Method and system for estimating motion state of underwater vehicle based on underwater acoustic positioning system

Abstract

The application discloses a water-borne body motion state estimation method and system based on a water acoustic positioning system, and the method comprises the following steps: acquiring an initial motion state of the water-borne body; the initial motion state comprises the speed, position of the water-borne body at an initial moment and the observation distance between the base array of the water acoustic positioning system and the water-borne body; taking the speed and position of the water-borne body as state variables and the observation distance as an observation variable, a state transition equation and an observation equation of an adaptive cubature Kalman filter algorithm are constructed; the observation distance of the water-borne body is acquired in real time, and the motion state of the water-borne body is estimated through the adaptive cubature Kalman filter algorithm. The application improves the adaptive cubature Kalman filter algorithm, realizes the estimation of unknown noises such as process noise variance and observation noise variance, weakens the impact of the uncertainty of noise statistical characteristics on the Kalman filter, effectively eliminates the water acoustic positioning outliers, and can improve the motion state estimation precision of the water-borne body.

Description

Technical Field

[0001] This invention belongs to the field of underwater vehicle navigation, specifically relating to a method and system for estimating the motion state of underwater vehicles based on an underwater acoustic positioning system. Background Technology

[0002] Underwater vehicles generally refer to various underwater devices such as submersibles and underwater robots. By locating and tracking the position, direction, and speed of underwater vehicles, they can effectively perform various underwater survey tasks in the underwater environment.

[0003] When underwater vehicles need to correct their navigation information, they must surface to receive signals from the Global Navigation Satellite System. This not only consumes time and energy but also significantly increases the likelihood of exposure. Underwater acoustic positioning systems, which can correct navigation information underwater and ensure the concealment of underwater vehicles, are currently a hot research topic. However, due to measurement errors in the sensors themselves, contamination from random noise, and interference from underwater acoustic reverberation, the observation data obtained by the sensors contains a large number of outliers, reducing reliability and ultimately leading to errors in the navigation information of underwater vehicles.

[0004] Currently, the main method for processing outliers in underwater acoustic positioning data is Kalman filtering and its variants. However, current algorithms based on Kalman filtering assume that the noise follows a Gaussian distribution. In reality, due to the superposition of ocean noise, self-noise, and sensor noise, the distribution of underwater noise statistical characteristics is uncertain, making traditional Kalman filtering methods no longer applicable.

[0005] Therefore, a new underwater acoustic localization noise processing method is needed to improve the accuracy of underwater body motion state estimation. Summary of the Invention

[0006] In view of this, the present invention proposes an underwater body motion state estimation method and system based on an underwater acoustic positioning system, which is used to solve the problem of insufficient accuracy in underwater body motion state estimation caused by unknown underwater noise statistical characteristics.

[0007] In a first aspect, this invention discloses a method for estimating the motion state of an underwater vehicle based on an underwater acoustic positioning system, the method comprising:

[0008] Acquire the initial motion state of the underwater vehicle; the initial motion state includes the velocity, position, and observation distance between the underwater vehicle and the array of the underwater acoustic positioning system at the initial moment;

[0009] Using the velocity and position of the underwater vehicle as state variables and the observation distance as the observation quantity, we construct the state transition equation and observation equation of the adaptive capacitive Kalman filter algorithm.

[0010] The observation distance of the underwater vehicle is acquired in real time, and the motion state of the underwater vehicle is estimated by an adaptive capacitive Kalman filter algorithm.

[0011] Based on the above technical solutions, preferably, the state transition equation and observation equation of the adaptive capacitive Kalman filter algorithm are as follows:

[0012]

[0013] Where k represents the sampling time, X(k)=[x(k)v x (k)y(k)v y (k)z(k)v z (k)] T Let [x(k), y(k), z(k)] represent the position of the body in the water, and [v...] represent the state variables. x (k),v y (k),v z [k] represents the velocity of the water-borne body; represents the state transition matrix, and T is the filtering period; The noise driving matrix is ​​represented by W(k-1) and V(k), which represent process noise and observation noise, respectively. The measurement is the observation distance between the array of the underwater acoustic positioning system and the underwater body.

