CN113589300A - Synthetic aperture sonar submerged target imaging enhancement method based on compressed sensing - Google Patents

Synthetic aperture sonar submerged target imaging enhancement method based on compressed sensing Download PDF

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CN113589300A
CN113589300A CN202110727970.1A CN202110727970A CN113589300A CN 113589300 A CN113589300 A CN 113589300A CN 202110727970 A CN202110727970 A CN 202110727970A CN 113589300 A CN113589300 A CN 113589300A
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CN113589300B (en
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易石
洪煜宸
丛卫华
刘竞扬
曾祥瑞
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715th Research Institute of CSIC
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S15/00—Systems using the reflection or reradiation of acoustic waves, e.g. sonar systems
    • G01S15/88—Sonar systems specially adapted for specific applications
    • G01S15/89—Sonar systems specially adapted for specific applications for mapping or imaging
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/52—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S15/00
    • G01S7/539—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S15/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
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    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
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    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
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Abstract

The invention provides a synthetic aperture sonar submerged target imaging enhancement method based on compressed sensing, which corrects the SAS range migration effect in a range-Doppler domain (RD) by utilizing nonlinear interpolation to complete two-dimensional spatial decoupling; and then, carrying out azimuth sparse imaging by using a compressed sensing method to obtain an enhanced target, and then fusing the target and the background to realize final imaging. The method enhances the imaging of the submerged target, greatly reduces the interference of the submarine reverberation, and provides powerful criteria for the identification of the submerged target.

