Artificial lateral line array optimization method for attitude estimation of underwater vehicle

Abstract

The invention relates to the field of artificial lateral line optimization layout, in particular to an artificial lateral line array optimization method for attitude estimation of an underwater vehicle, which comprises the following steps of: 1, forming a quaternion model, acquiring a vector parameter, and deducing a vector after rotation; 2, forming an artificial lateral line array, and constructing an underwater vehicle pressure field model; 3, deducing a relative root mean square error to obtain an effective estimation range of vector parameters; 4, determining a quaternion model vector parameter value range, and determining an artificial side line array layout parameter constraint condition; 5, determining an optimization target; and 6, verifying the reliability of the optimization method. By fusing an artificial lateral line array and a quaternion model and based on an array attitude estimation performance quantitative evaluation index system, the problem of accumulative errors existing in the integral operation process of an inertial sensing system is effectively avoided, and the problem of universal joint deadlock during Euler angle operation is solved.

Description

Technical Field

[0001] The present invention relates to the field of optimized layout of artificial lateral lines, and specifically relates to an optimization method for an artificial lateral line array for attitude estimation of an underwater vehicle. Background Art

[0002] Fish and aquatic amphibians have a special sensory system - the lateral line, which can be used to sense the hydrodynamic information around themselves and provide guarantee for their own complex movement behaviors. On the one hand, the bionic lateral line system provides a new idea for attitude estimation of underwater vehicles, effectively making up for the deficiency that cumulative errors will inevitably occur in the integral operation process of inertial sensing systems. On the other hand, although Euler angles can intuitively represent the attitude of the carrier, there are drawbacks of gimbal lock during its operation, and using the quaternion model can effectively reduce the computational complexity. Therefore, proposing a quantitative evaluation index for the attitude estimation performance of the artificial lateral line array, and judging the advantages and disadvantages of the array layout form based on this, and optimizing the layout of the artificial lateral line array, is of great significance for improving the accuracy of attitude estimation of underwater vehicles. Summary of the Invention

[0003] Aiming at the problems existing in the prior art, the purpose of the present invention is: based on the perception mechanism of aquatic organisms, to improve the deficiencies of traditional inertial sensing systems in attitude estimation, so as to improve the accuracy of underwater vehicle motion control, a new attitude estimation method integrating artificial lateral line sensing and the quaternion model is proposed, and a quantitative evaluation index for the attitude estimation performance of the artificial lateral line array is proposed based on the Cramer-Rao lower bound, so as to realize the optimized design of the artificial lateral line array of the underwater vehicle.

[0004] To achieve the above purpose, the technical solution adopted by the present invention is: an optimization method for an artificial lateral line array for attitude estimation of an underwater vehicle, including the following steps: Step 1: According to the rotation angle α and the unit rotation vector u to form a quaternion model q , obtain the vector parameters when using the quaternion model to represent the attitude of the underwater vehicle, and deduce the rotated vector v in matrix representation form from any vector v to be rotated of the underwater vehicle; Step 2: According to the lateral line perception principle, select pressure sensors to form an artificial lateral line array, determine the array layout form and the initial layout position of the pressure sensors, construct a pressure field model of the underwater vehicle, and form an array signal model; Step 3: Derive the Cramer-Rao lower bound of the quaternion model vector parameters considering Gaussian white noise according to the array signal model, and derive the relative root mean square error of the quaternion model vector parameter estimation based on the Cramer-Rao lower bound. Use this as a quantitative evaluation index for the array attitude estimation performance, obtain the effective estimation range of the vector parameters, and determine whether the array meets the attitude estimation requirements; Step 4: Determine the value range of the quaternion model vector parameters, and determine the constraint conditions of the artificial lateral line array layout parameters according to the underwater vehicle model parameters; Step 5: Determine the optimization objective, optimize the artificial lateral line array layout parameters, and obtain the simplest array that meets the attitude estimation requirements; Step 6: According to the obtained simplest array layout parameters, use Monte Carlo simulation to construct a randomized simulation analysis framework, integrate the least squares method to estimate the vector parameters of the quaternion model, obtain the average relative root mean square error under the simulation conditions, and compare it with the theoretical calculation value in S4 to complete the reliability verification of the optimization method.

