METHOD FOR PREDICTING MULTIPLE ACOUSTIC SCATTERING CHARACTERISTICS OF UNDERWATER VEHICLE, ELECTRONIC DEVICE, AND PROGRAM PRODUCT

Description

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation of international application of PCT application serial no. PCT/CN2025/122109 filed on Sep. 18, 2025, which claims the priority benefit of China application no. 202411508115.1 filed on Oct. 28, 2024. The entirety of each of the above mentioned patent applications is hereby incorporated by reference herein and made a part of this specification.

TECHNICAL FIELD

The present invention belongs to the technical field of underwater acoustic engineering, and particularly relates to a method for predicting multiple acoustic scattering characteristics of an underwater vehicle, an electronic device, and a program product.

BACKGROUND

Underwater vehicles, as key equipment for marine resource development, feature diverse types and complex shapes. Detection and identification on the underwater vehicles represent one of key development directions in underwater security work. Compared to methods such as optical, electromagnetic, or passive acoustic detection, active sonar is a more effective method for detecting and identifying an unmanned underwater vehicle. To improve accuracy and detection efficiency of an active sonar detection system, it is necessary to grasp echo characteristics of the unmanned underwater vehicle to a certain extent. For example, a remote operated vehicle (ROV), as a type of underwater vehicle, features complex structures such as an extendable operation chassis, a pod, a propeller tunnel, a propeller conduit, an electronics compartment, and a hydraulic unit. An incident acoustic wave undergoes multiple acoustic scattering on the surfaces of these structures. This is because the electronics compartment and the hydraulic unit of the ROV may be integrated as a single assembly. The extendable operation chassis carrying this combined assembly is made of an acoustic transmission material, allowing the acoustic wave to penetrate through the chassis. Furthermore, the ROV's thruster can also be a combined assembly. In a case of a thruster tunnel being designed as a concave surface, it will similarly cause acoustic scattering of the incident acoustic wave.

Therefore, when being tasked with performing complex missions, the underwater vehicle carries a variety of sensors and devices. Currently, there are limited researches on the fine echo features caused by multiple acoustic scattering from the complex surfaces due to such complex underwater vehicle carrying a variety of devices. Additionally, there is a lack of an efficient prediction model for multiple acoustic scattering of the complex underwater vehicle. Therefore, the complex underwater vehicle fails to meet the requirements of modern underwater security.

SUMMARY

According to one of embodiments of the present invention, a method for predicting multiple acoustic scattering characteristics of an underwater vehicle is provided, including: a pre-treating step, specifically including:

constructing a geometric model of the underwater vehicle for subsequent acoustic simulation and computation;

subdividing a mesh model of the underwater vehicle, specifically, performing mesh subdivision based on the geometric model to divide the underwater vehicle model into smaller units for numerical computation; and

establishing a multi-level grouped facet model for the underwater vehicle, dividing a surface of the underwater vehicle into a plurality of facets to simulate a complex structure of the underwater vehicle;

• • a solving step, specifically including:
  • accelerating a physical acoustics simulation process with an iterative algorithm; and
  • performing computation on scattered acoustic fields of different levels to acquire an accurate simulation result for a multiple scattering phenomenon of the underwater vehicle; and
  • a post-treating step, specifically including:
  • performing analysis to acquire acoustic characteristics of the underwater vehicle in an underwater environment, comprising a time-domain echo and a target strength of multiple acoustic scattering of the underwater vehicle.

BRIEF DESCRIPTION OF THE DRAWINGS

By reading the following detailed description with reference to the accompanying drawings, the above and other objectives, features and advantages of the exemplary implementations of the present invention will become easier to understand. In the drawings, a number of implementations of the present invention are shown in an exemplary and non-limiting manner, in which:

FIG. 1 is a flowchart of a method for predicting multiple acoustic scattering characteristics of an underwater vehicle according to one of embodiments of the present invention;

FIG. 2 is a two-dimensional schematic diagram of level l and level (l+1) according to one of embodiments of the present invention;

FIG. 3A is a simplified ROV geometric model and its facets according to one of embodiments of the present invention;

