Underwater gravity measurement method, device and equipment based on position observation and medium

Abstract

The invention provides an underwater gravity measurement method, device, equipment and medium based on position observation, the method is realized by gravity measurement based on Hampel and RTS smoothing filtering, and the method comprises the following steps: cleaning and identifying horizontal position data provided by a USBL by using Hampel filtering, and replacing outliers in the horizontal position data to obtain more reliable horizontal position data; the SINS system performs a moving base alignment process by means of the cleaned high-precision horizontal position data; the SINS system performs pure inertial navigation calculation, continuously provides navigation parameters, and suppresses accumulative errors of the inertial navigation system through RTS smooth filtering processing; and an original gravity anomaly result is calculated, and precision evaluation is carried out after filtering processing of an FIR low-pass filter. According to the method, the gravity data is efficiently and accurately processed under the condition that USBL data output is unstable, and the reliability of a gravity data processing result is improved while a high-precision gravity measurement result is obtained.

Description

Technical Field

[0001] The present invention belongs to the technical field of underwater gravity measurement, and particularly relates to an underwater gravity measurement method, device, equipment and medium based on position observation. Background Art

[0002] With the increasing demands for marine resource exploration and navigation, the development of underwater dynamic gravity measurement technology has become a hot topic in the field of geophysical exploration. High-precision and high-resolution near-seabed gravity data are the basic basis for exploring the distribution laws of marine geological structures and mineral resources and clarifying the storage status of geological bodies. They are also the key factors affecting the accurate calculation of the trajectories of underwater vehicles, the precise guidance technology of underwater weapons, and the long-term underwater autonomous navigation of submarines. However, due to the limited reception of radio signals in the marine environment, satellite navigation means relied on in marine and airborne gravity measurements cannot be used for underwater gravity measurement, and only underwater sensors can be used instead. This poses more stringent challenges to the performance of gravity sensor and data fusion technology.

[0003] In the prior art, a combined navigation scheme of a strapdown inertial navigation system (SINS), a Doppler velocity log (DVL), an ultra-short baseline underwater acoustic positioning system (USBL), and a depth gauge (DG) is widely adopted. For example, the inertial navigation solution results are fused with external sensor data using a federated filter to achieve strapdown underwater dynamic gravity measurement. However, due to the limited ability to suppress data noise in a complex ocean current environment, it is difficult to effectively filter out high-frequency noise in gravity anomaly calculation. In addition, there is also a method of dynamically selecting sensor data combinations through Kalman filtering to achieve real-time underwater dynamic gravity measurement. However, due to the lack of robustness to abnormal values of sensor data, especially when the USBL positioning signal is interfered by multipath effects or the DG depth data mutates, systematic errors are easily introduced. And in terms of data processing algorithms, the prior art mostly relies on traditional Kalman filtering or low-pass filtering, without fully considering the non-Gaussian noise characteristics of sensor data in a dynamic environment, resulting in insufficient stability of gravity anomaly calculation results. Even when using multi-source fusion methods, due to relying on specific sensors and not effectively solving the synchronization problem of position observation data, the improvement of measurement accuracy is limited.

[0004] On the other hand, in a complex underwater environment, if the sound wave emitted by the DVL towards the seabed during operation cannot reach the seabed for a long time, it will be difficult for its data quality to meet the requirements of the integrated navigation and positioning accuracy. In the absence of DVL observation data, for the integrated navigation method based on SINS, USBL, and DG, using USBL as the horizontal position observation can achieve the feedback and compensation of inertial navigation errors. However, the output data of USBL often shows a problem of decreased stability due to environmental factor interference, which also indirectly poses a challenge to the reliability of the gravity data processing process. There is an urgent need to explore how to efficiently and accurately process gravity data under the condition of unstable USBL data output, while ensuring high-precision gravity measurement results and improving the reliability of gravity data processing results. Therefore, it is particularly important to deeply explore and optimize the underwater gravity measurement data processing algorithm based on position observation. Summary of the Invention

[0005] To solve the above problems existing in the prior art, the present invention proposes an underwater gravity measurement method, device, equipment, and medium based on position observation.

