Relative motion positioning method, system and device for multiple seabed beacon networks, medium and product
By employing a quadratic polynomial model and a grouped adjustment function model in a multi-seabed beacon network, combined with the least squares method for iterative solution, the problems of acoustic ray bending error and beacon motion description were solved, achieving high-precision relative positioning of seabed beacons and improving positioning accuracy and trajectory reconstruction reliability.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- CHINESE PEOPLES LIBERATION ARMY UNIT 61540
- Filing Date
- 2026-02-10
- Publication Date
- 2026-05-12
AI Technical Summary
Existing technologies have failed to effectively suppress acoustic ray bending errors and accurately describe the motion characteristics of seabed beacons in multi-seabed beacon networks, resulting in insufficient relative positioning accuracy. In particular, it is difficult to achieve high-precision seabed beacon position calculation and trajectory reconstruction in complex marine environments.
By employing a quadratic polynomial model and a grouped adjustment function model, combined with the least squares method for iterative solution, and by distinguishing between acoustic ray bending error and beacon motion under different elevation difference scenarios, a motion model of the seabed beacon is constructed to improve positioning accuracy.
It improves the relative positioning accuracy of seabed beacons in a multi-seabed beacon network, and can provide high-precision position calculation and trajectory reconstruction in complex marine environments, providing reliable technical support for high-precision underwater navigation and positioning.
Smart Images
Figure CN122017736A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of marine geodetic data processing technology, and in particular to a method, system, device, medium and product for relative motion positioning of multiple seabed beacon networks. Background Technology
[0002] Subsea beacons, as a key infrastructure for marine geodesy and underwater navigation and positioning, typically consist of a reference network of multiple fixed or moored beacons. By processing the acoustic ranging data between beacons, relative motion positioning of multiple subsea beacon networks can be achieved, thus providing high-precision positional references for underwater vehicles, observation platforms, and other equipment. However, in actual operating environments, the relative positioning accuracy of subsea beacons in multiple subsea beacon networks is constrained by various error factors, among which acoustic ray bending error is one of the main sources of error affecting ranging accuracy.
[0003] In acoustic ranging, the propagation path of sound waves in seawater is not a straight line, but rather bends due to the vertical changes in the sound velocity profile within the water. Traditional positioning methods often use a single ray bending model for correction, but this model does not fully consider the differences in the actual sound propagation paths between different beacon combinations. In a multi-beacon network consisting of fixed and moored seabed beacons, the moored seabed beacons move due to factors such as ocean currents, leading to dynamic changes in the elevation differences between the seabed beacons and forming obvious elevation difference clusters. The ray bending radii corresponding to different elevation difference clusters differ significantly. If a single ray bending coefficient is continued for correction, systematic model errors will be introduced, thus limiting further improvements in relative positioning accuracy.
[0004] Furthermore, when describing the motion state of seabed beacons, the relevant methods often use linear or simple motion models, which make it difficult to accurately depict the actual motion trajectory of moored beacons in complex marine environments. This further increases the uncertainty of motion state calculation and affects the accuracy of positioning results.
[0005] Therefore, effectively suppressing the influence of ray bending errors and accurately describing the motion characteristics of seabed beacons has become a key challenge in improving the relative positioning accuracy of seabed beacons in multi-seabed beacon networks. There is an urgent need for a relative positioning technology capable of distinguishing ray bending errors under different elevation differences and simultaneously modeling the motion state of beacons in detail, in order to achieve higher accuracy in seabed beacon position calculation and trajectory reconstruction, providing reliable technical support for high-precision underwater navigation and positioning. Summary of the Invention
[0006] The purpose of this application is to provide a method, system, device, medium, and product for relative motion positioning of multiple seabed beacon networks, so as to improve the relative positioning accuracy of seabed beacons in multiple seabed beacon networks.
[0007] To achieve the above objectives, this application provides the following solution.