[0005] For the above artificial lateral line array optimization method for underwater vehicle attitude estimation, in Step 1, the basic form and matrix form of the quaternion model vector parameters are: , , , where: q 1 is the real part parameter, q 2, q 3, q 4 are the imaginary part parameters, i , j , k are three mutually orthogonal imaginary part unit vectors, sin is the sine function, and cos is the cosine function; The basic form of the rotated vector v′ is , where, q * represents the conjugate of the quaternion. According to the matrix form of the quaternion model, the rotated vector v′ is expressed using matrix multiplication as: .

[0006] For the above artificial lateral line array optimization method for underwater vehicle attitude estimation, Step 2 includes: Step 2-1: Set the center of gravity of the underwater vehicle to coincide with the origin of the coordinate system. The head array elements of the underwater vehicle are arranged in a cross shape, and the main body array elements are arranged linearly. The array consists of a total of M = 4M1 + 4M2 + 1 array elements, and obtain the static pressure p at each array element position of the artificial lateral line array d and the water depth where the array element is located: p 0 is the atmospheric pressure,ρ is the seawater density, g is the acceleration due to gravity; Step 2-2: Combining with the rotated vector v′ obtained by matrix multiplication in Step 1, the static pressure at each element position of the rotated array can be expressed as: , where, p 1, p 2, …, p M T is the static pressure at each element position, d 0 is the depth of the center of gravity position of the underwater vehicle, v 1, v 2, …, v M T is the rotation vector represented by each element, and T represents the transpose; Step 2-3: According to Step 2-2, the artificial lateral line array signal model is obtained as: , where: z n is the array observation signal, p n ; θ is the array theoretical signal containing the quaternion model vector parameters, θ = u x u y u z α T is the vector parameter to be estimated, w n is the Gaussian white noise with a mean of zero and a variance of σ 2 .

[0007] The above artificial lateral line array optimization method for underwater vehicle attitude estimation, the derivation of the Cramer-Rao lower bound of the quaternion model vector parameters considering Gaussian white noise in Step 3 includes: Step 3-1: Set the likelihood function of the Gaussian white noise P ( z ; θ ) satisfies the regularity condition, expressed as: , where exp is the exponential function with the real number e as the base; Step 3-2: According to the Cramer-Rao lower bound theory, for any unbiased estimator of the vector parameter θ ​​​​​​​ Estimated variance Satisfies the following equation: where, I ( θ ) is a vector parameter θ The Fisher information matrix of, and its reciprocal characterizes the vector parameter θ The Cramer-Rao lower bound of the estimated variance, E[] represents taking the mathematical expectation, ∂ represents taking the partial derivative, and ln represents taking the logarithm; Step 3-3: For the quaternion model vector parameter θ Its Fisher information matrix I ( θ ) is expressed as: , The vector parameter θ The Cramer-Rao lower bounds of each component Correspond to the diagonal elements of the inverse of the Fisher information matrix, and are expressed as: where: C ( u x ), C ( u y ), C ( u z ), C ( α ) are the Cramer-Rao lower bounds of the parameters u x , u y , u z , α respectively, I -1 ( θ ) is the inverse matrix of the Fisher information matrix, and diag[] represents extracting the diagonal elements of the matrix.

[0008] The above artificial lateral line array optimization method for underwater vehicle attitude estimation, step 3 further includes: Step 3-4: According to the Cramer-Rao lower bounds of each component of the vector parameter θ The relative root mean square errors of the quaternion model unit rotation vector and rotation angle can be respectively expressed as: , , where, ReRMSE( u) is the relative root mean square error of unit rotation vector estimation, ReRMSE( α ) is the relative root mean square error of rotation angle estimation, ∣ u ∣ is the modulus of the unit rotation vector; Step 3-5: Taking the relative root mean square errors of the unit rotation vector and rotation angle estimated by the quaternion model as the evaluation indexes of the array attitude estimation performance, and taking 10% relative root mean square error as the judgment basis, obtain the effective estimation ranges of the unit rotation vector and rotation angle of the quaternion model, and judge whether the array meets the attitude estimation requirements according to the size of the effective estimation range.