[0006] The present invention proposes an underwater gravity measurement method based on position observation, including: Step 110, building an underwater vehicle integrated navigation system composed of a strapdown inertial navigation system, an ultra-short baseline positioning system, and a depth gauge; Step 120, using a Hampel filter to clean the horizontal position data provided by the ultra-short baseline positioning system, identifying and replacing the outliers therein to obtain the cleaned horizontal position data; Step 130, the strapdown inertial navigation system uses the cleaned horizontal position data to perform moving base alignment and complete the initial alignment; Step 140, using the strapdown inertial navigation system to perform pure inertial navigation solution and continuously provide updated navigation parameters; the navigation parameters include the attitude, speed, and position of the underwater vehicle; Step 150, fusing the cleaned horizontal position data and the depth data obtained by depth measurement as observation quantities, inputting them into an RTS smoothing filter, using the RTS smoothing filter algorithm to perform smoothing filtering on the navigation parameters to obtain the navigation parameters after smoothing filtering, and using the navigation parameters after smoothing filtering to perform feedback correction on the strapdown inertial navigation system; Step 160, using the navigation parameters after smoothing filtering to obtain gravity measurement data, including calculating the Eötvös correction term and the normal gravity value, compensating the vertical motion acceleration according to the depth data provided by the depth gauge, and calculating the original gravity anomaly result according to the attitude information and vertical specific force provided by the strapdown inertial navigation system after feedback correction according to the gravity anomaly formula.

[0007] Furthermore, the underwater gravity measurement method based on position observation further includes: Step 170: Filter the original gravity anomaly result using a FIR low-pass filter to remove high-frequency noise components and obtain a smoothed high-precision gravity anomaly result; Step 180: Use the in-line coincidence accuracy evaluation method for repeated survey lines to evaluate the accuracy of gravity measurement data, including: Calculate the in-line coincidence accuracy of the th survey line : ; Wherein, is the total number of repeated survey lines; is the number of data points of the observation quantity for each repeated survey line; , is the gravity measurement data of the th point on the th survey line, is the average value of the gravity measurement data of the th point: .

[0008] The present invention also provides an underwater gravity measurement device based on position observation, which is used to implement the steps of the aforementioned underwater gravity measurement method based on position observation. The device includes: The first module is used to build an underwater vehicle integrated navigation system composed of a strapdown inertial navigation system, an ultra-short baseline positioning system, and a depth gauge; The second module is used to clean the horizontal position data provided by the ultra-short baseline positioning system using a Hampel filter, identify and replace the outliers therein, and obtain the cleaned horizontal position data; The third module is used for the strapdown inertial navigation system to perform moving base alignment using the cleaned horizontal position data to complete initial alignment; The fourth module is used to perform pure inertial navigation solution using the strapdown inertial navigation system to continuously provide updated navigation parameters; the navigation parameters include the attitude, speed, and position of the underwater vehicle; The fifth module is used to fuse the cleaned horizontal position data and the depth data obtained by depth measurement as observation quantities, input them into the RTS smoothing filter, use the RTS smoothing filter algorithm to perform smoothing filtering on the navigation parameters, obtain the smoothed navigation parameters, and use the smoothed navigation parameters to perform feedback correction on the strapdown inertial navigation system; The sixth module is used to obtain gravity measurement data by using the navigation parameters after smoothing filtering, including calculating the Eötvös correction term and the normal gravity value, compensating the vertical motion acceleration according to the depth data provided by the depth gauge, and calculating the original gravity anomaly result according to the attitude information and vertical specific force provided by the strapdown inertial navigation system after feedback correction, based on the gravity anomaly formula.

[0009] On the other hand, the present invention protects a computer device, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the steps of the foregoing underwater gravity measurement method based on position observation are implemented.

[0010] The present invention also provides a storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the foregoing underwater gravity measurement method based on position observation are implemented.

[0011] In summary, the present invention proposes an underwater gravity measurement method, device, equipment and medium based on position observation. Compared with the prior art, the advantages and beneficial effects of the method of the present invention include: (1) Using Hampel filtering to clean the horizontal position data and detect and replace the outliers in the data; according to the actual situation of the data and the cleaning requirements, the Hampel filtering process can be repeated to further remove the subtle outliers in the data and improve the accuracy and reliability of the data.

[0012] (2) Using RTS filtering for forward prediction and backward smoothing to obtain the optimal state estimation, enabling the system to output more accurate navigation parameters and using this information for feedback correction of the SINS system, thereby suppressing the cumulative error generated by the inertial navigation system due to long-term operation.