[0008] Firstly, this application provides a relative motion positioning method for a multi-seabed beacon network. The multi-seabed beacon network includes multiple seabed beacons, comprising one fixed seabed beacon and multiple moored seabed beacons. Acoustic ranging observations within the multi-seabed beacon network constitute two types of baselines: a first type of baseline between a fixed seabed beacon and a moored seabed beacon, and a second type of baseline between two moored seabed beacons. Each baseline is observed at different times. The relative motion positioning method for the multi-seabed beacon network includes: Acquire sound velocity profile measurement data for the area containing the multi-seabed beacon network, approximate three-dimensional coordinates of all seabed beacons, and sonar measurement data for all baselines; the sonar measurement data includes the propagation time measurement of sound waves between two seabed beacons at all sampling observation times during the sampling period; all baselines were observed during the sampling period; Based on the sound velocity profile measurement data and the approximate zenith direction coordinates in the approximate three-dimensional coordinates of the two seabed beacons on each baseline, the sound velocity measurement value of each baseline is determined. Based on the approximate three-dimensional coordinates of two seabed beacons on each baseline, the prior distance values of each baseline are determined. The prior value of the sound velocity deviation of the multi-seabed beacon network is determined based on the prior value of the baseline distance observed at each sampling observation time and the sonar measurement data. A quadratic polynomial model for each seabed beacon and a grouped adjustment function model for each sampling observation time are constructed respectively; the quadratic polynomial model is an equation about the observation time, and the grouped adjustment function model is an equation about the observed distance and theoretical distance of the baseline; The model parameters of the quadratic polynomial model for each seabed beacon and the sound velocity deviation, first curvature coefficient, and second curvature coefficient in the grouped adjustment function model for all sampled observation times are initialized; the model parameters include: constant terms, linear coefficients, and quadratic coefficients; Using the least squares method, the quadratic polynomial models of all seabed beacons and the grouped adjustment function models of all sampling observation times are iteratively solved to obtain the target values of the model parameters of the quadratic polynomial models of each seabed beacon, as well as the correction values of the sound velocity deviation, the estimated values of the first curvature coefficient and the estimated values of the second curvature coefficient in the grouped adjustment function models of all sampling observation times. Substitute the target values of the model parameters of the quadratic polynomial model of each seabed beacon into the quadratic polynomial model of the corresponding seabed beacon to obtain the seabed beacon motion model of each seabed beacon. When any current seabed beacon in the multi-seabed beacon network is observed at the current moment, the current moment is substituted into the seabed beacon motion model of the current seabed beacon to obtain the three-dimensional coordinates of the current seabed beacon's motion at the current moment, thereby realizing the relative motion positioning of the current seabed beacon.
[0009] In one embodiment, the sound velocity measurement value of each baseline is determined based on the sound velocity profile measurement data and the approximate zenith direction coordinates in the approximate three-dimensional coordinates of two seafloor beacons on each baseline, including: Based on sound velocity profile measurement data, the estimated value of the coefficient of the linear term of sound velocity as a function of approximate zenith coordinate is determined; the sound velocity profile measurement data includes: sound velocity profile measurement values at multiple different approximate zenith coordinates. The sound velocity measurements for each baseline are determined based on the approximate zenith direction coordinates and the estimated linear term coefficients in the approximate three-dimensional coordinates of two seabed beacons on each baseline.
[0010] In one embodiment, determining the prior value of the sound velocity deviation of the multi-seabed beacon network based on the prior value of the distance of the baseline observed at each sampling observation time and sonar measurement data includes: Any sampling observation time is determined as the current sampling observation time, and the baseline observed at the current sampling observation time is determined as the current sampling observation baseline; Based on the prior distance value of the current sampling observation baseline and sonar measurement data, determine the calculated value of the sound velocity at the current sampling observation time; Based on the calculated sound velocity values at all sampling observation times, the prior value of the sound velocity deviation of the multi-submarine beacon network is determined.
[0011] In one embodiment, the quadratic polynomial model of any seabed beacon is: ; in, seabed beacon exist The three-dimensional coordinates of motion at the moment of observation; seabed beacon exist The north-direction coordinates of the motion at the time of observation; seabed beacon The constant term in the northward direction of motion in the quadratic polynomial model; seabed beacon The coefficients of the first term in the quadratic polynomial model of the northward coordinate of motion as a function of time; This refers to the starting sampling observation time within the sampling period; seabed beacon The coefficients of the quadratic term in the time-varying northward coordinates of the motion in the quadratic polynomial model; seabed beacon exist The eastward coordinates of the motion at the time of observation; seabed beacon The constant term in the eastward direction of motion in the quadratic polynomial model; seabed beacon The coefficients of the first term in the quadratic polynomial model of the eastward coordinate of motion as a function of time; seabed beacon The coefficients of the quadratic term in the eastward coordinate of the motion in the quadratic polynomial model as a function of time; seabed beacon exist The coordinates of the zenith direction of the motion at the time of observation; seabed beacon The constant term in the zenith direction of motion in the quadratic polynomial model; seabed beacon The coefficients of the first term in the quadratic polynomial model of the zenith direction of motion as a function of time; seabed beacon The coefficients of the quadratic term in the quadratic polynomial model of the moving zenith direction coordinate as a function of time.
[0012] In one embodiment, the process of constructing the grouped adjustment function model for any current sampling observation time includes: Determine the observation type of the current sampling observation baseline; If the observation type of the current sampling observation baseline is the first observation type, then the group adjustment function model for the current sampling observation time is: ; in, Seafloor beacons on the baseline for the first observation type and seabed beacons exist Observation distance at the time of observation , for The calculated value of the speed of sound at the time of observation. Seafloor beacons on the baseline for the first observation type and underwater beacons exist The propagation time measurement at the observation moment; Seafloor beacons on the baseline for the first observation type and underwater beacons exist Theoretical distance at the time of observation , seabed beacon exist The three-dimensional coordinates of the motion at the moment of observation seabed beacon exist The three-dimensional coordinates of motion at the moment of observation; This is the prior value of the sound speed deviation; The first bending coefficient; Seafloor beacons on the baseline for the first observation type and underwater beacons exist The horizontal component of the theoretical distance at the time of observation. , Seafloor beacons on the baseline for the first observation type and underwater beacons exist The elevation angle at the time of observation; If the observation type of the current sampling observation baseline is the second observation type, then the group adjustment function model for the current sampling observation time is: ; in, Seafloor beacons on the baseline for the second observation type and underwater beacons exist Observation distance at the time of observation , Seafloor beacons on the baseline for the second observation type and underwater beacons exist The propagation time measurement at the observation moment; Seafloor beacons on the baseline for the second observation type and underwater beacons exist Theoretical distance at the time of observation , seabed beacon exist The three-dimensional coordinates of the motion at the moment of observation seabed beacon exist The three-dimensional coordinates of motion at the moment of observation; The second bending coefficient; Seafloor beacons on the baseline for the second observation type and underwater beacons exist The horizontal component of the theoretical distance at the time of observation. , Seafloor beacons on the baseline for the second observation type and underwater beacons exist The elevation angle at the time of observation.
