CN117970299A - Target echo time delay estimation method and system under strong direct background interference - Google Patents
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Abstract
The invention discloses a target echo time delay estimation method and system under strong direct background interference, belongs to the technical field of water acoustics and water sound signal processing, and aims to solve the technical problem that when a multiple-input-output sonar detection system detects a target, the direct sound interference intensity is far higher than the target echo intensity, and the target cannot be accurately positioned. The method comprises the following steps: based on a test signal transmitted by a transmitting platform and a received signal received by a receiving platform, estimating a strong direct interference channel by adopting an SBL sparse Bayesian learning algorithm; convolving the strong direct interference channel with the test signal to construct a cancellation signal, and subtracting the received signal from the cancellation signal to obtain a cancellation signal; and constructing an echo time delay optimization function based on the canceled signal by adopting an LASSO compression estimation algorithm, introducing a weighting matrix into the echo time delay optimization function, iterating the weighted echo time delay optimization function, and outputting echo time delay estimation. And the target positioning accuracy is improved.
Description
Technical Field
The invention relates to the technical field of underwater acoustics and underwater acoustic signal processing, in particular to a target echo time delay estimation method and system under strong direct background interference.
Background
In a multi-base active sonar detection system, a plurality of sonars distributed at different positions are used for detecting targets, echo signals of the targets are processed by using corresponding algorithms, finally, a plurality of sets of azimuth information and echo time delay estimation of the targets relative to the different sonars can be obtained, and the targets can be positioned by combining the information with a positioning model.
In the time delay estimation algorithm, the correlation method estimates the time delay difference between signals by searching for the delay peak value of the autocorrelation function of the signals, and the time delay estimation of the non-stationary signals still has larger error although the time delay estimation is simple and easy to realize. At present, various traditional delay estimation methods are generally applicable to narrowband signals, but for wideband signals, the traditional algorithm can not ensure the estimation accuracy of delay any more, and can not ensure that the algorithm has good convergence rate. In the process of target detection, the multiple-input-multiple-output sonar detection system can be influenced by factors such as environmental noise, reverberation, direct sound interference and the like. The direct sound interference has a much higher intensity than the target echo, which can cause the phenomenon that the target echo is submerged, and thus the target cannot be accurately positioned.
Disclosure of Invention
The invention aims to provide a target echo time delay estimation method and system under strong direct background interference, which are used for solving the technical problems that in the process of target detection of a multi-transmission one-reception sonar detection system, the influence of factors such as environmental noise, reverberation, direct sound interference and the like can be caused, and the target echo is submerged because the strength of the direct sound interference is far higher than the target echo strength, so that the target cannot be accurately positioned.
In order to achieve the above object, the present invention provides the following technical solutions:
The method for estimating the target echo time delay under the strong direct background interference comprises the following steps:
Step S10: based on a test signal transmitted by a transmitting platform and a received signal received by a receiving platform, estimating a strong direct interference channel by adopting an SBL sparse Bayesian learning algorithm;
step S20: convolving the strong direct interference channel with the test signal to construct a cancellation signal, and subtracting the received signal from the cancellation signal to obtain a cancellation signal;
Step S30: and constructing an echo delay optimization function based on the canceled signal by adopting an LASSO compression estimation algorithm, introducing a weighting matrix into the echo delay optimization function, iterating the weighted echo delay optimization function, and outputting echo delay estimation.
Compared with the prior art, the invention has the following technical effects:
According to the invention, under the strong direct interference background, a Weighted iteration target echo time delay estimation algorithm (namely Sparse Bayesian Learning-Weighted LASSO, SBL-WLASSO) based on interference cancellation is adopted, after the pre-estimation of a strong direct interference channel is realized, the strong direct interference component can be restored, and only the target echo part is reserved for further research by eliminating the strong direct interference part in a received signal through the idea of cancellation, so that the steady estimation of the target echo time delay under the strong direct interference background is realized, and the target positioning precision is improved.
The invention also provides a target echo time delay estimation system under strong direct background interference, which comprises:
The channel estimation module is used for estimating a strong direct interference channel by adopting an SBL sparse Bayesian learning algorithm based on the test signals transmitted by the transmitting platform and the received signals received by the receiving platform;
the cancellation signal construction module is used for carrying out convolution operation on the strong direct interference channel and the test signal to construct a cancellation signal, and carrying out subtraction operation on the received signal and the cancellation signal to obtain a cancellation signal;
And the echo delay estimation module is used for constructing an echo delay optimization function based on the canceled signal by adopting an LASSO compression estimation algorithm, introducing a weighting matrix for the echo delay optimization function, iterating the weighted echo delay optimization function and outputting echo delay estimation.