[0013] (3) Using a FIR low-pass filter to filter the original gravity anomaly data, with the cut-off frequency set to 300 Hz to remove the high-frequency noise components, thereby obtaining a smoother and more accurate gravity anomaly result. Description of the Drawings

[0014] Figure 1 is the flowchart of the steps of an underwater gravity measurement method based on position observation in the first embodiment of the present invention; Figure 2 is the schematic diagram of the cleaning process of the USBL position horizontal data based on Hampel filtering in the first embodiment of the present invention, where is the window length, is the median difference multiple threshold; Figure 3 is the schematic diagram of the algorithm architecture of an underwater gravity measurement method based on position observation in the first embodiment of the present invention. Detailed implementation manners

[0015] In order to make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present 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 only used to explain the present invention and are not used to limit the present invention.

[0016] The present invention provides an underwater gravity measurement method, device, equipment and medium based on position observation. By designing a gravity measurement method based on Hampel filtering and RTS smoothing filtering, the Hampel filtering is used to clean the horizontal position data, and the RTS filtering is used for forward prediction and backward smoothing to obtain the optimal state estimation, so as to compensate for the inertial navigation error and obtain high-precision and high-reliability gravity measurement results.

[0017] The Hampel filter is a non-linear filter based on statistical principles, mainly used to detect and remove outliers in signals, such as impulse noise or transient outliers. Specifically, the local statistical characteristics (such as median and absolute deviation) within the sliding window are used to determine whether a data point is an abnormal outlier.

[0018] The RTS smoothing filter is an optimized smoothing algorithm based on the Kalman filter, mainly used for state estimation. By performing forward and backward processing on all observation data, the accuracy of the past state estimation is improved.

[0019] In the first embodiment, referring to Figure 1 as shown, the present invention provides an underwater gravity measurement method based on position observation, specifically including: Step 110: Build an underwater integrated navigation system composed of a strapdown inertial navigation system (SINS), an ultra-short baseline positioning system (USBL) and a depth gauge (DG).

[0020] Step 120: Use the Hampel filter to clean the horizontal position data provided by the USBL, identify and replace the outliers therein, and obtain more reliable horizontal position data for subsequent calculation steps. The horizontal position data includes longitude and latitude data. The process of cleaning the USBL horizontal position data based on the Hampel filter is as Figure 2 shown.

[0021] First, input the original longitude and latitude data of the USBL containing outliers into the filter; then, considering the data frequency and quality of the USBL, set the filter parameters, including the window length and the median difference multiple threshold .

[0022] For high-frequency data (frequency ≥ 1 Hz), the window length is set to 10 - 20 data points, which can cover short-term noise and avoid excessive delay; for low-frequency data (frequency < 1 Hz), the window length is set to 5 - 10 data points to ensure there are enough samples to calculate the median and also avoid unreliable statistics due to too small a window. A dynamic adjustment mechanism is set: if the target moves fast or the noise is highly bursty, the window can be shortened; if the data is stable, the window can be appropriately extended.

[0023] Subsequently, the Hampel filtering algorithm is applied to detect and replace outliers in the data; if necessary, the above filtering process can be repeated according to the actual situation and cleaning requirements of the data to further remove minor outliers in the data and improve the accuracy and reliability of the data; finally, the cleaned data is output, which can be used for subsequent analysis, processing, or applications.

[0024] The Hampel filter first calculates the median of all data points in the horizontal position data set within the window : ; where is the median, is the -th data in the data set within the window , is the number of data points in the window, .

[0025] Then, the median of the distances from the data points in the window to the median is calculated. For each data point in the window, the median absolute deviation (MAD) is estimated: .

[0026] Calculate the median difference multiple of : ; Set the adjustable parameter as the threshold for . If , then the data point is considered an outlier and is replaced with the median .

[0027] Step 130: The SINS system performs the moving base alignment process by leveraging the cleaned high-precision horizontal position data. This step encompasses the precise estimation of the initial attitude angles (pitch angle, roll angle, and heading angle) of the gravitometer, and also involves the precise determination of the three-dimensional position coordinates and the initial velocity vector. The SINS system measures the specific force in the vehicle coordinate system through the accelerometer, combines the angular velocity information provided by the gyroscope to calculate the attitude matrix, converts the specific force to the navigation coordinate system and then separates the gravity vector components, and determines the initial attitude angle of the vehicle through the gravity projection relationship.

[0028] Step 140: After the initial alignment is completed, the SINS system enters the pure inertial navigation solution stage to continuously provide updated navigation parameters. The navigation parameters include the attitude, velocity, and position of the vehicle, and the position data includes horizontal position data and depth data.