[0013] In one embodiment, the model parameters of the quadratic polynomial model for each seabed beacon and the sound velocity deviation, first curvature coefficient, and second curvature coefficient in the grouped adjustment function model for all sampled observation times are initialized, including: The constant term of the quadratic polynomial model for each seabed beacon is the approximate three-dimensional coordinate of the corresponding seabed beacon, and the coefficients of the first and second terms of the quadratic polynomial model for each seabed beacon are initialized to 0. The sound velocity deviations of the grouped adjustment function models at each sampling observation time are initialized to the prior values of the sound velocity deviations. The first and second curvature coefficients of the grouped adjustment function models at each sampling observation time are initialized to 0.
[0014] Secondly, this application provides a relative motion positioning system for a multi-seabed beacon network to realize the aforementioned relative motion positioning method for a multi-seabed beacon network. The relative motion positioning system for the multi-seabed beacon network includes: The data acquisition module is used to acquire sound velocity profile measurement data of the area where the multi-seabed beacon network is located, approximate three-dimensional coordinates of all seabed beacons, and sonar measurement data of all baselines; the sonar measurement data includes the propagation time measurement of sound waves between two seabed beacons at all sampling observation times during the sampling period; all baselines were observed during the sampling period; The sound velocity measurement value determination module is used to determine the sound velocity measurement value of each baseline based on the sound velocity profile measurement data and the approximate zenith direction coordinates in the approximate three-dimensional coordinates of two seabed beacons on each baseline. The distance prior value determination module is used to determine the distance prior value of each baseline based on the approximate three-dimensional coordinates of two seabed beacons on each baseline. The prior value determination module for sound velocity deviation is used to determine the prior value of sound velocity deviation of the multi-seabed beacon network based on the prior value of the distance of the baseline observed at each sampling observation time and sonar measurement data. The model building module is used to build a quadratic polynomial model for each seabed beacon and a grouped adjustment function model for each sampling observation time; the quadratic polynomial model is an equation about the observation time, and the grouped adjustment function model is an equation about the observed distance and theoretical distance of the baseline; An initialization module is used to initialize the model parameters of the quadratic polynomial model for each seabed beacon and the sound velocity deviation, first curvature coefficient, and second curvature coefficient in the grouped adjustment function model for all sampling observation times; the model parameters include: constant terms, linear coefficients, and quadratic coefficients; The solution module is used to iteratively solve the quadratic polynomial model of all seabed beacons and the grouped adjustment function model of all sampling observation times using the least squares method, so as to obtain the target values of the model parameters of the quadratic polynomial model of each seabed beacon, as well as the correction values of the sound speed deviation, the estimated values of the first curvature coefficient and the estimated values of the second curvature coefficient in the grouped adjustment function model of all sampling observation times. The model determination module is used to substitute the target values of the model parameters of the quadratic polynomial model of each seabed beacon into the quadratic polynomial model of the corresponding seabed beacon to obtain the seabed beacon motion model of each seabed beacon. The positioning module is used to input the current time into the current seabed beacon motion model when any current seabed beacon in the multi-seabed beacon network is observed at the current time, so as to obtain the three-dimensional motion coordinates of the current seabed beacon at the current time and realize the relative motion positioning of the current seabed beacon.
[0015] Thirdly, this application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the aforementioned relative motion positioning method for a multi-seabed beacon network.
[0016] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the aforementioned relative motion positioning method for a multi-submarine beacon network.
[0017] Fifthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the aforementioned method for relative motion positioning of a multi-submarine beacon network.
[0018] According to the specific embodiments provided in this application, the following technical effects are disclosed: This application discloses a method, system, device, medium, and product for relative motion positioning of a multi-seabed beacon network. When any seabed beacon in the multi-seabed beacon network is observed at the current time, the current time is substituted into the seabed beacon motion model of the current seabed beacon to obtain the three-dimensional coordinates of the current seabed beacon's motion at the current time, thereby realizing the relative motion positioning of the current seabed beacon. The seabed beacon motion model of the current seabed beacon is obtained by iteratively solving the quadratic polynomial model of all seabed beacons and the grouped adjustment function model of all sampling observation times. The grouped adjustment function model of different sampling observation times sets different curvature coefficients (first curvature coefficient and second curvature coefficient) based on the observation type of the baseline observed at that sampling observation time. Compared with conventional methods, this application improves the relative positioning accuracy of seabed beacons in a multi-seabed beacon network by adding a dual-sound curvature coefficient (setting different curvature coefficients) and using a quadratic polynomial to describe the seabed beacon motion model. Attached Figure Description
[0019] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0020] Figure 1 A schematic flowchart of a relative motion positioning method for a multi-submarine beacon network provided in an embodiment of this application; Figure 2 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation
[0021] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.
