Maritime interference estimation and observer design method using multi-source sensor fusion

Abstract

The invention is suitable for the field of offshore platform or intelligent ship control, and provides an offshore interference estimation and observer design method using multi-source sensor fusion, comprising the following steps: S1, sensor data acquisition and preprocessing; s2, constructing an interference estimation model; s3, converting a state space model; according to the invention, based on a'time alignment + coordinate conversion 'structure of a sensor data acquisition and preprocessing module, the problems of time sequence deviation and coordinate system difference of multiple sensors are solved: time alignment ensures that data are synchronized at the frequency of 5Hz, and interference estimation delay caused by data asynchronization is avoided; coordinate conversion is combined with ship attitude and navigational speed to obtain accurate interference data under an absolute coordinate system, and compared with a traditional preprocessing method, the interference data accuracy is improved by 40%-50%, and reliable input is provided for subsequent modules.

Description

Technical Field

[0001] This invention belongs to the field of offshore platform or intelligent ship control, and particularly relates to a method for offshore interference estimation and observer design using multi-source sensor fusion. Background Technology

[0002] Traditional ship control systems mainly rely on basic sensors such as GPS and IMU to measure the ship's position, speed, and other statuses. They do not integrate wave and current sensor data. External disturbances such as currents and waves have high uncertainty and cannot be accurately predicted by conventional motion models, resulting in large disturbance estimation errors that affect navigation accuracy and stability.

[0003] In existing multi-sensor applications, the output frequencies of each sensor differ significantly (e.g., 10Hz for IMU, 5Hz for GPS, and 10min / time for wave sensors), resulting in time-series biases in the data. Furthermore, wave and current sensor measurements are based on the ship's coordinate system, which is inconsistent with the northeast-northeast coordinate system of GPS, making direct fusion impossible and further reducing the accuracy of interference estimation. In addition, existing interference estimation models do not incorporate ship heading and speed, calculating interference only based on local coordinate system data, failing to convert it into real interference in an absolute coordinate system. This leads to deviations in the direction and amplitude of interference compensation, making it difficult for the control system to effectively counteract environmental influences.

[0004] Traditional ship control systems rely solely on feedback control (such as PID control). In sea states 1-3, this results in position deviations of 0.6-3.2m, heading errors of 1.5-6.8°, and rudder angle change rates of 3.0-8.1° / s. These errors are even greater in complex sea states. Frequent rudder movements lead to high energy consumption and rapid equipment wear. Therefore, a multi-source sensor fusion approach for maritime disturbance estimation and observer design is needed to address these issues. Summary of the Invention

[0005] The purpose of this invention is to provide a method for estimating maritime interference and designing observers using multi-source sensor fusion, in order to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: A method for estimating maritime disturbances and designing observers using multi-source sensor fusion includes the following steps: S1. Sensor Data Acquisition and Preprocessing: The sensor data acquisition and preprocessing module acquires data from the inertial measurement unit (IMU), global positioning system (GPS), wave sensor, and water flow sensor. The acquired data is time-aligned and transformed to the northeast-east-northeast (NED) coordinate system. After obtaining the preprocessed data, it is output to the disturbance estimation model construction module. S2. Construction of Interference Estimation Model: Based on the received preprocessed data, the interference estimation model construction module constructs a wave interference model and a water flow interference model respectively. The two models are superimposed to obtain the initial model of total interference, which is then output to the state space model module. S3. State-space model transformation: The state-space model module transforms the initial model of total interference into state equations describing the dynamic changes of interference, and observation equations describing the relationship between sensor observation data and interference state. The state equations and observation equations are then output to the interference observer design module. S4. Interference Observer Estimation: The interference observer design module is based on the state equation and the observation equation. It uses the Kalman filter algorithm to first predict the interference state and covariance matrix according to the state equation, and then update the interference estimate by combining the sensor observation data. After obtaining the real-time interference estimate, it outputs it to the feedforward controller design module. S5. Feedforward control signal generation: The feedforward controller design module receives the real-time disturbance estimate and combines it with the desired control input obtained based on trajectory planning, target heading and speed to generate the final control signal, which is then output to the control execution module. S6. Control Execution: The control execution module adjusts the ship's rudder angle and throttle according to the final control signal, and drives the ship to perform corresponding navigation actions to counteract external interference caused by water flow and waves.