[0029] Step 150: Using the navigation parameters, fuse the real-time horizontal position data provided by the USBL and the depth data measured by the DG, and input them as the observation data into the RTS smoothing algorithm. Through the RTS smoothing filtering process, the system can output more accurate navigation parameters and use this information for feedback correction of the SINS system, thereby suppressing the cumulative error generated by the inertial navigation system during long-term operation.

[0030] The RTS smoothing filtering algorithm is mainly divided into two steps: forward recursion and backward recursion: (1) Forward recursion process Follow the prediction and update steps of the Kalman filter. First, use the dimensional system state quantity of the SINS as the error vector: ; Among them, is the attitude error, represents the velocity error, represents the position error, represents the gyro zero bias error, represents the accelerometer zero bias error, represents the transpose operation of the matrix.

[0031] Differentiate and expand the above error vector of the SINS with respect to time , that is, use the differential equation to describe the relationship between the error and time, and the error state equation can be obtained: , Among them, is the state transition matrix; is the noise transition matrix, and the matrix element is the projection of the gyro zero bias error and the accelerometer zero bias error; is the system noise; is the gyro random noise; is the accelerometer random noise. The matrix , , , , , , and represent the dynamic coupling relationships between different error terms, and the specific form is: , , , , , , , ; wherein, is the latitude data, represents the depth, is a constant representing the angular velocity of the Earth's rotation; , and respectively represent the eastward velocity, northward velocity, and upward velocity of the underwater vehicle relative to the Earth; represents the radius of curvature of the meridian, represents the radius of curvature of the prime vertical; , and respectively represent the eastward specific force component, northward specific force component, and upward specific force component.

[0032] Take the horizontal position data provided by USBL and the depth data provided by DG as the observation quantities: , wherein, is the horizontal position error, is the depth error; Using the said observation quantities, the observation equation is established as: , , In the formula, is the observation matrix, is the state vector, is the observation noise; The first in means ignoring the first 6 components of the state vector, including velocity error and attitude error; Indicates extracting the error components at the 7th to 9th positions in the state vector; the second one in Indicates ignoring the last 6 components of the state vector (3D gyroscope bias and 3D accelerometer bias).

[0033] The RTS filter adopts the measurement data at each time point one by one since the initial time point. Based on the state transition and observation equations, it recursively calculates the state estimation value and its corresponding state covariance matrix at each time point. This recursive process continues until the final time point. At this time, the obtained state estimation and state covariance matrix will be used as the starting benchmark for the backward recursive operation.

[0034] The specific calculation process of the RTS filter includes: 1) State prediction The state estimation at time ; Calculate the measurement : ; It can be calculated to obtain the state covariance matrix at time as: ; 2) Measurement update The filtering gain matrix : ; Calculate the state estimation value at time : ; The updated state covariance matrix is: .

[0035] (2) Backward recursion Starting from the final time , using the state estimation and state covariance matrix obtained from the forward recursion, as well as the measurement data for the entire time period, through the way of backward iteration, the state estimation at each time point is calculated and optimized in turn. During the backward recursion process, the state estimation at each time point will be adjusted according to the measurement data at subsequent time points, so as to obtain a more accurate state estimation result. The calculation process is as follows: 1) Initialization: ; 2) Recursive step: For , obtain the RTS smoother gain matrix : ; Calculate the smoothed state estimate and the state covariance matrix : , .

[0036] Step 160: Use the navigation parameters processed by smoothing filtering to obtain gravity measurement data, including calculating the Eötvös correction term and the normal gravity value, and compensating the vertical motion acceleration based on the depth observation data provided by DG. On this basis, using the attitude information and vertical specific force provided by the feedback-corrected SINS, the gravity anomaly information can be extracted more accurately.

[0037] Furthermore, the original gravity anomaly result can be calculated according to the gravity anomaly formula: ; where, is the vertical component of the disturbing gravity vector, is the normal gravity value, is the vertical specific force, is the vertical motion acceleration, represents the angular velocity of the Earth's rotation, and are the eastward and northward components of the velocity relative to the Earth, respectively, and are the radii of curvature of the meridian and prime vertical, respectively, is the geodetic latitude.

