CN114217321A - Multi-constraint matching processor positioning method - Google Patents

Multi-constraint matching processor positioning method Download PDF

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CN114217321A
CN114217321A CN202111434375.5A CN202111434375A CN114217321A CN 114217321 A CN114217321 A CN 114217321A CN 202111434375 A CN202111434375 A CN 202111434375A CN 114217321 A CN114217321 A CN 114217321A
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王晋晋
郭凯晖
邱龙皓
张志刚
付进
邹男
齐滨
郝宇
张光普
王逸林
王燕
梁国龙
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Abstract

Compared with the matching field positioning method of the original multi-constraint matching processor, the method only needs to additionally process and calculate the autocorrelation matrix in the calculation process, does not increase the calculation difficulty and complexity, simultaneously enables the original calculation result to be more accurate and convenient to search, greatly reduces the interference of other interference peaks or side lobes on a target peak, improves the identification degree of a main peak, and simultaneously enables the method to have certain improvement on the resistance to environmental mismatch due to the introduction of the weighted linear matching processor.

Description

Multi-constraint matching processor positioning method
Technical Field
The invention relates to the technical field of acoustic positioning of a matching field, in particular to a positioning method of a multi-constraint matching processor.
Background
Matching field localization is one of the localization methods commonly used in the acoustic field in recent years. The existing matching field positioning method is to obtain the specific position of a target by constructing a sound field model, calculating a copy sound field, and substituting sound field data collected by a receiving hydrophone and copy field data obtained by calculation into a matching processor. However, when matching field positioning is performed, a target positioning result is deviated due to deviation between a constructed copy field and an actual sound field, namely, the problem of environmental imbalance; or due to the existence of interference sources of the sound field acquired by the hydrophone and other conditions, other interference peaks exist on the ambiguity surface of the final positioning result, and the target peak point is difficult to search.
The existing general matching field positioning method is carried out in a sound pressure vertical array or horizontal array mode, the method mainly has two factors to influence and limit the positioning performance, firstly, a sound pressure hydrophone cannot provide angle information of any target sound source, and azimuth information must be acquired by means of array signal processing; secondly, for array processing, especially for vertical arrays commonly used in matching field positioning, the problems of mismatching such as inclination, array element depth, array element interval and the like exist, and the positioning effect is seriously influenced.
According to different processing ideas, the currently used matching field positioning methods can be classified into 3 types: multi-constraint methods, subspace methods, and statistical-like methods.
The present invention relates to a multi-constraint method. For example, for the multi-constrained processor method, a linear constraint method is adopted, and a plurality of adjacent points are arranged in a distance depth plane to ensure that the main lobe is similar to a Bartlett processor and the side lobe is similar to an MVDR under the condition of environmental mismatch. For a minimum variance processor, the measured data and the copied data are optimized in the sense that the output noise power is minimized.
By adopting the method, the multi-constraint processor has more side lobes or interference peaks when carrying out matching field positioning, so that the searching of the target peak becomes more troublesome; and because the size of the constraint area or the selection of the disturbance range, namely the mismatch problem of the environment, the influence on the processing result is also large, so that the positioning result does not necessarily meet the actual requirement.
It is because of the above problems that the improvement of the method is needed to make the positioning result more accurate, the positioning performance better and the resistance of the matching processor to the environmental mismatch become important research aspects.
Disclosure of Invention
The invention improves the original multi-constraint processing method to enhance the positioning performance and the positioning effect of the matching processor and simultaneously enhance the environmental mismatch resistance, and provides a multi-constraint matching processor positioning method, which provides the following technical scheme:
a multi-constraint matching processor positioning method, the method comprising the steps of:
step 1: determining the number of neighborhood constraint points; before the sound field of the target water area is collected, measuring environmental parameters of the target water area, including water depth, sound velocity gradient curve, water bottom flatness and rough terrain in the range of the target water area, water bottom sediment components, sediment density, sediment attenuation coefficient and sediment sound velocity;
step 2: acquiring sound field data of a water area where a target signal is located according to the environmental parameters of the measured water area, and constructing a water area sound field model;
and step 3: determining a matrix for a copy field according to the constructed water area sound field model;
and 4, step 4: acquiring underwater acoustic signals of target sound source in water area by using horizontal receiving hydrophone array to obtain measurement field matrix signals F1；
And 5: for measurement field matrix signal F1Performing autocorrelation calculation to determine an autocorrelation matrix R;
step 6: substituting the measurement field matrix, the copy field matrix and the autocorrelation matrix into a cost function of the multi-constraint processor to obtain a positioning result of the original matching processor;
and 7: and (4) carrying out difference on the cost functions of the multi-constraint matching processors before and after the autocorrelation matrix processing to obtain the difference value of the positioning result of each sound field point, and carrying out more accurate positioning on the target.
