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

Abstract

The present invention provides a sub-block-sub-band-based multi-receiving array synthetic aperture sonar imaging method and system, relating to the field of image processing technology. The method comprises: obtaining a two-dimensional spectral phase expression of the receiving array echo signal to obtain an accurate two-dimensional spectral expression; obtaining an equivalent monostatic echo baseband signal through monobasic conversion; performing range compression on the equivalent monostatic echo baseband signal to obtain a data reconstruction signal; performing block processing on the data reconstruction signal in the range time domain to obtain multiple sub-blocks; performing range-frequency domain sub-band processing within the current sub-block, independently processing the multiple sub-band data within the sub-block to obtain low-resolution results for the multiple sub-bands, and performing coherent superposition to obtain a high-resolution image of the current sub-block; obtaining multiple high-resolution images corresponding to the multiple sub-blocks; and splicing all high-resolution images along the range time domain to obtain the result of the full mapping swath. The present invention significantly reduces the influence of azimuth-range-frequency domain coupling of the two-dimensional spectrum.

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] During synthetic aperture sonar (SAS) imaging, ignoring the azimuth-range coupling of the two-dimensional spectrum can lead to severe defocusing, which in turn distorts the imaging results. Azimuth-range coupling in the two-dimensional spectrum is generally caused by range migration. Because the azimuth-range coupling is minimal in narrowband signals and narrow swaths, high-order terms can generally be ignored. However, in broadband signals and wide swaths, the azimuth-range coupling in the two-dimensional spectrum is significant, and high-order terms cannot be ignored.

[0003] To reduce the influence of azimuth-range coupling in two-dimensional spectra, the most direct approach currently is to use a migration algorithm to directly compensate for the nonlinear mapping of range and frequency in the two-dimensional frequency domain. However, this method suffers from low interpolation efficiency. Another approach is to use a linear frequency modulation (LFM) scaling algorithm to perform FFT calculations and phase multiplications. While this avoids interpolation and significantly improves computational efficiency, the LFM scaling algorithm does not address the spatial variation of azimuth-range coupling in the two-dimensional spectrum with range. As the target azimuth angle or the mapping bandwidth increases, the azimuth-range coupling increases, and the influence of the azimuth-range coupling term away from the center point becomes greater, resulting in non-negligible higher-order terms. In particular, the further away from the center point, the more severe the defocus, which in turn limits the mapping swath width.

[0004] To reduce the impact of azimuth-range coupling at the boundaries of the imaging region, continuous uniform blocking in the range direction can be used to address the range-variance problem of the azimuth-range coupling term. This can be achieved by simply shrinking the imaging region to reduce the residual error after azimuth-range coupling compensation. However, in the case of severe azimuth-range coupling, continuous uniform blocking in the range direction results in very densely packed scoring blocks, which is not only computationally inefficient but also makes the azimuth-range coupling term highly dependent on range frequency. Therefore, using only range-time blocking to reduce the impact of azimuth-range coupling is not effective. 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 to 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] Performing block processing on the data reconstruction signal in the 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 obtaining corresponding low-resolution results for 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 high-resolution images are obtained corresponding to multiple sub-blocks; and all high-resolution images are spliced ​​along the range and time domain to obtain the result of the entire mapping band.

[0012] Furthermore, 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.

[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] Furthermore, the single-base conversion includes:

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

[0016] Perform delay compensation in the range-frequency-azimuth-time domain;

[0017] The data are rearranged in the time domain using the offset of the data of the mth receiving array on the azimuth axis as the 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 distance migration term; represents the coefficient of the quadratic distance compression term; Represents the coefficient of the cubic distance compression term.

[0021] Furthermore, 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 azimuthal 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; 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 sub-band.

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

[0027]

[0028] Among them, CAT[·] represents the distance time domain splicing function, for pP(l,f a ) performs 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 range 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 multi-receiving array synthetic aperture sonar imaging method and system based on sub-block-sub-band, which can achieve the following beneficial effects compared with the existing technology:

[0031] This method performs block processing in the range-time domain to generate multiple sub-blocks. It then performs range-frequency-domain sub-band processing within each sub-block, independently processing the multiple sub-band data within the sub-block to produce low-resolution results for multiple frequency sub-bands. Finally, through coherent superposition and image fusion, high-resolution, full-band imaging is achieved. In the case of broadband signals and wide mapping, this method not only significantly reduces the effects of azimuth-range-frequency coupling in the two-dimensional spectrum, ensuring high-resolution imaging results, but also ensures the independence of each sub-block and sub-band, improving the algorithm's real-time performance and, consequently, 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 following briefly introduces the drawings required for use in the embodiments or the description of the prior art. 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 any creative work.

