A dual-sensor side-line array layout optimization method for underwater target positioning

Abstract

The present invention relates to the field of exploration, detection and positioning, and specifically to a dual-sensor side line array layout optimization method for underwater target positioning, including: S1: establishing a pressure field model and a velocity field model for underwater vehicle detection targets; S2: constructing a signal model of a dual-sensor array according to the physical model parameters of the underwater vehicle; S3: obtaining an information matrix for characterizing a lower bound; S4: determining an array performance evaluation index, and comprehensively evaluating the positioning performance of the dual-sensor side line array; S5: determining constraint conditions according to model parameters; S6: taking the number of sensors of the underwater vehicle as the optimization target, optimizing the number of array elements, determining the number and position distribution of array pressure and flow velocity sensors, and completing the synchronous optimization of the layout of the dual-sensor side line array of the underwater vehicle. The present invention adopts the lower bound theory to fuse the dual-sensor side line array signals, and provides evaluation indicators for the dual-sensor side line array layout synchronous optimization problem, thereby reducing the array design cost and improving the detection accuracy and efficiency.

Description

Technical Field

[0001] The present invention relates to the field of exploration, detection and positioning, and in particular to a dual-sensor side line array layout optimization method for underwater target positioning. Background Art

[0002] In complex marine environments, fish can sense flow fields and detect targets with their vision and lateral line systems, thereby ensuring their own survival. Therefore, a variety of lateral line systems based on structural bionics and principle bionics have been derived, and the performance of sensors has been greatly improved. However, the existing lateral line sensor array layout mostly relies on empirical guidance and lacks quantitative theoretical support; and most of them are one-dimensional linear arrays with relatively simple structures; most importantly, the design of artificial lateral line arrays similar to the surface neuromasts and duct neuromasts of fish has not been realized. Therefore, it is necessary to design an artificial lateral line array that combines pressure and flow rate, and use theoretical models as a guide to judge the advantages and disadvantages of the array layout method to obtain the optimal layout plan, thereby improving the accuracy and efficiency of target detection.

[0003] Therefore, those skilled in the art are in urgent need of a method that can judge the pros and cons of array layout methods, propose a quantitative comprehensive evaluation index for the positioning performance of a dual-sensing side line array, and obtain an optimal layout solution. Summary of the invention

[0004] In view of the problems existing in the prior art, the purpose of the present invention is to provide a sensor array layout optimization method for a lateral line system that combines pressure and flow velocity sensing for underwater vehicles, so as to make up for the shortcomings of empirical design.

[0005] To achieve the above object, the technical solution adopted by the present invention is: a dual-sensor side line array layout optimization method for underwater target positioning, comprising the following steps:

[0006] S1: Establish the pressure field model and velocity field model of underwater vehicle detection target;

[0007] S2: according to the physical model parameters of the underwater vehicle, setting the initial layout positions of the pressure sensor and the flow velocity sensor of the underwater vehicle, constructing a signal model of a dual sensor array, and generating a theoretical pressure signal and velocity signal data set;

[0008] S3: Comprehensively considering the factors causing the measurement error of the dual sensor array, and obtaining the Fisher information matrix according to the signal model of the dual sensor array to characterize the Cramer-Rao lower bound;

[0009] S4: Determine the array performance evaluation index, obtain the array effective detection area, and quantitatively and comprehensively evaluate the positioning performance of the dual-sensing side line array;

[0010] S5: determining constraint conditions according to the model parameters of the underwater vehicle;

[0011] S6: Taking the number of sensors of the underwater vehicle as the optimization target, the number of array elements of the dual sensor array is optimized, the optimal number and position distribution of array pressure and flow velocity sensors are determined, and the synchronous optimization of the dual sensor side line array layout of the underwater vehicle is completed.

[0012] The above dual-sensor side line array layout optimization method for underwater target positioning, said S1 comprises: assuming that the underwater radius is a The dipole target has an amplitude of s , the frequency is ω If the sinusoidal motion is that of a vibrating dipole, the sensor can detect the r =( x s , y s , z s )For the hydrodynamic signal generated by the surrounding fluid disturbance, the pressure field model and velocity field model of the underwater dipole target can be obtained according to the fluid mechanics point source and point sink potential flow model:

[0013] The pressure field model is: ,

[0014] The velocity field model is: , where ρ represents the fluid density and t represents time.

