技大学 | Micro-motion target high-resolution time-frequency diagram reconstruction method based on sparse Bayesian learning |
| CN111998935A (en) * | 2020-08-11 | 2020-11-27 | 南京理工大学 | Time/frequency decomposition method for explosive shock wave signal based on variational modal decomposition |
Family Cites Families (1)
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| US8335955B2 (en) * | 2008-06-24 | 2012-12-18 | Siemens Aktiengesellschaft | System and method for signal reconstruction from incomplete data |
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Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106772224A (en) * | 2016-12-28 | 2017-05-31 | 武汉大学 | A kind of L-type array estimating two-dimensional direction-of-arrival algorithm of use time frequency analysis |
| CN107301434A (en) * | 2017-07-28 | 2017-10-27 | 西安交通大学 | Blind separation hybrid matrix method of estimation based on synchronous compression Short Time Fourier Transform |
| CN109120562A (en) * | 2018-08-06 | 2019-01-01 | 电子科技大学 | One kind is added up matched MFSK signal frequency estimation method based on frequency spectrum |
| CN111025257A (en) * | 2019-12-31 | 2020-04-17 | 西安电子科技大学 | Micro-motion target high-resolution time-frequency diagram reconstruction method based on sparse Bayesian learning |
| CN111998935A (en) * | 2020-08-11 | 2020-11-27 | 南京理工大学 | Time/frequency decomposition method for explosive shock wave signal based on variational modal decomposition |
Non-Patent Citations (1)
| Title |
|---|
| 时频信号的非参数加窗稀疏协方差迭代分析法;王悦斌;张建秋;;飞控与探测;20190125(第01期);全文 * |
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