[0033] Figure 1This is a schematic diagram of a method flow 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 This is a schematic diagram of the positional relationship between the transmitting array and the receiving array of a multi-receiving array synthetic aperture sonar imaging method and system based on sub-block-sub-band provided by the present invention;

[0035] Figure 3 This 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 This 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 This 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-blocks and sub-bands 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] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings and embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within 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 "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a method, step, or system comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such method, step, or system. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the method, step, or system comprising the element.

[0041] like Figure 1As shown, the present invention provides a multi-receiving array synthetic aperture sonar imaging method based on sub-block and 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 to obtain an accurate two-dimensional spectrum expression of the receiving array.

[0043] The derivation of the two-dimensional spectrum expression is key to the frequency-domain line-by-line algorithm and forms the foundation for subsequent imaging. Specifically, in the process of solving the precise two-dimensional spectrum expression, after expanding the two-dimensional spectrum phase expression of the echo baseband signal to the third-order terms at the range frequency, the higher-order phase terms are not simply ignored. Instead, they are retained and accurately compensated for 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 phase dwell principle is utilized to obtain a two-dimensional spectrum phase expression of the receiving array m echo signal based on the precise distance history.

[0046] More specifically, the two-dimensional spectrum 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 center frequency of the signal; 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 as follows 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 It 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 azimuthal compression term, represents the coefficient of 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 designed to remove the array element dependencies in the two-dimensional spectral phase and convert the multi-receive array data into single-array data. In this way, the subsequent imaging process only needs to be performed 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-base conversion is an accurate two-dimensional spectrum expression for each receiving array, that is, Figure 3 As shown, FFT stands for Fast Fourier Transform, and IFFT stands for Inverse Fast Fourier Transform. The algorithm consists of six Fourier transforms / inverse transforms and ten phase multiplications. Multiple precise two-dimensional spectral expressions corresponding to multiple receiving arrays are then simultaneously converted to single basis.

[0061] The array element dependency term in the precise two-dimensional spectrum expression is removed, that is, the term related to the receiving array number m in the precise two-dimensional spectrum expression is removed, thereby obtaining 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 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 with the offset of the data of the mth receiving array on the azimuth axis as the 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, the first filter H is used phase,m (r) Fixed compensation in the time domain in an accurate two-dimensional spectral expression.

[0068] The first filter H phase,m (r) can 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] ΔR at the reference distance m (r c ) to replace the value within 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 characteristics 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 obtaining the equivalent monostatic echo baseband signal, the third filter is used to perform range 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 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 divided 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 distance, making the imaging method more applicable to large survey swaths.

[0086] As a preferred embodiment, 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 term at the center of the entire survey swath. 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 survey swaths.

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

[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 azimuthal 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; 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 range frequency, 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 SS are 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 and residual tertiary distance compression 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 range 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, and process multiple sub-band data within the sub-block separately to obtain low-resolution results of multiple frequency sub-bands.

[0107] Range-frequency subband processing uses piecewise linear approximation in the frequency domain to convert high-order phases into linear phases in each subband, thereby reducing the coupling of high-order phases with 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] Among them, 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 range sampling rate; N r Indicates the number of frequency sampling points.

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

[0112]

[0113] in,

[0114] As a preferred embodiment, range-frequency subband processing includes dividing the range-frequency domain into multiple subbands within each sub-block, performing piecewise linear approximation on the quadratic and cubic terms of the range frequency within each sub-band, and converting the high-order phase within each sub-band into a linear phase. This results in a two-dimensional spectrum containing only linear terms, ensuring that the two-dimensional spectrum error within each sub-band is less than π / 4, thereby reducing azimuth-range coupling.

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

[0116] Divide the frequency full sampling point k, the number of frequency domain full sampling points is N r , divided into Q blocks, 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 points.

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

[0120] The piecewise linear approximation process is as follows: after dividing k, the sub-block SS after high-order phase compensation is Bulk (f r ,f a ; The discretization expression SS of r) Bulk (k,f a ;pN P +n) in the quadratic term k 2 and the cubic term k 3 Linear approximation is performed within 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 is 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, within sub-block p, traverse the frequency sub-band q, and obtain the maximum phase error within the current sub-block p:

[0130]

[0131] Among them, k m 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] Separately processing multiple sub-band data within a sub-block to obtain corresponding low-resolution results for multiple frequency sub-bands includes: correcting the linear phase using a fast implementation of Chirp-Z transform to obtain a low-resolution result for each frequency sub-band.