The technical effect of the target echo time delay estimation system under the strong direct background interference provided by the invention is the same as that of the target echo time delay estimation method under the strong direct background interference, and the discussion is not repeated here.
Drawings
Other and further objects and advantages will become apparent from the following description. The drawings are intended to illustrate examples of the various forms of the invention. The drawings are not to be construed as illustrating all the ways in which the invention may be made and used. Variations and substitutions of the various components of the invention are, of course, possible. The invention resides as well in the sub-combinations and sub-systems of the elements described and in the methods of using them.
In the drawings:
FIG. 1 is a block diagram of the overall process of a target echo delay estimation method under strong direct background interference;
fig. 2 is a strong direct interference channel impulse response;
FIG. 3 is a target echo generalized channel impulse response;
FIG. 4 is a graph of the target echo delay estimation results for four algorithms in a strong direct interference background;
FIG. 5 is a comparison of delay estimation performance at different signal-to-noise ratios;
Fig. 6 is a schematic flow chart of a target echo delay estimation method under strong direct background interference.
Detailed Description
In order to make the technical problems, technical schemes and beneficial effects to be solved more clear, the invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
Embodiment one:
As shown in fig. 1 to 6, the method for estimating target echo delay under strong direct background interference provided by the invention comprises the following steps:
Step S10: based on a test signal transmitted by a transmitting platform and a received signal received by a receiving platform, estimating a strong direct interference channel by adopting an SBL sparse Bayesian learning algorithm;
Step S20: convolving the strong direct interference channel with the test signal to construct a cancellation signal, and subtracting the received signal from the cancellation signal to obtain a cancellation signal;
Step S30: and constructing an echo time delay optimization function based on the canceled signal by adopting an LASSO compression estimation algorithm, introducing a weighting matrix into the echo time delay optimization function, iterating the weighted echo time delay optimization function, and outputting echo time delay estimation.
The specific implementation method comprises the following steps:
The implementation scheme of the invention comprises the following specific steps:
step 1: and (5) carrying out strong direct channel pre-estimation by using an SBL algorithm.
(1): The transmitting platform firstly transmits a test signal x and obtains a receiving signal y from the receiving platform; then, toplitz (toeplitz) operators are acted on the test signals x to obtain corresponding dictionary matrixes phi; finally, relevant parameter setting is carried out: let the maximum iteration number be r max, the termination threshold be e, and the noise variance be σ 2.
(2): And initializing iteration parameters, namely enabling the super-parameter matrix lambda (0)＝IL to have iteration times r=0.
(3): E-step, the expectation of the hidden variable Z is calculated.
μ＝σ-2ΣΦHy
(4): M-step, solving the maximum likelihood estimation of the parameters.
(5): Judging whether iteration is continued or not, if r is smaller than r max, making r=r+1, and repeating the steps 3 to 4; if r=r max orThe iteration is terminated.
(6): Outputting a pre-estimated strong direct interference channel
Step 2: and constructing a cancellation signal, and calculating with the received signal to obtain a cancellation signal.
As shown in the following two calculation formulas, a cancellation signal is constructed through convolution operation of a channel and a transmission signal; and then subtracting the received signal from the cancellation signal to obtain the cancellation signal.
Step 3: constructing echo time delay optimization function according to LASSO (compression estimation) idea
After the canceled signal is obtained, an optimization function of echo time delay is constructed by using the thought of LASSO
The main path of the generalized channel impulse response is taken as the arrival time of the target echo. Due to the influence of factors such as marine environment noise and multi-path phenomenon, the echo time delay estimation of the main path is severely interfered, so that the echo time delay is difficult to accurately estimate.
Aiming at the problems, when a weighting matrix U is introduced, the time delay optimization function becomes
Wherein the expression of the weighting matrix U is that
Splitting H into H 1 and H 2, substituting the H 1 and H 2 into a weighted experiment optimization function, and solving parameters: for the parameter H 1, firstly conducting derivation, and obtaining:
H1 i+1＝(UHU+ρI)-1(UHy+ρ(H2 i-wi))
Order the And updating the H 2, at this time, solving the problem of non-conduction at the position of H 2 =0 by using a soft threshold operator, (the formula w has no specific meaning, and is just for facilitating subsequent calculation, y has the same meaning as the above, lambda is a regularization parameter, the strength of sparsity is controlled, ρ is a temporary variable), and defining the soft threshold operator as follows:
by setting the threshold value kappa, the sparsity of elements in the vector H to be estimated is ensured, and the updating expression of H 2 is as follows:
At the time of the j-th iteration, The weighting coefficients, which can be regarded as corresponding channel estimation positions, are:
For the corresponding value of the mth position in the i-1 th iteration estimation, the fine tuning factor epsilon ensures that the denominator of the above is non-zero, and is generally set to be a smaller positive number (10 -4).