Preferably, the step 3 specifically comprises: adopting a Kraken model or a Bellhop model, and when the water bottom is flat and is a low-frequency far field, calculating the sound field model by adopting a Kraken simple normal wave model; when the water bottom has undulating terrain and is a high-frequency sound field, a Bellhop ray model is adopted for calculation, and the obtained copy field uses a matrix F2And (4) showing.
Preferably, the step 6 specifically includes: substituting the measurement field matrix, the copy field matrix and the autocorrelation matrix into a cost function of the multi-constraint processor to obtain a positioning result of the original matching processor, wherein the positioning result is represented by the following formula:
Figure BDA0003381092310000031
preferably, the step 7 specifically includes:
step 7.1: the constant m is calculated from the autocorrelation matrix and the measurement field sound pressure and is represented by the following formula:
Figure BDA0003381092310000032
wherein, K is the number of receiving array elements, and m is a constant greater than 0;
adding the autocorrelation matrix and the diagonal matrix with diagonal elements of m to obtain a matrix Rm，RmRepresented by the formula:
Rm＝R+m·En
wherein E isnIs an n-order identity matrix;
step 7.2: substituting the measured field matrix and the copy field matrix into a cost function of a multi-constraint processor, and then processing an autocorrelation matrix of the measured field matrix to obtain a matrix RmIn the matrix RmSubstituting the autocorrelation matrix R into the cost function of the multi-constraint processor yields the following fuzzy surface expression:
Figure BDA0003381092310000041
step 7.3: and (3) subtracting the cost functions of the multi-constraint matching processors before and after the autocorrelation matrix processing to obtain an expression of the difference value of the positioning result of each sound field point, and performing variable separation and simplification on the expression, wherein the difference value equation is expressed by the following formula:
Figure BDA0003381092310000042
the measurement field matrices are all deterministic constants, and each term in the above equation is replaced by the following parameters:
Figure BDA0003381092310000043
Figure BDA0003381092310000044
Figure BDA0003381092310000045
the difference function Delta is expressed by the above parametersMCThen there is deltaMC＝t·k0+k1；
Step 7.4: will PMCmThe following expression can be obtained by expanding, separating and simplifying the expression of (a):
Figure BDA0003381092310000046
Figure BDA0003381092310000047
Figure BDA0003381092310000048
Figure BDA0003381092310000049
when the environmental parameters are deviated within a certain range, the target is still accurately positioned.
Preferably, the value of m is determined according to the actually measured sound field or autocorrelation matrix, and is generally selected from the range of 0.01-0.000001.
The invention has the following beneficial effects:
compared with the matching field positioning method of the original multi-constraint matching processor, the method only needs to additionally process and calculate the autocorrelation matrix in the calculation process, does not increase the difficulty and complexity of calculation, simultaneously enables the original calculation result to be more accurate and convenient to search, greatly reduces the interference of other interference peaks or side lobes to a target peak, improves the identification degree of a main peak, and simultaneously enables the method to have certain improvement on the resistance to environmental mismatch due to the introduction of the weighted linear matching processor.