[0038] Step 170, since the original gravity anomaly result contains a large amount of high-frequency noise components, an FIR low-pass filter is used to filter the original gravity anomaly data to remove the high-frequency noise components, so as to obtain a smoother and more accurate gravity anomaly result. The FIR (Finite Impulse Response) low-pass filter is a linear digital filter, characterized in that the impulse response decays to zero within a finite time, and by suppressing the high-frequency components, only the low-frequency signals are allowed to pass.

[0039] Step 180, adopt the in-line coincidence accuracy evaluation method for repeated survey lines to evaluate the accuracy of the gravity measurement data: ; where, is the in-line coincidence accuracy of the th survey line; is the total number of repeated survey lines; is the number of observation points for each repeated survey line; is the th survey line and the th gravity observation value of the point; is the th average value of the gravity observation of the point; is the difference between and

[0040] In this embodiment, the specific algorithm architecture process of the underwater gravity measurement method based on position observation is as Figure 3 shown.

[0041] In the second embodiment of the present invention, in step 120, considering the characteristics that USBL data has high precision but is vulnerable to environmental influence to generate singular values, a median difference multiple threshold can be set.

[0042] For high-frequency data (frequency ≥ 1 Hz), the window length can be set to 15 data points; for low-frequency data (frequency < 1 Hz), the window length is set to 8 data points. The dynamic adjustment mechanism is set as follows: if the target movement speed is fast or the noise burst is strong, the window can be shortened by 5 - 7 points; if the data is stable, the window can be appropriately extended by 16 - 20 points.

[0043] Furthermore, in this embodiment, in step 170, the cut-off frequency of the FIR low-pass filter is set to 300 Hz.

[0044] In all the foregoing embodiments of the present invention, after cleaning the navigation data obtained by the ultra-short baseline positioning system using Hampel filtering, horizontal position data is obtained; after completing the initial alignment of the moving base using the strapdown inertial navigation system, pure inertial navigation solution is performed; the horizontal position data after Hampel filtering is integrated with the depth data given by the depth gauge, and all the observed data is input into the RTS smoothing filtering algorithm module to output a more accurate integrated navigation result, which is then fed back to the SINS system for error correction to slow down the growth of cumulative errors. Using these optimized navigation parameters, the original gravity data can be obtained. Finally, the original gravity anomaly data is filtered, and the accuracy of the obtained result is evaluated to ensure the accuracy and reliability of the gravity data.

[0045] In the third embodiment of the present invention, an underwater gravity measurement device based on position observation is provided. Using the device to implement the steps of the foregoing underwater gravity measurement method based on position observation, the device includes: A first module for building an underwater vehicle integrated navigation system composed of a strapdown inertial navigation system, an ultra-short baseline positioning system, and a depth gauge; A second module is used to clean the horizontal position data provided by the ultra-short baseline positioning system by using a Hampel filter, identify and replace the outliers therein, and obtain the cleaned horizontal position data; A third module is used for the strapdown inertial navigation system to perform moving base alignment by using the cleaned horizontal position data and complete the initial alignment; A fourth module is used to perform pure inertial navigation solution by using the strapdown inertial navigation system and continuously provide updated navigation parameters; the navigation parameters include the attitude, speed and position of the underwater vehicle; A fifth module is used to fuse the cleaned horizontal position data and the depth data obtained by measurement by a depth gauge as observation quantities, input them into an RTS smoothing filter, perform smoothing filtering processing on the navigation parameters by using the RTS smoothing filtering algorithm, obtain the navigation parameters after smoothing filtering processing, and perform feedback correction on the strapdown inertial navigation system by using the navigation parameters after smoothing filtering processing; A sixth module is used to obtain gravity measurement data by using the navigation parameters after smoothing filtering processing, including calculating the Eötvös correction term and the normal gravity value, compensating the vertical motion acceleration according to the depth data provided by the depth gauge, and calculating the original gravity anomaly result according to the gravity anomaly formula by using the attitude information and vertical specific force provided by the strapdown inertial navigation system after feedback correction.

[0046] On the other hand, in one embodiment of the present invention, a computer device is provided. The device may be a server. The device includes a processor, a memory, a network interface and a database connected through a system bus. Among them, the processor of the device is used to provide computing and control capabilities. The memory of the device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the device is used to store underwater gravity measurement data based on position observations. The network interface of the device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, the underwater gravity measurement method based on position observations is implemented.

[0047] Those skilled in the art can understand that the description of the technical features of the device in the above embodiments does not constitute a limitation on all devices to which the solution of the present invention is applied. The specific device may include more or fewer components, or combine some components, or have different component arrangements.