[0015] In the above dual-sensor side line array layout optimization method for underwater target positioning, S2 comprises:

[0016] S2-1: Assume that M pressure sensors are arranged linearly and N flow rate sensors are randomly distributed within a certain range to form a dual-sensor side line array signal model.

[0017] ,

[0018] ,

[0019] ,in, is the collected signal matrix, D ( x s , y s , z s ) is the array signal response matrix, p ( x s , y s, z s ) is the array pressure signal response vector, v ( x s , y s , z s ) is the array velocity signal response vector, is the noise matrix, is the unit trigonometric function signal matrix, is a unit sinusoidal signal, is a unit cosine signal, L is the number of snaps;

[0020] S2-2: Based on S1, determine the flow field target detection area, obtain the signal amplitudes at the positions of the pressure sensor and the flow velocity sensor, and generate an array response signal data set.

[0021] In the above-mentioned dual-sensor sideline array layout optimization method for underwater target positioning, in S3, the error factors include response vector mismatch errors caused by the pressure sensor, the flow rate sensor and the dual-sensor array itself, and measurement errors caused by hydrodynamic environment noise.

[0022] In the above dual-sensor side line array layout optimization method for underwater target positioning, S3 comprises:

[0023] S3-1: According to the Cramer-Rao lower bound theorem, the inverse of the information matrix I(θ) is used to characterize the unbiased estimator The estimated variance lower bound of ,in, represents the estimated variance, E[] represents the mathematical expectation, represents partial derivative, ln represents logarithm, and P(x;θ) represents the probability density function of parameter θ;

[0024] S3-2: Assume that the error factor obeys a Gaussian distribution with zero mean and equal variance, and obtain the response vector m The probability density function of P ( m ; r ) and the probability density function of Gaussian white noise P ( x ; r ), respectively expressed as: ,

[0025] , where σ m 2 represents the variance of the response vector, σ n2 represents the noise variance;

[0026] S3-3: Based on the vector parameters of three-dimensional space r =[ x , y , z ], the information matrix of the response vector mismatch I m ( r ) and hydrodynamic environmental noise information matrix I n ( r ) can be expressed as:

[0027] ,

[0028] ;

[0029] S3-4: Information matrix based on response vector mismatch I m ( r ) and hydrodynamic environmental noise information matrix I n ( r ) The final information matrix is: .

[0030] In the above dual-sensor side line array layout optimization method for underwater target positioning, S4 comprises:

[0031] S4-1: Relative distance between estimated position and actual position through target Δr The relative root mean square error characterizes the comprehensive positioning accuracy of the array, and the relative root mean square error satisfies the following inequality: , where ReRMSE represents the relative root mean square error, tr[] represents the trace of the matrix, R ( r ) is the distance between the target and the array,

[0032] S4-2: Based on the right side of the inequality, the Cramer-Rao lower bound of the relative root mean square error of the side line array for positioning the target in the positioning area is obtained, and a quantitative comprehensive evaluation index of the dual-sensor array positioning performance is defined to characterize the performance that the array can achieve.

[0033] In the above dual-sensor sideline array layout optimization method for underwater target positioning, in S5, the constraints include array element quantity constraints of pressure sensors and flow rate sensors, array element spacing constraints, array geometry constraints, and array positioning performance constraints.

[0034] In the above dual-sensor side-line array layout optimization method for underwater target positioning, in S6, a multi-island genetic algorithm is used to optimize the layout of the dual-sensor side-line array.

[0035] In the above dual-sensor side line array layout optimization method for underwater target positioning, S6 comprises:

[0036] S6-1: Assume that the array consists of M pressure sensors and N flow rate sensors, and each satisfies the constraints on the number of array elements;

[0037] S6-2: Assume that the Cramer-Rao lower bound of the relative positioning error of the dual-sensor side line array is less than 10% for effective positioning, and divide the positioning area into effective positioning area and invalid positioning area with 10% as the isosurface, and calculate the volume of the effective positioning area V valid , effective positioning area volume V valid Total volume of the positioning area V total The ratio of meets the array positioning performance constraint;

[0038] S6-3: Taking the total number of array sensors M+N as the minimum as the optimization goal, the multi-island genetic algorithm is used for optimization;

[0039] S6-4: When the termination iteration conditions are met, the array parameters and the volume of the array effective positioning area are output, thereby obtaining the optimal layout of the dual-sensing side line array.