[0014] Based on the above technical solutions, preferably, the step of estimating the motion state of the underwater vehicle using the adaptive capacitive Kalman filter algorithm specifically includes:

[0015] The predicted state value is obtained by performing a time update based on standard capacitive Kalman filtering;

[0016] Calculate the process noise variance estimate based on the predicted state value and calculate the predicted state variance;

[0017] Based on the predicted state variance, a measurement update based on standard capacitive Kalman filtering is performed to obtain the predicted observation value at the current time.

[0018] The error between the actual observation distance of the underwater body and the predicted observation value at the current time is calculated to obtain the current time information, and the information variance is obtained by the sliding window estimation method.

[0019] Calculate the estimated observation noise variance based on the innovation variance and the predicted observations at the current time;

[0020] Calculate the filter gain based on the estimated variance of the observed noise;

[0021] The estimated state value at the current time is updated based on the predicted observations and the filter gain at the current time.

[0022] Based on the above technical solutions, preferably, the calculation of the filter gain based on the estimated observation noise variance specifically includes:

[0023] The predicted observation variance and the cross-covariance between the predicted state value and the predicted observation value are calculated based on the estimated observation noise variance, and the pseudo-observation matrix is ​​updated.

[0024] The filter gain is calculated based on the predicted observation variance and the cross-covariance between the predicted state value and the predicted observation value.

[0025] Based on the above technical solutions, preferably, the formula for calculating the noise variance estimate in the calculation process is as follows:

[0026]

[0027] in, P is the estimated variance of the process noise at time k-1. ss (k) is the estimated value of the predicted state variance at time k; For the volume point propagating through the state transition equation, Let ω be the predicted state value at time k; n is the dimension of the state vector, i = 1, 2, ..., 2n, 2n is the number of volume points, and ω is the value of the predicted state at time k. i Let be the weight of the i-th volume point.

[0028] Based on the above technical solutions, preferably, the formula for calculating the estimated variance of the observation noise is as follows:

[0029]

[0030]

[0031] in, To estimate the variance of the observed noise, P εε (k) represents the innovation variance at time k, j = 1, 2, ..., m, m is the total number of windows, and ε(kj) represents the innovation at time kj; For the volume point propagated through the observation equation, Let k be the predicted observation value at time k.

[0032] Based on the above technical solutions, the preferred formula for calculating the filter gain is:

[0033] K(k) = Pxz(k)(Pzz(k)) - 1

[0034]

[0035]

[0036] Among them, Pzz (k) represents the predicted observation variance, P xz (k) represents the cross-covariance between the predicted state value and the predicted observation value.

[0037] In a second aspect, the present invention discloses an underwater vehicle motion state estimation system based on an underwater acoustic positioning system, the system comprising:

[0038] Initialization module: used to acquire the initial motion state of the underwater vehicle; the initial motion state includes the velocity, position, and observation distance between the underwater vehicle and the array of the underwater acoustic positioning system at the initial moment;

[0039] The filtering algorithm module is used to construct the state transition equation and observation equation of the adaptive capacitive Kalman filter algorithm, with the velocity and position of the underwater body as state variables and the observation distance as the observation quantity.

[0040] State estimation module: used to obtain the observation distance of the underwater vehicle in real time, and to estimate the motion state of the underwater vehicle through an adaptive capacitive Kalman filter algorithm.

[0041] A third aspect of the present invention discloses an electronic device comprising: at least one processor, at least one memory, a communication interface, and a bus;

[0042] The processor, memory, and communication interface communicate with each other through the bus.

[0043] The memory stores program instructions that can be executed by the processor, which invokes the program instructions to implement the method as described in the first aspect of the present invention.

[0044] In a fourth aspect, the present invention discloses a computer-readable storage medium storing computer instructions that cause a computer to perform the method described in the first aspect of the present invention.

[0045] The present invention has the following advantages over the prior art:

[0046] 1) This invention proposes an adaptive capacitive Kalman filter algorithm that can estimate noise online. It constructs state transition equations and observation equations using the velocity and position of the underwater vehicle as state variables and the observation distance as the observation quantity. These equations are used to eliminate outliers in underwater acoustic localization and estimate the motion state of the underwater vehicle, which can effectively improve the estimation accuracy.