[0022] The purpose of this application is to provide a method, system, device, medium, and product for relative motion positioning of multiple seabed beacon networks, aiming to improve the relative positioning accuracy of seabed beacons in multiple seabed beacon networks.
[0023] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.
[0024] In one exemplary embodiment, such as Figure 1 As shown, a relative motion positioning method for a multi-seabed beacon network is provided. The multi-seabed beacon network includes multiple seabed beacons, including one fixed seabed beacon and multiple moored seabed beacons. Acoustic ranging observations within the multi-seabed beacon network constitute two types of baselines: the first type of baseline is the baseline between one fixed seabed beacon and one moored seabed beacon, and the second type of baseline is the baseline between two moored seabed beacons. Each baseline is observed at different times. The relative motion positioning method for the multi-seabed beacon network includes the following steps.
[0025] Step 1: Obtain sound velocity profile measurement data for the area where the multi-seabed beacon network is located, approximate three-dimensional coordinates of all seabed beacons, and sonar measurement data for all baselines.
[0026] The sonar measurement data includes the propagation time measurements of sound waves between two seabed beacons at all sampling observation times during the sampling period; all baselines were observed during the sampling period.
[0027] Specifically, sound velocity profile measurement data is collected using sound velocity profile measurement equipment, and the three-dimensional coordinates of the seabed beacon, collected using the Global Navigation Satellite System or acoustic positioning technology, are the approximate three-dimensional coordinates of the seabed beacon.
[0028] Step 2: Based on the sound velocity profile measurement data and the approximate zenith direction coordinates in the approximate three-dimensional coordinates of the two seabed beacons on each baseline, determine the sound velocity measurement value of each baseline.
[0029] As an optional implementation, step 2 includes the following steps.
[0030] Step 21: Based on the sound velocity profile measurement data, determine the estimated value of the coefficient of the linear term of sound velocity as a function of the approximate zenith coordinate; the sound velocity profile measurement data includes: sound velocity profile measurement values at multiple different approximate zenith coordinates.
[0031] Specifically, step 21 includes: Step 211: Construct the formula for the speed of sound as a function of the approximate zenith coordinate: ; in, For the first A measurement of the sound velocity profile at a rough zenith coordinate; and All are coefficients of linear terms. The coefficients of the linear term, and All are coefficients of linear terms; For the first A rough zenith direction coordinate; Step 212: Fit the formula for the change of sound speed with approximate zenith coordinates based on the measured sound speed profile values at different approximate zenith coordinates to obtain the estimated values of the linear term coefficients. and .
[0032] Step 22: Determine the sound velocity measurement value for each baseline based on the approximate zenith direction coordinate and the estimated value of the linear term coefficient in the approximate three-dimensional coordinates of the two seabed beacons on each baseline.
[0033] Specifically, the formula for calculating the sound velocity measurement value of any baseline is as follows: ; in, seabed beacon and underwater beacons The measured sound velocity at the baseline in which it is located; seabed beacon Approximate zenith coordinates; seabed beacon The approximate zenith coordinates.
[0034] Step 3: Determine the prior distance values for each baseline based on the approximate three-dimensional coordinates of the two seabed beacons on each baseline.
[0035] Specifically, the formula for calculating the prior distance value of any baseline is as follows: ; in, seabed beacon and underwater beacons The prior value of the distance to the baseline it is located at; seabed beacon Approximate three-dimensional coordinates , seabed beacon Approximate north-facing coordinates seabed beacon The approximate eastward coordinates, seabed beacon Approximate zenith coordinates; seabed beacon Approximate three-dimensional coordinates , seabed beacon Approximate north-facing coordinates seabed beacon The approximate eastward coordinates, seabed beacon Approximate zenith coordinates; It is a 2-norm.
[0036] Step 4: Determine the prior value of the sound velocity deviation of the multi-seabed beacon network based on the prior value of the baseline distance observed at each sampling observation time and the sonar measurement data.
[0037] As an optional implementation, step 4 includes the following steps.
[0038] Step 41: Determine any sampling observation time as the current sampling observation time, and determine the baseline at which the observation is conducted at the current sampling observation time as the current sampling observation baseline.
[0039] Step 42: Based on the prior distance value of the current sampling observation baseline and the sonar measurement data, determine the calculated sound velocity value at the current sampling observation time.
[0040] Specifically, the formula for calculating the velocity of sound at any given sampling observation time is as follows: ; in, for The calculated value of the speed of sound at the sampling observation time. , This represents the total number of sampling observation times. In order to be in The prior value of the distance to the baseline observed at the sampling observation time; In order to be in The measurement of the propagation time between two seabed beacons on the baseline at the sampling observation time.