[0040] In the above dual-sensor side line array layout optimization method for underwater target positioning, in S6-1: the pressure sensor randomly generates the carrier head array element spacing angle that satisfies the array element spacing constraint condition α The distance between the main array element and the carrier d , and determine whether the array meets the length constraint and the array element layout meets the geometric constraints of the head curve and the body straight line;

[0041] The flow velocity sensor randomly generates array element coordinates and meets the constraints of array element quantity, array element spacing and array geometry.

[0042] The beneficial effects of the dual-sensor side line array layout optimization method for underwater target positioning of the present invention are: according to the shape and size of the specific underwater vehicle physical model, the sensor layout range is preset, according to the actual positioning requirements, the array signal data set is generated by establishing a dual-sensor side line array signal model, and then the pressure signal and the velocity signal are fused in combination with the Fisher information matrix, which is used to characterize the Cramer-Rao lower bound of the comprehensive positioning performance of the dual-sensor side line array, and the comprehensive positioning performance of the pressure and flow velocity dual-sensor side line array is provided with a quantitative evaluation index, and finally the dual-sensor side line array layout is synchronously optimized by using a multi-island genetic algorithm. The present invention adopts the Cramer-Rao lower bound theory to well fuse the dual-sensor side line array signals, and provides a quantitative evaluation index for the dual-sensor side line array layout synchronization optimization problem, reduces the array design cost, avoids array information redundancy, improves detection accuracy and efficiency, and lays a theoretical foundation for scientifically designing and developing artificial side line arrays for underwater target detection and building an experimental platform. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 Schematic diagram of a three-dimensional dipole source model in an embodiment of the present invention;

[0044] Figure 2 This is a layout diagram of a dual-sensing side line array of a quarter underwater vehicle in an embodiment of the present invention;

[0045] Figure 3 It is a flow chart of multi-island genetic algorithm array layout optimization in an embodiment of the present invention;

[0046] Figure 4 This is an overall flow chart of synchronous optimization of the dual-sensing side line array layout in an embodiment of the present invention. DETAILED DESCRIPTION

[0047] In order to enable those skilled in the art to better understand the purpose and technical solutions of the present invention, the purpose and technical solutions of the present invention are described below in conjunction with specific implementation methods and drawings.

[0048] Example 1

[0049] The overall layout optimization process of the dual-sensor side line array layout optimization method for underwater target positioning of the present invention is as follows: Figure 4 As shown, the following steps are included.

[0050] S1: Establish the pressure field model and velocity field model for underwater vehicle to detect targets.

[0051] S2: According to the physical model parameters of the underwater vehicle, the initial layout positions of the pressure sensor and the flow velocity sensor of the underwater vehicle are set, the signal model of the dual sensor array is constructed, and the theoretical pressure signal and velocity signal data sets are generated.

[0052] S3: Taking into account the factors that cause the measurement error of the dual sensor array, the corresponding Fisher information matrix is ​​obtained according to the signal model of the dual sensor array to characterize the Cramer-Rao lower bound.

[0053] S4: The relative root mean square error of the array's detection of targets at different positions is used as the array performance evaluation index to obtain the array's effective detection area, thereby achieving a quantitative and comprehensive evaluation of the dual-sensing side line array positioning performance.

[0054] S5: Determine constraints such as array element quantity constraint, array element spacing constraint, array geometry constraint, array positioning performance constraint, etc. according to the underwater vehicle model parameters.

[0055] S6: Taking the minimum number of sensors for the underwater vehicle as the optimization target, the number of array elements of the dual-sensor array is optimized, the number and position distribution of the optimal array pressure and flow velocity sensors are determined, and the synchronous optimization of the dual-sensor side line array layout of the underwater vehicle is completed.

[0056] Example 2

[0057] In order to enable those skilled in the art to better understand the overall layout optimization process, the steps in Example 1 are specifically explained below.

[0058] S1, establish the pressure field and velocity field model of underwater vehicle detection target.

[0059] like Figure 1 As shown, the underwater radius is a The dipole target has an amplitude of s , the frequency is ω If the sinusoidal motion is that of a vibrating dipole, the sensor can detect the r =( x s , y s , z s ) The hydrodynamic signals generated by the surrounding fluid disturbance can be used to obtain the pressure field and velocity field models of the underwater dipole target based on the two typical potential flow models in fluid mechanics: point source and point sink potential flow models.

[0060] (1),

[0061] (2), where ρ represents the fluid density and t represents the time.

[0062] S2, set the initial layout positions of pressure and flow velocity sensors according to the physical model parameters of the underwater vehicle, form a collaborative array signal model, and generate theoretical pressure and flow velocity data sets. Here, the data set provides a data basis.