[0047] 2) This invention estimates the process noise variance based on the predicted state value and the predicted state variance estimate. It calculates the current time's innovation based on innovation theory and estimates the innovation variance of the first m windows using the sliding window technique. Based on the innovation variance and the current time's predicted observation value, it estimates the observation noise variance. This can effectively estimate unknown noise, weaken the impact of the uncertainty of noise statistical characteristics on the Kalman filter, improve the robustness of the Kalman filter, and improve the accuracy of motion state estimation.

[0048] 3) Based on the process noise variance estimation and observation noise variance estimation, this invention calculates the estimated state variance and filter gain according to the minimum variance criterion, and updates the estimated state value at the current time according to the predicted observation value and filter gain at the current time, which can achieve fast tracking. Attached Figure Description

[0049] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0050] Figure 1 This is a schematic diagram of the underwater acoustic positioning principle of the present invention;

[0051] Figure 2 This is a flowchart of the underwater body motion state estimation method based on an underwater acoustic positioning system according to the present invention.

[0052] Figure 3 This is a schematic diagram of the adaptive capacitive Kalman filter algorithm of the present invention. Detailed Implementation

[0053] The technical solutions of the present invention will be clearly and completely described below with reference to the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0054] The underwater acoustic positioning system consists of an array and a beacon. The array first emits an acoustic signal of a certain frequency, and the beacon transmits the signal back after recognizing it. The array then determines the relative position of the beacon by the round-trip time of the acoustic signal. In this invention, a beacon is mounted on the underwater hull, and the array is installed on the mother ship to estimate the motion state of the underwater hull. Figure 1 shows a schematic diagram of the underwater acoustic positioning principle of this invention.

[0055] Please see Figure 2This invention proposes a method for estimating the motion state of an underwater vehicle based on an underwater acoustic positioning system. The method includes:

[0056] S1. Obtain the initial motion state of the underwater vehicle.

[0057] The underwater vehicle obtains the observation distance and position measured by the array through underwater acoustic communication, and obtains the velocity based on the velocity sensor on its own equipment. This velocity sensor can be an inertial navigation system or a Doppler velocimeter, etc.

[0058] The initial motion state of the underwater vehicle at the initial moment is obtained, namely the velocity and position of the underwater vehicle at the initial moment, and the observation distance between the underwater acoustic positioning system array and the underwater vehicle.

[0059] S2. Using the velocity and position of the underwater body as state variables and the observation distance as the observation quantity, construct the state transition equation and observation equation of the adaptive capacitive Kalman filter algorithm.

[0060] The state transition equation and observation equation of the adaptive capacitive Kalman filter algorithm are as follows:

[0061] X(k)=F(k-1)X(k-1)+G(k-1)W(k-1) (1)

[0062] Z(k)=h(X(k))+V(k) (2)

[0063] Where k represents the sampling time, X(k)=[x(k)v x (k)y(k)v y (k)z(k)v z (k)] T Let [x(k), y(k), z(k)] represent the position of the body in the water, and [v...] represent the state variables. x (k),v y (k),v z [k] represents the velocity of the water-borne body; represents the state transition matrix, and T is the filtering period; The noise driving matrix is ​​represented by W(k-1) and V(k), which represent process noise and observation noise, respectively. The measurement is the observation distance between the array of the underwater acoustic positioning system and the underwater body.

[0064] S3. Real-time acquisition of the observation distance of the underwater vehicle, and estimation of the motion state of the underwater vehicle using an adaptive capacitive Kalman filter algorithm.

[0065] Figure 3The diagram shows the principle of the adaptive capacitive Kalman filter algorithm of this invention. The entire filtering process can be divided into a filter calculation channel, a noise estimation channel, and a gain calculation channel. The filter calculation channel calculates a suboptimal estimate of the motion state based on the predicted state value, the predicted observation value, and the filter gain. The noise estimation channel estimates the observation noise variance and process noise variance based on the innovation variance of the first m windows and the pseudo-measurement matrix. The gain calculation channel calculates the estimated state variance and the filter gain according to the minimum variance criterion.

[0066] Step S3, which involves estimating the motion state of the underwater vehicle using an adaptive capacitive Kalman filter algorithm, specifically includes:

[0067] S31. Perform time updates based on standard capacitive Kalman filtering to obtain the predicted state values.

