Multi-receiving-array synthetic aperture sonar imaging method and system based on sub-block-sub-band

Abstract

The invention provides a sub-block-sub-band-based multi-receiving-array synthetic aperture sonar imaging method and system, and relates to the technical field of image processing. The method comprises the following steps: acquiring a two-dimensional spectrum phase expression of a receiving array echo signal to obtain an accurate two-dimensional spectrum expression; obtaining an equivalent single-base echo baseband signal through single-base conversion; performing distance compression on the equivalent single-base echo baseband signal to obtain a data reconstruction signal; performing block processing on the data reconstruction signal in a distance time domain to obtain a plurality of sub-blocks; performing distance frequency domain sub-band processing in the current sub-block, independently processing multiple sub-band data in the sub-block, correspondingly obtaining a low-resolution result of multiple sub-bands, and performing coherent superposition to obtain a high-resolution image of the current sub-block; the plurality of sub-blocks correspondingly obtain a plurality of high-resolution images; and all the high-resolution images are spliced along the distance time domain to obtain a whole swath result. According to the invention, the azimuth distance frequency domain coupling influence of the two-dimensional frequency spectrum is reduced to a great extent.

Description

Technical Field

[0001] The present invention relates to the field of image processing technology, and in particular to a sub-block-sub-band based multi-receiving array synthetic aperture sonar imaging method and system. Background Art

[0002] In the synthetic aperture sonar (SAS) imaging process, if the azimuth-range coupling effect of the two-dimensional spectrum is ignored, it will cause serious defocusing, which will cause the imaging result to be distorted. The azimuth-range coupling of the two-dimensional spectrum is generally caused by range migration. Since the azimuth-range coupling of the two-dimensional spectrum is not large when the transmitted signal is a narrowband signal and a narrow mapping band, the high-order terms can generally be ignored. However, in the case of broadband signals and wide mapping, the azimuth-range coupling of the two-dimensional spectrum is large, so the high-order terms cannot be ignored.

[0003] In order to reduce the influence of azimuth-distance coupling of two-dimensional spectrum, the most direct method is to use migration algorithm to directly compensate the nonlinear mapping term of distance frequency in two-dimensional frequency domain, but the interpolation efficiency of this method is not high. There is also the use of linear frequency modulation variable algorithm to perform FFT operation and phase multiplication operation. Although interpolation is avoided and the operation efficiency is significantly improved, the linear frequency modulation variable algorithm does not solve the problem that the azimuth-distance coupling term of two-dimensional spectrum will vary with distance. When the target azimuth angle or mapping bandwidth increases, the azimuth-distance coupling increases, and the influence of the azimuth-distance coupling term that deviates from the center point will become larger, resulting in the high-order terms that cannot be ignored. In particular, the farther away from the center point, the more serious the defocusing, which in turn leads to a very limited mapping band width.

[0004] In order to reduce the influence of the azimuth-range coupling term on the boundary of the imaging area, the range-dependent continuous uniform block division can also be used to solve the range-dependent space-variation problem of the azimuth-range coupling term, and the residual error after the azimuth-range coupling term compensation can be reduced by simply reducing the imaging area. However, in the case of severe azimuth-range coupling, the range-dependent continuous uniform block division will make the score blocks very dense, which is not only inefficient in calculation, but also makes the azimuth-range coupling term heavily dependent on the range frequency. Therefore, it is not obvious to reduce the influence of the azimuth-range coupling term only by using the range-time domain block division. Summary of the invention

[0005] Based on this, it is necessary to provide a sub-block-sub-band based multi-receiving array synthetic aperture sonar imaging method and system to address the above technical problems, which solves the problem that the azimuth-range frequency domain coupling of the two-dimensional spectrum cannot be ignored in the case of broadband signals and wide mapping.

[0006] In one aspect, the present invention provides a sub-block-sub-band based multi-receiving array synthetic aperture sonar imaging method, the method comprising:

[0007] Obtain the two-dimensional spectrum phase expression of the receiving array echo signal, expand the range frequency and retain it to the third-order term, and obtain the accurate two-dimensional spectrum expression of the receiving array;

[0008] Removing the element dependency in the precise two-dimensional spectrum expression by monostatic conversion to obtain an equivalent monostatic echo baseband signal; performing range compression on the equivalent monostatic echo baseband signal to obtain a data reconstruction signal;

[0009] The data reconstruction signal is processed in a block manner in a range time domain to obtain a plurality of sub-blocks;

[0010] Performing range frequency domain sub-band processing within the current sub-block, processing multiple sub-band data within the sub-block separately, and correspondingly obtaining low-resolution results of multiple frequency sub-bands;

[0011] The low-resolution results of all frequency sub-bands within the current sub-block are coherently superimposed to obtain a high-resolution image of the current sub-block; multiple sub-blocks correspond to multiple high-resolution images; and all high-resolution images are spliced ​​along the range and time domain to obtain the result of the entire surveying band.