FIG. 3B is a mesh model corresponding to FIG. 3A;

FIG. 3C is a multi-level grouping model corresponding to FIG. 3A;

FIG. 4 is a pairing schematic diagram of far-field groups in a computation region according to one of embodiments of the present invention;

FIG. 5A is a simulation result of a scattered echo time versus angle of a work-class ROV model according to one of embodiments of the present invention;

FIG. 5B is an experimental result corresponding to FIG. 5A;

FIG. 6 is a schematic diagram of acoustic target strength pointing distribution (80 kHz) of a work-class ROV according to one of embodiments of the present invention; and

FIG. 7 is a schematic diagram of an influence of a number of subdivided facets on a computation time in the proposed method according to one of embodiments of the present invention.

DESCRIPTION OF THE EMBODIMENTS

Acoustic scattering characteristics refer to a phenomenon in which acoustic waves are scattered in all directions following a certain rule as they encounter an obstacle in propagating through a medium or due to inhomogeneity of the medium. This scattering phenomenon is closely related to physical characteristics, a shape and a size of the obstacle or the medium, a wavelength of the acoustic waves, and other factors. In an research on an unmanned underwater vehicle, the acoustic scattering characteristics are particularly important because these characteristics significantly affect detection, positioning, and communication capabilities of the underwater vehicle in water.

In an existing solution, a research on the acoustic scattering characteristics of the unmanned underwater vehicle primarily focuses on experimentally measuring its acoustic target strength. However, an experimental measurement imposes certain requirements for an experiment field, a to-be-tested model, personnel, etc., resulting in a high economic cost and a long testing cycle. This is often unacceptable for rapid research, development, and design. In addition, it is also very difficult to achieve the acoustic scattering experimental measurement on a non-cooperative target for the underwater vehicle. Therefore, a research on modeling the acoustic scattering characteristics of a complex underwater vehicle against the target is of great significance.

Main modeling methods for the acoustic scattering characteristics of the complex underwater target can be broadly categorized into two classes: low-frequency numerical methods and mid-to-high-frequency approximation methods. Among them, the low-frequency numerical methods, represented by a finite element method and a boundary element method, offer the advantages of high accuracy and stability. However, as a target scale increases, they also have the disadvantages of a high computational load and a high memory requirement.

In recent years, most researches have shifted towards using high-frequency approximation methods to solve the acoustic scattering problem of a large-scale complex target. An acoustic ray bouncing method, when accounting for a coupling effect, essentially only considers the coupling effect caused by a specular reflection. For a concave surface target with a large curvature, this method has the problem of ray divergence, leading to low computational accuracy. A planar element method based on Kirchhoff approximation offers high computational efficiency, but it does not account for multiple acoustic scattering effects, resulting in lower computational accuracy for acoustic scattering for complex underwater target.

Here, the high-frequency approximation method refers to a simplified computation method used in a case of a high acoustic wave frequency. It is based on an assumption of geometric acoustics, where at a high frequency, acoustic wave propagation and scattering can be treated by light analogy, with the acoustic waves primarily exhibiting reflection and diffraction when encountering the target. This method is particularly useful for treating the complex large-scale target because it can reduce computational complexity.

The acoustic ray bouncing method, as a high-frequency approximation numerical method, divides incident acoustic waves into multiple acoustic beams and computes a reflection direction and an energy loss of each acoustic beam on a target surface using a geometric acoustics method. This method accounts for multiple reflections of the acoustic beams on the target surface and obtains an overall scattered field of the target by superposing scattered fields generated by all the acoustic beams.

The Kirchhoff approximation, as a classical acoustic scattering theory, assumes incident and scattered waves at every point on the target surface can be approximated by planar waves. The planar element method based on the Kirchhoff approximation works by decomposing the target into numerous small planar elements, computing a scattering contribution of each element to the acoustic waves, and then superposing these scattered fields to predict the overall acoustic scattering characteristics of the target.