[0007] A further technical solution, step S1, "time alignment" specifically includes the following sub-steps: S11. Determine the reference frequency: Use the GPS output frequency (5Hz) as the reference frequency for time alignment; S12. Low-frequency sensor data alignment: For wave sensors and flow sensors with an output frequency of 10 min / time, alignment data at any given time is calculated using a linear interpolation formula. The interpolation formula is as follows: ,in, This represents the interpolation result at time t. For a moment Observations at; S13, High-frequency sensor data alignment: For IMUs with an output frequency of 10Hz, downsampling is performed according to the 5Hz frequency of GPS, and one IMU data point is extracted every 0.2s to achieve time synchronization with GPS data.

[0008] A further technical solution, in step S1, is the "Northeast Earth (NED) coordinate system transformation" for wave interference, which specifically includes the following sub-steps: S14. Constructing the rotation matrix: Constructing a rotation matrix that incorporates the ship's attitude. , ,in For roll angle, For pitch angle, For heading angle; S15. Calculate the ship speed vector: Based on the wave interference after the ship's real-time speed conversion... With heading angle Construct the ship speed vector The ship's speed vector ; S16. Wave Interference Coordinate Transformation: Transform the wave interference coordinates in the ship's coordinate system. Through rotation matrix When combined with the ship speed influence term, the wave disturbance is converted to a northeast coordinate system, and the formula is as follows: ,in t For time intervals.

[0009] In a further technical solution, the "constructing wave interference model and water flow interference model" step S2 in the interference estimation model construction module specifically includes the following sub-steps: S21. Constructing a wave interference model: Based on wave height in preprocessed data. ,cycle Phase ,direction A periodic wave disturbance model is constructed, and the formula is as follows: ; S22. Constructing a water flow disturbance model: Based on the water flow velocity in the preprocessed data. Water flow direction A uniform water flow disturbance model is constructed, and the formula is: ; S23. Calculate the initial model of total disturbance: Superimpose the wave disturbance model and the water flow disturbance model using vectors to obtain the initial model of total disturbance, as shown in the formula: .

[0010] A further technical solution, step S3, "converting into state equations and observation equations," specifically includes the following sub-steps: S31. Constructing the state equations: using the total disturbance as the initial model. As a state variable, a control input is introduced. ( For ship speed, For heading), zero-mean Gaussian process noise (covariance is) Q ), construct the state equation, the formula is ,in, A State transition matrix, B The input matrix; S32. Constructing the observation equation: using sensor observations Zero-mean Gaussian observation noise is introduced as the output. (covariance is) ), and the observation matrix Let the identity matrix be denoted as , and construct the observation equation as follows: .

[0011] A further technical solution, step S4, "predicting and updating the interference estimate using the Kalman filter algorithm," specifically includes the following sub-steps: S41. Initialization parameters: Set the initial interference estimate. Initial covariance matrix ( (as the identity matrix), and determine the process noise covariance. Q ( Covariance of observation noise R ( ); S42. Predicting Interference Status: Based on the current interference estimate... With control input ,calculate The predicted disturbance value at time t is given by the formula: Simultaneously calculate the prediction covariance matrix, using the formula: ; S43. Update interference estimate: Receiver Sensor observations at time First calculate the Kalman gain. Then, based on the Kalman gain correction, the predicted interference value is obtained, resulting in the updated interference estimate, as shown in the formula: Finally, update the covariance matrix using the following formula: .

[0012] A further technical solution, step S5, "generating the final control signal," specifically includes the following sub-steps: S51. Calculate the feedforward compensation signal: based on the real-time interference estimate obtained in step S4. Combined with the optimized gain matrix The formula for calculating the feedforward compensation signal is as follows: ,in, It is a feedforward control compensation signal; It is the estimated state of the disturbance; S52. Obtain the desired control input: Calculate the desired control input based on the deviation between the ship's target trajectory and its current state using a PID or LQR algorithm. ; S53. Synthesizing the final control signal: The feedforward compensation signal is superimposed with the desired control input to obtain the final control signal, as shown in the formula below. ,in, It is the control input obtained based on the ship dynamics model and target trajectory planning.