[0012] Furthermore, the block processing includes: dividing the data reconstruction signal into a plurality of sub-blocks in the range time domain, and performing high-order phase compensation on the center of each sub-block.

[0013] Furthermore, the range frequency domain subband processing includes: dividing the range frequency domain into multiple subbands within each subblock, and performing piecewise linear approximation processing on the quadratic and cubic terms of the range frequency within each subband, and converting the high-order phase within each subband into a linear phase.

[0014] Further, the single-base conversion includes:

[0015] Performing fixed compensation in the time domain of the precise two-dimensional spectrum expression;

[0016] Delay compensation is performed in the range frequency domain and azimuth time domain;

[0017] The data is rearranged in the time domain using the offset of the data of the mth receiving array on the azimuth axis as an interval to compensate for the double basic deformation term.

[0018] Furthermore, the data reconstruction signal SS(f r ,f a ; r) is:

[0019]

[0020] Among them, W r (·) represents the range spectrum envelope; W a (·) represents the azimuth spectrum envelope; fr Indicates the distance frequency; f a represents the Doppler frequency; j represents an imaginary number; r represents the vertical distance between the target and the track; f c represents the center frequency of the signal; c represents the speed of sound in water; represents the coefficient of the azimuth compression term; represents the coefficient of the distance migration term; represents the coefficient of the quadratic distance compression term; Represents the coefficient of the cubic distance compression term.

[0021] Further, the sub-block SS after the high-order phase compensation is performed Bulk (f r ,f a ; r) is expressed as:

[0022]

[0023] Among them, W r (·) represents the range spectrum envelope; W a (·) represents the azimuth spectrum envelope; f r Indicates the distance frequency; f a represents the Doppler frequency; j represents an imaginary number; represents the coefficient of the azimuth compression term, f c represents the center frequency of the signal; c represents the speed of sound in water; v represents the speed of movement; r represents the vertical distance between the target and the track; Represents the coefficient of distance migration term; r p represents the center of the sub-block; represents the coefficient of the quadratic distance compression term; represents the coefficient of the cubic distance compression term; for and When r=r p The higher-order terms remaining from the Taylor expansion at .

[0024] Furthermore, piecewise linear approximation processing is performed on the quadratic and cubic terms of the distance frequency in each sub-band, including taking the tangent of the phase of each segment at the center of the frequency sub-block as the approximate value within the sub-block, so that the center can be fully compensated and no phase jump occurs at the boundary points.

[0025] Furthermore, the linear phase is corrected using a fast implementation of Chirp-Z transform to obtain a low-resolution result for each frequency subband.

[0026] Furthermore, the spliced ​​signal expression pP(l,f a )for:

[0027]

[0028] Where CAT[·] represents the distance time domain splicing function, for pP(l,f a ) for azimuth compression; l represents the discretized distance time domain sampling point; f a represents the Doppler frequency; p represents the sub-block number; P represents the number of sub-blocks; N P Indicates the size of the sub-block; w r (·) represents the range envelope; τ represents the fast-changing time; f s Indicates the distance sampling frequency; W a (·) represents the azimuth spectrum envelope; j represents an imaginary number; Represents the phase multiplication factor.

[0029] On the other hand, the present invention also proposes a sub-block-sub-band based multi-receiving array synthetic aperture sonar imaging system, comprising a memory, a processor and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of any of the above methods.

[0030] In general, the present invention provides a sub-block-sub-band based multi-receiving array synthetic aperture sonar imaging method and system, which can achieve the following beneficial effects compared with the prior art:

[0031] The present invention obtains multiple sub-blocks by performing block processing in the distance time domain; and performs distance frequency domain sub-band processing inside the current sub-block, and processes multiple sub-band data inside the sub-block separately, and obtains low-resolution results of multiple frequency sub-bands correspondingly; finally, high-resolution, full-band imaging results are formed through coherent superposition and image fusion. In the case of broadband signals and wide mapping, not only the azimuth distance frequency domain coupling effect of the two-dimensional spectrum is greatly reduced, and high-resolution imaging results are met, but also each different sub-block and different sub-band are independent, which improves the real-time performance of the algorithm and thus improves the imaging efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0033] Figure 1It is a method flow diagram of a sub-block-sub-band based multi-receiving array synthetic aperture sonar imaging method and system provided by the present invention;