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FIG. 1 is a block flow diagram of the present invention;
FIG. 2 is a schematic diagram of a constructed water sound field model;
FIG. 3 is a sound velocity curve of a water area sound field according to the present invention, the sound velocity curve being a sound velocity curve of a water area in Jilin Songhua lake autumn;
FIG. 4 is a multi-constrained matching processor localization result without processing the autocorrelation matrix of the measurement field;
FIG. 5 is a positioning result of the multi-constraint matching processor after the measurement field autocorrelation matrix is processed by the present invention;
FIG. 6 is a graph of the distance positioning results of the present invention for water depth mismatches before and after autocorrelation matrix processing of the survey field;
FIG. 7 is a distance positioning result curve of the present invention before and after the autocorrelation matrix processing of the measurement field for the sound velocity mismatch of the water area;
FIG. 8 is a graph of the distance positioning results of the present invention for water bed bottom material mismatch before and after autocorrelation matrix processing of the measurement field;
FIG. 9 is a distance positioning result curve of the present invention for water bottom terrain mismatch before and after the autocorrelation matrix processing of the survey field.
Detailed Description
The present invention will be described in detail with reference to specific examples.
The first embodiment is as follows:
as shown in fig. 1 to 9, the specific optimized technical solution adopted to solve the above technical problems of the present invention is: the invention relates to a multi-constraint matching processor positioning method.
Step 1: before collecting a sound field of a target water area, measuring environmental parameters of the target water area, namely water depth, a sound velocity gradient curve, water bottom flatness and rough topography of the target water area range, water bottom sediment components, sediment density, a sediment attenuation coefficient and a sediment sound velocity;
step 2: constructing a sound field model according to the previously measured environmental parameters;
and step 3: and calculating the previous sound field model by adopting a Kraken model or a Bellhop model. Both models are usually selected according to the actually constructed sound field model. Generally, when the water bottom is flat and is a low-frequency far field, the sound field model adopts a Kraken normal wave model to calculate the water bottom; and when the water bottom has undulating terrain and is a high-frequency sound field, the Bellhop ray model is adopted for calculation. In this example, the Bellhop ray model is used to perform the copy field calculation, and the calculated copy field is represented by a matrix F2Represents;
and 4, step 4: acquiring underwater acoustic signals of target sound source in water area by using horizontal receiving hydrophone array, and making the acquired measuring field signal be F1；
And 5: to the measurement field matrix F1Performing autocorrelation calculation to obtain an autocorrelation matrix R;
step 6: substituting the measured field matrix, the copy field matrix and the autocorrelation matrix into a cost function of the multi-constraint processor to obtain an expression of a positioning result of the original matching processor, wherein the expression comprises the following steps:
Figure BDA0003381092310000061
and 7: the constant m is calculated by the autocorrelation matrix and the measurement field sound pressure, and the specific formula is as follows:
Figure BDA0003381092310000062
wherein, K is the number of receiving array elements, m is a constant greater than 0, and the value thereof is determined according to the actual situation and is generally selected between 0.01 and 0.000001;
adding the autocorrelation matrix and the diagonal matrix with diagonal elements of m to obtain a matrix Rm，RmThe specific expression is as follows:
Rm＝R+m·En
wherein E isnIs an n-order identity matrix;
substituting the measured field matrix and the copy field matrix into a cost function of a multi-constraint processor, and then processing an autocorrelation matrix of the measured field matrix to obtain a matrix RmIn the matrix RmSubstituting the autocorrelation matrix R into the cost function of the multi-constraint processor yields the following fuzzy surface expression:
Figure BDA0003381092310000071
the cost functions of the multi-constraint matching processors before and after the autocorrelation matrix processing are differentiated to obtain an expression of the difference value of each sound field point positioning result, and then the expression is subjected to variable separation and simplification, so that the difference value equation can be expressed as the following form:
Figure BDA0003381092310000072
from the above equation, it can be seen that since m is a constant calculated before, the measurement field matrices are all deterministic constants, thereby allowing the terms in the above equation to be replaced by the following parameters
Figure BDA0003381092310000073
Figure BDA0003381092310000074
Figure BDA0003381092310000075
The difference function Delta is expressed by the above parametersMCThen there is deltaMC＝t·k0+k1(ii) a By selecting the appropriate m, k can be made0Is a constant greater than 0Number, hence ΔMCCan be regarded as a linear function with the coefficient larger than 0, and the variable t is the matching field positioning cost function expression of the original multi-constraint processor. Thus, ΔMCIs related to the result of the original matching field localization, P of the original matching field localizationMCThe larger the value of (A), the larger the value of (A)MCThe larger the value of (A); the target peak value of the fuzzy surface in the original matching field positioning can be effectively amplified, and the value of the non-target peak is effectively inhibited, so that the result of target positioning is highlighted, and the target searching is facilitated.