[0013] Further technical solutions also include the following steps: S7. Hardware Deployment and Algorithm Execution: Configure at least one processor and at least one memory connected to the processor. Store program instructions that can be executed by the processor in the memory. When the processor calls the program instructions, execute steps S1 to S6 in sequence to realize the estimation and observation of marine interference. In step S1, wind speed and wind direction data acquisition can be added. In step S2, the interference model is expanded to a "wave + water flow + wind speed" interference model to adapt to the dynamic positioning scenario of offshore platforms or intelligent ships.

[0014] Compared with the prior art, the beneficial effects of the present invention are: This invention addresses the issues of timing deviation and coordinate system differences among multiple sensors based on a "time alignment + coordinate transformation" structure in the sensor data acquisition and preprocessing module. Time alignment ensures data synchronization at a 5Hz frequency, avoiding interference estimation delays caused by data asynchrony. Coordinate transformation, combined with ship attitude and speed, yields accurate interference data in an absolute coordinate system. Compared to traditional preprocessing methods, the accuracy of interference data is improved by 40%-50%, providing reliable input for subsequent modules. This invention, based on the "Kalman filtering + dynamic Q / R adjustment" structure of the interference observer design module, combined with the "wave-current fusion" design of the interference estimation model, achieves accurate tracking of time-varying interference: a small Q value ensures the stability of the filter under stable sea conditions, a large Q value enhances the tracking capability under strong sea conditions, a small R value of high-precision sensors increases the observation weight, and a large R value of low-precision sensors relies on model prediction; compared with traditional fixed model observers, the interference estimation error is reduced by 35%-50% under different sea conditions, adapting to complex and ever-changing marine environments; This invention, based on the "interference compensation + desired input superposition" structure of the feedforward controller design module, combined with the "rudder angle-throttle linkage adjustment" of the control execution module, significantly improves control performance: the position deviation in sea state 1 is reduced from 0.6m to 0.4m (↓33%), and the heading error is reduced from 1.5° to 0.8° (↓47%); the position deviation in sea state 3 is reduced from 3.2m to 1.6m (↓50%), and the heading error is reduced from 6.8° to 2.9° (↓57%). At the same time, the rudder angle change rate is reduced by 33%-46%, which improves navigation stability and reduces energy consumption and rudder wear.

[0015] To more clearly illustrate the structural features and effects of the present invention, the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. Attached Figure Description

[0016] Figure 1 This is an overall flowchart of the present invention; Figure 2This is a flowchart of the sensor data acquisition and preprocessing process of the present invention; Figure 3 The flowchart for constructing the interference estimation model of this invention; Figure 4 This is a flowchart illustrating the state-space model transformation of the present invention; Figure 5 This is a flowchart of the interference observer estimation method of the present invention; Figure 6 This is a flowchart illustrating the generation of the feedforward control signal in this invention; Figure 7 This is a comparison table of the control effects of the present invention under different sea states. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0018] The specific implementation of the present invention will be described in detail below with reference to specific embodiments.

[0019] like Figures 1-7 As shown, this embodiment of the invention provides a method for maritime interference estimation and observer design using multi-source sensor fusion, including the following steps: S1. Sensor Data Acquisition and Preprocessing: The sensor data acquisition and preprocessing module acquires data from the inertial measurement unit (IMU), global positioning system (GPS), wave sensor, and water flow sensor. The acquired data is time-aligned and transformed to the northeast-east-northeast (NED) coordinate system. After obtaining the preprocessed data, it is output to the disturbance estimation model construction module. S2. Construction of Interference Estimation Model: Based on the received preprocessed data, the interference estimation model construction module constructs a wave interference model and a water flow interference model respectively. The two models are superimposed to obtain the initial model of total interference, which is then output to the state space model module. S3. State-space model transformation: The state-space model module transforms the initial model of total interference into state equations describing the dynamic changes of interference, and observation equations describing the relationship between sensor observation data and interference state. The state equations and observation equations are then output to the interference observer design module. S4. Interference Observer Estimation: The interference observer design module is based on the state equation and the observation equation. It uses the Kalman filter algorithm to first predict the interference state and covariance matrix according to the state equation, and then update the interference estimate by combining the sensor observation data. After obtaining the real-time interference estimate, it outputs it to the feedforward controller design module. S5. Feedforward control signal generation: The feedforward controller design module receives the real-time disturbance estimate and combines it with the desired control input obtained based on trajectory planning, target heading and speed to generate the final control signal, which is then output to the control execution module. S6. Control Execution: The control execution module adjusts the ship's rudder angle and throttle according to the final control signal, and drives the ship to perform corresponding navigation actions to counteract external interference caused by water flow and waves.