[0034] Figure 2 It is a schematic diagram of the positional relationship between the transmitting array and the receiving array of a multi-receiving array synthetic aperture sonar of a sub-block-sub-band based multi-receiving array synthetic aperture sonar imaging method and system provided by the present invention;

[0035] Figure 3 It is a schematic diagram of an imaging algorithm flow of a sub-block-sub-band based multi-receiving array synthetic aperture sonar imaging method and system provided by the present invention;

[0036] Figure 4 It is a piecewise linear approximation schematic diagram of a sub-block-sub-band based multi-receiving array synthetic aperture sonar imaging method and system provided by the present invention;

[0037] Figure 5 It is a schematic diagram of a fast implementation method of ChirpZ transform of a multi-receiving array synthetic aperture sonar imaging method and system based on sub-block-sub-band provided by the present invention;

[0038] Figure 6 It is a schematic diagram of imaging results of a sub-block-sub-band based multi-receiving array synthetic aperture sonar imaging method and system provided by the present invention. DETAILED DESCRIPTION

[0039] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in combination with the drawings and embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work belong to the scope of protection of the present invention.

[0040] It should be noted that, in the description of the embodiments of the present invention, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a method, step or system including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such method, step or system. In the absence of further restrictions, an element defined by the sentence "includes a ..." does not exclude the existence of other identical elements in the method, step or system including the element.

[0041] like Figure 1As shown, the present invention provides a multi-receiving array synthetic aperture sonar imaging method based on sub-block-sub-band, which includes derivation of two-dimensional spectrum, single basis conversion processing, range time domain sub-block processing, range frequency domain sub-band processing and image fusion. Specifically:

[0042] Step 101: Obtain a two-dimensional spectrum phase expression of the receiving array echo signal, expand the range frequency and retain it to the third-order term, and obtain an accurate two-dimensional spectrum expression of the receiving array.

[0043] The derivation process of the two-dimensional spectrum expression is the key to the frequency domain line-by-line algorithm and the basis for subsequent imaging. That is, in the process of solving the accurate two-dimensional spectrum expression, after the two-dimensional spectrum phase expression of the echo baseband signal is expanded to the third-order term of the range frequency, the high-order phase terms are not simply ignored, but retained, and then accurately compensated through subsequent processing.

[0044] It should be noted that the two-dimensional spectrum phase expression can be directly obtained based on the established multi-receiver array synthetic aperture sonar platform.

[0045] As an embodiment, the two-dimensional spectrum phase expression of the receiving array m echo signal is obtained based on the precise distance history by utilizing the phase dwell principle.

[0046] More specifically, the two-dimensional spectral phase expression SS m ′(f r ,f a ; r) can be:

[0047]

[0048] Among them, W r (·) represents the range spectrum envelope; f r represents the distance frequency; f a represents the Doppler frequency; c represents the speed of sound in water; f c represents the signal center frequency; v represents the platform movement speed; D T Indicates the size of the transmitting array; D R Indicates the receiving array size; d m represents the baseline length from the mth receiving array to the transmitting array; r represents the vertical distance between the target and the track; K r Indicates the frequency modulation slope;

[0049]

[0050] It should be noted that the array in the multi-receiver array synthetic aperture sonar platform can be Figure 2 As shown, the precise distance history of the receiving array m relative to the target It can be obtained based on the non-stop-and-go assumption and the phase center approximation.

[0051] Specific, precise distance history Can be:

[0052]

[0053] Where r is the vertical distance between the target and the track; v is the platform speed; t is the azimuth time; d m represents the baseline length from the mth receiving array to the transmitting array; c represents the speed of sound in water; ΔR m (r) = (2vr / c + d m ) 2 / (4r).

[0054] As an embodiment, the range frequency of the two-dimensional spectrum phase expression is expanded and retained to the third-order term, and the non-negligible high-order phase is retained to obtain the accurate two-dimensional spectrum expression of the receiving array.

[0055] Specific, accurate two-dimensional spectrum expression SS m (f r ,f a ; r) is:

[0056]

[0057] Among them, W r (·) represents the range spectrum envelope; W a (·) represents the azimuth spectrum envelope; f r Indicates the distance frequency; f a represents the Doppler frequency; j represents an imaginary number; f c represents the center frequency of the signal; c represents the speed of sound in water; ΔR m (r) = (2vr / c + d m ) 2 / (4r), v represents the speed of movement; r represents the vertical distance between the target and the track, d m Indicates the baseline length from the mth receiving array to the transmitting array; The coefficient of the distance compression term, K r Indicates the frequency modulation slope; represents the coefficient of the azimuth compression term, represents the coefficient of the distance migration term; represents the coefficient of the quadratic distance compression term; represents the coefficient of the cubic distance compression term;

[0058] Step 102: removing the element dependency in the precise two-dimensional spectrum expression by monostatic conversion to obtain an equivalent monostatic echo baseband signal; performing range compression on the equivalent monostatic echo baseband signal to obtain a data reconstruction signal.