Description

Synthetic aperture sonar submerged target imaging enhancement method based on compressed sensing
Technical Field
The invention relates to sonar and a sonar signal processing method, belongs to the fields of synthetic aperture sonar, target identification and the like, and mainly relates to a synthetic aperture sonar submerged target imaging enhancement method based on compressed sensing.
Background
Synthetic Aperture Sonar (SAS) is used as an active ocean observation system, a large aperture base array is virtualized by using the movement of a small aperture physical array, low-frequency long-distance high-resolution imaging detection can be realized, and rapid underwater environment exploration, underwater mine exploration and identification, seabed landform mapping and long-term change monitoring are realized in the military aspect. For imaging of a submerged target at a water bottom boundary, due to the interference of a reverberation background of a current bottom echo, the existing synthetic aperture sonar, whether being a side-looking sonar, a downward-looking sonar or an oblique-looking sonar, has the problem that the targets are difficult to distinguish, and how to improve the identification capability of the submerged target is a problem to be solved urgently by the existing sonar.
In recent years, sparse sampling signal processing technology is rapidly developed, an imaging method based on compressed sensing is widely concerned in the field of Synthetic Aperture Radar (SAR), and different from the traditional method based on matched filtering processing, as long as signals meet the precondition of sparseness in a certain specific transform domain, distortion-free reconstruction of the signals can be realized at high probability by using a small amount of observation data (far lower than the Shannon Nyquist sampling rate); the method fully utilizes the prior information of the scattering coefficient of the target, and is an optimization problem based on L1 norm regularization. Different from synthetic aperture radars, synthetic aperture sonar cannot ignore the problem of range migration due to the influence of sound velocity and working environment, and a conventional two-dimensional space reconstruction method cannot be applied to an SAS.
Disclosure of Invention
The invention aims to overcome the defects in the prior art, provides a synthetic aperture sonar sunk target imaging enhancement method based on compressed sensing, solves the problems that a seabed sunk target is weak in characteristics and easy to mask seabed reverberation, integrates the compressed sensing technology into synthetic aperture imaging sonar, reduces the interference of seabed reverberation, enhances the sunk target imaging, and provides powerful criteria for sunk target identification.
The object of the present invention is achieved by the following technical means. A synthetic aperture sonar sunk target imaging enhancement method based on compressed sensing is characterized in that synthetic aperture sonar carries out uniform linear motion along the x-axis direction, and the coordinate of a sunk target P is (x)i,yi) Sonar emissionThe signal is a linear frequency modulation signal, and the method specifically comprises the following steps:
the method comprises the following steps: acquiring a sonar echo signal to obtain a formula (1);
Figure BDA0003138224530000011
wherein t is the fast time from the distance direction (y-axis direction), A is the scattering coefficient of the target, c is the sound velocity in water, f0Is a carrier frequency, KrFor adjusting the frequency, tau, of the transmitted signalnIs slow time of azimuth direction and has (-N/2 < N < N/2), wa(τn) As a function of the azimuth window, wr(t-2R(τn) C) is a window function of the emission signal, R (tau)n) Is a sunk target P (x)i,yi) Instantaneous slope distance to azimuth sampling point;
step two: completing range-direction pulse compression, performing azimuth-direction Fourier transform, and converting signals into a range-Doppler domain to obtain a formula (2);
Figure BDA0003138224530000021
wherein R isrd(τn) The range migration curve of the RD domain comprises:
Figure BDA0003138224530000022
step three: completing the decoupling of the azimuth direction and the distance direction by utilizing nonlinear interpolation;
step IV: completing target scene imaging by using an azimuth compressed sensing technology;
step five: establishing an observation matrix phi, completing the reconstruction of scattering coefficients of a target field, and obtaining target images at the same distance;
step (c): circularly performing the step IV on all the distance directions in the target field to finish the sparse imaging of the full target field;
step (c): and (c) weighting and adding the imaging result completed in the step (c) and the traditional synthetic aperture result, and performing multi-stage image filtering processing to obtain a final target image so as to realize the enhancement of the sinking target.
Furthermore, in the third step, the distance migration curve correction is completed according to the formula (4), and the two-dimensional decoupling of the direction and the distance is realized;
Figure BDA0003138224530000023
furthermore, in the fourth step, a compressed sensing SAS freedom degree strategy is formulated according to requirements, and the freedom degree is selected to carry out corresponding strategy optimization according to different data;
in the azimuth observation range LobEstablishing a compressed sensing SAS sparse base at the inner distance and the single distance, and obtaining a formula (5); after selecting the degree of freedom, the arbitrary distance R after two-dimensional space demodulationeAnd according to the characteristic of azimuth matched filtering, setting the sparse basis of compressed sensing as follows:
Figure BDA0003138224530000024
wherein
Figure BDA0003138224530000025
At a distance ReUpward azimuthal chirp slope, LobFor the observation range, PRF is the pulse repetition frequency.
Furthermore, in the fifth step, establishing an observation matrix phi, and completing the reconstruction of the scattering coefficient of the target field according to the formula (6) to obtain target images at the same distance;
if the observation matrix Φ is a gaussian kernel matrix, the sensing matrix Θ Φ Ψ satisfies the RIP criterion, and the minimum l in equation (6) is solved by an orthogonal matching pursuit algorithm1Norm to obtain scattering coefficient S (R) of target scenee) Completing compressed sensing sparse imaging of the target scene;
min||S(Re)||1,s.t.f＝ΘS＝ΦΨS(Re) (6)。
the invention has the beneficial effects that: the method overcomes the spatial coupling problem caused by motion, solves the restriction of range migration on a compressed sensing method, and enhances the target masked in the current bottom echo by utilizing the characteristic that compressed sensing can well image the target sparsely distributed in the distance dimension, thereby improving the identification capability of the sinking target.
Drawings
Fig. 1 is a sonar operation schematic diagram.
FIG. 2 is a schematic diagram of target azimuth resolution under the compressive sensing SAS multiple degrees of freedom.
FIG. 3 is a diagram illustrating the imaging result of a conventional synthetic aperture method on a submerged target
FIG. 4 is a diagram illustrating compressed sensing sparse imaging results.
Fig. 5 is a diagram illustrating the result of compressed sensing synthetic aperture imaging.
Detailed Description
The invention will be described in detail below with reference to the following drawings:
the invention provides a compressive sensing imaging technology adaptive to an SAS (Serial attached SCSI). the SAS range migration effect is corrected in a range-Doppler domain (RD) by utilizing nonlinear interpolation to complete two-dimensional spatial decoupling; and then, carrying out azimuth sparse imaging by using a compressed sensing method to obtain an enhanced target, and then fusing the target and the background to realize final imaging.
The algorithm principle is as follows:
a synthetic aperture submerged target imaging enhancement method based on compressed sensing utilizes a linear frequency modulation emission signal to study submerged target imaging with the assumption of a synthetic aperture sonar equivalent phase center. The research is mainly divided into three parts: 1. a synthetic aperture RD algorithm based on nonlinear interpolation; 2. implementation of compressed sensing in SAS; 3. and (4) reinforcing the submerged target.
Synthetic aperture RD algorithm based on non-linear interpolation
Assuming that the synthetic aperture sonar performs uniform linear motion along the azimuth direction (x-axis direction), as shown in fig. 1, the coordinate of the bottom-sinking target P is (x)i,yi) The sonar transmitting signal is a linear frequency-modulated signal, and the sonar array receivesThe resulting echo can be represented by equation (1):
Figure BDA0003138224530000041
wherein t is the fast time from the distance direction (y-axis direction), A is the scattering coefficient of the target, c is the sound velocity in water, f0Is a carrier frequency, KrFor adjusting the frequency, tau, of the transmitted signalnIs slow time of azimuth direction and has (-N/2 < N < N/2), wa(τn) As a function of the azimuth window, wr(t-2R(τn) C) is a window function of the emission signal, R (tau)n) Is a sunk target P (x)i,yi) Instantaneous slope to azimuth sample point.
Obtaining a range-Doppler domain (RD) signal form through range-direction pulse compression and azimuth-direction Fourier transform:
Figure BDA0003138224530000042
wherein R isrd(τn) The range migration curve of the RD domain comprises:
Figure BDA0003138224530000043
to eliminate the coupling of the distance direction and the azimuth direction due to the platform motion, i.e. to eliminate the second term in the equation, we use a non-linear interpolation method here:
Figure BDA0003138224530000044
where m is the nonlinear interpolation kernel. The difference method effectively completes the decoupling of the direction and the distance.
Implementation of compressed sensing in SAS:
in an observation range with an aperture or larger, the number and the positions of targets existing in the azimuth direction meet the space sparsity and randomness required by a compressed sensing theory, and then the compressed sensing method can be used for replacing the traditional azimuth matched filtering so as to improve the azimuth resolution and the grating lobe resistance of the targets.
Because the imaging of the traditional synthetic aperture sonar on the submerged target is interfered by the submarine reverberation, the identification difficulty is high, the imaging is easy to be masked in a submarine background, and the compressed sensing-based SAS algorithm can image the targets at the same distance in a certain degree of freedom, so that the target information is obtained. As shown in fig. 2, in the selection of the degree of freedom, the imaging azimuth resolution of the SAS algorithm based on compressed sensing under a smaller degree of freedom (1-sparse, 2-sparse) is better than that of the conventional algorithm for single-point target imaging at the same distance, the sidelobe suppression effect is obvious, and after the selected degree of freedom is improved, (4-sparse, 10-sparse) target resolution approaches that of the conventional algorithm and still presents advantages. Therefore, the selection of the degree of freedom can carry out corresponding strategy optimization according to different data so as to achieve the optimal effect.
After selecting the degree of freedom, the arbitrary distance R after two-dimensional space demodulationeAnd according to the characteristic of azimuth matched filtering, setting the sparse basis of compressed sensing as follows:
Figure BDA0003138224530000051
wherein
Figure BDA0003138224530000052
At a distance ReUpward azimuthal chirp slope, LobFor the observation range, PRF is the pulse repetition frequency.
At this time, let the observation matrix Φ be a gaussian kernel matrix, and then the sensing matrix Θ Φ Ψ satisfies the RIP criterion, and the minimum l in the solution (6) can be solved by using an Orthogonal Matching Pursuit (OMP) algorithm1Norm to obtain scattering coefficient S (R) of target scenee) And completing compressed sensing sparse imaging of the target scene.
min||S(Re)||1,s.t.f＝ΘS＝ΦΨS(Re) (6)
Submerged target enhancement
And weighting and adding the formed image and the traditional synthetic aperture result, and carrying out image processing of multi-stage filtering such as normalization, background equalization and the like to finally obtain the enhanced image of the sinking target.
The specific processing flow of the algorithm is as follows:
the method comprises the following steps: and acquiring a sonar echo signal to obtain an expression (1).
Step two: and (3) completing range-direction pulse compression, performing azimuth-direction Fourier transform, and converting the signals into a range-Doppler domain to obtain an expression (2).
Step three: and (4) finishing the distance migration curve correction according to the formula (4) to realize the two-dimensional decoupling of the direction and the distance.
Step IV: and making a compressive sensing SAS freedom degree strategy according to the requirements.
Step five: in the azimuth observation range LobAnd establishing a compressed sensing SAS sparse base at the inner distance and the single distance, and obtaining an equation (5).
Step (c): and establishing an observation matrix phi, and finishing the reconstruction of the scattering coefficient of the target field according to the formula (6) to obtain target images at the same distance.
Step (c): and (6) circularly performing the step (iv) on all the distances in the target field to complete the sparse imaging of the whole target field.
Step (v): and (4) carrying out weighted addition on the imaging result finished in the step (c) and the traditional synthetic aperture result, and then carrying out multistage image filtering processing to obtain a final target image so as to realize the enhancement of the sinking target.
Actual data analysis
The bottom-sinking target detection test is carried out on a collusion lake. The sound velocity measurement value in the water of the test area is 1495 m/s, and the average water depth is 60 m; before the test, a sunk cylindrical iron target with the length of 2 meters and the diameter of 30 centimeters is distributed at the lake bottom. The sonar platform is towed by a mother ship to work, and the towed body is 20 meters deep into the water. The sonar emission signal adopts a linear frequency modulation signal with the working frequency of 15-30 kHz.
Fig. 3 is an imaging result of a conventional synthetic aperture method. As can be seen from the figure, the sonar platform is about 42 meters from the lake bottom, and the submerged target is located at the proper bottom. Due to the complex topography of the lake bottom, the traditional synthetic aperture method easily enables the submerged target to be masked in a reverberation background, and the identification difficulty is high, and as shown in fig. 3, the submerged target is easily distinguished as a lake bottom rock protrusion due to the fact that the submerged target is flush with the near-sighted lake bottom. FIG. 4 shows the sparse imaging result of compressive sensing on synthetic aperture target field, and the reflection characteristics of the submerged target are highlighted through the comparison of the known positions of the target. Under the condition of unknown target information, the result obtained by using a compressed sensing synthetic aperture method is shown in fig. 5, and it can be seen from the graph that the signal-to-noise ratio of the submerged target is greatly improved, the background energy of the lake bottom is greatly reduced, and powerful criteria are provided for target identification.
It should be understood that equivalent substitutions and changes to the technical solution and the inventive concept of the present invention should be made by those skilled in the art to the protection scope of the appended claims.