Will PMCmThe following expression can be obtained by expanding, separating and simplifying the expression of (a):
Figure BDA0003381092310000081
similarly, the following parameters were varied for each of the above equations:
Figure BDA0003381092310000082
Figure BDA0003381092310000083
Figure BDA0003381092310000084
as can be seen from the above equation, the parameter l is close to the linear matching processor PCMFPIf k is to be2And k is1If viewed as weights, the multi-constrained processor after transformation can be viewed as the result of the linear matching processor after weighting. And because the linear matching processor has stronger robustness when the environmental parameters change, compared with the original multi-constraint matching processor, the improved multi-constraint method has certain robustness in the aspect of resisting environmental mismatch, so that when the environmental parameters deviate in a certain range, a target can still be subjected to more robustnessAnd (4) accurate positioning.
The second embodiment is as follows:
the invention adopts a group of lake experimental data, and the water area environment model is constructed by taking the pine flower lake as a prototype. By utilizing the technical scheme, the sound source with the distance of 250m and the depth of about 20m is positioned and calculated, the receiving hydrophone is a 10-element horizontal array, the array element interval is 0.33m, and underwater target sound source signals are 10 line spectrum signals with the fundamental frequency of 56 Hz.
The method comprises the following steps: measuring the sound field environment parameters of the water area of the Songhua lake;
step two: constructing a sound field environment model, and obtaining that the depth of a measured water area is about 50m according to measurement, so that the water depth of the sound field model is set to be 50m, and the horizontal range is set to be 500 m; measuring the sound velocity gradient of the water area for multiple times, and performing six-order fitting on the measured data to obtain a sound velocity curve as shown in FIG. 2; the bottom of the model is flat; the water bottom substrate adopts a base layer parameter, namely the density rho is 1.8g/cm3The attenuation coefficient alpha is 0.15 dB/lambda, and the substrate sound velocity v is 1600 m/s; the sound field model obtained finally according to the above is shown in fig. 3;
step three: setting a model sound source signal as a single-frequency signal of 504 Hz; this frequency is chosen because of the line spectrum at 504Hz, which is most apparent from the previous spectral bins, but is not within the scope of the present invention. Therefore, only the model sound source signal is required to be set to 504Hz, and then the Kraken model is utilized to calculate the sound field to obtain a copy field result;
step four: carrying out data acquisition on the sound field to obtain a measurement field signal;
step five: performing autocorrelation calculation on the measurement field to obtain an autocorrelation matrix, and substituting the autocorrelation matrix and copy field data into a multi-constraint matching processor to obtain a calculated ambiguity image, which is specifically shown in fig. 4;
step six: selecting a proper constant m according to the self-correlation matrix of the measuring field calculated previously and the sound pressure of the measuring field, wherein m is 2.2204 multiplied by 10-12(ii) a Adding the autocorrelation matrix and the diagonal matrix with diagonal elements of m, and replacing the original autocorrelation matrix with the newly obtained matrixSubstituting the image into a multi-constraint matching processor to obtain an improved ambiguity image of the multi-constraint matching field positioning method, as shown in FIG. 5;
comparing the blurred surface image of the original multi-constraint method shown in fig. 4 with the blurred surface image of the improved multi-constraint method shown in fig. 5, it can be seen that when the autocorrelation matrix of the measurement field is not processed, more interference peaks exist in the obtained blurred surface, the overall image is messy, and the identification and search of the maximum peak are greatly influenced; after the autocorrelation matrix of the measurement field proposed in the invention is processed, the obtained fuzzy image is clearer and clearer, the interference peak is suppressed, the side lobe is reduced, and the main peak is more prominent and easy to identify. Recombining the previously derived differences ΔMCThe theoretical formula of the method shows that the theoretical derivation of the method for improving the positioning effect of the multi-constraint matching processor is consistent with the actual application result, and the positioning effect and the positioning performance of the multi-constraint matching processor are effectively improved.