[0059] The single-base conversion process is to remove the array element dependency in the two-dimensional spectral phase and convert the multi-receive array data into single-array data, so that the subsequent imaging process only needs to be executed once, and there is no need to image each array element one by one, which is beneficial to the efficiency of subsequent imaging processing.

[0060] It should be noted that the single basis conversion is an accurate two-dimensional spectrum expression for each receiving array, that is, Figure 3 As shown, FFT is fast Fourier transform and IFFT is fast inverse Fourier transform. The algorithm flow includes six Fourier transform / inverse transform and ten phase multiplications. Multiple receiving arrays correspond to multiple accurate two-dimensional spectrum expressions, and then single basis conversion is performed on multiple accurate two-dimensional spectrum expressions simultaneously.

[0061] The array element dependency term in the accurate two-dimensional spectrum expression is removed, that is, the term related to the receiving array number m in the accurate two-dimensional spectrum expression is removed, so as to obtain an equivalent monostatic echo baseband signal.

[0062] The equivalent monobasic echo baseband signal SS′(f r ,f a ; r) can be expressed as:

[0063]

[0064] Among them, W r (·) represents the range spectrum envelope; W a (·) represents the azimuth spectrum envelope; f r Indicates the distance frequency; f a represents the Doppler frequency; j represents an imaginary number; The coefficient of the distance compression term, K r Indicates the frequency modulation slope; r represents the vertical distance between the target and the track; f c represents the center frequency of the signal; c represents the speed of sound in water; represents the coefficient of the azimuth compression term; represents the coefficient of the distance migration term; represents the coefficient of the quadratic distance compression term; Represents the coefficient of the cubic distance compression term.

[0065] As an embodiment, the single-base conversion includes: performing fixed compensation in the time domain in the precise two-dimensional spectrum expression; performing delay compensation in the range frequency domain-azimuth time domain; and rearranging the data in the time domain using the offset of the data of the mth receiving array on the azimuth axis as an interval to compensate for the double-base deformation term.

[0066] In other words, the precise two-dimensional spectrum can be processed sequentially through the first filter, the second filter and the conversion processor to obtain an equivalent monostatic echo baseband signal.

[0067] Specifically, using the first filter H phase,m (r) Fixed compensation in the time domain in an accurate two-dimensional spectral expression.

[0068] The first filter H phase,m (r) may be:

[0069]

[0070] Where, j represents an imaginary number; f c represents the center frequency of the signal; c represents the speed of sound in water; ΔR m (r) = (2vr / c + d m ) 2 / (4r), v represents the speed of movement; r represents the vertical distance between the target and the track, d m Indicates the baseline length from the mth receiving array to the transmitting array.

[0071] Using ΔR at the reference distance m (r c ) to replace the value in the mapping band, and then use the second filter H delay,m (f r ) performs delay compensation in the range-frequency domain and azimuth-time domain.

[0072] The second filter H delay,m (f r ) can be:

[0073]

[0074] Where, j represents an imaginary number; f r represents the distance frequency; c represents the speed of sound in water; ΔR m (r) = (2vr / c + d m ) 2 / (4r), v represents the speed of movement; r represents the vertical distance between the target and the track, d m Indicates the baseline length from the mth receiving array to the transmitting array.

[0075] Since the transmitting / receiving arrays in the multi-receiving array synthetic aperture sonar have different positions, the exact two-dimensional spectrum expression SS m (f r ,f a ; The second exponential term in r) is the double basis deformation term.

[0076] According to the time-shift characteristic of the Fast Fourier Transform (FFT), the double-base deformation term will cause the data of the mth receiving array to shift by d on the azimuth axis. m Therefore, in order to offset the influence of the double-base deformation term, the conversion processor uses the offset of the data of the mth receiving array on the azimuth axis as the interval, that is, to compensate for the double-base deformation term with d m / 2 as the interval to rearrange the data in the time domain.

[0077] After the equivalent monostatic echo baseband signal is obtained, the third filter is used to perform distance compression on the equivalent monostatic echo baseband signal to obtain a data reconstruction signal.

[0078] As an embodiment, the data reconstruction signal SS(f r ,f a ; r) can be:

[0079]

[0080] Among them, W r (·) represents the range spectrum envelope; W a (·) represents the azimuth spectrum envelope; f r Indicates the distance frequency; f a represents the Doppler frequency; j represents an imaginary number; r represents the vertical distance between the target and the track; f c represents the center frequency of the signal; c represents the speed of sound in water; represents the coefficient of the azimuth compression term; represents the coefficient of the distance migration term; represents the coefficient of the quadratic distance compression term; Represents the coefficient of the cubic distance compression term.