Claims (4)

1. A synthetic aperture sonar submerged target imaging enhancement method based on compressed sensing is characterized by comprising the following steps: the synthetic aperture sonar moves linearly at a constant speed along the x-axis direction, and the coordinate of the bottom sinking target P is (x)i,yi) The sonar transmitting signal is a chirp signal, and the method specifically comprises the following steps:
the method comprises the following steps: acquiring a sonar echo signal to obtain a formula (1);
Figure FDA0003138224520000011
wherein t is the distance fast time, A is the scattering coefficient of the target, c is the sound velocity in water, f0Is a carrier frequency, KrFor adjusting the frequency, tau, of the transmitted signalnIs slow time of azimuth direction and has (-N/2 < N < N/2), wa(τn) As a function of the azimuth window, wr(t-2R(τn) C) is a window function of the emission signal, R (tau)n) Is a sunk target P (x)i,yi) Instantaneous slope distance to azimuth sampling point;
step two: completing range-direction pulse compression, performing azimuth-direction Fourier transform, and converting signals into a range-Doppler domain to obtain a formula (2);
Figure FDA0003138224520000012
wherein R isrd(τn) The range migration curve of the RD domain comprises:
Figure FDA0003138224520000013
step three: completing the decoupling of the azimuth direction and the distance direction by utilizing nonlinear interpolation;
step IV: completing target scene imaging by using an azimuth compressed sensing technology;
step five: establishing an observation matrix phi, completing the reconstruction of scattering coefficients of a target field, and obtaining target images at the same distance;
step (c): circularly performing the step IV on all the distance directions in the target field to finish the sparse imaging of the full target field;
step (c): and (c) weighting and adding the imaging result completed in the step (c) and the traditional synthetic aperture result, and performing multi-stage image filtering processing to obtain a final target image so as to realize the enhancement of the sinking target.
2. The compressed sensing-based synthetic aperture sonar undersea target imaging enhancement method of claim 1, wherein: thirdly, completing the correction of a range migration curve according to the formula (4) to realize the two-dimensional decoupling of the direction and the range;
Figure FDA0003138224520000014
3. the compressed sensing-based synthetic aperture sonar undersea target imaging enhancement method of claim 1, wherein: in the fourth step, a compressed sensing SAS freedom degree strategy is formulated according to requirements, and the freedom degree is selected to carry out corresponding strategy optimization according to different data;
in the azimuth observation range LobEstablishing a compressed sensing SAS sparse base at the inner distance and the single distance, and obtaining a formula (5); after selecting the degree of freedom, the arbitrary distance R after two-dimensional space demodulationeAnd according to the characteristic of azimuth matched filtering, setting the sparse basis of compressed sensing as follows:
Figure FDA0003138224520000021
wherein
Figure FDA0003138224520000022
At a distance ReUpward azimuthal chirp slope, LobFor the observation range, PRF is the pulse repetition frequency.
4. The compressed sensing-based synthetic aperture sonar undersea target imaging enhancement method of claim 1, wherein: establishing an observation matrix phi, and finishing the reconstruction of the scattering coefficient of the target field according to the formula (6) to obtain target images at the same distance;
if the observation matrix Φ is a gaussian kernel matrix, the sensing matrix Θ Φ Ψ satisfies the RIP criterion, and the minimum l in equation (6) is solved by an orthogonal matching pursuit algorithm1Norm to obtain scattering coefficient S (R) of target scenee) Completing compressed sensing sparse imaging of the target scene;
min||S(Re)||1,s.t.f＝ΘS＝ΦΨS(Re) (6)。
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