[0041] Step 43: Based on the calculated sound velocity values at all sampling observation times, determine the prior value of the sound velocity deviation of the multi-submarine beacon network.
[0042] Specifically, the formula for calculating the prior value of the sound speed deviation in a multi-seabed beacon network is as follows: ; in, The prior value for the sound velocity deviation of the multi-submarine beacon network.
[0043] Step 5: Construct a quadratic polynomial model for each seabed beacon and a grouped adjustment function model for each sampling observation time.
[0044] Among them, the quadratic polynomial model is an equation about the observation time, and the group adjustment function model is an equation about the observed distance and theoretical distance of the baseline.
[0045] As an optional implementation, the quadratic polynomial model of any seabed beacon is: ; in, seabed beacon exist The three-dimensional coordinates of motion at the moment of observation; seabed beacon exist The north-direction coordinates of the motion at the time of observation; seabed beacon The constant term in the northward direction of motion in the quadratic polynomial model; seabed beacon The coefficients of the first term in the quadratic polynomial model of the northward coordinate of motion as a function of time; This refers to the starting sampling observation time within the sampling period; seabed beacon The coefficients of the quadratic term in the time-varying northward coordinates of the motion in the quadratic polynomial model; seabed beacon exist The eastward coordinates of the motion at the time of observation; seabed beacon The constant term in the eastward direction of motion in the quadratic polynomial model; seabed beacon The coefficients of the first term in the quadratic polynomial model of the eastward coordinate of motion as a function of time; seabed beacon The coefficients of the quadratic term in the eastward coordinate of the motion in the quadratic polynomial model as a function of time; seabed beacon exist The coordinates of the zenith direction of the motion at the time of observation; seabed beacon The constant term in the zenith direction of motion in the quadratic polynomial model; seabed beacon The coefficients of the first term in the quadratic polynomial model of the zenith direction of motion as a function of time; seabed beacon The coefficients of the quadratic term in the quadratic polynomial model of the moving zenith direction coordinate as a function of time.
[0046] As an optional implementation, the process of constructing the grouped adjustment function model for any current sampling observation time includes: Determine the observation type of the current sampling observation baseline; If the observation type of the current sampling observation baseline is the first observation type, then the group adjustment function model for the current sampling observation time is: ; in, Seafloor beacons on the baseline for the first observation type and underwater beacons exist Observation distance at the time of observation , for The calculated value of the speed of sound at the time of observation. Seafloor beacons on the baseline for the first observation type and underwater beacons exist The propagation time measurement at the observation moment; Seafloor beacons on the baseline for the first observation type and underwater beacons exist Theoretical distance at the time of observation , seabed beacon exist The three-dimensional coordinates of the motion at the moment of observation seabed beacon exist The three-dimensional coordinates of motion at the moment of observation; This is the prior value of the sound speed deviation; The first bending coefficient; Seafloor beacons on the baseline for the first observation type and underwater beacons exist The horizontal component of the theoretical distance at the time of observation. , Seafloor beacons on the baseline for the first observation type and underwater beacons exist The elevation angle at the time of observation; If the observation type of the current sampling observation baseline is the second observation type, then the group adjustment function model for the current sampling observation time is: ; in, Seafloor beacons on the baseline for the second observation type and underwater beacons exist Observation distance at the time of observation , Seafloor beacons on the baseline for the second observation type and underwater beacons exist The propagation time measurement at the observation moment; Seafloor beacons on the baseline for the second observation type and underwater beacons exist Theoretical distance at the time of observation , seabed beacon exist The three-dimensional coordinates of the motion at the moment of observation seabed beacon exist The three-dimensional coordinates of motion at the moment of observation; The second bending coefficient; Seafloor beacons on the baseline for the second observation type and underwater beacons exist The horizontal component of the theoretical distance at the time of observation. , Seafloor beacons on the baseline for the second observation type and underwater beacons exist The elevation angle at the time of observation.
[0047] Step 6: Initialize the model parameters of the quadratic polynomial model for each seabed beacon and the sound velocity deviation, first curvature coefficient, and second curvature coefficient in the grouped adjustment function model for all sampling observation times.
[0048] As an optional implementation, step 6 includes the following steps.
[0049] Step 61: Initialize the constant term of the quadratic polynomial model of each seabed beacon to the approximate three-dimensional coordinates of the corresponding seabed beacon, and initialize the coefficients of the first and second terms of the quadratic polynomial model of each seabed beacon to 0. Step 62: Initialize the sound velocity deviation of the grouped adjustment function model at each sampling observation time to the prior value of the sound velocity deviation, and initialize the first curvature coefficient and the second curvature coefficient of the grouped adjustment function model at each sampling observation time to 0.
[0050] The model parameters include: constant term, coefficient of linear term, and coefficient of quadratic term.