[0020] In this embodiment, the problem of isolated modules and ineffective data fusion in traditional systems is solved by coordinating the entire process of "sensor acquisition - preprocessing - disturbance modeling - state space transformation - observer estimation - feedforward control - execution". Among them, the preprocessing module provides a synchronous and unified coordinate system data foundation for disturbance modeling, the observer module corrects the disturbance estimation in real time based on the dynamic model, and the feedforward control module directly uses the disturbance estimation to generate compensation signals. The modules form a closed loop, which significantly improves the linkage between disturbance estimation and control and ensures control accuracy under complex sea conditions.

[0021] Specifically, step S1, "time alignment," includes the following sub-steps: S11. Determine the reference frequency: Use the GPS output frequency (5Hz) as the reference frequency for time alignment; S12. Low-frequency sensor data alignment: For wave sensors and flow sensors with an output frequency of 10 min / time, alignment data at any given time is calculated using a linear interpolation formula. The interpolation formula is as follows: ,in, This represents the interpolation result at time t. For a moment Observations at; S13, High-frequency sensor data alignment: For IMUs with an output frequency of 10Hz, downsampling is performed according to the 5Hz frequency of GPS, and one IMU data point is extracted every 0.2s to achieve time synchronization with GPS data.

[0022] In this embodiment, time alignment is based on GPS, and a combination strategy of "interpolation + downsampling" is adopted for different frequency sensors: low-frequency sensors supplement high-frequency data through linear interpolation to avoid data loss; high-frequency sensors reduce redundancy and reduce computational load through downsampling. This design solves the problem of timing deviation of multiple sensors, so that all data are synchronized at a frequency of 5Hz, providing time-consistent data for subsequent interference modeling. Compared with the traditional "single frequency adaptation" method, the data utilization rate is improved.

[0023] Specifically, the "Northeast Earth (NED) coordinate system transformation" in step S1, which is a transformation for wave interference, includes the following sub-steps: S14. Constructing the rotation matrix: Constructing a rotation matrix that incorporates the ship's attitude. , ,in For roll angle, For pitch angle, For heading angle; S15. Calculate the ship speed vector: Based on the wave interference after the ship's real-time speed conversion... With heading angle Construct the ship speed vector The ship's speed vector ; S16. Wave Interference Coordinate Transformation: Transform the wave interference coordinates in the ship's coordinate system. Through rotation matrix When combined with the ship speed influence term, the wave disturbance is converted to a northeast coordinate system, and the formula is as follows: ,in t For time intervals.

[0024] In this embodiment, coordinate transformation introduces a rotation matrix. (Integrating roll, pitch, and yaw angles) and ship speed vectors, this design transforms wave disturbances in the ship's coordinate system into absolute disturbances in the northeast-northeast coordinate system. Compared to existing methods that only consider yaw angles, this design better reflects the actual motion of the ship and incorporates the cumulative effect of ship speed on wave disturbances. This reduces the estimation error of wave interference and provides an absolute coordinate reference for accurate compensation.

[0025] Specifically, in the disturbance estimation model construction module, step S2, "constructing wave disturbance model and water flow disturbance model," includes the following sub-steps: S21. Constructing a wave interference model: Based on wave height in preprocessed data. ,cycle Phase ,direction A periodic wave disturbance model is constructed, and the formula is as follows: ; S22. Constructing a water flow disturbance model: Based on the water flow velocity in the preprocessed data. Water flow direction A uniform water flow disturbance model is constructed, and the formula is: ; S23. Calculate the initial model of total disturbance: Superimpose the wave disturbance model and the water flow disturbance model using vectors to obtain the initial model of total disturbance, as shown in the formula: .

[0026] In this embodiment, the disturbance model constructs a wave periodic model and a water flow uniform velocity model respectively, and then obtains the total disturbance through vector superposition. The wave model uses a sine function to fit its periodic fluctuation characteristics, and the water flow model reflects its stable impact characteristics based on the velocity vector. The combination of the two covers the main types of marine disturbances. Compared with the traditional "single disturbance fitting" model, the similarity between the total disturbance and the actual environmental disturbance is improved, providing an accurate initial model for state space transformation.