[0009] In the above artificial lateral line array optimization method for underwater vehicle attitude estimation, step 4 further includes: Step 4-1: Determine the value range of the rotation angle. In the spherical coordinate system, the unit rotation vector is represented by the polar angle ψ and the azimuth angle φ as: ; Step 4-2: According to the size limitations of the sensor itself and the physical model of the underwater vehicle, the artificial lateral line array is arranged according to the constraint conditions, and the constraint conditions include the number of array elements, the element interval, the element position, the array length, and the array attitude estimation performance.

[0010] In the above artificial lateral line array optimization method for underwater vehicle attitude estimation, step 5 includes: Step 5-1: Set N h = 4M1 + 1 head array elements and N b = 4M2 main body array elements to form an array, and the array meets the constraint conditions; Step 5-2: According to the geometric characteristics of the physical model of the underwater vehicle, the head array elements of the array are arranged along a curve, and the main body array elements are arranged along a straight line. The main body array elements randomly generate the main body element interval distance Δd that meets the element interval constraint conditions, and judge whether the main body array meets the array length constraint conditions. If not, correct it according to the length L of the underwater vehicle body and the number of main body array elements M2 / 4. The correction formula is: . To avoid repeated constraints, the head element interval angle β can be calculated from the arc length of the head half-arm array and the number of elements M1: ; Step 5-3: Taking the total number of array elements M = N h + N b being the least and the array attitude estimation performance being the best as the optimization objectives, set a reasonable weight ratio of the total number of array elements to the array attitude estimation performance, and use the archived micro-genetic algorithm for multi-objective optimization; Step 5-4: When the termination iteration condition is met, obtain the simplest array that meets the attitude estimation requirements, and output the array layout parameters.

[0011] The above artificial lateral line array optimization method for underwater vehicle attitude estimation, the said step 6 includes: Step 6-1: According to the layout parameters of the simplest array obtained in step 5, introduce random noise interference, and use Monte Carlo simulation to generate 1000 groups of simulation data; Step 6-2: According to the generated simulation data, use the least squares method to estimate the vector parameters of the quaternion model, and statistically analyze the estimation errors of all Monte Carlo simulation samples, so as to obtain the relative root mean square error of the unit rotation vector and rotation angle estimation of the simulation quaternion model; Step 6-3: Compare the relative root mean square error between the vector parameter simulation and the theoretical estimation. If the deviation is within the preset confidence interval, it proves that the above optimization method is reliable in a statistical sense, thus realizing the closed-loop verification of the artificial lateral line array optimization method.

[0012] The beneficial effects of the artificial lateral line array optimization method for underwater vehicle attitude estimation of the present invention are as follows: By integrating the artificial lateral line array and the quaternion model, a quantitative evaluation index system for the array attitude estimation performance based on the Cramer-Rao lower bound is proposed, effectively avoiding the cumulative error problem existing in the integral operation process of the inertial sensing system and overcoming the gimbal lock problem during Euler angle operation; Combining the underwater vehicle pressure field model with the rotation matrix of the quaternion model, an artificial lateral line array signal model is constructed, and the Cramer-Rao lower bound theory is used to realize the quantitative characterization of the underwater vehicle attitude parameter estimation, providing a quantitative basis for the array layout parameter optimization; Finally, through Monte Carlo simulation and least squares estimation, the reliability evaluation system of the artificial lateral line array optimization design method from theoretical derivation to simulation verification is improved. On the premise of ensuring the attitude estimation accuracy, the simplest array layout parameters that meet the constraint conditions are obtained, significantly reducing the computational complexity and design cost, and enhancing the robustness and real-time performance of the underwater vehicle attitude estimation in complex underwater environments, laying a theoretical and methodological foundation for the scientific design, development and engineering application of the artificial lateral line array for underwater vehicle attitude estimation. Description of the Drawings

[0013] Figure 1 Schematic diagram of the vector parameters of the quaternion model in the embodiment of the present invention; Figure 2 Schematic diagram of the lateral line array of the underwater vehicle in the embodiment of the present invention; Figure 3 Schematic diagram of the unit rotation vector in the spherical coordinate system in the embodiment of the present invention; Figure 4Flowchart of the optimized layout of the archived micro-genetic algorithm array in the embodiment of the present invention; Figure 5 Flowchart of the optimized design method for the artificial lateral line array layout in the embodiment of the present invention. Specific implementation manners

[0014] To enable those skilled in the art to better understand the technical solution of the present invention, the technical solution of the present invention will be described below in conjunction with specific implementation manners and the accompanying drawings.