To address the limitations in the aforementioned methods, the present invention proposes a method for rapidly predicting multiple acoustic scattering characteristics of a complex underwater vehicle. The method employs an iterative physical acoustics multi-level grouping model, which approximates a multiple scattered acoustic field of the underwater vehicle as a coherent superposition result of scattered acoustics fields of different levels from discrete facets within a non-empty group. This method offers the advantages of clear physical significance, and high computational accuracy and efficiency.

According to one or more embodiments, the method for rapidly predicting the multiple acoustic scattering characteristics of the complex underwater vehicle totally consists of two main parts: firstly, pre-treating, in which the multi-level grouped facet model of the complex underwater vehicle is established with the octree structure; and secondly, the divided-level scattered acoustic fields of discrete facets within the non-empty group are computed with the iterative physical acoustics acceleration algorithm, then the divided-level scattered acoustic fields of the facets are coherently superposed to obtain an overall divided-level scattered acoustic field of the target, and finally, the scattered acoustic fields of different levels are coherently superposed to obtain the overall multiple scattered acoustic field of the target. Based on the above solution, the multiple acoustic scattering characteristics of the complex underwater vehicle can be rapidly predicted, as shown in FIG. 1.

The pre-treating includes:

• • constructing a geometric model of the underwater vehicle for subsequent acoustic simulation and computation;
  • subdividing a mesh model of the underwater vehicle, specifically, performing mesh subdivision based on the geometric model to divide the underwater vehicle model into smaller units for numerical computation; and
  • establishing a multi-level grouped facet model for the underwater vehicle, dividing a surface of the underwater vehicle into a plurality of facets to simulate a complex structure of the underwater vehicle.

A further solving process includes:

• • accelerating a physical acoustics simulation process with an iterative algorithm; and
  • performing computation on scattered acoustic fields of different levels to acquire a more accurate simulation result for a multiple scattering phenomenon of the underwater vehicle.

Further post-treating includes:

• • analyzing acoustic characteristics of the underwater vehicle in an underwater environment, including a time-domain echo and a target strength of multiple acoustic scattering of the complex underwater vehicle.

From construction of the geometric model to computation of the acoustic characteristics, each step is designed to more accurately simulate and predict an acoustic behavior of the underwater vehicle in the underwater environment.

A method for constructing the multi-level grouped facet model is to establish the multi-level grouped facet model with the octree structure. The octree structure is a tree-like data structure used for three-dimensional spatial data, dividing a three-dimensional space into multiple cubic units. Each unit can be further subdivided into 8 smaller cubic units. This process can be performed recursively. A specific process is as follows:

For a three-dimensional case, a solution region is enclosed by a cube, which is then subdivided into 8 sub-cubes, and this level is denoted as level 1;

• • each sub-cube is further subdivided into 8 smaller sub-cubes to obtain level 2; and
  • obtaining finer levels by analogy, thereby completing octree-based multi-level grouping of a target, wherein a number of sub-cubes at level l is 8^l. A grouping criterion here is:

First, definitions of a parent group and a parent level, a child level and a child group, and a distant relative group are provided. Let a current level be level l, a finer level obtained by subdividing the current level is level (l+1). In this case, level l is the parent level of level (l+1); and level (l+1) is the child level of level l. A non-empty group on the parent level is called the parent group, and a non-empty group on its child level is called the child group. If two groups at level (l+1) are near-field groups, and their parent groups are far-field groups, they are distant relative groups, as shown in FIG. 2.

Here, the significance of grouping lies in describing physical characteristics at different levels through parent groups and child groups, including the scattering characteristics of the acoustic waves across regions of varying sizes. Groups that interact with each other at a relatively long distance are described as the distant relative groups, for solving the problem of long-distance acoustic wave propagation. This grouping method allows for abstraction and simplification of the problem at different levels, thereby improving the computational efficiency.

As shown in FIG. 2, a square is subdivided into smaller squares, each representing one group. These groups are labeled as the parent groups, the child groups, or the distant relative groups, to indicate their positions and relationships within the octree structure. With a source group as a computational starting point, facets contained in it are used for computing the scattered acoustic fields generated by a direct action of the incident acoustic waves. Primary distinctions among a near group, the distant relative group, and the far-field group lie in their distances from the source group and their interaction methods. An interaction between the near groups requires precise computation, whereas an interaction between the distant relative group and the far-field group can be treated with the approximation method.