[0063] like Figure 2 As shown in the figure, inspired by the lateral line of fish, the pressure sensors are arranged linearly and the flow sensors are randomly distributed within a certain range to form a dual-sensor lateral line array model. The dual-sensor lateral line array signal model composed of M pressure sensors and N flow sensors can be expressed as:

[0064] (3),

[0065] (4) ,

[0066] (5),

[0067] in, is the collected signal matrix, D ( x s , y s , z s ) is the array signal response matrix, p ( x s , y s , z s )=[ p 1 ( x s , y s , z s ),…, p M ( x s , y s , z s )] T is the array pressure signal response vector, v ( x s , y s , z s )=[ x M+1 ( x s , ys , z s ),…, x M+N ( x s , y s , z s )] T is the array velocity signal response vector, is the noise matrix, is the unit trigonometric function signal matrix, is a unit sinusoidal signal, is a unit cosine signal, L The number of snapshots.

[0068] On the basis of step S1, the size of the flow field area around the underwater vehicle equipped with the sideline array is determined according to actual needs. By determining the flow field target detection area, the signal amplitude at each sensor position is calculated in Matlab to generate an array response signal data set.

[0069] S3, comprehensively considering the factors that may cause array measurement errors, the corresponding Fisher information matrix is ​​obtained according to the established dual-sensor sideline array signal model to characterize the Cramer-Rao lower bound.

[0070] According to the Cramer-Rao lower bound theorem, assuming that the probability density function of parameter θ is P(x;θ), then its unbiased estimator is The lower bound of the estimated variance can be characterized by the inverse of the Fisher information: (6),

[0071] in, represents the estimated variance, E[] represents the mathematical expectation, It means partial derivative, and ln means logarithm.

[0072] It can be seen from the array signal model in step S2 that the error sources mainly include two aspects: one is the response vector mismatch error caused by the sensor and the array itself, and the other is the measurement error caused by the hydrodynamic environment noise.

[0073] Assuming that both errors follow a Gaussian distribution with zero mean and equal variance, the response vector m =[ m 1 ,…, m M ,…, m M+N ] T The probability density function of P (m ; r ) and the probability density function of Gaussian white noise P ( x ; r ) can be expressed as:

[0074] (7),

[0075] (8),

[0076] Among them, σ m 2 represents the variance of the response vector, σ n 2 represents the noise variance.

[0077] For vector parameters in three-dimensional space r =[ x , y , z ], Fisher information matrix considering response vector mismatch and hydrodynamic ambient noise I m ( r )and I n ( r ) can be expressed as:

[0078] (9),

[0079] (10).

[0080] Combining equations (8) and (9), the final Fisher information matrix is: (11).

[0081] S4, the Cramer-Rao lower bound of the relative root mean square error of the array in detecting targets at different positions is used as a quantitative comprehensive evaluation index for the positioning performance of the dual-sensor array to obtain the effective detection area of ​​the array.

[0082] The overall positioning accuracy of the array can be determined by the relative distance between the estimated position and the actual position of the target. Δr The relative root mean square error ReRMSE is used to characterize the

[0083] For the three-dimensional positioning problem, according to the Cramer-Rao lower bound theorem, the relative root mean square error satisfies the following inequality: (12), where ReRMSE represents the relative root mean square error, tr[] represents the trace of the matrix, R ( r ) is the distance between the target and the array.

[0084] The right side of the inequality is the Cramer-Rao lower bound of the relative root mean square error of the side line array for positioning the target in the positioning area, which is defined as a quantitative comprehensive evaluation index of the dual-sensor array positioning performance, representing the optimal performance that the array can achieve.

[0085] S5, determining constraints such as array element quantity constraint, array element spacing constraint, array geometry constraint, array positioning performance constraint, etc. according to the underwater vehicle model parameters.

[0086] Considering the size limitations of the sensor itself and the physical model of the underwater vehicle, the sensor layout position must meet certain constraints. The specific constraints are shown in Table 1.

[0087] Table 1 Dual-sensing side line array layout constraints

[0088] .