[0015] Embodiment 1 An optimized method for an artificial lateral line array for underwater vehicle attitude estimation includes the following steps.

[0016] S1. Construct a method for representing the attitude of an underwater vehicle based on a quaternion model.

[0017] S2. According to the principle of lateral line perception, select pressure sensors to form an artificial lateral line array, determine the array layout form and the initial layout position of the pressure sensors, construct a pressure field model of the underwater vehicle, and form an array signal model.

[0018] S3. Deduce the Cramer-Rao lower bound of the vector parameters of the quaternion model considering Gaussian white noise according to the array signal model.

[0019] S4. Deduce the relative root mean square error of the vector parameter estimation of the quaternion model according to the Cramer-Rao lower bound, use this as a quantitative evaluation index for the array attitude estimation performance, obtain the effective estimation range of the vector parameters, and determine whether the array meets the attitude estimation requirements.

[0020] S5. Determine the value range of the vector parameters of the quaternion model, determine the constraint conditions for the layout parameters of the artificial lateral line array according to the model parameters of the underwater vehicle. Each element of the artificial lateral line array needs to be arranged on the underwater vehicle according to parameters such as quantity and interval. The constraint conditions are used to limit various constraints when the elements of the array are arranged on the vehicle. For example, the quantity constraint limits the upper and lower limits of the number of elements, and the interval constraint limits that the minimum interval between elements cannot be less than the size of one sensor and the maximum cannot exceed certain parameters of the underwater vehicle, and so on.

[0021] S6. Determine the optimization objective, optimize the layout parameters of the artificial lateral line array, and obtain the simplest array that meets the attitude estimation requirements.

[0022] S7. According to the obtained simplest array layout parameters, use Monte Carlo simulation to construct a randomized simulation analysis framework, integrate the least squares method to estimate the vector parameters of the quaternion model, obtain the average relative root mean square error under the simulation conditions, and compare it with the theoretical calculation value in S4 to complete the reliability verification of the above optimization method.

[0023] Example 2 As Figures 1 - 5 shown, an optimization method for an artificial lateral line array for underwater vehicle attitude estimation includes the following steps.

[0024] Step 1: Construct an underwater vehicle attitude representation method based on the quaternion model.

[0025] Underwater vehicle attitude estimation has three degrees of freedom. The quaternion model has four parameters and one constraint condition, and can effectively avoid singularity problems when representing three-dimensional attitudes. The quaternion model q consists of a rotation angle α and a unit rotation vector u =( u x , u y , u z ). When using the quaternion model to represent the underwater vehicle attitude, the vector parameters are as Figure 1 shown, and its basic form and matrix form are as follows: (1), (2), (3), where: q 1 is the real part parameter, q 2, q 3, q 4 are the imaginary part parameters, i , j , k are three mutually orthogonal imaginary part unit vectors, sin is the sine function, and cos is the cosine function.

[0026] For any vector to be rotated v =( v x , v y , v z ) of the underwater vehicle, its rotated vector v ′ can be expressed by the following formula: (4), where: q * represents the conjugate of the quaternion.

[0027] For the convenience of subsequent model derivation, according to the matrix form of the quaternion model and using matrix multiplication, the matrix form of the above formula (4) can be expressed as: (5).

[0028] Step 2: According to the lateral line perception principle, select pressure sensors to form an artificial lateral line array, determine the array layout form and the initial layout positions of the pressure sensors, construct an underwater vehicle pressure field model, and form an array signal model.

[0029] According to the fish lateral line perception principle, the neuromast can respond acutely to the pressure field changes caused by weak underwater flows. Therefore, pressure sensors are selected to form an artificial lateral line array. As Figure 2 shown, assuming that the center of gravity of the underwater vehicle coincides with the origin of the coordinate system, the head array elements of the vehicle are arranged in a cross shape, the main body array elements are arranged linearly, and the overall arrangement is axisymmetric. Then the array is composed of a total of M = 4M1 + 4M2 + 1 array elements. The static pressure at each position of the artificial lateral line array p and the water depth d where the array element is located have the following relationship: (6), where: p 0 is the atmospheric pressure, ρ is the seawater density, g is the acceleration due to gravity.