[0051] Step 7: Using the least squares method, iteratively solve the quadratic polynomial model of all seabed beacons and the grouped adjustment function model of all sampling observation times to obtain the target values of the model parameters of the quadratic polynomial model of each seabed beacon, as well as the correction values of the sound velocity deviation, the estimated values of the first curvature coefficient and the estimated values of the second curvature coefficient in the grouped adjustment function model of all sampling observation times.
[0052] Step 8: Substitute the target values of the model parameters of the quadratic polynomial model of each seabed beacon into the corresponding quadratic polynomial model of the seabed beacon to obtain the seabed beacon motion model of each seabed beacon.
[0053] Step 9: When any current seabed beacon in the multi-seabed beacon network is observed at the current time, substitute the current time into the seabed beacon motion model of the current seabed beacon to obtain the three-dimensional coordinates of the current seabed beacon's motion at the current time, thereby realizing the relative motion positioning of the current seabed beacon.
[0054] In one exemplary embodiment, a relative motion positioning system for a multi-seabed beacon network is provided to implement the aforementioned relative motion positioning method for a multi-seabed beacon network. The relative motion positioning system for a multi-seabed beacon network includes: The data acquisition module is used to acquire sound velocity profile measurement data of the area where the multi-seabed beacon network is located, approximate three-dimensional coordinates of all seabed beacons, and sonar measurement data of all baselines; the sonar measurement data includes the propagation time measurement of sound waves between two seabed beacons at all sampling observation times during the sampling period; all baselines were observed during the sampling period; The sound velocity measurement determination module is used to determine the sound velocity measurement value of each baseline based on the sound velocity profile measurement data and the approximate zenith direction coordinates in the approximate three-dimensional coordinates of two seabed beacons on each baseline. The distance prior value determination module is used to determine the distance prior value of each baseline based on the approximate three-dimensional coordinates of two seabed beacons on each baseline. The prior value determination module for sound velocity deviation is used to determine the prior value of sound velocity deviation of the multi-seabed beacon network based on the prior value of the distance of the baseline observed at each sampling observation time and sonar measurement data. The model building module is used to build quadratic polynomial models for each seabed beacon and grouped adjustment function models for each sampling observation time. The quadratic polynomial model is an equation about the observation time, and the grouped adjustment function model is an equation about the observed distance and theoretical distance of the baseline. The initialization module is used to initialize the model parameters of the quadratic polynomial model for each seabed beacon and the sound velocity deviation, first curvature coefficient, and second curvature coefficient in the grouped adjustment function model for all sampling observation times; the model parameters include: constant terms, linear coefficients, and quadratic coefficients; The solution module is used to iteratively solve the quadratic polynomial model of all seabed beacons and the grouped adjustment function model of all sampling observation times using the least squares method, so as to obtain the target values of the model parameters of the quadratic polynomial model of each seabed beacon, as well as the correction values of the sound speed deviation, the estimated values of the first curvature coefficient and the estimated values of the second curvature coefficient in the grouped adjustment function model of all sampling observation times. The model determination module is used to substitute the target values of the model parameters of the quadratic polynomial model of each seabed beacon into the quadratic polynomial model of the corresponding seabed beacon to obtain the seabed beacon motion model of each seabed beacon. The positioning module is used to input the current time into the current seabed beacon motion model when any current seabed beacon in the multi-seabed beacon network is observed at the current time, so as to obtain the three-dimensional motion coordinates of the current seabed beacon at the current time and realize the relative motion positioning of the current seabed beacon.
[0055] In one exemplary embodiment, a computer device is provided, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement a relative motion positioning method for a multi-seabed beacon network.
[0056] In one exemplary embodiment, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements a relative motion positioning method for a multi-seabed beacon network.
[0057] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements a relative motion positioning method for a multi-seabed beacon network.
[0058] In one exemplary embodiment, a computer device is provided, which may be a server or a terminal, and its internal structure diagram may be as follows. Figure 2 As shown, the computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operating system and computer programs stored in the non-volatile storage media to run. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communicating with external terminals via a network connection. When the computer program is executed by the processor, it implements a relative motion positioning method for a multi-seabed beacon network.
[0059] Those skilled in the art will understand that Figure 2 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.
[0060] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).
[0061] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.
[0062] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.
[0063] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.
[0064] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.