Step seven: the positioning deviation condition when the environment is mismatched is simulated through simulation, and the frequency of the simulated sound source is changed into a single-frequency signal of 1000 Hz. And (3) simulating the condition of environment mismatch by changing the environment parameters of the sound field model constructed in the figure 3 on the basis of a single variable. Firstly, changing the water depth to simulate the condition of water depth mismatch, wherein the water depth change range is [40m, 60m ], and if the water depth of 50m is taken as the complete matching condition, obtaining the deviation percentage of the original multi-constraint method and the improved multi-constraint method to the positioning of the distance by simulation as shown in figure 6;
step eight: and (3) changing the sound velocity parameter of the sound field model constructed in the figure 3 to simulate the situation of sound velocity mismatch in water by using a single variable as a principle. Because the sound velocity of the original model is a sound velocity curve formed by fitting the water area measurement result, in order to represent different sound velocity changes more conveniently, the sound velocity mismatch condition of the original model is simulated by using an equal sound velocity gradient model, the change range of the new sound velocity model is [1480m/s, 1520m/s ], and the deviation percentage of the original multi-constraint method and the improved multi-constraint method obtained by simulation to distance positioning is shown in figure 7;
step nine: and (3) changing the substrate parameters of the sound field model constructed in the figure 3 to simulate the condition when the water bottom substrate is mismatched by using a single variable principle. Since there are a plurality of parameters that affect the substrate, the same parameters will differ from substrate to substrate. Only the change in the substrate sound velocity is selected here to simulate the substrate mismatch case. For example, for a deposited layer, the density ρ is 1.7g/cm3The attenuation coefficient α is 0.13dB/λ, which is comparable to the parameters of the substratum, but when its depth varies within 2.5m, the change in the speed of sound of the substratum varies from 1520m/s to 1580 m/s. Therefore, the original model substrate is changed into a deposition layer, and the sound velocity of the substrate ranges from [1520m/s, 1580m/s]The deviation percentage of the original multi-constraint method and the improved multi-constraint method obtained by simulation to the distance positioning is shown in fig. 8;
step ten: and (3) changing the substrate parameters of the sound field model constructed in the figure 3 to simulate the condition when the underwater terrain environment is mismatched on the basis of a single variable. Due to the fact that the underwater topography is various and nearly irregular, the change condition of the topography cannot be quantitatively represented through a specific change curve or a change parameter; therefore, in order to simulate the change of submarine topography, it is assumed that a triangular soil slope with a height of 2m and a bottom of 30m exists, then the submarine topography is respectively simulated by flat, one soil slope, three soil slopes, five soil slopes and seven soil slopes, and the soil slopes are uniformly distributed on the submarine, so as to achieve the purpose of simulating the mismatch of the submarine topography, and the deviation percentage of the original multi-constraint method and the improved multi-constraint method obtained by simulation to the distance positioning is shown in fig. 9;
compared with the positioning result deviation images when the environmental parameters are mismatched, the improved multi-constraint positioning method has certain performance improvement in the aspect of resisting environmental mismatch and stronger robustness compared with the original multi-constraint positioning method; this is consistent with the rationale derivation.
The third concrete embodiment:
in order to achieve the above-mentioned research objective, the following technical solutions are proposed:
firstly, the number of neighborhood constraint points needs to be determined, and the number M of the constraint points is obtained by the following formula:
M＝2·Ndim+1；
in the above formula, NdimRepresenting dimension, and the two-dimensional situation of the matching field positioning research is the two-dimensional case, namely the depth and the horizontal distance, N dim2; therefore, it can be determined that there are five neighboring sound source points, one of which is an original sound source point, and the values of the sound source points are (r, z), (r ± Δ r, z) (r, z ± Δ z); a constraint vector d can be obtained from the constraint points, but the calculation of the constraint vector d relates to the sound pressure of a measurement field, and the following process is discussed in detail;
secondly, sound field data acquisition needs to be carried out on a water area where the target signal is located, the number of hydrophones is assumed to be K, and for simplifying understanding and calculation explanation, the measurement field data is adopted to be a ternary vertical array, namely, the acquired data is F1＝[a b c]TDuring actual measurement, the number of the array elements is strictly set according to actual conditions;
constructing a sound field model of the collected water area;
and (3) performing simulated sound field calculation by using a Kraken normal wave or Bellhop ray model, wherein a specific calculation method is selected according to a sound field environment and a sound propagation model. In general, when the near-field shallow sea model is used and the seabed is flat, a normal wave model or a ray model can be adopted, and a Bellhop ray model can be used when the seabed has terrain.