[0030] Combined with the quaternion model rotation matrix obtained by matrix multiplication in Step 1, the static pressure at each position of the rotated array can be expressed as: (7), where: p 1, p 2, …, p M T is the static pressure at each array element position, d 0 is the depth of the center of gravity position of the underwater vehicle, v 1, v 2, …, v M T is the rotation vector represented by each array element, and T represents the transpose.

[0031] Then the artificial lateral line array signal model can be expressed as: (8), where: z n is the array observation signal, p n ; θ is the array theoretical signal containing the quaternion model vector parameters, θ = u x u y u z α T is the vector parameter to be estimated, w n ​​​​​​is Gaussian white noise with a mean of zero and a variance of σ 2 .

[0032] Step 3: Derive the Cramer-Rao lower bound of the quaternion model vector parameters considering Gaussian white noise according to the array signal model.

[0033] Assume that the likelihood function of Gaussian white noise P ( z ; θ ) satisfies the regular condition and can be expressed as: (9), where: exp is the exponential function with the real number e as the base.

[0034] According to the Cramer-Rao lower bound theory, for any unbiased estimator θ of the vector parameter , the estimation variance satisfies the following equation: (10), where: I ( θ ) is the Fisher information matrix of the vector parameter θ , and its reciprocal represents the Cramer-Rao lower bound of the estimation variance of the vector parameter θ . E[] represents the mathematical expectation, ∂ represents the partial derivative, and ln represents the logarithm.

[0035] For the quaternion model vector parameter θ , its Fisher information matrix I ( θ ) can be expressed as: (11).

[0036] The Cramer-Rao lower bounds θ of the components of the vector parameter correspond to the diagonal elements of the inverse of the corresponding Fisher information matrix and can be expressed as: (12), where: C ( u x ), C ( u y ), C ( u z ), C ( α ) are the parameters u x , u y respectively.u z , α Cramer-Rao lower bound of I -1 ( θ ) is the inverse matrix of the Fisher information matrix, and diag[] represents extracting the diagonal elements of the matrix.

[0037] Step 4: Derive the relative root mean square error of the quaternion model vector parameter estimation according to the Cramer-Rao lower bound, and use this as a quantitative evaluation index for the array attitude estimation performance to obtain the effective estimation range of the vector parameters, and determine whether the array meets the attitude estimation requirements.

[0038] Quaternion model vector parameters θ The estimation accuracy can be characterized by the relative root mean square error ReRMSE of the unit rotation vector and rotation angle estimation respectively. According to the Cramer-Rao lower bound of each component of the vector parameter θ The relative root mean square errors of the unit rotation vector and rotation angle of the quaternion model can be expressed as: (13), (14), where: ReRMSE( u ) is the relative root mean square error of the unit rotation vector estimation, ReRMSE( α ) is the relative root mean square error of the rotation angle estimation, and ∣ u ∣ is the modulus of the unit rotation vector.

[0039] Taking the relative root mean square errors of the unit rotation vector and rotation angle estimation of the quaternion model as the evaluation indexes of the array attitude estimation performance, and taking 10% relative root mean square error as the judgment basis, the effective estimation ranges of the unit rotation vector and rotation angle of the quaternion model are obtained, and it is determined whether the array meets the attitude estimation requirements according to the size of the effective estimation range.

[0040] Step 5: Determine the value range of the quaternion model vector parameters, and determine the constraint conditions of the artificial lateral line array layout parameters according to the underwater vehicle model parameters.

[0041] Considering the symmetry of the underwater vehicle attitude transformation, first, determine the value range of the rotation angle to be 0~90°; as Figure 3 shown, the unit rotation vector in the spherical coordinate system can be represented by the polar angle ψ and the azimuth angle φ , and thus determine the value ranges of the polar angle ψ and the azimuth angle φ to be 0~90°. The conversion relationship between the unit rotation vector and the polar angle ψ and the azimuth angle φ in the spherical coordinate system can be expressed as: (15).

[0042] Considering the size limitations of the sensor itself and the physical model of the underwater vehicle, the layout parameters of the artificial lateral line array need to meet certain constraint conditions. The specific constraint conditions for the number of array elements, element spacing, element position, array length, and array attitude estimation performance are shown in Table 1.

[0043] Table 1 Constraint Conditions for Artificial Lateral Line Array Layout Parameters .