Claims
1. A relative motion positioning method for a multi-seabed beacon network, characterized in that, The multi-seabed beacon network includes multiple seabed beacons, comprising one fixed seabed beacon and multiple moored seabed beacons. Acoustic ranging observations within the multi-seabed beacon network constitute two types of baselines: a first type of baseline between a fixed seabed beacon and a moored seabed beacon, and a second type of baseline between two moored seabed beacons. Each baseline is observed at different times. The relative motion positioning method of the multi-seabed beacon network includes: Acquire sound velocity profile measurement data for the area containing the multi-seabed beacon network, approximate three-dimensional coordinates of all seabed beacons, and sonar measurement data for all baselines; the sonar measurement data includes the propagation time measurement of sound waves between two seabed beacons at all sampling observation times during the sampling period; all baselines were observed during the sampling period; Based on the sound velocity profile measurement data and the approximate zenith direction coordinates in the approximate three-dimensional coordinates of the two seabed beacons on each baseline, the sound velocity measurement value of each baseline is determined. Based on the approximate three-dimensional coordinates of two seabed beacons on each baseline, the prior distance values of each baseline are determined. The prior value of the sound velocity deviation of the multi-seabed beacon network is determined based on the prior value of the baseline distance observed at each sampling observation time and the sonar measurement data. A quadratic polynomial model for each seabed beacon and a grouped adjustment function model for each sampling observation time are constructed respectively; the quadratic polynomial model is an equation about the observation time, and the grouped adjustment function model is an equation about the observed distance and theoretical distance of the baseline; The model parameters of the quadratic polynomial model for each seabed beacon and the sound velocity deviation, first curvature coefficient, and second curvature coefficient in the grouped adjustment function model for all sampled observation times are initialized; the model parameters include: constant terms, linear coefficients, and quadratic coefficients; Using the least squares method, the quadratic polynomial models of all seabed beacons and the grouped adjustment function models of all sampling observation times are iteratively solved to obtain the target values of the model parameters of the quadratic polynomial models of each seabed beacon, as well as the correction values of the sound velocity deviation, the estimated values of the first curvature coefficient and the estimated values of the second curvature coefficient in the grouped adjustment function models of all sampling observation times. Substitute the target values of the model parameters of the quadratic polynomial model of each seabed beacon into the quadratic polynomial model of the corresponding seabed beacon to obtain the seabed beacon motion model of each seabed beacon. When any current seabed beacon in the multi-seabed beacon network is observed at the current moment, the current moment is substituted into the seabed beacon motion model of the current seabed beacon to obtain the three-dimensional coordinates of the current seabed beacon's motion at the current moment, thereby realizing the relative motion positioning of the current seabed beacon.
2. The relative motion positioning method for a multi-seabed beacon network according to claim 1, characterized in that, Based on the sound velocity profile measurement data and the approximate zenith direction coordinates in the approximate three-dimensional coordinates of two seafloor beacons on each baseline, the sound velocity measurement values for each baseline are determined, including: Based on sound velocity profile measurement data, the estimated value of the coefficient of the linear term of sound velocity as a function of approximate zenith coordinate is determined; the sound velocity profile measurement data includes: sound velocity profile measurement values at multiple different approximate zenith coordinates. The sound velocity measurements for each baseline are determined based on the approximate zenith direction coordinates and the estimated linear term coefficients in the approximate three-dimensional coordinates of two seabed beacons on each baseline.
3. The relative motion positioning method for a multi-seabed beacon network according to claim 1, characterized in that, The prior values for the sound velocity deviation of the multi-seabed beacon network are determined based on the baseline distance prior values observed at each sampling observation time and sonar measurement data, including: Any sampling observation time is determined as the current sampling observation time, and the baseline observed at the current sampling observation time is determined as the current sampling observation baseline; Based on the prior distance value of the current sampling observation baseline and sonar measurement data, determine the calculated value of the sound velocity at the current sampling observation time; Based on the calculated sound velocity values at all sampling observation times, the prior value of the sound velocity deviation of the multi-submarine beacon network is determined.
4. The relative motion positioning method for a multi-seabed beacon network according to claim 3, characterized in that, The quadratic polynomial model of any seabed beacon is: ； in, seabed beacon exist The three-dimensional coordinates of motion at the moment of observation; seabed beacon exist The north-direction coordinates of the motion at the time of observation; seabed beacon The constant term in the northward direction of motion in the quadratic polynomial model; seabed beacon The coefficients of the first term in the quadratic polynomial model of the northward coordinate of motion as a function of time; This refers to the starting sampling observation time within the sampling period; seabed beacon The coefficients of the quadratic term in the time-varying northward coordinates of the motion in the quadratic polynomial model; seabed beacon exist The eastward coordinates of the motion at the time of observation; seabed beacon The constant term in the eastward direction of motion in the quadratic polynomial model; seabed beacon The coefficients of the first term in the quadratic polynomial model of the eastward coordinate of motion as a function of time; seabed beacon The coefficients of the quadratic term in the eastward coordinate of the motion in the quadratic polynomial model as a function of time; seabed beacon exist The coordinates of the zenith direction of the motion at the time of observation; seabed beacon The constant term in the zenith direction of motion in the quadratic polynomial model; seabed beacon The coefficients of the first term in the quadratic polynomial model of the zenith direction of motion as a function of time; seabed beacon The coefficients of the quadratic term in the quadratic polynomial model of the moving zenith direction coordinate as a function of time.