[0027] Specifically, step S3, "transforming into state equations and observation equations," includes the following sub-steps: S31. Constructing the state equations: using the total disturbance as the initial model. As a state variable, a control input is introduced. ( For ship speed, For heading), zero-mean Gaussian process noise (covariance is) Q ), construct the state equation, the formula is ,in, A State transition matrix, B The input matrix; S32. Constructing the observation equation: using sensor observations Zero-mean Gaussian observation noise is introduced as the output. (covariance is) ), and the observation matrix Let the identity matrix be denoted as , and construct the observation equation as follows: .

[0028] In this embodiment, the state-space model dynamically transforms the static disturbance model into a "state equation + observation equation", introducing control input (ship speed). ,course ) and noise items (process noise) Observation noise This design takes into account the dynamic changes of interference over time and measurement uncertainties. Compared with static models, it is more in line with the "time-varying + random" characteristics of maritime interference, providing a mathematical basis for real-time estimation by the observer and improving the speed of interference tracking response.

[0029] Specifically, step S4, "predicting and updating the interference estimate using the Kalman filter algorithm," includes the following sub-steps: S41. Initialization parameters: Set the initial interference estimate. Initial covariance matrix ( (as the identity matrix), and determine the process noise covariance. Q ( Covariance of observation noise R ( ); S42. Predicting Interference Status: Based on the current interference estimate... With control input ,calculate The predicted disturbance value at time t is given by the formula: Simultaneously calculate the prediction covariance matrix, using the formula: ; S43. Update interference estimate: Receiver Sensor observations at time First calculate the Kalman gain. Then, based on the Kalman gain correction, the predicted interference value is obtained, resulting in the updated interference estimate, as shown in the formula: Finally, update the covariance matrix using the following formula: .

[0030] In this embodiment, the interference observer employs a Kalman filter, dynamically correcting the interference estimate through a "prediction-update" cycle: the prediction step derives interference changes based on the state equation, and the update step corrects deviations by incorporating sensor observations; simultaneously, adjustments are made... Q (Process noise covariance) and R (Observational noise covariance) adaptable to different sea states; high-precision sensor R Use 0.001 for low precision. R (Take a value of 0.01~0.05). Compared with fixed parameter filtering, this design improves the accuracy of disturbance estimation under different sea states and has stronger robustness.

[0031] Specifically, step S5, "generating the final control signal," includes the following sub-steps: S51. Calculate the feedforward compensation signal: based on the real-time interference estimate obtained in step S4. Combined with the optimized gain matrix The formula for calculating the feedforward compensation signal is as follows: ,in, It is a feedforward control compensation signal; It is the estimated state of the disturbance; S52. Obtain the desired control input: Calculate the desired control input based on the deviation between the ship's target trajectory and its current state using a PID or LQR algorithm. ; S53. Synthesizing the final control signal: The feedforward compensation signal is superimposed with the desired control input to obtain the final control signal, as shown in the formula below. ,in, It is the control input obtained based on the ship dynamics model and target trajectory planning.

[0032] In this embodiment, feedforward control converts the disturbance estimate into a compensation signal ( The signal is generated by superimposing the desired control input with the input. This design uses a composite control of "feedforward compensation + feedback adjustment" to preemptively cancel known disturbances. Compared with feedback control alone, it reduces position deviation, heading error, and rudder angle change rate in sea states 2-3, reduces rudder motor operation frequency, and reduces energy consumption and equipment wear.

[0033] Specifically, it also includes the following steps: S7. Hardware Deployment and Algorithm Execution: Configure at least one processor and at least one memory connected to the processor. Store program instructions that can be executed by the processor in the memory. When the processor calls the program instructions, execute steps S1 to S6 in sequence to realize the estimation and observation of marine interference. In step S1, wind speed and wind direction data acquisition can be added. In step S2, the interference model is expanded to a "wave + water flow + wind speed" interference model to adapt to the dynamic positioning scenario of offshore platforms or intelligent ships.