[0089] In Table 1, R is the head radius of the underwater vehicle; B is the main body size of the underwater vehicle; b p is the pressure sensor size; b v M is the size of the flow sensor; h is the number of head pressure sensors; M b N is the number of main pressure sensors; h N is the number of head flow velocity sensors; b is the number of main flow velocity sensors; α ph is the head pressure sensor spacing angle; d pb is the distance between the main pressure sensors; α vh is the head flow velocity sensor spacing angle; d vb is the distance between the main flow velocity sensors; ( x ph , y ph , z ph ) is the layout coordinates of the head pressure sensor; ( x pb , y pb , z pb ) is the layout coordinate of the main pressure sensor; ( x vh , y vh , zvh ), are the layout coordinates of the head flow velocity sensors; x vb , y vb , z vb ), are the layout coordinates of the main body flow velocity sensors; V valid is the volume of the effective positioning area of the array; V total is the total volume of the positioning area; η is the lower limit of the array positioning performance.

[0090] S6. With the minimum number of sensors as the optimization goal, optimize the number of elements of the dual-sensing side-line array, determine the optimal number and position distribution of the array pressure and flow velocity sensors, so as to complete the synchronous optimization of the underwater vehicle pressure and flow velocity dual-sensing side-line array layout.

[0091] The optimization process of the multi-island genetic algorithm for the dual-sensing side-line array layout is as Figure 3 shown.

[0092] First, assume that the array consists of M = M h + M b pressure sensors and N = N h + N b flow velocity sensors, and each satisfies its own element number constraint condition.

[0093] For the pressure sensors, randomly generate the element interval angle α of the carrier head and the element interval distance d of the carrier main body that satisfy the element interval constraint condition, and judge whether the array satisfies the length constraint condition. The element layout satisfies the geometric constraint conditions of the head curve and the main body straight line, as Figure 2 shown by the dotted line.

[0094] For the flow velocity sensors, randomly generate the element coordinates within the Figure 2 dotted line frame of the line, and satisfy the relevant constraint conditions such as the number of elements, element interval, and array geometry.

[0095] Assume that the Cramer-Rao lower bound of the relative positioning error of the dual-sensing side-line array is less than 10% as effective positioning. Then, divide the positioning area into an effective positioning area and an ineffective positioning area with 10% as the isosurface, and calculate the volume V valid of the effective positioning area, and the ratio of it to the total volume V total of the positioning area needs to satisfy the array positioning performance constraint condition.

[0096] Finally, the multi-island genetic algorithm is used to optimize the array with the minimum total number of array sensors M+N as the optimization goal. When the termination iteration conditions are met, the array parameters such as the number of array elements and the array element spacing and the volume of the array effective positioning area are output, thereby obtaining the optimal layout of the dual-sensing side line array.

[0097] The above embodiments are only for illustrating the inventive concept and features of the present invention, and their purpose is to enable those skilled in the art to understand the content of the present invention and implement it accordingly, and they cannot be used to limit the protection scope of the present invention. Any equivalent changes or modifications made based on the essence of the content of the present invention should be included in the protection scope of the present invention.

Claims

1. A dual-sensor side line array layout optimization method for underwater target positioning, characterized in that: The following steps are involved: S1: Establish the pressure field model and velocity field model for underwater vehicle detection targets, including: Assume the underwater radius is a The dipole target has an amplitude of s , the frequency is ω If the sinusoidal motion is that of a vibrating dipole, the sensor can detect the r =( x s , y s , z s )For the hydrodynamic signal generated by the surrounding fluid disturbance, the pressure field model and velocity field model of the underwater dipole target can be obtained according to the fluid mechanics point source and point sink potential flow model: The pressure field model is: , The velocity field model is: , where ρ represents the fluid density and t represents the time; S2: According to the physical model parameters of the underwater vehicle, the initial layout positions of the pressure sensor and the flow velocity sensor of the underwater vehicle are set, a signal model of the dual sensor array is constructed, and a theoretical pressure signal and velocity signal data set is generated, including: S2-1: Assume that M pressure sensors are arranged linearly and N flow rate sensors are randomly distributed within a certain range to form a dual-sensor side line array signal model. ， ， ,in, is the collected signal matrix, D ( x s , y s , z s ) is the array signal response matrix, p ( x s , y s , z s ) is the array pressure signal response vector, v ( x s , y s , z s ) is the array velocity signal response vector, is the noise matrix, is the unit trigonometric function signal matrix, is a unit sinusoidal signal, is a unit cosine signal, L is the number of snaps; S2-2: Based on S1, determine the flow field target detection area, obtain the signal amplitude at the position of the pressure sensor and the flow velocity sensor, and generate an array response signal data set; S3: Comprehensively considering the factors causing the measurement error of the dual sensor array, and obtaining the Fisher information matrix according to the signal model of the dual sensor array to characterize the Cramer-Rao lower bound; S4: Determine the array performance evaluation index, obtain the array effective detection area, and quantitatively and comprehensively evaluate the positioning performance of the dual-sensing side line array; S5: determining constraint conditions according to the model parameters of the underwater vehicle; S6: Taking the number of sensors of the underwater vehicle as the optimization target, the number of array elements of the dual sensor array is optimized, the optimal number and position distribution of array pressure and flow velocity sensors are determined, and the synchronous optimization of the dual sensor side line array layout of the underwater vehicle is completed.