Further, as shown in FIG. 4, a criteria for the far-field group includes:

A number of groups D(i,j) between group i and group j is used for judging whether the two groups are the far-field groups. If D(i,j)>D_b, they are the far-field groups. If D(i,j)<D_b, they are the near-field groups. Here, D; is a positive integer, and a value of D_b can be used to control computational precision of the algorithm. In general, a criteria for far fields at different levels is:

D b ( l ) = ⌈ 1.5 Δ l λ ⌉ + 1 ,

• • where ┌⋅┐ represents a rounding-up function, and Δ_l represents an edge length of the groups at level l.

For example, due to a highly complex geometric configuration of the ROV physical model, it is impractical to directly construct a multi-level grouped facet model for it. Therefore, without substantially affecting acoustic scattering of the ROV model, the simplified ROV model retains a main frame and components, as shown in FIG. 3A. The retained main frame and components include bow anti-collision rubber, a protective plate, a buoyancy block, a power module, an electronics module, a multifunctional module, a 80 L hydraulic source, 2 stern hydraulic thrusters, 8 power thrusters, and a telescopic tray. The simplified ROV model is discretized into 532378 triangular facets, as shown in FIG. 3B. According to the method of the embodiment of the present invention, a seven-level grouping method is employed, with level 3 selected as a thickest level. Multi-level grouping of facets is shown in FIG. 3C.

The iterative physical acoustics acceleration algorithm includes:

• • using the Helmholtz integral formula, where the scattered acoustic field at any arbitrary field point in a space is given by

ϕ s ( r B | r A ) = G radi ⁢ Φ ( 1 ) + G radi ⁢ ∑ q = 2 Q ⁢ Φ ( q ) , ( 1 ⁢ a ) Φ ( q ) = G mul ⁢ Φ ( q - 1 ) , q ≥ 2 , ( 1 ⁢ b )

• • wherein r_A is a coordinate vector of an acoustic source A; r_B is a coordinate vector of a field point B; G^mul is a coupling scattering kernel function matrix; G^radi is an external field radiation function matrix; G^radiΦ⁽¹⁾ represents a first-level scattered field of the target;

G r ⁢ a ⁢ d ⁢ i ⁢ Σ q = 2 Q ⁢ Φ ( q )

represents a sum of high-level scattered fields of the target; Φ^(q) represents a q^th-level surface acoustic field of the target; and Φ^(q-1) represents a (q−1)^th-level acoustic field on the surface of target.

As shown in FIG. 4, assuming that group i and group j form a far-field group pair, while facet m and facet n belong to group i and group j respectively. At this time, a distance R_nm between facets m and n can be expressed as:

R n ⁢ m = ❘ "\[LeftBracketingBar]" r c ⁢ n - r c ⁢ m ❘ "\[RightBracketingBar]" = ❘ "\[LeftBracketingBar]" R ji + R nj - R mi ❘ "\[RightBracketingBar]" , ( 2 )

• • where R_ji=r_cj−r_ci, R_mi=r_cm−r_ci, R_nj=r_cn−r_cj, r_cm and r_cn are vectors of geometric center positions of facet m and facet n; and r_ci and r_cj are vectors of geometric center positions of group i and group j. According to Taylor series expansion, an α^th power term

R n ⁢ m a

of the distance R_nm between facet m and facet n can be approximated as

R n ⁢ m a ≈ R n a + R m a , ( 3 )

• • where a power exponent is α=1, −1, −2;

R n a

is α^th power term of a center distance related term between facet n and group j;

R m a

is an α^th power term of a center distance related term between facet m and group i. Thus, a coupling scattering kernel function between facet m and facet n can be approximated as

G ji mul = D j II ⁢ C i l + D j I ⁢ C i II , ( 4 )

where

C i I ⁢ and ⁢ C i II

represent type I anu type II aggregation factors transferred from a center of facet m to a geometric center of group i; and

D j I ⁢ and ⁢ D j II

represent type i anu type II divergence factors transferred from a geometric center of group j to a center of facet n.

