High frequency adaptive dynamic positioning vessel state estimation method

Abstract

The application relates to the field of ship motion control and discloses a high-frequency adaptive dynamic positioning ship state estimation method, which comprises the following steps: constructing a system model and a measurement model of a three-degree-of-freedom motion state estimation of a ship horizontal plane; expanding the model, taking a dominant frequency of high-frequency motion and a relative damping coefficient as state variables and measurement information; collecting ship position, heading, wind speed and direction and propeller rotating speed information by using a position reference system and sensors; performing frequency spectrum analysis through fast Fourier transform to extract the dominant frequency of high-frequency motion; performing state estimation by using extended Kalman filtering or unscented Kalman filtering, separating and eliminating the high-frequency motion component, and outputting a low-frequency motion state for dynamic positioning control. The method can improve state estimation precision and convergence by dynamically estimating high-frequency parameters and combining time domain and frequency domain analysis, reduce propeller wear and energy consumption, adapt to complex marine environments and improve dynamic positioning control performance.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of ship motion control, and particularly relates to a high-frequency adaptive dynamic positioning ship state estimation method. BACKGROUND

[0002] The main function of the dynamic positioning system of the water surface ship is to realize automatic control of three degrees of freedom of position and heading, so that the ship can maintain the set position, heading and trajectory. Due to the complexity of the marine environment, the ship is disturbed by wind, wave and current. Among them, the wind speed and direction are easy to measure, and the wind load is easy to calculate, and the wind feedforward control strategy is usually used to compensate the influence of wind on the ship position. The force of the sea current on the ship is usually assumed to be a slowly changing environmental force; the wave action makes the force and motion state of the ship become very complex, and the force of the wave action on the ship includes the first-order wave force and the second-order wave force. Among them, the second-order wave force makes the ship produce slow drift motion, and the first-order wave force makes the ship produce high-frequency reciprocating motion. Therefore, the ship motion at sea is the superposition of high-frequency motion and low-frequency motion, and the position information measured by the position measurement system is also a high-low frequency mixed signal. For the high-frequency position signal, from the perspective of reducing the wear of the propeller and reducing the energy consumption, the dynamic positioning system does not need to control it. In addition, the data measured by the position and heading sensor contains a certain amount of noise signal, which will affect the stability of the whole system. The role of state estimation is to separate the high and low frequency motion information, filter out the high frequency signal and environmental noise signal from the position and heading information, and only pass the low frequency signal to the controller, so that the controller automatically controls the low frequency position and heading of the ship.

[0003] Kalman filter is a recursive algorithm, which estimates the ship motion state from the ship mathematical model and the sensor information with noise, and separates the low frequency signal and the high frequency signal. For a linear system with known process noise and measurement noise, the classical Kalman filter is an optimal state estimation filter. In reality, the process noise and measurement noise are time-varying under the influence of the marine environment, and the dynamic positioning system is a complex nonlinear system, so the extended Kalman filter is used in engineering applications.

[0004] Due to the variability and uncertainty of the working environment of the dynamic positioning ship, the wave frequency and noise in the motion model are uncertain. The uncertainty of the parameters will greatly affect the filtering accuracy, and even cause the divergence of the filter, and thus affect the performance of the whole dynamic positioning control system. In view of the disadvantages of using fixed high-frequency motion model parameters in the filtering algorithm, the parameters are introduced into the state estimation model as estimated variables and measurement information to be updated in real time, so as to better estimate and eliminate the high-frequency motion information, improve the convergence and accuracy of the state estimation, and thus help to improve the accuracy of the DP control system. SUMMARY

[0005] The present application aims to at least solve one of the technical problems existing in the related art. To this end, the present application provides a high-frequency adaptive dynamic positioning ship state estimation method.

[0006] A high-frequency adaptive dynamic positioning ship state estimation method,

[0007] S1, a system model and a measurement model for estimating the three-degree-of-freedom motion state of a dynamic positioning ship in a horizontal plane are constructed, the system model includes a low-frequency motion model, a high-frequency motion model, a thruster thrust model, and a wind load model, and the measurement model is used to describe the measurement information of the ship position and heading;

[0008] S2, the system model and the measurement model are expanded, the dominant frequencies and relative damping coefficients of the longitudinal, lateral, and heading directions in the high-frequency motion model are added to the system model as state variables, and the measurement values of the dominant frequencies are added to the measurement model as measurement information, to obtain the expanded system model and measurement model;

[0009] S3, real-time position, heading, relative wind speed and direction, and thruster speed information of the ship are collected to generate measurement data, and the collection of the measurement data is realized through a position reference system and a sensor system;

[0010] S4, based on the measurement data collected in S3, position and heading data within a time window are extracted, frequency spectrum analysis of three-degree-of-freedom motion in the longitudinal, lateral, and heading directions is performed, and the dominant frequencies of high-frequency motion are extracted as dominant frequency measurement values;

[0011] S5, based on the expanded system model and measurement model in S2, using a Kalman filtering algorithm, combining the measurement data in S3 and the dominant frequency measurement values in S4, state estimation is performed, high-frequency motion components are separated and removed, and low-frequency motion state estimation information is obtained.