[0068] Cholesky decomposition of the estimated state variance P(k-1) at time k-1 yields:

[0069] P(k-1)=S(k-1)S T (k-1) (3)

[0070] Where S(k-1) is the square root of the estimated state variance P(k-1) at time k-1.

[0071] Calculate the volume point:

[0072]

[0073] in, For the i-th volume point, ξ i For the i-th volume vector, This represents the estimated state value at time k-1.

[0074] Propagating the volume point through the state transition equation:

[0075]

[0076] in, Let f be the i-th volume point propagated through the state transition equation, and let f be the state transition equation of formula (1).

[0077] By weighted summing of the volume points propagated through the state transition equation, the predicted state value at time k is obtained:

[0078]

[0079] S32. Calculate the process noise variance estimate based on the predicted state value, and calculate the predicted state variance.

[0080] Step S32 specifically includes the following sub-steps:

[0081] S321. Calculate the innovation at time kj using the sliding window method and obtain the pseudo-observation matrix at time kj. Calculate the estimated value of the predicted state variance using the innovation at time kj and the pseudo-observation matrix.

[0082]

[0083] Among them, P ss (k) represents the estimated variance of the predicted state at time k; j = 1, 2, ..., m, where m is the number of time windows, H + (kj) represents the pseudo-inverse matrix of the pseudo-observation matrix H(kj) at time kj, satisfying H + (kj)H(kj)=I, where I is the identity matrix; Let ε(kj) be the estimated variance of the observation noise at time kj, and let ε(kj) be the information at time kj.

[0084] S323. Calculate the process noise variance estimate based on the predicted state variance estimate.

[0085] The formula for calculating the process noise variance estimate is:

[0086]

[0087] in, G(k-1) is the estimated variance of the process noise at time k-1, and G(k-1) is the noise driving matrix at time k-1. For the volume point propagating through the state transition equation, Let ω be the predicted state value at time k; n is the dimension of the state vector, i = 1, 2, ..., 2n, 2n is the number of volume points, and ω is the value of the predicted state at time k. i Let be the weight of the i-th volume point.

[0088] S323. Calculate the variance of the predicted state.

[0089]

[0090] Where P(kk-1) is the predicted state variance at time k.

[0091] S33. Based on the predicted state variance, perform measurement updates based on standard capacitive Kalman filtering to obtain the predicted observations at the current time.

[0092] Performing Cholesky decomposition on the predicted state variance yields:

[0093] P(kk-1)=S(kk-1)S T (kk-1) (10)

[0094] Calculate the volume point:

[0095]

[0096] Propagation of volume points through observation equations:

[0097]

[0098] Where h(·) represents the observation equation of formula (2), Let k be the volume point propagated through the observation equation at time k.

[0099] We obtain the predicted observation value at the current time by weighted summation of the volume points propagated through the observation equation:

[0100]

[0101] in, Let k be the predicted observation value at time k.

[0102] S34. Calculate the error between the actual observation distance of the underwater body and the predicted observation value at the current time to obtain the current information, and obtain the information variance through the sliding window estimation method.

[0103] Calculate the current information:

[0104]

[0105] Where ε(k) represents the current information, and Z(k) represents the actual observation distance of the underwater vehicle.

[0106] The variance of the innovation for the first m windows is obtained using the sliding window estimation method.

[0107]

[0108] Among them, P εε ε(k) represents the variance of the innovation at time k, and ε(kj) represents the innovation at time kj. j = 1, 2, ..., m, where m is the total number of windows. More windows result in more accurate estimation, but also increase the computational burden. Generally, m = 5 is chosen.

[0109] S35. Calculate the estimated value of the observation noise variance based on the information variance and the predicted observation value at the current time.

[0110] The formula for calculating the variance estimate of the observed noise is:

[0111]

[0112] in, Let k be the estimated variance of the observation noise. For the volume point propagated through the observation equation, Let k be the predicted observation value at time k.

[0113] The noise estimation channel of this invention estimates the process noise variance based on the predicted state value and the predicted state variance estimate. It calculates the current time's innovation based on innovation theory and estimates the innovation variance of the first m windows using the sliding window technique. Based on the innovation variance and the current time's predicted observation value, it estimates the observation noise variance. This can effectively estimate unknown noise, reduce the impact of the uncertainty of noise statistical characteristics on the Kalman filter, improve the robustness of the Kalman filter, and enhance the accuracy of motion state estimation.