[0044] Among them, R is the radius of the head of the underwater vehicle; L is the length of the main body of the underwater vehicle; b is the diameter of the array element; M1 is the number of array elements in the half-arm of the head; M2 is the number of array elements in the quarter main body; Δd and β are the element spacing distance and spacing angle respectively; ( x h , y h , z h ) are the layout coordinates of the head pressure sensor; ( x b , y b , z b ) are the layout coordinates of the main body pressure sensor; δ is the offset distance of the center of the head sphere of the underwater vehicle relative to the center of gravity; V u , V α are the unit rotation vector and the effective estimation range of the rotation angle of the quaternion model respectively; T u , T α are the unit rotation vector and the actual total estimation range of the rotation angle of the quaternion model respectively; η is the lower limit of the array attitude estimation performance.

[0045] Step 6, determine the optimization objective, optimize the layout parameters of the artificial lateral line array, and obtain the simplest array that meets the attitude estimation requirements.

[0046] The optimization process of the artificial lateral line array layout by the archived micro-genetic algorithm is as Figure 4 shown.

[0047] First, assume that the array consists of N h = 4M1 + 1 head array elements and N b = 4M2 main body array elements, and meet the constraint conditions of the number of array elements.

[0048] For the main array elements, randomly generate the spacing between the main array elements that satisfies the array element spacing constraint condition Δd , and determine whether the main array satisfies the array length constraint condition. If not, correct it according to the length of the underwater vehicle body L and the number of a quarter of the main array elements M2. The correction formula is as follows: (16).

[0049] To avoid repeated constraints, the head array element spacing angle β can be calculated from the arc length of the head half-arm array and the number of array elements M1, that is: (17).

[0050] According to the geometric characteristics of the physical model of the underwater vehicle, the head array elements are arranged along a curve, and the main array elements are arranged along a straight line. The generated array element coordinates need to satisfy the array element position constraint conditions.

[0051] It is considered that the Cramer-Rao lower bound of the relative root mean square error of the unit rotation vector and the rotation angle estimation of the quaternion model is less than 10% as an effective estimation. Calculate the effective estimation ranges of the unit rotation vector and the rotation angle respectively. Taking 90% as the lower limit of the array attitude estimation performance, the ratios of the effective estimation ranges of the unit rotation vector and the rotation angle to the actual total estimation range need to satisfy the array attitude estimation performance constraint conditions.

[0052] Finally, with the total number of array elements M = N h + N b being the minimum and the optimal array attitude estimation performance as the optimization objectives, set the reasonable weight ratios of the total number of array elements and the array attitude estimation performance, and use the archived micro-genetic algorithm for multi-objective optimization. When the termination iteration condition is satisfied, obtain the simplest array that meets the attitude estimation requirements, and output the array layout parameters.

[0053] Step 7, according to the obtained simplest array layout parameters, use Monte Carlo simulation to construct a randomized simulation analysis framework, integrate the least squares method to estimate the vector parameters of the quaternion model, obtain the average relative root mean square error under the simulation conditions, and compare it with the theoretical calculation value in S4 to complete the reliability verification of the above optimization method.

[0054] According to the layout parameters such as the number of elements, element spacing, and element position of the simplest array obtained in step 6, introduce random noise interference, and use Monte Carlo simulation to generate 1000 groups of simulation data.

[0055] According to the generated simulation data, the least squares method is used to estimate the vector parameters of the quaternion model, and the estimation errors of all Monte Carlo simulation samples are statistically analyzed, so as to obtain the relative root mean square errors of the unit rotation vector and rotation angle estimation of the simulation quaternion model.

[0056] Compare the relative root mean square errors of the vector parameter simulation and the theoretical estimation. If the deviation is within the preset confidence interval, it proves that the above optimization design method is reliable in the statistical sense, thus realizing the closed-loop verification of the artificial lateral line array optimization design method.

[0057] The overall process of the optimization design method for the artificial lateral line array layout is as Figure 5 shown.

[0058] The above embodiments are only for illustrating the structural concept and characteristics of the present invention, and the purpose is to enable those of ordinary skill in the art to understand the content of the present invention and implement it accordingly. It should not be used to limit the protection scope of the present invention. Any equivalent changes or modifications made according to the essence of the content of the present invention should be covered within the protection scope of the present invention.