Selecting a column vector from the calculated copy field, which is assumed to be F2＝[x y z]T. With F2As sound field data at a point of the copy field;
performing autocorrelation calculation on the measurement field matrix to obtain an autocorrelation matrix R;
substituting the autocorrelation matrix R, the measurement field matrix and the copy field matrix into a multi-constraint processor expression P to obtain a primary valence function expression PMC：
Figure BDA0003381092310000121
Wherein the constraint vector d in the above formula is determined by the sound pressure of each of the neighboring sound source points and the original sound source point, if let F1 (1)Sound pressure, F, representing the original source point1 (m)Representing the sound pressures of other nearby sound source points, the d vector is expressed as follows:
d＝[d1,d2,d3,d4,d5]＝[F1 (1)F1 (1),F1 (1)F1 (2),......,F1 (1)F1 (m)],1≤m≤M；
by adding the diagonal elements of the autocorrelation matrix and comparing the result with an equation associated with m, the resulting constant m should satisfy the following inequality:
Figure BDA0003381092310000122
the value of m in the above formula is selected according to the actual sound pressure and the autocorrelation matrix, and is generally selected from 0.01 to 0.000001, so that when m is located in the denominator in the subsequent calculation, the result can be larger than zero.
Adding the autocorrelation matrix R and a diagonal matrix, wherein diagonal elements of the diagonal matrix are m, and obtaining the matrix R after additionm；
In a matrix RmSubstituting the original autocorrelation matrix R into the cost function P of the multi-constraint processor to obtain a cost function expression PMCm：
Figure BDA0003381092310000123
Then P is addedMCAnd PMCmThe difference between the two is used to obtain a difference expression delta before and after transformationMCWill be aMCBy performing separation transformation and simplification, the following expression can be obtained:
Figure BDA0003381092310000131
by the expression of the difference ΔMCIt can be seen that when m is selected to ensure that the calculated value in the parenthesis on the right side of the first term is greater than 0, the equation can be regarded as a linear function, and the variation is the cost function P of the original MC processorMCThe variation coefficient is a constant greater than 0, and the addition constant is also greater than 0;
therefore, according to the above situation, it can be seen that the difference before and after transformation of the cost function is related to the magnitude of the value at each point obtained by the primary price function, that is, the larger the peak value is, the larger the difference before and after transformation is, and the smaller the peak value is, the smaller the difference is. Therefore, the result is equivalent to phase-change suppression of interference peaks in the target positioning result, so that the target peak is highlighted, and the target searching is facilitated.
Besides side lobe suppression, the resistance of the improved multi-constraint matching field positioning method in the aspect of environmental mismatch is also improved to a certain extent. If P is to beMCmThe following expression is obtained by expanding the molecular terms and then separating and changing the molecular terms:
Figure BDA0003381092310000132
as can be seen from the above formula, PMCmIn the second term of the split transform, the part in parenthesis is close to the computational expression of the linear matching processor. Therefore, if the first term of the expression is taken as a constant and the coefficient part of the second term is taken as a weight, the multi-constraint matching processor after transformation can be regarded as a result of weighting operation performed by the linear matching processor at each grid position. And because the operation of the linear processor is relatively insensitive to the change condition of the environmental parameters, compared with the original multi-constraint matching field positioning method, the matching field method after transformation can obviously reduce the influence of the environmental mismatch on the positioning performance of the matching field processor.
The above description is only a preferred embodiment of the multi-constraint matching processor positioning method, and the protection scope of the multi-constraint matching processor positioning method is not limited to the above embodiments, and all technical solutions belonging to the idea belong to the protection scope of the present invention. It should be noted that modifications and variations which do not depart from the gist of the invention will be those skilled in the art to which the invention pertains and which are intended to be within the scope of the invention.