[0081] As an embodiment, the second filter H rc (f r ) can be:

[0082]

[0083] in, The coefficient of the distance compression term, K r Indicates the frequency modulation slope; f r Represents the distance frequency.

[0084] Step 103: The data reconstruction signal is processed into blocks in the range time domain to obtain multiple sub-blocks.

[0085] Range-time sub-block processing is used to reduce the coupling of high-order phases with range, making the imaging method more suitable for large swath situations.

[0086] As a preferred embodiment, the block processing includes: dividing the data reconstruction signal into multiple sub-blocks in the range time domain, and performing high-order phase compensation on the center of each sub-block, rather than just compensating the high-order phase terms at the center of the entire mapping zone. This can reduce the variation of the azimuth-range coupling in the two-dimensional spectrum with distance, thereby making the imaging method more suitable for large mapping zones.

[0087] It should be noted that if the data reconstruction signal is evenly divided into P sub-blocks in the range time domain, the size of each sub-block is: N P =N r / P,N r Represents the distance sampling point; the discretized distance time domain sampling point is: l = pN P +n, p represents the number of the sub-block, ranging from: p = -P / 2, -P / 2+1, ..., P / 2-1; the distance discretization within the sub-block p is r' = r p +(nN P / 2)·c / (2f s ), r p represents the center of the sub-block, f s Represents the sampling frequency in the range direction.

[0088] As an embodiment, the sub-block SS after high-order phase compensation is performed Bulk (f r ,f a ; r) can be expressed as:

[0089]

[0090] Among them, W r (·) represents the range spectrum envelope; W a (·) represents the azimuth spectrum envelope; f r Indicates the distance frequency; f a represents the Doppler frequency; j represents an imaginary number; represents the coefficient of the azimuth compression term, f c represents the center frequency of the signal; c represents the speed of sound in water; v represents the speed of movement; r represents the vertical distance between the target and the track; Represents the coefficient of distance migration term; r p Indicates the center of the sub-block; represents the coefficient of the quadratic distance compression term; represents the coefficient of the cubic distance compression term; for and When r=r p The higher-order terms remaining from the Taylor expansion at .

[0091] Perform high-order phase compensation SS on any sub-block HOP (f r ,f a ; r) can be expressed as:

[0092]

[0093] Among them, SS(f r ,f a ; r) represents the data reconstruction signal; represents the residual high-order phase error after compensation.

[0094] It should be noted that f r It represents the frequency in the range direction, and its coefficient is only related to the azimuth frequency and distance.

[0095] More specifically, the process of high-order phase compensation includes:

[0096] First, the high-order phase compensation expression SS HOP (f r ,f a ; r) and When r=r p Taylor expansion is performed at the position and retained to the first-order term for subsequent processing, thereby obtaining the intermediate phase expression SS ARD,m (f r ,f a ; r); then the intermediate phase expression SS ARD,m (f r ,f a ; r) Perform consistent phase compensation to remove f r The distance-independent terms in the first, second and third coefficients of the compensated sub-block SS are finally obtained. Bulk (f r ,f a ; r).

[0097] Intermediate phase expression SS ARD,m (f r ,f a ; r) can be:

[0098]

[0099]

[0100] in, for and When r=r p The higher-order terms remaining from the Taylor expansion at .

[0101]

[0102] Since the phase of the residual high-order terms cannot be ignored, the intermediate phase expression SS ARD,m (f r ,f a ; r) Perform consistent phase compensation to remove f r The distance-independent terms in the first, second, and third coefficients of make each sub-block center r p If the distance migration, secondary distance compression and tertiary distance compression are zero, the remaining terms are only the residual distance migration, residual secondary distance compression term and residual tertiary distance compression term of the distance space variation.

[0103] In addition, based on the center r of each sub-block p Distance migration Secondary distance compression Three-dimensional distance compression And the distance frequency f r The phase multiplication factor can be obtained

[0104] Phase multiplication factor The expression can be:

[0105]

[0106] Step 104: Perform range frequency domain sub-band processing within the current sub-block, process multiple sub-band data within the sub-block separately, and obtain corresponding low-resolution results of multiple frequency sub-bands.

[0107] The range frequency domain subband processing converts the high-order phase into a linear phase in each subband through piecewise linear approximation in the frequency domain, thereby reducing the coupling of the high-order phase with the range frequency in each subband.

[0108] It should be noted that the distance frequency within the sub-block p can be discretized as:

[0109] f r = k·f s / N r ;

[0110] Where k represents the distance frequency domain full sampling point, the range is k = -N r / 2,-N r / 2+1,...,N r / 2-1;f s Indicates the sampling rate in the distance direction; N r Indicates the number of frequency sampling points.