[0012] Further, the system model in S1 includes:

[0013] a low-frequency motion model describing the low-frequency motion state of the ship in the longitudinal, lateral, and heading directions;

[0014] a high-frequency motion model describing high-frequency reciprocating motion caused by first-order wave forces;

[0015] a thruster thrust model describing the longitudinal, lateral, and heading thrust generated by the thruster;

[0016] a wind load model describing the wind load calculated based on the relative wind speed and direction;

[0017] The measurement model includes ship position and heading information measured based on a position reference system and a sensor system.

[0018] Further, the system model after dimension expansion in the S2 step expands the state vector to include low-frequency motion state, high-frequency motion state, environmental disturbance load, and dominant frequency and relative damping coefficient of high-frequency motion, and the measurement model includes position, heading, and measurement information of dominant frequency.

[0019] Further, the position reference system in the S3 step includes at least one of a satellite navigation system, an underwater acoustic positioning system, a laser positioning system, or a microwave positioning system; and the sensor system includes at least one of a compass, a vertical plane reference system, or a wind speed and direction instrument.

[0020] Further, the measurement data collected in the S3 step includes longitudinal position, lateral position, heading angle, relative wind speed, relative wind direction, and rotation speed of each propeller.

[0021] Further, the spectrum analysis in the S4 step adopts a fast Fourier transform method, specifically including:

[0022] performing spectrum analysis on the position and heading data collected in the S3 step to obtain power spectrum distribution of the longitudinal, lateral, and heading directions;

[0023] sorting the power spectrum distribution to extract the frequency corresponding to the maximum power spectrum value as the dominant frequency measurement value of the longitudinal, lateral, and heading directions.

[0024] Further, the length of the time window in the S4 step is set according to the ship motion characteristics and ocean environmental conditions, and is dynamically updated in a fixed time interval.

[0025] Further, the Kalman filtering algorithm in the S5 step is an extended Kalman filter or an unscented Kalman filter, and the state estimation includes estimation of low-frequency motion state, environmental disturbance load, dominant frequency of high-frequency motion, and relative damping coefficient.

[0026] Further, the separated high-frequency motion component in the S5 step includes high-frequency reciprocating motion caused by first-order wave force, and the low-frequency motion state estimation information includes longitudinal position, lateral position, heading angle, and corresponding speed information of the ship.

[0027] Further, it also includes,

[0028] S6, outputting the low-frequency motion state estimation information obtained in the S5 step to a dynamic positioning control system for position and heading control of the ship;

[0029] The low-frequency motion state estimation information output in the S6 step is used to generate propeller control instructions to achieve position keeping and heading stability of the ship in complex ocean environments.

[0030] The one or more technical solutions in the embodiments of the present application have at least one of the following technical effects:

[0031] 1. The present application provides a high-frequency adaptive dynamic positioning ship state estimation method, which can separate high-frequency and low-frequency motion information of the ship, thereby filtering out high-frequency signals and environmental noise signals from position and heading information, and only transmitting low-frequency signals to the controller, so that the controller automatically controls the low-frequency position and heading of the ship.

[0032] 2. The present application expands the time domain state estimation model based on the advantages of the filtering algorithm, introduces high-frequency model parameters as state variables to be estimated, and uses fast Fourier transform method in frequency domain to analyze the spectrum of high and low frequency mixed motion information, and takes the extracted dominant frequency as part of the measurement information in the time domain state estimation measurement model. The combination of time domain and frequency domain methods greatly improves the global adaptability of the algorithm, can suppress filter divergence, adapt to the change of dominant frequency caused by complex changes of marine environment, so that the high-frequency motion information component can be extracted and removed as much as possible, and the present application can accurately estimate the ship motion state and environmental force, and has high practical value.