[0136] Since the two-dimensional spectrum contains only linear terms, which can be corrected using the Chirp-Z transform, the present invention uses the Chirp-Z transform to correct the linear phase in the two-dimensional spectrum, reducing the coupling of high-order phases with range frequency, making it 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 cumulative error accumulated by 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 The approximate expression for shows that it no longer contains the quadratic and cubic range frequency terms. The linear range frequency term can be eliminated through range migration correction, which can be efficiently implemented using the Chirp-Z transform. Therefore, after the Chirp-Z transform, the range-Doppler domain data for 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 way to implement 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 be more efficient, frequency domain convolution can generally be achieved by multiplying in the time domain, and finally multiplying by The result after range migration correction and secondary range compression is obtained.

[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 band.

[0149] It should be noted that the processing between different sub-blocks and different sub-bands is independent and can be implemented in parallel by 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 results 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 Sigmoid 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] Among them, CAT[·] represents the distance time domain splicing function, for pP(l,f a ) performs 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 range 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 aforementioned embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that this application is not limited to the order of the actions described. According to this 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 this specification are all preferred embodiments, and the actions and modules involved are not necessarily required by this 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 merely schematic. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods, 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 separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0163] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or 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, 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 a number of instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application.

[0165] Those skilled in the art will appreciate that all or part of the various circuits in the above embodiments may be implemented by instructing related hardware through a program, and the program may be stored in a computer-readable memory, which may include a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0166] The above is only an exemplary embodiment of the present disclosure and cannot be used to limit the scope of the present disclosure. 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 herein, those skilled in the art will easily think of the implementation scheme of the present disclosure. This application is intended to cover any variation, 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 art that are not recorded in the present disclosure. The description and examples are to be 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 can 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 merely 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 scope of protection 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 to 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; Performing block processing on the data reconstruction signal in the range time domain to obtain a plurality of sub-blocks; Performing range-frequency subband processing within the current sub-block, individually processing multiple sub-band data within the sub-block to obtain corresponding low-resolution results for multiple frequency sub-bands; the range-frequency sub-band processing includes: dividing each sub-block into multiple sub-bands in the range-frequency domain, and performing piecewise linear approximation processing on the quadratic and cubic terms of the range frequency within each sub-band to convert the high-order phase within each sub-band into a linear phase; wherein the piecewise linear approximation processing 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 points; and correcting the linear phase using a fast implementation of the Chirp-Z transform to obtain a low-resolution result for each frequency sub-band; 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 high-resolution images are obtained corresponding to multiple sub-blocks; and all high-resolution images are spliced ​​along the range and time domain to obtain the result of the entire mapping band.

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 multiple 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-receive 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; Perform delay compensation in the range-frequency-azimuth-time domain; First The offset of the data of the receiving arrays on the azimuth axis is used as the interval, and the data is rearranged in the time domain to compensate for the double basic deformation term.

4. 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 for: ； in, Represents the range spectrum envelope; represents the azimuth spectrum envelope; represents the distance frequency; represents the Doppler frequency; represents an imaginary number; ; Indicates the vertical distance between the target and the track; Indicates the signal center frequency; represents the speed of sound in water; ; , represents the coefficient of the azimuth compression term, Indicates the speed of movement; , represents the coefficient of distance migration term; , represents the coefficient of the quadratic distance compression term; , represents the coefficient of the cubic distance compression term.

5. The sub-block-sub-band based multi-receiving array synthetic aperture sonar imaging method according to claim 4, characterized in that: Sub-block after high-order phase compensation The expression is: ； in, Represents the range spectrum envelope; represents the azimuth spectrum envelope; represents the distance frequency; represents the Doppler frequency; represents an imaginary number; , represents the coefficient of the azimuth compression term, ; Indicates the signal center frequency; represents the speed of sound in water; Indicates the speed of movement; Indicates the vertical distance between the target and the track; , represents the coefficient of distance migration term; 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 exist The higher-order terms remaining from the Taylor expansion at .

6. 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 for: ； in, represents the distance time domain splicing function, Perform azimuth compression; Represents the discretized distance time domain sampling points; represents the Doppler frequency; Indicates the number of the sub-block; Indicates the number of sub-blocks; Indicates the size of the sub-block; Indicates the distance envelope; Indicates fast-changing time; Indicates the range sampling frequency; represents the azimuth spectrum envelope; represents an imaginary number; Represents the phase multiplication factor.

7. 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 6.

Images