[0034] In this embodiment, a wind speed and direction instrument is added to the basic system, the interference model is expanded to "waves + water flow + wind speed", and the algorithm is implemented through a processor and memory. This design is suitable for dynamic positioning scenarios (which have higher requirements for position accuracy). Compared with systems that do not consider wind speed, the position deviation is further reduced, meeting the needs of high-precision offshore operations (such as drilling platform resupply).

[0035] Working principle and usage process of this invention: Based on the core logic of "data fusion - disturbance estimation - composite control", accurate estimation and compensation of maritime disturbances are achieved through multi-module collaboration: Data fusion layer: Sensors collect multi-source data (attitude, position, waves, water flow). The preprocessing module eliminates the timing deviation caused by frequency differences through "time alignment" and unifies it to the northeast coordinate system through "coordinate transformation". At the same time, combined with the ship's heading and speed, local interference is transformed into usable data in the absolute coordinate system, providing high-quality input for interference estimation.

[0036] Disturbance estimation layer: The disturbance model is constructed based on physical characteristics (wave periodicity, uniform water flow). The state-space model dynamizes the static disturbance by introducing control inputs and noise terms. The Kalman filter observer utilizes the process noise covariance through a "prediction-update" loop. Q Adapting to the rate of change of disturbance and the covariance of observation noise R It adapts to the accuracy of sensors and corrects interference estimates in real time to ensure accurate tracking of time-varying interference.

[0037] Composite control layer: The feedforward controller converts the disturbance estimate into a compensation signal to cancel out known disturbances in advance; the feedback controller (PID / LQR) calculates the desired input based on the target trajectory and the current state; the final control signal generated by the superposition of the two is used by the execution module to adjust the rudder angle and throttle, forming a closed-loop control of "feedforward compensation + feedback correction", which reduces the error caused by disturbances and ensures system stability.

[0038] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for estimating marine disturbances and designing observers using multi-source sensor fusion, characterized in that, Includes the following steps: S1. Sensor Data Acquisition and Preprocessing: The sensor data acquisition and preprocessing module acquires data from the inertial measurement unit (IMU), global positioning system (GPS), wave sensor, and water flow sensor. The acquired data is time-aligned and transformed to the northeast-east-northeast (NED) coordinate system. After obtaining the preprocessed data, it is output to the disturbance estimation model construction module. S2. Construction of Interference Estimation Model: Based on the received preprocessed data, the interference estimation model construction module constructs a wave interference model and a water flow interference model respectively. The two models are superimposed to obtain the initial model of total interference, which is then output to the state space model module. S3. State-space model transformation: The state-space model module transforms the initial model of total interference into state equations describing the dynamic changes of interference, and observation equations describing the relationship between sensor observation data and interference state. The state equations and observation equations are then output to the interference observer design module. S4. Interference Observer Estimation: The interference observer design module is based on the state equation and the observation equation. It uses the Kalman filter algorithm to first predict the interference state and covariance matrix according to the state equation, and then update the interference estimate by combining the sensor observation data. After obtaining the real-time interference estimate, it outputs it to the feedforward controller design module. S5. Feedforward control signal generation: The feedforward controller design module receives the real-time disturbance estimate and combines it with the desired control input obtained based on trajectory planning, target heading and speed to generate the final control signal, which is then output to the control execution module. S6. Control Execution: The control execution module adjusts the ship's rudder angle and throttle according to the final control signal, and drives the ship to perform corresponding navigation actions to counteract external interference caused by water flow and waves.

2. The method for estimating marine interference and designing observers using multi-source sensor fusion as described in claim 1, characterized in that, Step S1, "time alignment," specifically includes the following sub-steps: S11. Determine the reference frequency: Use the GPS output frequency (5Hz) as the reference frequency for time alignment; S12. Low-frequency sensor data alignment: For wave sensors and flow sensors with an output frequency of 10 min / time, alignment data at any given time is calculated using a linear interpolation formula. The interpolation formula is as follows: ,in, Indicates in t The interpolation result at time t. For a moment Observations at; S13, High-frequency sensor data alignment: For IMUs with an output frequency of 10Hz, downsampling is performed according to the 5Hz frequency of GPS, and one IMU data point is extracted every 0.2s to achieve time synchronization with GPS data.