[0033] Additional aspects and advantages of the present application will be partially given in the following description, partially will become obvious from the following description, or will be understood through the practice of the present application. BRIEF DESCRIPTION OF DRAWINGS

[0034] In order to more clearly illustrate the technical solutions in the present application or prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description are some embodiments of the present application, and those skilled in the art can also obtain other drawings according to these drawings without creative labor.

[0035] Figure 1 is a flow chart of the high-frequency adaptive dynamic positioning ship state estimation method provided by the embodiments of the present application.

[0036] Figure 2 is a longitudinal position and velocity estimation comparison chart provided by the embodiments of the present application.

[0037] Figure 3 is a transverse position and velocity estimation comparison chart provided by the embodiments of the present application.

[0038] Figure 4 is a heading angle and angular velocity estimation comparison chart provided by the embodiments of the present application.

[0039] Figure 5 is an environmental disturbance load estimation comparison chart provided by the embodiments of the present application.

[0040] Figure 6 is a comparison chart of the main frequency output results provided by the embodiment of the present application. DETAILED DESCRIPTION

[0041] To make the objects, technical solutions and advantages of the present application clearer, the technical solutions in the present application will be described below in connection with the drawings in the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative labor fall within the scope of protection of the present application. The following embodiments are used to illustrate the present application, but cannot be used to limit the scope of the present application.

[0042] The present application aims at the variability and uncertainty of the working environment of a dynamically positioned ship, the low filtering accuracy or even filtering divergence caused by the uncertainty of wave frequency, noise and other parameters in the motion model, and proposes a high-frequency adaptive dynamically positioned ship state estimation method. The state estimation model is expanded in the time domain, high-frequency model parameters are introduced as state variables to be estimated, and the expanded state estimation is performed based on the extended Kalman filter or unscented Kalman filter algorithm; in the frequency domain, the fast Fourier transform method is used to analyze the spectrum of the high-low frequency mixed motion information, and the extracted main frequency is used as part of the measurement information in the time domain state estimation measurement model. The time domain and frequency domain methods are combined to further improve the convergence and accuracy of state estimation.

[0043] As shown in Figure 1 , a flow chart of the high-frequency adaptive dynamically positioned ship state estimation method is shown.

[0044] The high-frequency adaptive dynamically positioned ship state estimation method comprises:

[0045] S1: constructing a system model and a measurement model of a three-degree-of-freedom motion state estimation of a dynamically positioned ship in the horizontal plane, wherein the system model comprises a three-degree-of-freedom low-frequency motion mathematical model, a high-frequency motion mathematical model, a thruster thrust model and a wind load model of the dynamically positioned ship in the horizontal plane;

[0046] comprising the following steps:

[0047] B1: establishing a three-degree-of-freedom low-frequency motion mathematical model of a dynamically positioned ship in the horizontal plane under ocean environment as formula (1):

[0048] (1)

[0049] wherein,

[0050] is the longitudinal position of the ship in the ship coordinate system , the lateral position and heading state vector, i.e. ;

[0051] U is the state vector of the ship's motion velocity and angular velocity in the body coordinate system, , is the longitudinal velocity, is the lateral velocity, is the heading angular velocity;

[0052] b is the environmental disturbance load in three degrees of freedom of north, east and heading,

[0053] is a three-dimensional diagonal matrix containing time constants,

[0054] is a three-dimensional diagonal matrix representing the amplitude of the environmental disturbance load,

[0055] is a zero-mean Gaussian white noise vector;

[0056] is the coordinate transformation matrix,

[0057] T represents the matrix transpose;

[0058] M is the ship's inertia matrix, ,

[0059] is the ship's mass, is the ship's moment of inertia, is the longitudinal coordinate of the ship's center of mass, is the longitudinal hydrodynamic acceleration derivative, is the lateral hydrodynamic acceleration derivative, is the coupling hydrodynamic acceleration derivative of heading to lateral, is the coupling hydrodynamic acceleration derivative of lateral to heading, is the heading hydrodynamic acceleration derivative;

[0060] is the ship's damping matrix, , is the longitudinal hydrodynamic velocity derivative, is the lateral hydrodynamic velocity derivative, is the coupling hydrodynamic velocity derivative of heading to lateral, is the coupling hydrodynamic velocity derivative of lateral to heading, is the heading hydrodynamic velocity derivative;

[0061] is the thrust vector, , 、 、 are the longitudinal, lateral and bow yaw three degrees of freedom thrust (moment) generated by the propeller, respectively;

[0062] is the wind load vector, , 、 、 are the longitudinal, lateral and bow yaw three degrees of freedom wind load, respectively;

[0063] is a three-dimensional diagonal matrix, representing the amplitude of the process noise,

[0064] is a zero-mean Gaussian white noise vector.