Claims

1. An optimization method for an artificial lateral line array used for attitude estimation of an underwater vehicle, characterized in that It includes the following steps: Step 1: According to the rotation angle α and the unit rotation vector u to form a quaternion model q , obtain the vector parameters when representing the attitude of the underwater vehicle using the quaternion model. From any vector v to be rotated of the underwater vehicle, deduce the rotated vector v ' in matrix representation form; Step 2: According to the principle of lateral line perception, select pressure sensors to form an artificial lateral line array, determine the array layout form and the initial layout position of the pressure sensors, construct an underwater vehicle pressure field model, and form an array signal model; Step 3: Deduce the Cramer-Rao lower bound of the quaternion model vector parameters considering Gaussian white noise according to the array signal model, and deduce the relative root mean square error of the quaternion model vector parameter estimation based on the Cramer-Rao lower bound. Use this as a quantitative evaluation index for the array attitude estimation performance, obtain the effective estimation range of the vector parameters, and determine whether the array meets the attitude estimation requirements; Step 4: Determine the value range of the quaternion model vector parameters, and determine the constraint conditions for the artificial lateral line array layout parameters according to the underwater vehicle model parameters; Step 5: Determine the optimization objective, optimize the artificial lateral line array layout parameters, and obtain the simplest array that meets the attitude estimation requirements; Step 6: According to the obtained simplest array layout parameters, use Monte Carlo simulation to construct a randomized simulation analysis framework, integrate the least squares method to estimate the vector parameters of the quaternion model, obtain the average relative root mean square error under the simulation conditions, and compare it with the theoretical calculation value in S4 to complete the reliability verification of the optimization method.

2. The optimized method for an artificial lateral line array for underwater vehicle attitude estimation according to claim 1, characterized in that In Step 1, the basic form and matrix form of the quaternion model vector parameters are: , , , where: q 1 is the real part parameter, q 2, q 3, q 4 are the imaginary part parameters, i , j , k are three mutually orthogonal imaginary unit vectors, sin is the sine function, and cos is the cosine function; The basic form of the rotated vector v′ is , where q * represents the conjugate of the quaternion. According to the matrix form of the quaternion model, the rotated vector v′ is expressed using matrix multiplication as: .

3. The optimized method for artificial lateral line array for underwater vehicle attitude estimation according to claim 2, characterized in that, The said Step 2 includes: Step 2-1: Set the center of gravity of the underwater vehicle to coincide with the origin of the coordinate system. The head elements of the underwater vehicle are arranged in a cross pattern, and the main body elements are arranged linearly. The array consists of a total of M = 4M1 + 4M2 + 1 elements. Obtain the static pressure at the positions of each element of the artificial lateral line array p and the water depth where the element is located d The relationship between them is: , where p 0 is the atmospheric pressure, ρ is the seawater density, g is the acceleration due to gravity; Step 2-2: Combine the rotated vector v′ obtained by using matrix multiplication in Step 1. The static pressure at the positions of each element of the rotated array can be expressed as: , where p 1, p 2, … , p M T is the static pressure at the position of each array element, d 0 is the depth of the center of gravity position of the underwater vehicle, v 1, v 2, … , v M T is the rotation vector represented by each array element, and T represents the transpose;​​ Step 2-3: According to Step 2-2, the artificial lateral line array signal model is obtained as follows: , where: z n is the array observation signal, p n ; θ is the array theoretical signal containing quaternion model vector parameters, θ = u x u y u z α T is the vector parameter to be estimated, w n is the Gaussian white noise with a mean of zero and a variance of σ 2 .​​​​ 4. The method for optimizing an artificial lateral line array for underwater vehicle attitude estimation according to claim 3, wherein In the said Step 3, deducing the Cramer-Rao lower bound of the quaternion model vector parameters considering Gaussian white noise according to the array signal model includes: Step 3-1: Set the likelihood function of Gaussian white noise P ( z ; θ ) satisfies the regularity condition, expressed as: , where exp is the exponential function with a real number e as the base; Step 3-2: According to the Cramer-Rao lower bound theory, for any unbiased estimator θ of the vector parameter the estimation variance satisfies the following equation: , where I ( θ ) is the Fisher information matrix of the vector parameter θ , and its reciprocal represents the Cramer-Rao lower bound of the estimated variance of the vector parameter θ . E[] represents taking the mathematical expectation, ∂ represents taking the partial derivative, and ln represents taking the logarithm; Step 3-3: For the quaternion model vector parameter θ , its Fisher information matrix I ( θ ) is expressed as: ， Vector parameter θ Cramer-Rao lower bound of each component Each diagonal element of the inverse of the corresponding Fisher information matrix, expressed as: , where: C ( u x ), C ( u y ) C ( u z ) C ( α ) are respectively the Cramer-Rao lower bounds of the parameters u x , u y , u z , α , and I -1 ( θ ) is the inverse matrix of the Fisher information matrix, and diag[] represents extracting the diagonal elements of the matrix.