After grouping, a term on a right hand side of equation (1b) can be approximated in a multilevel form:

G j mul ⁢ Φ ( q - 1 ) = Σ i L ∈ N j L V ⁢ G j L ⁢ i L mul ⁢ Φ i L ( q - 1 ) + Σ l = 2 L ⁢ Σ i l ∈ R j l J - V ( D j l II ⁢ C i l II ⁢ Φ i l ( q - 1 ) + D j l I ⁢ C i l I ⁢ Φ i l ( q - 1 ) ) , ( 5 )

• • where i_l and j_l represent group i and group j at level l; N_jl represents group i_l and group j_l forming a near-field group pair; R_jl represents group i_l and group j_l forming a distant relative group pair; J is a number of non-empty groups; and V is a number of near-field groups. A coupling effect between facets is decomposed by equation (5) into two terms: a first term

Σ i L ∈ N j L V ⁢ G j L ⁢ i L mul ⁢ Φ i L ( q - 1 )

represents a coupling effect between facets of a finest-level near-field group pair; and a second term

Σ l = 2 L ⁢ Σ i l ∈ R j l J - V ( D j l II ⁢ C i l II ⁢ Φ i l ( q - 1 ) + D j l I ⁢ C i l I ⁢ Φ i l ( q - 1 ) )

represents a coupling effect between facets of a distant relative group pair at each level. Equation (5) is substituted into equation (la) to obtain:

ϕ s ( r B | r A ) = ∑ j J G j radi ⁢ Φ i ( 1 ) + ∑ j J G j radi ⁢ ∑ q = 2 Q [ ∑ i L ∈ N j L V G j L ⁢ i L m ⁢ u ⁢ l ⁢ Φ i L ( q ) + ∑ l = 2 L ∑ i l ∈ R j l J - V ( D j l II ⁢ C i l II ⁢ Φ i l ( q ) + D j l I ⁢ C i l I ⁢ Φ i l ( q ) ) ] . ( 6 ⁢ a ) G j mul ⁢ Φ ( q - 1 ) = Σ g i ∈ near j V ⁢ G ji mul ⁢ Φ i ( q - 1 ) + Σ g i ∈ far j J - V ( D j II ⁢ C i II ⁢ Φ i ( q - 1 ) + D j I ⁢ C i I ⁢ Φ i ( q - 1 ) ) , ( 6 ⁢ b )

Equation (6) is a theoretical equation of the multilevel grouping iterative physical acoustics acceleration algorithm

Σ j J ⁢ G j radi ⁢ Φ i ( 1 )

• • in a term on a right hand side in equation (6a) represents a first-level scattered acoustic field of the target obtained by coherent superposition of first-level scattered acoustic fields of discrete facets within the non-empty groups;

∑ j J G j radi [ ∑ i L ∈ N j L V G j L ⁢ i L mul ⁢ Φ i L ( q ) + ∑ I = 2 L ∑ i l ∈ R j l J - V ( D j l II ⁢ C i l II ⁢ Φ i l ( q ) + D j l I ⁢ C j l I ⁢ Φ i l ( q ) ) ]

• • in the term on the right hand side in equation (6a) represents a q(≥2)^th-level scattered acoustic field of the target obtained by coherent superposition of q(≥2)^th-level scattered acoustic fields of discrete facets within the non-empty groups; and

∑ j J G j radi ⁢ ∑ q = 2 Q [ ∑ i L ∈ N J L V G j L ⁢ i L mul ⁢ Φ i L ( q ) + ∑ l = 2 L ∑ i l ∈ R j l J - V ( D j l II ⁢ C i l II ⁢ Φ i l ( q ) + D j l I ⁢ C i l I ⁢ Φ i l ( q ) ) ]

• • in the term on the right hand side in equation (6a) represents an overall multiple scattered acoustic field of the target obtained by coherent superposition of scattered acoustic fields of different levels. For a problem of the number of the facets being N, a computational complexity of each level in equation (6) is approximated as N, and equation (6) can typically consist of log N levels, resulting in an overall computational complexity being O(N log N).