[0114] S36. Calculate the filter gain based on the estimated variance of the observation noise.

[0115] S361. Calculate the predicted observation variance and the cross-covariance between the predicted state value and the predicted observation value based on the estimated observation noise variance, and update the pseudo-observation matrix.

[0116] Calculate the predicted observation variance based on the estimated observation noise variance:

[0117]

[0118] Among them, P zz (k) represents the predicted observation variance at time k.

[0119] Calculate the cross-covariance between the predicted state values ​​and the predicted observations:

[0120]

[0121] Among them, P xz (k) represents the cross-covariance between the predicted state value and the predicted observation value at time k.

[0122] S362. Update the pseudo-observation matrix:

[0123] H(k)=(Pxz(k)) T P- 1 (kk-1) (19)

[0124] Where H(k) is the pseudo-observation matrix at time k.

[0125] S363. Calculate the filter gain based on the predicted observation variance and the cross-covariance between the predicted state value and the predicted observation value.

[0126] The formula for calculating the filter gain is:

[0127] K(k) = Pxz(k)(Pzz(k)) - 1 (20)

[0128] Where K(k) is the filter gain at time k.

[0129] S37. Update the estimated state value at the current time based on the predicted observation value and the filter gain at the current time.

[0130] Update the estimated state variance based on the filter gain:

[0131] P(k)=P(kk-1)-K(k)Pzz(k)K T (k) (21)

[0132] Where P(k) is the estimated state variance at time k.

[0133] Update the estimated state value at the current time step:

[0134]

[0135] in, Let k be the estimated state value at time k.

[0136] This invention proposes an adaptive capacitive Kalman filter algorithm capable of online noise estimation. Using the velocity and position of the underwater vehicle as state variables and the observation distance as the observation quantity, it constructs state transition equations and observation equations. Based on process noise variance estimation and observation noise variance estimation, it calculates the estimated state variance and filter gain according to the minimum variance criterion. The estimated state value at the current moment is updated based on the predicted observation value and filter gain. This method can quickly eliminate outliers in underwater acoustic localization, estimate the motion state of the underwater vehicle, and effectively improve estimation accuracy.

[0137] Corresponding to the above method embodiments, the present invention also proposes an underwater vehicle motion state estimation system based on an underwater acoustic positioning system, the system comprising:

[0138] Initialization module: used to acquire the initial motion state of the underwater vehicle; the initial motion state includes the velocity, position, and observation distance between the underwater vehicle and the array of the underwater acoustic positioning system at the initial moment;

[0139] The filtering algorithm module is used to construct the state transition equation and observation equation of the adaptive capacitive Kalman filter algorithm, with the velocity and position of the underwater body as state variables and the observation distance as the observation quantity.

[0140] State estimation module: used to obtain the observation distance of the underwater vehicle in real time, and to estimate the motion state of the underwater vehicle through an adaptive capacitive Kalman filter algorithm.

[0141] It should be noted that, in the embodiments of this application, the above system embodiments and method embodiments are one-to-one correspondences. For a brief description of the system embodiments, please refer to the method embodiments.

[0142] The present invention also discloses an electronic device, comprising: at least one processor, at least one memory, a communication interface, and a bus; wherein the processor, memory, and communication interface communicate with each other through the bus; the memory stores program instructions executable by the processor, and the processor calls the program instructions to implement the aforementioned method of the present invention.

[0143] The present invention also discloses a computer-readable storage medium that stores computer instructions, which cause the computer to implement all or part of the steps of the method described in the embodiments of the present invention. The storage medium includes various media capable of storing program code, such as a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0144] The system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, meaning they can be distributed across multiple network units. Those skilled in the art can select some or all of the modules to achieve the purpose of this embodiment without any inventive effort, based on actual needs.