The method for estimating the target echo time delay under the strong direct background interference and the beneficial effects thereof are further described in detail below by combining with specific embodiments.
As can be seen from (a) to (c) in fig. 4, in the strong direct interference context, for three conventional delay estimation algorithms, namely, matched filtering, phase-shift generalized cross-correlation (GCC-phas) and frequency-sliding generalized cross-correlation (WSVD-FS-GCC): the matched filtering algorithm still keeps a large number of false peaks after the time delay estimation is carried out; although the amplitude of the pseudo peaks is weakened by the phase transformation generalized cross correlation algorithm, the number of the pseudo peaks is still more; after the main path is effectively strengthened by the frequency sliding generalized cross correlation algorithm, the false peak is effectively restrained. However, there is a common problem with these three algorithms: compared with the strong direct interference component, the target echo energy is weaker, so the time delay estimation results obtained by the three algorithms are the arrival time of the strong direct interference, and are not the arrival time of the target echo with weaker energy. The graph (d) shows that after the weighted iterative algorithm (SBL-WLASSO) based on interference cancellation is used for pre-estimating the strong direct interference channel, an interference cancellation component is constructed, a weighting matrix is introduced into the residual component obtained after cancellation, an iterative mode is adopted, a main path with the maximum amplitude is emphasized, noise with smaller amplitude is restrained, and multiple paths are emphasized, and then the stable estimation of the target echo time delay under the strong direct interference background is realized.
As can be seen from fig. 5, the delay estimation performance of the SBL-WLASSO algorithm is better than that of the other three comparison algorithms under the same signal-to-noise ratio, and the delay root mean square error of the SBL-WLASSO algorithm can be reduced to 0 as the signal-to-noise ratio is gradually increased.
According to the invention, under the strong direct interference background, a Weighted iteration target echo time delay estimation algorithm (namely Sparse Bayesian Learning-Weighted LASSO, SBL-WLASSO) based on interference cancellation is adopted, after the pre-estimation of a strong direct interference channel is realized, the strong direct interference component can be restored, and only the target echo part is reserved for further research by eliminating the strong direct interference part in a received signal through the idea of cancellation, so that the steady estimation of the target echo time delay under the strong direct interference background is realized, and the target positioning precision is improved.
As an implementation manner, an SBL sparse bayesian learning algorithm is adopted to estimate a strong direct interference channel, and the method comprises the following steps:
Applying Toplitz Toeplitz operators to the test signals to obtain a dictionary matrix, and setting the maximum iteration times, a termination threshold and noise variance for the dictionary matrix;
constructing a super-parameter matrix and initializing iteration parameters of the super-parameter matrix;
And carrying out iterative solution on hidden variable expectations of the dictionary matrix and maximum likelihood estimation of iteration parameters based on the super-parameter matrix until the iteration times of the dictionary matrix are the same as the maximum iteration times or the square of the second norm of the two adjacent iteration times is less than or equal to a termination threshold, and outputting a pre-estimated strong direct interference channel.
And obtaining a pre-estimated value of the strong direct interference signal through the constructed dictionary matrix, the super-parameter matrix and settlement of the dictionary matrix and the super-parameter matrix, and providing a data basis for accurately positioning a target.
As an implementation manner, the weighted echo delay optimization function is iterated, and echo delay estimation is output, which includes the following steps:
Splitting the obtained estimated value of the strong direct interference channel into a first parameter and a second parameter;
Substituting the first parameter and the second parameter into a weighted echo time delay optimization function respectively to solve, and updating the second parameter by using a derivative result of the first parameter;
and iterating the updated second parameter expression, and outputting an echo delay estimated value.
The calculation of the echo delay estimated value is facilitated by splitting the estimated value of the strong direct interference channel.
As an embodiment, after updating the second parameter using the calculation result of the first parameter; before iterating the updated second parameter expression, the method further comprises the following steps:
defining a soft threshold operator for the second parameter, and setting a threshold value for the soft threshold operator;
and introducing a fine tuning factor to ensure the sparsity of elements in the vector to be estimated.
And smooth calculation of the echo delay estimated value is ensured through the defined soft threshold operator and the introduced fine tuning factor.