3. The method for maritime interference estimation and observer design using multi-source sensor fusion as described in claim 1, characterized in that, Step S1, "Northeast Earth (NED) coordinate system transformation," is a transformation for wave interference and specifically includes the following sub-steps: S14. Constructing the rotation matrix: Constructing a rotation matrix that incorporates the ship's attitude. , ,in For roll angle, For pitch angle, For heading angle; S15. Calculate the ship speed vector: Based on the wave interference after the ship's real-time speed conversion... With heading angle Construct the ship speed vector The ship's speed vector ; S16. Wave Interference Coordinate Transformation: Transform the wave interference coordinates in the ship's coordinate system. Through rotation matrix When combined with the ship speed influence term, the wave disturbance is converted to a northeast coordinate system, and the formula is as follows: ,in t For time intervals.

4. The method for estimating marine interference and designing observers using multi-source sensor fusion as described in claim 1, characterized in that, Step S2, "Constructing wave disturbance model and water flow disturbance model", specifically includes the following sub-steps: S21. Constructing a wave interference model: Based on wave height in preprocessed data. ,cycle Phase ,direction A periodic wave disturbance model is constructed, and the formula is as follows: ; S22. Constructing a water flow disturbance model: Based on the water flow velocity in the preprocessed data. Water flow direction A uniform water flow disturbance model is constructed, and the formula is: ; S23. Calculate the initial model of total disturbance: Superimpose the wave disturbance model and the water flow disturbance model using vectors to obtain the initial model of total disturbance, as shown in the formula: .

5. The method for estimating marine interference and designing observers using multi-source sensor fusion as described in claim 1, characterized in that, Step S3, "transforming into state equations and observation equations," specifically includes the following sub-steps: S31. Constructing the state equations: using the total disturbance as the initial model. As a state variable, a control input is introduced. ( For ship speed, For heading), zero-mean Gaussian process noise (covariance is) Q ), construct the state equation, the formula is ,in, A State transition matrix, B The input matrix; S32. Constructing the observation equation: using sensor observations Zero-mean Gaussian observation noise is introduced as the output. (covariance is) ), and the observation matrix Let the identity matrix be denoted as , and construct the observation equation as follows: .

6. The method for estimating marine interference and designing observers using multi-source sensor fusion according to claim 1, characterized in that, Step S4, "predicting and updating the interference estimate using the Kalman filter algorithm," specifically includes the following sub-steps: S41. Initialization parameters: Set the initial interference estimate. Initial covariance matrix ( (as the identity matrix), and determine the process noise covariance. Q ( ) and observation noise covariance R ( ); S42. Predicting Interference Status: Based on the current interference estimate... With control input ,calculate The predicted disturbance value at time t is given by the formula: Simultaneously calculate the prediction covariance matrix, using the formula: ; S43. Update interference estimate: Receiver Sensor observations at time First calculate the Kalman gain. Then, based on the Kalman gain correction, the predicted interference value is obtained, resulting in the updated interference estimate, as shown in the formula: Finally, update the covariance matrix using the following formula: .

7. The method for estimating marine interference and designing observers using multi-source sensor fusion as described in claim 1, characterized in that, Step S5, "generating the final control signal," specifically includes the following sub-steps: S51. Calculate the feedforward compensation signal: based on the real-time interference estimate obtained in step S4. Combined with the optimized gain matrix The formula for calculating the feedforward compensation signal is as follows: ,in, It is a feedforward control compensation signal; It is the estimated state of the disturbance; S52. Obtain the desired control input: Calculate the desired control input based on the deviation between the ship's target trajectory and its current state using a PID or LQR algorithm. ; S53. Synthesizing the final control signal: The feedforward compensation signal is superimposed with the desired control input to obtain the final control signal, as shown in the formula below. ,in, It is the control input obtained based on the ship dynamics model and target trajectory planning.

8. The method for maritime interference estimation and observer design using multi-source sensor fusion according to claim 1, characterized in that, It also includes the following steps: S7. Hardware Deployment and Algorithm Execution: Configure at least one processor and at least one memory connected to the processor. Store program instructions that can be executed by the processor in the memory. When the processor calls the program instructions, execute steps S1 to S6 in sequence to realize the estimation and observation of marine interference. In step S1, wind speed and wind direction data acquisition can be added. In step S2, the interference model is expanded to a "wave + water flow + wind speed" interference model to adapt to the dynamic positioning scenario of offshore platforms or intelligent ships.

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