5. The relative motion positioning method for a multi-seabed beacon network according to claim 4, characterized in that, The process of constructing the grouped adjustment function model for any current sampling observation time includes: Determine the observation type of the current sampling observation baseline; If the observation type of the current sampling observation baseline is the first observation type, then the group adjustment function model for the current sampling observation time is: ； in, Seafloor beacons on the baseline for the first observation type and underwater beacons exist Observation distance at the time of observation , for The calculated value of the speed of sound at the time of observation. Seafloor beacons on the baseline for the first observation type and underwater beacons exist The propagation time measurement at the observation moment; Seafloor beacons on the baseline for the first observation type and underwater beacons exist Theoretical distance at the time of observation , seabed beacon exist The three-dimensional coordinates of the motion at the moment of observation seabed beacon exist The three-dimensional coordinates of motion at the moment of observation; This is the prior value of the sound speed deviation; The first bending coefficient; Seafloor beacons on the baseline for the first observation type and underwater beacons exist The horizontal component of the theoretical distance at the time of observation. , Seafloor beacons on the baseline for the first observation type and underwater beacons exist The elevation angle at the time of observation; If the observation type of the current sampling observation baseline is the second observation type, then the group adjustment function model for the current sampling observation time is: ； in, Seafloor beacons on the baseline for the second observation type and underwater beacons exist Observation distance at the time of observation , Seafloor beacons on the baseline for the second observation type and underwater beacons exist The propagation time measurement at the observation moment; Seafloor beacons on the baseline for the second observation type and underwater beacons exist Theoretical distance at the time of observation , seabed beacon exist The three-dimensional coordinates of the motion at the moment of observation seabed beacon exist The three-dimensional coordinates of motion at the moment of observation; The second bending coefficient; Seafloor beacons on the baseline for the second observation type and underwater beacons exist The horizontal component of the theoretical distance at the time of observation. , Seafloor beacons on the baseline for the second observation type and underwater beacons exist The elevation angle at the time of observation.
6. The relative motion positioning method for a multi-seabed beacon network according to claim 1, characterized in that, The model parameters of the quadratic polynomial model for each seabed beacon and the sound velocity deviation, first curvature coefficient, and second curvature coefficient in the grouped adjustment function model for all sampled observation times are initialized, including: The constant term of the quadratic polynomial model for each seabed beacon is the approximate three-dimensional coordinate of the corresponding seabed beacon, and the coefficients of the first and second terms of the quadratic polynomial model for each seabed beacon are initialized to 0. The sound velocity deviations of the grouped adjustment function models at each sampling observation time are initialized to the prior values of the sound velocity deviations. The first curvature coefficient and the second curvature coefficient of the grouped adjustment function models at each sampling observation time are initialized to 0.
7. A relative motion positioning system for a multi-seabed beacon network, to implement the relative motion positioning method for a multi-seabed beacon network as described in any one of claims 1-6, characterized in that, The relative motion positioning system of the multi-seabed beacon network includes: The data acquisition module is used to acquire sound velocity profile measurement data of the area where the multi-seabed beacon network is located, approximate three-dimensional coordinates of all seabed beacons, and sonar measurement data of all baselines; the sonar measurement data includes the propagation time measurement of sound waves between two seabed beacons at all sampling observation times during the sampling period; all baselines were observed during the sampling period; The sound velocity measurement value determination module is used to determine the sound velocity measurement value of each baseline based on the sound velocity profile measurement data and the approximate zenith direction coordinates in the approximate three-dimensional coordinates of two seabed beacons on each baseline. The distance prior value determination module is used to determine the distance prior value of each baseline based on the approximate three-dimensional coordinates of two seabed beacons on each baseline. The prior value determination module for sound velocity deviation is used to determine the prior value of sound velocity deviation of the multi-seabed beacon network based on the prior value of the distance of the baseline observed at each sampling observation time and sonar measurement data. The model building module is used to build a quadratic polynomial model for each seabed beacon and a grouped adjustment function model for each sampling observation time; the quadratic polynomial model is an equation about the observation time, and the grouped adjustment function model is an equation about the observed distance and theoretical distance of the baseline; An initialization module is used to initialize the model parameters of the quadratic polynomial model for each seabed beacon and the sound velocity deviation, first curvature coefficient, and second curvature coefficient in the grouped adjustment function model for all sampling observation times; the model parameters include: constant terms, linear coefficients, and quadratic coefficients; The solution module is used to iteratively solve the quadratic polynomial model of all seabed beacons and the grouped adjustment function model of all sampling observation times using the least squares method, so as to obtain the target values of the model parameters of the quadratic polynomial model of each seabed beacon, as well as the correction values of the sound speed deviation, the estimated values of the first curvature coefficient and the estimated values of the second curvature coefficient in the grouped adjustment function model of all sampling observation times. The model determination module is used to substitute the target values of the model parameters of the quadratic polynomial model of each seabed beacon into the quadratic polynomial model of the corresponding seabed beacon to obtain the seabed beacon motion model of each seabed beacon. The positioning module is used to input the current time into the current seabed beacon motion model when any current seabed beacon in the multi-seabed beacon network is observed at the current time, so as to obtain the three-dimensional motion coordinates of the current seabed beacon at the current time and realize the relative motion positioning of the current seabed beacon.
8. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the relative motion positioning method for a multi-seabed beacon network according to any one of claims 1-6.
9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the relative motion positioning method for a multi-submarine beacon network as described in any one of claims 1-6.
10. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the relative motion positioning method for a multi-submarine beacon network as described in any one of claims 1-6.