5. The optimized method for an artificial lateral line array for underwater vehicle attitude estimation according to claim 4, wherein The said Step 3 also includes: Step 3-4: According to the Cramer-Rao lower bounds of the components of the vector parameter θ The relative root mean square errors of the unit rotation vector and the rotation angle of the quaternion model can be respectively expressed as: ， , where ReRMSE( u ) is the relative root mean square error of the unit rotation vector estimation, ReRMSE( α ) is the relative root mean square error of the rotation angle estimation, and | u | is the modulus of the unit rotation vector; Step 3-5: Take the relative root mean square error of the unit rotation vector and rotation angle estimation of the quaternion model as the evaluation index of the array attitude estimation performance. Take 10% relative root mean square error as the judgment basis, obtain the effective estimation range of the unit rotation vector and rotation angle of the quaternion model, and determine whether the array meets the attitude estimation requirements according to the size of the effective estimation range.

6. The optimized method for artificial lateral line array for underwater vehicle attitude estimation according to claim 5, characterized in that The said Step 4 also includes: Step 4-1: Determine the value range of the rotation angle. In the spherical coordinate system, the unit rotation vector is represented by the polar angle ψ and the azimuth angle φ as follows: ； Step 4-2: According to the size limitations of the sensors themselves and the physical model of the underwater vehicle, the artificial lateral line array is arranged according to the constraint conditions, and the constraint conditions include the number of elements, the element interval, the element position, the array length, and the array attitude estimation performance.

7. The optimized method for an artificial lateral line array for underwater vehicle attitude estimation according to claim 6, characterized in that The said Step 5 includes: Step 5-1: Set N h = 4M1 + 1 header array elements and N b = 4M2 main array elements to form an array, and the array satisfies the constraint condition; Step 5-2: According to the geometric characteristics of the underwater vehicle physical model, the array head elements are arranged along a curve, and the main body elements are arranged in a straight line. The main body elements randomly generate a main body element interval distance that satisfies the element interval constraint condition Δd , and determine whether the main body array meets the array length constraint condition. If not, according to the length of the underwater vehicle main body L and one-quarter of the number of main body elements M2, it is corrected. The correction formula is: , to avoid duplicate constraints, the angular interval of the head array elements β can be calculated from the arc length of the head half-arm array and the number of array elements M1: ; Step 5-3: Take the total number of array elements M = N h + N b Taking the minimum number and the optimal array attitude estimation performance as the optimization objectives, set the reasonable weight ratio of the total number of array elements to the array attitude estimation performance, and use the archived micro-genetic algorithm for multi-objective optimization; Step 5-4: When the termination iteration condition is met, obtain the simplest array that meets the attitude estimation requirements, and output the array layout parameters.

8. The optimized method for artificial lateral line array for underwater vehicle attitude estimation according to claim 7, wherein The said Step 6 includes: Step 6-1: According to the layout parameters of the simplest array obtained in Step 5, introduce random noise interference, and use Monte Carlo simulation to generate 1000 groups of simulation data; Step 6-2: According to the generated simulation data, use the least squares method to estimate the vector parameters of the quaternion model, and statistically analyze the estimation errors of all Monte Carlo simulation samples, so as to obtain the relative root mean square error of the unit rotation vector and rotation angle estimation of the simulation quaternion model; Step 6-3: Compare the relative root mean square error between the vector parameter simulation and the theoretical estimation. If the deviation is within the preset confidence interval, it proves that the above optimization method is reliable in a statistical sense, thus realizing the closed-loop verification of the artificial lateral line array optimization method.

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