[0111] Then the sub-block SS after high-order phase compensation is Bulk (f r ,f a ; r) can be discretized as:

[0112]

[0113] in,

[0114] As a preferred embodiment, the range frequency domain subband processing includes: dividing the range frequency domain into multiple subbands within each subblock, and performing piecewise linear approximation processing on the quadratic term and the cubic term of the range frequency within each subband, and converting the high-order phase within each subband into a linear phase. In this way, the two-dimensional spectrum only contains linear terms, and the two-dimensional spectrum error within each subband can be guaranteed to be less than π / 4, thereby reducing the azimuth range coupling.

[0115] Specifically, each sub-block is divided into multiple sub-bands in the range frequency domain, including:

[0116] Divide the frequency sampling points k, the total number of frequency domain sampling points is N r , divided equally into Q blocks, then the size of each subband is N Q =N r / Q, the distance frequency domain full sampling point k can be expressed as: k = qN Q +i.

[0117] Where q represents the subband number, ranging from q = -Q / 2, -Q / 2+1, ..., Q / 2-1; the frequency sampling point i in subband q = 0, 1, ..., N Q -1.

[0118] The piecewise linear approximation processing of the quadratic and cubic terms of the distance frequency in each sub-band includes: taking the tangent of the phase of each segment at the center of the frequency sub-block as the approximate value within the sub-block, so that the center can be fully compensated and no phase jump occurs at the boundary point.

[0119] In other words, the high-order phase in each segment is approximately considered to be equal to the linear phase after approximation. The step size and number of segments depend on whether the high-order phase error of the two-dimensional spectrum in the sub-band meets the high-resolution imaging requirement of less than π / 4.

[0120] The piecewise linear approximation process is as follows: after k is divided, the sub-block SS after high-order phase compensation is Bulk (f r ,f a ; r) discretization expression SS Bulk (k,f a ; pN P +n) 2 and the cubic term k 3 Linear approximation is performed in each subband, and then the following is obtained: Figure 4 Schematic diagram of the piecewise linear approximation shown.

[0121] Specifically, the quadratic term k 2 and the cubic term k 3 The approximate expressions are:

[0122]

[0123] After piecewise linear approximation, the subband two-dimensional spectrum SS corresponding to the qth subband in sub-block p PLA (qN Q +i,f a ; pN P +n) can be expressed as:

[0124]

[0125] in, The phase error caused by the piecewise linear approximation can be expressed as:

[0126]

[0127] based on and The cumulative error obtained by accumulating the three is:

[0128]

[0129] According to the frequency full sampling point k = qN Q +i, in sub-block p, traverse the frequency sub-band q, and get the maximum phase error in the current sub-block p:

[0130]

[0131] Among them, k m It means that after traversing subband q, The distance frequency sampling point corresponding to the maximum value.

[0132] Next, traverse the distance time domain sub-block p, according to the distance time domain sampling point l = pN P+n, and get the maximum phase error in the entire range direction.

[0133] The maximum phase error can be expressed as:

[0134]

[0135] The multiple sub-band data within the sub-block are processed separately to obtain low-resolution results of the multiple frequency sub-bands, including: correcting the linear phase by using a fast implementation method of Chirp-Z transform to obtain a low-resolution result of each frequency sub-band.

[0136] Since the two-dimensional spectrum contains only linear terms, which can be corrected by Chirp-Z transformation, the present invention uses Chirp-Z transformation to correct the linear phase in the two-dimensional spectrum, which can reduce the coupling of high-order phase with distance frequency, and is thus suitable for broadband signals.

[0137] The Chirp-Z transform is used to implement frequency domain convolution through time domain multiplication.

[0138] More specifically, after block division and piecewise linear approximation, appropriate P and Q values ​​are selected to ensure that each frequency sub-block has Then based on and The accumulated error of the three can be ignored, and the focusing process in each sub-block can meet the requirements of high-resolution imaging.

[0139] From the quadratic term k 2 and the cubic term k 3 It can be seen from the approximate expression that it no longer contains the quadratic and cubic terms of the range frequency, while the linear term of the range frequency can be eliminated by range migration correction, and can be efficiently implemented using Chirp-Z transform. Therefore, after Chirp-Z transform processing, the range Doppler domain data of sub-block p and range sub-band q can be obtained.