As an implementation manner, the weighting coefficients of the obtained corresponding channel estimation positions are:
Wherein, As a weighting coefficient,/>For the corresponding value of the mth position in the i-1 th iteration estimation, epsilon is a fine tuning factor and is set to be a positive number of 10 -4.
Embodiment two:
On the basis of the first embodiment, the invention also provides a target echo delay estimation system under strong direct background interference, which comprises:
The channel estimation module is used for estimating a strong direct interference channel by adopting an SBL sparse Bayesian learning algorithm based on the test signals transmitted by the transmitting platform and the received signals received by the receiving platform;
The cancellation signal construction module is used for carrying out convolution operation on the strong direct interference channel and the test signal to construct a cancellation signal, and carrying out subtraction operation on the received signal and the cancellation signal to obtain a cancellation signal;
The echo delay estimation module is used for constructing an echo delay optimization function based on the canceled signal by adopting an LASSO compression estimation algorithm, introducing a weighting matrix into the echo delay optimization function, iterating the weighted echo delay optimization function and outputting echo delay estimation.
According to the invention, under the strong direct interference background, a Weighted iteration target echo time delay estimation algorithm (namely Sparse Bayesian Learning-Weighted LASSO, SBL-WLASSO) based on interference cancellation is adopted, after the pre-estimation of a strong direct interference channel is realized, the strong direct interference component can be restored, and only the target echo part is reserved for further research by eliminating the strong direct interference part in a received signal through the idea of cancellation, so that the steady estimation of the target echo time delay under the strong direct interference background is realized, and the target positioning precision is improved.
As an implementation manner, the channel estimation module is further configured to apply Toplitz toeplitz operators to the test signals to obtain a dictionary matrix, and set a maximum iteration number, a termination threshold and a noise variance for the dictionary matrix;
constructing a super-parameter matrix and initializing iteration parameters of the super-parameter matrix;
And carrying out iterative solution on hidden variable expectations of the dictionary matrix and maximum likelihood estimation of iteration parameters based on the super-parameter matrix until the iteration times of the dictionary matrix are the same as the maximum iteration times or the square of the second norm of the two adjacent iteration times is less than or equal to a termination threshold, and outputting a pre-estimated strong direct interference channel.
And obtaining a pre-estimated value of the strong direct interference signal through the constructed dictionary matrix, the super-parameter matrix and settlement of the dictionary matrix and the super-parameter matrix, and providing a data basis for accurately positioning a target.
As an implementation manner, the echo delay estimation module is further configured to split the obtained estimated value of the strong direct interference channel into a first parameter and a second parameter;
substituting the first parameter and the second parameter into a weighted echo time delay optimization function respectively to solve, and updating the second parameter by using the calculation result of the first parameter;
and iterating the updated second parameter expression, and outputting an echo delay estimated value.
The calculation of the echo delay estimated value is facilitated by splitting the estimated value of the strong direct interference channel.
As an implementation manner, the echo delay estimation module is further configured to define a soft threshold operator for the second parameter, and set a threshold value for the soft threshold operator;
and introducing a fine tuning factor to ensure the sparsity of elements in the vector to be estimated.
And smooth calculation of the echo delay estimated value is ensured through the defined soft threshold operator and the introduced fine tuning factor.
As an implementation manner, the weighting coefficients of the corresponding channel estimation positions obtained by the echo time delay estimation module are as follows:
Wherein, As a weighting coefficient,/>For the corresponding value of the mth position in the i-1 th iteration estimation, epsilon is a fine tuning factor and is set to be a positive number of 10 -4.
In the description of the above embodiments, particular features, structures, materials, or characteristics may be combined in any suitable manner in any one or more embodiments or examples.
Of course, various changes and substitutions can be made to the above description, and all such changes and substitutions are intended to be within the spirit and scope of the invention. Therefore, the invention should not be limited, except as by the appended claims and equivalents thereof.
Claims (10)
1. The target echo time delay estimation method under the strong direct background interference is characterized by comprising the following steps of:
Step S10: based on a test signal transmitted by a transmitting platform and a received signal received by a receiving platform, estimating a strong direct interference channel by adopting an SBL sparse Bayesian learning algorithm;
step S20: convolving the strong direct interference channel with the test signal to construct a cancellation signal, and subtracting the received signal from the cancellation signal to obtain a cancellation signal;
Step S30: and constructing an echo delay optimization function based on the canceled signal by adopting an LASSO compression estimation algorithm, introducing a weighting matrix into the echo delay optimization function, iterating the weighted echo delay optimization function, and outputting echo delay estimation.