[0065] B2: Establishing the mathematical model of high-frequency motion of the ship is formula (2):

[0066] (2)

[0067] wherein:

[0068] (ωi, i = 1, 2, 3) represent the wave intensity;

[0069] (ζi, i = 1, 2, 3) represent the relative damping coefficient; (ωi, i = 1, 2, 3) represent the wave dominant frequency,

[0070] is the transfer function of the model,

[0071] is a virtual variable,

[0072] is a virtual variable,

[0073] ( i = 1, 2, 3) represent the longitudinal, lateral and bow yaw three degrees of freedom, respectively;

[0074] Formula (2) is expressed as a state space form, which is formula (3):

[0075] (3)

[0076] In the formula,

[0077] is the high-frequency state vector of the ship;

[0078] represents the longitudinal surge of high-frequency motion,​​ represents the high-frequency sway position, represents the high-frequency yaw angle, represents the integral of represents the integral of represents the integral of

[0079] is a zero-mean Gaussian white noise vector;

[0080] is a three-dimensional vector representing the high-frequency sway, sway position and yaw angle, respectively;

[0081] , , are coefficient matrices, respectively;

[0082] wherein,

[0083] I is a unit matrix of dimension 3 x 3.

[0084] B3: Establish a model of the thruster thrust of the dynamically positioned ship.

[0085] The thrust and torque generated by all the thrusters can be represented by the vector (longitudinal force, lateral force and yaw moment) as formula (4):

[0086] (4)

[0087] c is the control input, is the input control variable, , is the rotational speed of each thruster;

[0088] The thrust coefficient matrix K is a diagonal matrix composed of the thrust coefficients of i thrusters, which are generally obtained by the thrust curve or real ship identification;

[0089] B is a control matrix describing the thruster configuration, which is related to the arrangement position of i thrusters and the type of thrusters.

[0090] B4: Establish a wind load model as formula (5):

[0091] (5) ​​​​​​​​​

[0092] wherein:

[0093] , , are the longitudinal, transverse, and bow-wise wind loads of the ship hull, respectively;

[0094] , and are the non-dimensional wind load coefficients of the ship hull in longitudinal, transverse, and bow-wise directions, respectively, which are generally obtained through wind tunnel tests or CFD simulation analysis; is the relative wind direction;

[0095] is the relative wind speed;

[0096] is the air density;

[0097] and are the positive and lateral projection areas of the ship hull, respectively;

[0098] is the overall length of the ship hull.

[0099] B5: Establish the state estimation measurement model equation (6):

[0100] (6)

[0101] wherein is a zero-mean Gaussian white noise three-dimensional vector.

[0102] B6: By integrating the above models, the state estimation nonlinear mathematical model equation (7) of the dynamically positioned ship is obtained:

[0103] (7)

[0104] The state estimation model equation (8) of the dynamically positioned ship is obtained by expressing equation (7) in state space form:

[0105] (8)

[0106] wherein:

[0107] represents the system measurement,

[0108] the state vector is a 15-dimensional state vector;

[0109] the nonlinear state transition function ;

[0110] E is the noise coefficient matrix, ;

[0111] is a zero-mean Gaussian white noise vector;

[0112] is an observation matrix, .

[0113] S2: dimensionally expanding the system model and the measurement model, adding the dominant frequency and the relative damping coefficient of the longitudinal, lateral and heading directions in the mathematical model of the high frequency motion in the system model as part of the state estimation vector to the system model, and adding the dominant frequency measurement of the longitudinal, lateral and heading directions as part of the measurement to the measurement model, thereby obtaining the system model and the measurement model of the state estimation of the three-degree-of-freedom motion in the horizontal plane of the dynamically positioned ship after dimensionally expanding;

[0114] comprising the following steps:

[0115] C1: establishing the mathematical model formula (9) of the dominant frequency and the relative damping coefficient of the longitudinal, lateral and heading directions in the mathematical model of the high frequency motion:

[0116] (9)

[0117] wherein,

[0118] is a dominant frequency vector,

[0119] is a three-dimensional diagonal matrix containing time constants,

[0120] is a three-dimensional diagonal matrix, representing the amplitude of the dominant frequency,

[0121] is a zero-mean Gaussian white noise vector.

[0122] is a relative damping coefficient vector,

[0123] is a three-dimensional diagonal matrix, representing the amplitude of the relative damping coefficient,

[0124] is a zero-mean Gaussian white noise vector.