[0140] Specifically, the specific expression of range Doppler domain data can be:

[0141]

[0142] According to the Bluestein equation, 2ni=n 2 +i 2 -(ni) 2 , we can get:

[0143]

[0144] Among them, g(qN Q ,f a ) and h(qNQ ,f a ) are respectively expressed as:

[0145]

[0146] A fast implementation of the Chirp-Z transform operation is as follows Figure 5 As shown, due to pP(pN P +n,f a ) is equivalent to PP PLA (qN Q +i,f a ; pN P +n) multiplied by exp(-j·h(qN Q ,f a )N P i), then multiply by exp(j·h(qN Q ,f a )i 2 ), and finally with exp(-j·h(qN Q ,f a )i 2 ) to perform convolution. Therefore, in order to make the calculation more efficient, frequency domain convolution can generally be achieved by multiplying in the time domain, and finally multiplying by The result is obtained after range migration correction and secondary range compression.

[0147] Step 105: coherently superimpose the low-resolution results of all frequency sub-bands within the current sub-block to obtain a high-resolution image of the current sub-block; obtain multiple high-resolution images corresponding to multiple sub-blocks; and splice all high-resolution images along the range and time domain to obtain the result of the entire surveying band.

[0148] Image fusion is to coherently superimpose the low-resolution results of different sub-bands in a certain sub-block to form a high-resolution result, and then sequentially splice the high-resolution results of the range sub-blocks in the time domain to form the focusing result of the entire mapping belt.

[0149] It should be noted that the processing between different sub-blocks and different sub-bands is independent and can be implemented in parallel through multiple processors to improve the real-time performance of the algorithm.

[0150] Since the bandwidth of each subband is 1 / N of the total bandwidth P , therefore, the distance resolution is only 1 / N of the original signal P , the sub-band is a low-resolution result. The high-resolution result of the range sub-block p can be obtained by coherently superimposing the low-resolution results of Q range sub-bands.

[0151] Specifically, the high-resolution result pP(pN P +n,f a ) can be expressed as:

[0152]

[0153] Among them, w r (·) represents the envelope in the distance direction, which is in the form of a Symplectic function; (pN P +n) / f s Indicates the time corresponding to the actual distance coordinate of the target. Since (pN P +n) / f s With f a is irrelevant, so the range migration has been corrected.

[0154] The range Doppler domain data is the range full time domain data obtained by splicing along the range direction. Therefore, the spliced ​​signal expression pP(l,f a )for:

[0155]

[0156] Where CAT[·] represents the distance time domain splicing function, for pP(l,f a ) for azimuth compression; l represents the discretized distance time domain sampling point; f a represents the Doppler frequency; p represents the sub-block number; P represents the number of sub-blocks; N P Indicates the size of the sub-block; w r (·) represents the range envelope; τ represents the fast-changing time; f s Indicates the distance sampling frequency; W a (·) represents the azimuth spectrum envelope; j represents an imaginary number; Represents the phase multiplication factor.

[0157] The azimuthally compressed signal is subjected to azimuth IFFT to obtain the final two-dimensional time domain focusing result. The result is as follows Figure 6 shown.

[0158] On the other hand, the present invention provides a sub-block-sub-band based multi-receiving array synthetic aperture sonar imaging system, comprising a memory, a processor and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of any one of the above methods.

[0159] The technical solution of the system is consistent with the technical solution of the method and will not be described in detail here.

[0160] It should be noted that, for the above-mentioned various embodiments, for the sake of simplicity of description, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, and according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present application.

[0161] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, please refer to the relevant description of other embodiments. In the several embodiments provided in this application, it should be understood that the disclosed method or system can be implemented in other ways. For example, the embodiments described above are only schematic, such as the division of the units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed.

[0162] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0163] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0164] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, which is stored in a memory and includes several instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application.

[0165] A person skilled in the art can understand that all or part of the various circuits in the above-mentioned embodiments can be completed by entering a program to instruct related hardware. The program can be stored in a computer-readable memory, and the memory can include: a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0166] The above is only an exemplary embodiment of the present disclosure, and the scope of the present disclosure cannot be limited thereto. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the specification and practicing the disclosure here, those skilled in the art will easily think of the implementation scheme of the present disclosure. This application is intended to cover any modification, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the technical field not recorded in the present disclosure. The description and examples are regarded as exemplary only, and the scope and spirit of the present disclosure are defined by the claims.