2. The method for estimating target echo time delay under strong direct background interference according to claim 1, wherein the estimating of the strong direct interference channel by using SBL sparse bayesian learning algorithm comprises the following steps:
applying Toplitz Toeplitz operators to test signals to obtain a dictionary matrix, and setting maximum iteration times, a termination threshold and noise variance for the dictionary matrix;
Constructing a super-parameter matrix, and initializing iteration parameters of the super-parameter matrix;
And carrying out iterative solution on hidden variable expectations of the dictionary matrix and maximum likelihood estimation of the iteration parameters based on the super-parameter matrix until the iteration times of the dictionary matrix are the same as the maximum iteration times or two norms square of the adjacent iteration times are smaller than or equal to the termination threshold, and outputting a pre-estimated strong direct interference channel.
3. The method for estimating the target echo delay under strong direct background interference according to claim 1, wherein the step of iterating the weighted echo delay optimization function to output the echo delay estimate comprises the steps of:
Splitting the obtained estimated value of the strong direct interference channel into a first parameter and a second parameter;
Substituting the first parameter and the second parameter into the weighted echo time delay optimization function respectively to solve, and updating the second parameter by using the derivative result of the first parameter;
and iterating the updated second parameter expression, and outputting an echo delay estimated value.
4. A method of estimating a target echo delay in strong direct background interference according to claim 3, wherein after updating the second parameter using the calculation result of the first parameter; before the iteration of the updated second parameter expression, the method further comprises the following steps:
Defining a soft threshold operator for the second parameter, and setting a threshold value for the soft threshold operator;
and introducing a fine tuning factor to ensure the sparsity of elements in the vector to be estimated.
5. The method for estimating target echo time delay under strong direct background interference according to claim 1, wherein the weighting coefficients of the obtained corresponding channel estimation positions are:
Wherein, As a weighting coefficient,/>For the corresponding value of the mth position in the i-1 th iteration estimation, epsilon is a fine tuning factor and is set to be a positive number of 10 -4.
6. An estimation system based on the target echo delay estimation method under strong direct background interference according to any one of claims 1 to 5, comprising:
The channel estimation module is used for estimating a strong direct interference channel by adopting an SBL sparse Bayesian learning algorithm based on the test signals transmitted by the transmitting platform and the received signals received by the receiving platform;
the cancellation signal construction module is used for carrying out convolution operation on the strong direct interference channel and the test signal to construct a cancellation signal, and carrying out subtraction operation on the received signal and the cancellation signal to obtain a cancellation signal;
And the echo delay estimation module is used for constructing an echo delay optimization function based on the canceled signal by adopting an LASSO compression estimation algorithm, introducing a weighting matrix for the echo delay optimization function, iterating the weighted echo delay optimization function and outputting echo delay estimation.
7. The system of claim 6, wherein the channel estimation module is further configured to apply Toplitz toeplitz operator to the test signal to obtain a dictionary matrix, and set a maximum iteration number, a termination threshold, and a noise variance for the dictionary matrix;
Constructing a super-parameter matrix, and initializing iteration parameters of the super-parameter matrix;
And carrying out iterative solution on hidden variable expectations of the dictionary matrix and maximum likelihood estimation of the iteration parameters based on the super-parameter matrix until the iteration times of the dictionary matrix are the same as the maximum iteration times or two norms square of the adjacent iteration times are smaller than or equal to the termination threshold, and outputting a pre-estimated strong direct interference channel.
8. The system for estimating the target echo delay under the strong direct background interference according to claim 6, wherein the echo delay estimation module is further configured to split the obtained estimated value of the strong direct interference channel into a first parameter and a second parameter;
Substituting the first parameter and the second parameter into the weighted echo time delay optimization function respectively to solve, and updating the second parameter by using the calculation result of the first parameter;
and iterating the updated second parameter expression, and outputting an echo delay estimated value.
9. The system of claim 8, wherein the echo delay estimation module is further configured to define a soft threshold operator for the second parameter, and set a threshold for the soft threshold operator;
and introducing a fine tuning factor to ensure the sparsity of elements in the vector to be estimated.
10. The system for estimating target echo time delay under strong direct background interference according to claim 6, wherein the weighting coefficients of the corresponding channel estimation positions obtained by the echo time delay estimation module are:
Wherein, As a weighting coefficient,/>For the corresponding value of the mth position in the i-1 th iteration estimation, epsilon is a fine tuning factor and is set to be a positive number of 10 -4.
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