[0125] C2: establishing the measurement model formula (10) of the dominant frequency in the mathematical model of the high frequency motion:

[0126] (10)

[0127] wherein, represents a dominant frequency measurement vector; is a three-dimensional vector of zero-mean Gaussian white noise.

[0128] C3: add the system model and measurement model established in C1 and C2 to the model formula (8) in step S1, thereby expanding the state estimation model to obtain an expanded state estimation model formula (11):

[0129] (11)

[0130] In the formula: represents an expanded Gaussian white noise vector;

[0131] represents an expanded measurement noise;

[0132] represents an expanded system measurement,

[0133] the expanded state vector is a 21-dimensional state vector;

[0134] a nonlinear state transition function ;

[0135] is an expanded noise coefficient matrix, ;

[0136] is an expanded observation matrix, .

[0137] S3: obtain the ship position, heading, and relative wind speed and direction measurement information using the position reference system and the sensor system, measure and obtain the ship propeller speed information;

[0138] The position reference system usually includes, but is not limited to, satellite navigation systems such as Beidou, GPS, GLONASS, and underwater positioning systems, relative position measurement systems such as lasers and microwaves; the sensor system usually includes, but is not limited to, compasses, vertical plane reference systems, and wind speed and direction indicators.

[0139] S4: update and extract the position and heading measurement information in the time window from the current time to a certain time in the past at a fixed time interval, and perform three-degree-of-freedom motion spectrum analysis in the longitudinal, lateral, and heading directions to extract the dominant frequency of the longitudinal, lateral, and heading high-frequency motion as the dominant frequency measurement value;

[0140] comprising the following steps:

[0141] D1: use the fast Fourier transform method to perform spectrum analysis on the high and low frequency mixed motion measurement information of the ship in the longitudinal, lateral, and heading directions at a fixed time interval, to obtain the longitudinal, lateral, and heading power spectrum distribution corresponding to different frequencies;

[0142] D2: Sort the power spectrum of the longitudinal, lateral and heading directions using the sorting algorithm, and take the frequency at the maximum power spectrum as the dominant frequency of each dimension.

[0143] S5: Perform state estimation using the extended Kalman filter method or the unscented Kalman filter method to separate and eliminate high-frequency motion, and thus use the optimal low-frequency motion state estimation information for dynamic positioning motion control.

[0144] comprising the following steps:

[0145] E1: Calculate the thrust of each propeller and the longitudinal, lateral and heading forces (moments) of the ship:

[0146] The formula (4) is used to calculate .

[0147] E2: Calculate the wind load in the longitudinal, lateral and heading directions of the ship:

[0148] The formula (5) is used to calculate .

[0149] E3: Estimate and output the three-degree-of-freedom high and low frequency motion states, environmental disturbance loads, dominant frequencies and relative damping coefficients using the extended Kalman filter method or the unscented Kalman filter method, i.e. complete the estimation and output of the state vector , and pass the low-frequency motion state estimation and environmental disturbance load to the next level of control system.

[0150] S6: Output the low-frequency motion state estimation information obtained in step S5 to the dynamic positioning control system for position and heading control of the ship.

[0151] The low-frequency motion state estimation information output to the dynamic positioning control system is used to generate propeller control instructions to achieve position keeping and heading stability of the ship in complex marine environments.

[0152] Figure 2 , Figure 3 , Figure 4 The comparative results of longitudinal position, longitudinal velocity, lateral position, lateral velocity, heading and heading angle estimation are shown. It can be seen that using the method can eliminate more high-frequency motion components, and the estimation result is smoother, which is beneficial to the motion control of the next level, thereby better reducing the propeller wear.

[0153] Figure 5 The comparative results of environmental disturbance load estimation are shown. It can be seen that the environmental disturbance load estimated using the method is smoother, which is beneficial to the motion control of the next level, and can better reduce the propeller wear.

[0154] Figure 6 The contrast results of the dominant frequency output are shown, and it can be seen that the dominant frequency estimated using the method is smoother, and the jitter of the dominant frequency output by the spectrum analysis method is reduced.