[0048] In another embodiment, a storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of the aforementioned underwater gravity measurement method based on position observations are implemented.

[0049] Those of ordinary skill in the art can understand that all or part of the processes of implementing the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, database, or other medium used in the embodiments provided in the present application can include non-volatile and / or volatile memories. Non-volatile memories can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memories can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and Rambus dynamic RAM (RDRAM), etc.

[0050] Matters not described in this invention are well-known techniques.

[0051] The technical features of the above embodiments can be combined arbitrarily. For the sake of concise description, 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, it should be considered as the scope described in this specification.

[0052] The above-described embodiments only represent several implementation manners of the present invention. The description is relatively specific and detailed, but it should not be construed as a limitation on the scope of the invention. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several modifications and improvements can still be made, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the appended claims.

Claims

1. An underwater gravity measurement method based on position observation, characterized in that Including: Step 110: Establish an underwater vehicle integrated navigation system composed of a strapdown inertial navigation system, an ultra-short baseline positioning system, and a depth gauge; Step 120: Use a Hampel filter to clean the horizontal position data provided by the ultra-short baseline positioning system, identify and replace the outliers therein, and obtain the cleaned horizontal position data; Step 130: The strapdown inertial navigation system uses the cleaned horizontal position data to perform moving base alignment and complete initial alignment; Step 140: Use the strapdown inertial navigation system to perform pure inertial navigation solution and continuously provide updated navigation parameters; the navigation parameters include the attitude, speed, and position of the underwater vehicle; Step 150: Fuse the cleaned horizontal position data and the depth data obtained by measurement with the depth gauge as observation quantities, input them into the RTS smoothing filter, use the RTS smoothing filter algorithm to perform smoothing filtering on the navigation parameters, obtain the navigation parameters after smoothing filtering, and use the navigation parameters after smoothing filtering to perform feedback correction on the strapdown inertial navigation system; Step 160: Use the navigation parameters after smoothing filtering to obtain gravity measurement data, including calculating the Eötvös correction term and the normal gravity value, compensating the vertical motion acceleration based on the depth data provided by the depth gauge, and calculating the original gravity anomaly result according to the gravity anomaly formula using the attitude information and vertical specific force provided by the strapdown inertial navigation system after feedback correction.

2. The underwater gravity measurement method based on position observation according to claim 1, wherein In step 120, using a Hampel filter to clean the horizontal position data provided by the ultra-short baseline positioning system, identify and replace the outliers therein, and obtain the cleaned horizontal position data, including: Set the filtering parameters of the Hampel filter, including the window length and the median difference multiple threshold ; Set different window lengths according to the data frequency of the ultra-short baseline positioning system, and adopt a dynamic adjustment mechanism: if the target moves fast or the noise is highly bursty, shorten the window; if the data is stable, extend the window; Input the horizontal position data containing outliers provided by the ultra-short baseline positioning system into the Hampel filter; the horizontal position data includes longitude data and latitude data; Apply the Hampel filtering algorithm to detect and replace the outliers in the data to obtain the filtered horizontal position data, including: Calculate all data points in the horizontal position data set within the window length of the filter, and the median of all data points in the data set, as well as the median difference multiple of the data points , where is the function for calculating the median; ​ Set adjustable parameters , as a threshold to filter outliers at abnormal points: If , then the data point is considered abnormal, and the median is used to replace ; Set the number of times to perform repeated Hampel filtering , remove the minor outliers in the filtered horizontal position data, and obtain the cleaned horizontal position data.

3. The underwater gravity measurement method based on position observation according to claim 2, wherein In step 130, the strapdown inertial navigation system uses the cleaned horizontal position data to perform moving base alignment and complete initial alignment, including: Estimate the initial attitude angles of the underwater vehicle, and the attitude angles include pitch angle, roll angle, and heading angle; Calculate the three-dimensional position coordinates and the initial velocity vector.

4. The underwater gravity measurement method based on position observation according to claim 3, wherein In step 150, using the RTS smoothing filter algorithm to perform smoothing filtering on the navigation parameters, including: Step 151, perform forward recursion, including obtaining the state vector of the strapdown inertial navigation system, and using the observation to perform state prediction and measurement update for the navigation parameters at each moment to obtain the state estimate and the state covariance matrix, , is the final moment; Step 152, perform backward recursion, including starting from the final moment and, by means of reverse iteration, using the state estimate and state covariance matrix obtained from forward recursion, as well as the data of all observables, calculate and optimize the state estimate at each moment in sequence.