To further illustrate the effects of the embodiments of the present invention, the following computational example is provided.

For 0-360-degree horizontal omnidirectional time-domain echo simulation of the work-class ROV shown in FIG. 3A, FIG. 3B, and FIG. 3C, a linear frequency modulation signal for a short pulse duration of 4 ms and at 60 kHz-120 kHz is used as a transmitted signal. Acoustic wave transmission and reception points are located 16.8 m and 6.8 m away from the model's acoustic center respectively.

FIG. 5A and FIG. 5B respectively present time-angle distribution simulation and an experimental result of received echo pulse sequences in various azimuths, where an abscissa represents an incident azimuth angle; an ordinate represents an echo pulse time; a color represents an echo amplitude; θ_i=0° corresponds to stern incidence; θ_i=90° and 270° correspond to abeam incidence; and θ_i=180 corresponds to bow incidence. As shown in FIG. 5A and FIG. 5B, echo structures from the experiment and simulation are in good agreement, both exhibiting an external contour feature of “W-W”. Multiple scattering echo bright spot distribution of the target can be clearly observed in echoes, which demonstrates that the method of the present invention can accurately predict multiple scattering echo bright spot distribution for the complex underwater vehicle, providing identifiable and fine features for underwater target recognition.

0-360-degree horizontal omnidirectional target strength simulation is performed on the work-class ROV shown in FIG. 3A, FIG. 3B, and FIG. 3C. FIG. 6 presents comparison results between the experiment and simulation of the ROV model's acoustic target strength at 80 kHz, where θ_i=0° corresponds to stern incidence; θ_i=90° and 270° correspond to abeam incidence; and θ_i=180° corresponds to bow incidence. It can be seen from FIG. 6, the experimental and simulation results are in good agreement, which validates effectiveness of the proposed method of the present invention for computing the scattered acoustic field of the complex underwater vehicle. Both the experimental and simulation results exhibit a “W-W” shaped azimuthal feature, with target strength peaks observed near a bow, a stern, and two abeam directions.

With continued taking the work-class ROV shown in FIG. 3A, FIG. 3B, and FIG. 3C as an example, we analyze a relationship between a computation time of the proposed method and the number of discrete facets N. A logarithmic proportional relationship curve of a required CPU time versus the number of discrete facets N is shown in FIG. 7. It can be seen from FIG. 7, the computational advantage of the proposed rapid method becomes fully evident as the number of facets increases. The relationship between the computation time and the number of facets is of an O(N log N) level, which is consistent with previous analysis.

Therefore, the present invention has the beneficial effects that: the method for rapidly predicting the multiple acoustic scattering characteristics of the complex underwater vehicle proposed by the present invention is used for rapidly predicting the omni directional time-domain echoes and the target strength for the multiple scattered echoes of the complex underwater vehicle. This provides theoretical support for predicting the fine features of the multiple scattered echoes of the complex underwater vehicle and improving a capability of underwater monitoring sonar for identifying the complex underwater vehicle.

It should be understood that in the embodiments of the present invention, the term “and/or” merely describes an association relationship between associated objects, indicating that three relationships may exist. For example, A and/or B may represent three cases: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character “/” in this document generally indicates that the associated objects before and after it are in an “or” relationship.

A person of ordinary skill in the art may realize that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, computer software, or a combination of the two. To clearly illustrate the interchangeability of hardware and software, the components and steps of each example have been generally described in terms of functions in the above description. Whether these functions are executed in hardware or software depends on the specific application and design constraints of the technical solution. A person skilled in the art may use different methods to implement the described functions for each specific application, but such implementation shall not be deemed to go beyond the scope of the present invention.

If an integrated unit is implemented as a software functional unit and sold or used as an independent product, it may be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, may be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which may be a personal computer, a server, a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage media include: U disk, mobile hard disk, Read-Only Memory (ROM), Random Access Memory (RAM), magnetic disk, optical disc, and other media that can store program codes.

The above descriptions are only specific implementations of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of various equivalent modifications or substitutions within the technical scope disclosed by the present invention, and these modifications or substitutions shall all be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.