[0167] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. As long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0168] It will be easily understood by those skilled in the art that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A multi-receive array synthetic aperture sonar imaging method based on sub-block-sub-band, characterized in that: The method comprises: Obtain the two-dimensional spectrum phase expression of the receiving array echo signal, expand the range frequency and retain it to the third-order term, and obtain the accurate two-dimensional spectrum expression of the receiving array; Removing the element dependency in the precise two-dimensional spectrum expression by monostatic conversion to obtain an equivalent monostatic echo baseband signal; performing range compression on the equivalent monostatic echo baseband signal to obtain a data reconstruction signal; The data reconstruction signal is processed in a block manner in a range time domain to obtain a plurality of sub-blocks; Performing range frequency domain sub-band processing within the current sub-block, processing multiple sub-band data within the sub-block separately, and correspondingly obtaining low-resolution results of multiple frequency sub-bands; The low-resolution results of all frequency sub-bands within the current sub-block are coherently superimposed to obtain a high-resolution image of the current sub-block; multiple sub-blocks correspond to multiple high-resolution images; and all high-resolution images are spliced ​​along the range and time domain to obtain the result of the entire surveying belt.

2. The sub-block-sub-band based multi-receiving array synthetic aperture sonar imaging method according to claim 1, characterized in that: The block processing includes: dividing the data reconstruction signal into a plurality of sub-blocks in the range time domain, and performing high-order phase compensation on the center of each sub-block.

3. The sub-block-sub-band based multi-receiving array synthetic aperture sonar imaging method according to claim 1, characterized in that: The range frequency domain subband processing includes: dividing each sub-block into multiple sub-bands in the range frequency domain, performing piecewise linear approximation processing on the quadratic term and the cubic term of the range frequency in each sub-band, and converting the high-order phase in each sub-band into a linear phase.

4. The sub-block-sub-band based multi-receiving array synthetic aperture sonar imaging method according to claim 1, characterized in that: The single-base conversion includes: Performing fixed compensation in the time domain of the precise two-dimensional spectrum expression; Delay compensation is performed in the range frequency domain and azimuth time domain; The data is rearranged in the time domain using the offset of the data of the mth receiving array on the azimuth axis as an interval to compensate for the double basic deformation term.

5. The sub-block-sub-band based multi-receiving array synthetic aperture sonar imaging method according to claim 2, characterized in that: The data reconstruction signal SS(f r ,f a ; r) is: Among them, W r (·) represents the range spectrum envelope; W a (·) represents the azimuth spectrum envelope; f r Indicates the distance frequency; f a represents the Doppler frequency; j represents an imaginary number; r represents the vertical distance between the target and the track; f c represents the center frequency of the signal; c represents the speed of sound in water; represents the coefficient of the azimuth compression term; represents the coefficient of the distance migration term; represents the coefficient of the quadratic distance compression term; Represents the coefficient of the cubic distance compression term.

6. The sub-block-sub-band based multi-receiving array synthetic aperture sonar imaging method according to claim 5, characterized in that: Sub-block SS after high-order phase compensation Bulk (f r ,f a ; r) is expressed as: Among them, W r (·) represents the range spectrum envelope; W a (·) represents the azimuth spectrum envelope; f r Indicates the distance frequency; f a represents the Doppler frequency; j represents an imaginary number; represents the coefficient of the azimuth compression term, f c represents the center frequency of the signal; c represents the speed of sound in water; v represents the speed of movement; r represents the vertical distance between the target and the track; Represents the coefficient of distance migration term; r p represents the center of the sub-block; represents the coefficient of the quadratic distance compression term; represents the coefficient of the cubic distance compression term; for and When r=r p The higher-order terms remaining from the Taylor expansion at .

7. The sub-block-sub-band based multi-receiving array synthetic aperture sonar imaging method according to claim 3, characterized in that: The piecewise linear approximation processing of the quadratic and cubic terms of the distance frequency in each sub-band includes: taking the tangent of the phase of each segment at the center of the frequency sub-block as the approximate value within the sub-block, so that the center can be fully compensated and no phase jump occurs at the boundary point.

8. The sub-block-sub-band based multi-receiving array synthetic aperture sonar imaging method according to claim 3, characterized in that: The linear phase is corrected using a fast implementation of the Chirp-Z transform to obtain a low-resolution result for each frequency subband.

9. The sub-block-sub-band based multi-receiving array synthetic aperture sonar imaging method according to claim 1, characterized in that: The spliced ​​signal expression pP(l,f a )for: Where CAT[·] represents the distance time domain splicing function, for pP(l,f a ) for azimuth compression; l represents the discretized distance time domain sampling point; f a represents the Doppler frequency; p represents the sub-block number; P represents the number of sub-blocks; N P Indicates the size of the sub-block; w r (·) represents the range envelope; τ represents the fast-changing time; f s Indicates the distance sampling frequency; W a (·) represents the azimuth spectrum envelope; j represents an imaginary number; Represents the phase multiplication factor.

10. A sub-block-sub-band based multi-receive array synthetic aperture sonar imaging system, comprising a memory, a processor and a computer program stored in the memory, characterized in that: The processor executes the computer program to implement the steps of the method according to any one of claims 1 to 9.

Images