5. The underwater gravity measurement method based on position observation according to claim 4, wherein In step 151, the forward recursion process includes: Input the navigation parameters output by the strapdown inertial navigation system into the RTS smoothing filter; Take the state vector of the dimensional system as the error vector: ； wherein, is the attitude error, represents the velocity error, represents the position error, represents the gyro zero bias error, represents the accelerometer zero bias error, represents the transpose operation of the matrix; Differentiate the state vector with respect to time and expand it to obtain the error state equation: ， Wherein, ； is the state transition matrix, and the sub-matrices , , , , , , and represent the dynamic coupling relationships between different error terms; ； is the noise transfer matrix; is the system noise; is the projection of the gyro zero bias error and the accelerometer zero bias error; is the gyro random noise; is the accelerometer random noise; Take the cleaned horizontal position data and the depth data obtained by measuring with a depth gauge as the observed quantities , and input them into the RTS smoothing filter, where is the horizontal position error, is the depth error; Use the observation quantities to establish an observation equation: ， Among them, is the observation matrix, is the state vector, is the observation noise; The first in indicates ignoring the first 6 components of the state vector, including velocity error and attitude error; indicates extracting the 7th to 9th position error components in the state vector; The second in indicates ignoring the last 6 components of the state vector; Perform State prediction at a moment, including: Obtaining state estimation using the error state equation ; Calculate the observed quantity using the observation equation ; Calculate and obtain State covariance matrix at a moment ; Perform Measurement update at a moment, including: Calculate the filtering gain matrix ; Calculation State estimation value at a moment ; Updated state covariance matrix .

6. The underwater gravity measurement method based on position observation according to claim 5, characterized in that In step 152, the backward recursion process includes: Initialization: ; Design the recursion steps: For time instant , obtain the smoother gain matrix for RTS smoothing filtering: ； The calculated smoothed state estimate and the state covariance matrix : ； 。 7. The underwater gravity measurement method based on position observation according to claim 6, characterized in that, Also including: Step 170: Use a FIR low-pass filter to filter the original gravity anomaly result to remove high-frequency noise components and obtain a smoothed high-precision gravity anomaly result; Step 180: Use the in-line repeatability accuracy evaluation method to evaluate the accuracy of the gravity measurement data, including: Calculate the coincidence accuracy within the repeated line of the th survey line: ； Among them, is the total number of repeated survey lines; is the number of data points of the observed quantity for each repeated survey line; , is the th survey line and the th point's gravity measurement data, is the average value of the gravity measurement data of the th point: 。 8. An underwater gravity measurement device based on position observation, characterized in that, Use the device to implement the steps of the underwater gravity measurement method based on position observation as described in claim 1, and the device includes: The first module is used to build an underwater vehicle integrated navigation system composed of a strapdown inertial navigation system, an ultra-short baseline positioning system, and a depth gauge; The second module is used to clean the horizontal position data provided by the ultra-short baseline positioning system by using Hampel filtering, identify and replace the outliers therein, and obtain the cleaned horizontal position data; The third module is used for the strapdown inertial navigation system to use the cleaned horizontal position data to perform the moving base alignment process and complete the initial alignment; The fourth module is used to perform pure inertial navigation solution by using the strapdown inertial navigation system and continuously provide updated navigation parameters; the navigation parameters include the attitude, speed, and position of the underwater vehicle; The fifth module is used to fuse the cleaned horizontal position data and the depth data obtained by measurement with the depth gauge as the observation quantity, input it into the RTS smoothing filter, use the RTS smoothing filter algorithm to perform smoothing filtering on the navigation parameters, obtain the navigation parameters after smoothing filtering, and use the navigation parameters after smoothing filtering to perform feedback correction on the strapdown inertial navigation system; The sixth module is used to obtain gravity measurement data by using the navigation parameters after smoothing filtering, including calculating the Eötvös correction term and the normal gravity value, compensating the vertical motion acceleration according to the depth data provided by the depth gauge, and calculating the original gravity anomaly result according to the gravity anomaly formula by using the attitude information and the vertical specific force provided by the strapdown inertial navigation system after feedback correction.

9. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the underwater gravity measurement method based on position observation according to any one of claims 1-7.

10. A storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the underwater gravity measurement method based on position observation according to any one of claims 1-7.

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