Claims (5)

1. A multi-constraint matching processor positioning method is characterized in that: the method comprises the following steps:
step 1: determining the number of neighborhood constraint points; before the sound field of the target water area is collected, measuring environmental parameters of the target water area, including water depth, sound velocity gradient curve, water bottom flatness and rough terrain in the range of the target water area, water bottom sediment components, sediment density, sediment attenuation coefficient and sediment sound velocity;
step 2: acquiring sound field data of a water area where a target signal is located according to the environmental parameters of the measured water area, and constructing a water area sound field model;
and step 3: determining a matrix for a copy field according to the constructed water area sound field model;
and 4, step 4: acquiring underwater acoustic signals of target sound source in water area by using horizontal receiving hydrophone array to obtain measurement field matrix signals F1；
And 5: for measurement field matrix signal F1Performing autocorrelation calculation to determine an autocorrelation matrix R;
step 6: substituting the measurement field matrix, the copy field matrix and the autocorrelation matrix into a cost function of the multi-constraint processor to obtain a positioning result of the original matching processor;
and 7: and (4) carrying out difference on the cost functions of the multi-constraint matching processors before and after the autocorrelation matrix processing to obtain the difference value of the positioning result of each sound field point, and carrying out more accurate positioning on the target.
2. Root of herbaceous plantThe method as claimed in claim 1, wherein said method comprises: the step 3 specifically comprises the following steps: adopting a Kraken model or a Bellhop model, and when the water bottom is flat and is a low-frequency far field, calculating the sound field model by adopting a Kraken simple normal wave model; when the water bottom has undulating terrain and is a high-frequency sound field, a Bellhop ray model is adopted for calculation, and the obtained copy field uses a matrix F2And (4) showing.
3. The method of claim 2, wherein: the step 6 specifically comprises the following steps: substituting the measurement field matrix, the copy field matrix and the autocorrelation matrix into a cost function of the multi-constraint processor to obtain a positioning result of the original matching processor, wherein the positioning result is represented by the following formula:
Figure FDA0003381092300000011
Figure FDA0003381092300000026
4. a method as claimed in claim 3, wherein said method further comprises: the step 7 specifically comprises the following steps:
step 7.1: the constant m is calculated from the autocorrelation matrix and the measurement field sound pressure and is represented by the following formula:
Figure FDA0003381092300000021
wherein, K is the number of receiving array elements, and m is a constant greater than 0;
adding the autocorrelation matrix and the diagonal matrix with diagonal elements of m to obtain a matrix Rm，RmRepresented by the formula:
Rm＝R+m·En
wherein E isnIs an n-order identity matrix;
step 7.2: substituting the measured field matrix and the copy field matrix into a cost function of a multi-constraint processor, and then processing an autocorrelation matrix of the measured field matrix to obtain a matrix RmIn the matrix RmSubstituting the autocorrelation matrix R into the cost function of the multi-constraint processor yields the following fuzzy surface expression:
Figure FDA0003381092300000022
step 7.3: and (3) subtracting the cost functions of the multi-constraint matching processors before and after the autocorrelation matrix processing to obtain an expression of the difference value of the positioning result of each sound field point, and performing variable separation and simplification on the expression, wherein the difference value equation is expressed by the following formula:
Figure FDA0003381092300000023
the measurement field matrices are all deterministic constants, and each term in the above equation is replaced by the following parameters:
Figure FDA0003381092300000024
Figure FDA0003381092300000025
Figure FDA0003381092300000031
the difference function Delta is expressed by the above parametersMCThen there is deltaMC＝t·k0+k1；
Step 7.4: will PMCmThe following expression can be obtained by expanding, separating and simplifying the expression of (a):
Figure FDA0003381092300000032
Figure FDA0003381092300000033
Figure FDA0003381092300000034
when the environmental parameters are deviated within a certain range, the target is still accurately positioned.
5. The method of claim 4, wherein: the value of m is determined according to the actually measured sound field or autocorrelation matrix, and is generally selected from the range of 0.01-0.000001.
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