2. The dual-sensor side line array layout optimization method for underwater target positioning according to claim 1, characterized in that: In S3, the error factors include response vector mismatch errors caused by the pressure sensor, the flow rate sensor and the dual sensor array itself, and measurement errors caused by hydrodynamic environment noise.

3. The dual-sensor side line array layout optimization method for underwater target positioning according to claim 2, characterized in that: The S3 includes: S3-1: According to the Cramer-Rao lower bound theorem, the inverse of the information matrix I(θ) is used to characterize the unbiased estimator The estimated variance lower bound of ,in, represents the estimated variance, E[] represents the mathematical expectation, represents partial derivative, ln represents logarithm, and P(x;θ) represents the probability density function of parameter θ; S3-2: Assume that the error factor obeys a Gaussian distribution with zero mean and equal variance, and obtain the response vector m The probability density function of P ( m ; r ) and the probability density function of Gaussian white noise P ( x ; r ), respectively expressed as: ， , where σ m 2 represents the variance of the response vector, σ n 2 represents the noise variance; S3-3: Based on the vector parameters of three-dimensional space r =[ x , y , z ], the information matrix of the response vector mismatch I m ( r ) and hydrodynamic environmental noise information matrix I n ( r ) can be expressed as: ， ； S3-4: Information matrix based on response vector mismatch I m ( r ) and hydrodynamic environmental noise information matrix I n ( r ) The final information matrix is: .

4. The dual-sensor side line array layout optimization method for underwater target positioning according to claim 3 is characterized in that: The S4 includes: S4-1: Relative distance between estimated position and actual position through target Δr The relative root mean square error characterizes the comprehensive positioning accuracy of the array, and the relative root mean square error satisfies the following inequality: , where ReRMSE represents the relative root mean square error, tr[] represents the trace of the matrix, R ( r ) is the distance between the target and the array, S4-2: Based on the right side of the inequality, the Cramer-Rao lower bound of the relative root mean square error of the side line array for positioning the target in the positioning area is obtained, and a quantitative comprehensive evaluation index of the dual-sensor array positioning performance is defined to characterize the optimal performance that the array can achieve.

5. The dual-sensor side line array layout optimization method for underwater target positioning according to claim 4, characterized in that: In S5, the constraints include array element quantity constraints, array element spacing constraints, array geometry constraints, and array positioning performance constraints of the pressure sensor and the flow rate sensor.

6. The dual-sensor side line array layout optimization method for underwater target positioning according to claim 5, characterized in that: In S6, the multi-island genetic algorithm is used to optimize the layout of the dual-sensing side line array.

7. The dual-sensor side line array layout optimization method for underwater target positioning according to claim 6, characterized in that: The S6 includes: S6-1: Assume that the array consists of M pressure sensors and N flow rate sensors, and each satisfies the constraints on the number of array elements; S6-2: Assume that the Cramer-Rao lower bound of the relative positioning error of the dual-sensor side line array is less than 10% for effective positioning, and divide the positioning area into effective positioning area and invalid positioning area with 10% as the isosurface, and calculate the volume of the effective positioning area V valid , effective positioning area volume V valid Total volume of the positioning area V total The ratio of meets the array positioning performance constraint; S6-3: Taking the total number of array sensors M+N as the minimum as the optimization goal, the multi-island genetic algorithm is used for optimization; S6-4: When the termination iteration conditions are met, the array parameters and the volume of the array effective positioning area are output, thereby obtaining the optimal layout of the dual-sensing side line array.

8. The dual-sensor side line array layout optimization method for underwater target positioning according to claim 7, characterized in that: In S6-1: the pressure sensor randomly generates the carrier head array element spacing angle that meets the array element spacing constraint condition α The distance between the main array element and the carrier d , and determine whether the array meets the length constraint and the array element layout meets the geometric constraints of the head curve and the body straight line; The flow velocity sensor randomly generates array element coordinates and meets the constraints of array element quantity, array element spacing and array geometry.

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