[0155] Finally, it should be noted that: the above examples are only used to illustrate the technical solutions of the present application, but not to limit them; although the present application has been described in detail with reference to the foregoing examples, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing examples, or make equivalent replacement for part of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A high-frequency adaptive dynamic positioning method for ship state estimation, characterized in that, S1. Construct a system model and a measurement model for estimating the three-degree-of-freedom motion state of a dynamically positioned ship in the horizontal plane. The system model includes a low-frequency motion model, a high-frequency motion model, a thruster model, and a wind load model. The measurement model is used to describe the measurement information of the ship's position and heading. S2, the system model and measurement model are expanded in dimension by adding the dominant longitudinal, lateral and bow directions and relative damping coefficients in the high-frequency motion model as state variables to the system model, and adding the measured values ​​of the dominant frequencies as measurement information to the measurement model, thus obtaining the expanded system model and measurement model. S3, collect the ship's real-time position, heading, relative wind speed and direction, and propeller speed information, and generate measurement data. The acquisition of the measurement data is achieved through a position reference system and a sensor system. S4. Within a fixed time interval, based on the measurement data collected in step S3, extract the position and heading data within the time window, perform spectral analysis of the longitudinal, lateral, and heading three-degree-of-freedom motion, and extract the dominant frequency of the high-frequency motion as the dominant frequency measurement value. S5, based on the expanded system model and measurement model from step S2, uses the Kalman filter algorithm, combined with the measurement data from step S3 and the dominant frequency measurement values ​​from step S4, to perform state estimation, separate and remove high-frequency motion components, and obtain low-frequency motion state estimation information.

2. The high-frequency adaptive dynamic positioning ship state estimation method according to claim 1, characterized in that, The system model in step S1 includes: Low-frequency motion model, describing the low-frequency motion state of a ship in the longitudinal, lateral, and bow directions; A high-frequency motion model describes the high-frequency reciprocating motion caused by first-order wave force. Thruster thrust model, describing the longitudinal, lateral, and bow thrust generated by the thruster; Wind load model, describing wind loads calculated based on relative wind speed and wind direction; The measurement model includes ship position and heading information measured based on a position reference system and a sensor system.

3. The high-frequency adaptive dynamic positioning ship state estimation method according to claim 1, characterized in that, In step S2, the expanded system model extends the state vector to include low-frequency motion state, high-frequency motion state, environmental interference load, and the dominant frequency and relative damping coefficient of high-frequency motion. The measurement model includes measurement information of position, heading, and dominant frequency.

4. The high-frequency adaptive dynamic positioning ship state estimation method according to claim 1, characterized in that, The position reference system in step S3 includes at least one of a satellite navigation system, an underwater acoustic positioning system, a laser positioning system, or a microwave positioning system; the sensor system includes at least one of a compass, a vertical plane reference system, or an anemometer.

5. The high-frequency adaptive dynamic positioning ship state estimation method according to claim 4, characterized in that, The measurement data collected in step S3 includes the ship's longitudinal position, lateral position, heading angle, relative wind speed, relative wind direction, and the rotational speed of each propeller.

6. The high-frequency adaptive state estimation method for a dynamically positioned vessel according to claim 1, wherein, the spectrum analysis in the S4 step adopts a fast Fourier transform method, and specifically comprises: performing spectrum analysis on the position and heading data collected in the S3 step to obtain power spectrum distributions of the longitudinal direction, the lateral direction and the heading direction; sorting the power spectrum distributions to extract frequencies corresponding to maximum values of the power spectrum distributions as dominant frequency measurement values of the longitudinal direction, the lateral direction and the heading direction.

7. The high-frequency adaptive state estimation method for a dynamically positioned vessel according to claim 6, wherein, a length of the time window in the S4 step is set according to vessel motion characteristics and ocean environment conditions, and is dynamically updated in a fixed time interval.

8. The high-frequency adaptive state estimation method for a dynamically positioned vessel according to claim 1, wherein, the Kalman filtering algorithm in the S5 step is an extended Kalman filtering algorithm or an unscented Kalman filtering algorithm, and the state estimation includes estimation of low-frequency motion states, environmental disturbance loads, high-frequency motion dominant frequencies and relative damping coefficients.

9. The high-frequency adaptive state estimation method for a dynamically positioned vessel according to claim 8, wherein, the separated high-frequency motion component in the S5 step includes high-frequency reciprocating motion caused by a first-order wave force, and the low-frequency motion state estimation information includes longitudinal position, lateral position, heading angle and corresponding speed information of the vessel.

10. The high frequency adaptive dynamic positioning vessel state estimation method of claim 1, wherein, Further comprising, S6, outputting the low-frequency motion state estimation information obtained in the S5 step to a dynamic positioning control system for position and heading control of the vessel; the low-frequency motion state estimation information output in the S6 step is used to generate a thruster control instruction to achieve position keeping and heading stabilization of the vessel in a complex ocean environment.

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