[0145] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., 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 the motion state of an underwater vehicle based on an underwater acoustic positioning system, characterized in that, The method includes: Acquire the initial motion state of the underwater vehicle; the initial motion state includes the velocity, position, and observation distance between the underwater vehicle and the array of the underwater acoustic positioning system at the initial moment; Using the velocity and position of the underwater vehicle as state variables and the observation distance as the observation quantity, we construct the state transition equation and observation equation of the adaptive capacitive Kalman filter algorithm. The observation distance of the underwater vehicle is acquired in real time, and the motion state of the underwater vehicle is estimated using an adaptive capacitive Kalman filter algorithm, specifically including: The predicted state value is obtained by performing a time update based on standard capacitive Kalman filtering; The process noise variance estimate is calculated based on the predicted state values, and the predicted state variance is also calculated. Calculated using the sliding window method k - j Stay up-to-date with the latest news and get the latest updates. k - j The pseudo-observation matrix at time step, through k - j The estimated state variance is calculated using the information and pseudo-observation matrix at time step: ； in, Let be the estimated variance of the predicted state at time k; j =1,2,…, m , m Number of time windows represent k - j pseudo-observation matrix at time step The pseudo-inverse matrix satisfies I is the identity matrix; for k - j The estimated variance of the observation noise at time t. for k - j The latest news; The process noise variance estimate is calculated based on the predicted state variance estimate. The formula for calculating the process noise variance estimate is as follows: ； in, for k The noise driving matrix at time -1 for k Estimated process noise variance at time -1 for k The estimated variance of the predicted state at time t; For the volume point propagating through the state transition equation, for k The predicted state value at time; n For the state vector dimension, i =1,2,…,2 n ,2 n The number of volume points, For the first i The weight of each volume point; Calculate the predicted state variance; Based on the predicted state variance, a measurement update based on standard capacitive Kalman filtering is performed to obtain the predicted observation value at the current time. The error between the actual observation distance of the underwater body and the predicted observation value at the current time is calculated to obtain the current time information, and the information variance is obtained by the sliding window estimation method. Calculate the estimated observation noise variance based on the innovation variance and the predicted observations at the current time; The formula for calculating the estimated variance of the observation noise is as follows: ； ； in, To estimate the variance of the observed noise, for k The new information variance at time, j =1,2,…, m , m The total number of windows, for k - j The latest news; For the volume point propagated through the observation equation, for k Predicted observations at time; Calculate the filter gain based on the estimated variance of the observed noise; The estimated state value at the current time is updated based on the predicted observations and the filter gain at the current time.

2. The underwater vehicle motion state estimation method based on an underwater acoustic positioning system according to claim 1, characterized in that, The state transition equation and observation equation of the adaptive capacitive Kalman filter algorithm are as follows: ； in, Represents the sampling time. For state variables, Represents the location of the water-based body. Represents the speed of the water-borne body; This represents the state transition matrix, where T is the filtering period; Represents the noise driving matrix. and These represent process noise and observation noise, respectively. The measurement is the observation distance between the array of the underwater acoustic positioning system and the underwater body; h(·) represents the observation equation.

3. The underwater vehicle motion state estimation method based on an underwater acoustic positioning system according to claim 2, characterized in that, The calculation of the filter gain based on the estimated observation noise variance specifically includes: The predicted observation variance and the cross-covariance between the predicted state value and the predicted observation value are calculated based on the estimated observation noise variance, and the pseudo-observation matrix is ​​updated. The filter gain is calculated based on the predicted observation variance and the cross-covariance between the predicted state value and the predicted observation value.

4. The underwater vehicle motion state estimation method based on an underwater acoustic positioning system according to claim 3, characterized in that, The formula for calculating the filter gain is: ； ； ； in, To predict the observed variance, This is the cross-covariance between the predicted state value and the predicted observation value.

5. An underwater vehicle motion state estimation system based on an underwater acoustic positioning system, employing the underwater vehicle motion state estimation method based on an underwater acoustic positioning system as described in any one of claims 1-4, characterized in that, The system includes: Initialization module: used to acquire the initial motion state of the underwater vehicle; the initial motion state includes the velocity, position, and observation distance between the underwater vehicle and the array of the underwater acoustic positioning system at the initial moment; The filtering algorithm module is used to construct the state transition equation and observation equation of the adaptive capacitive Kalman filter algorithm, with the velocity and position of the underwater body as state variables and the observation distance as the observation quantity. State estimation module: used to obtain the observation distance of the underwater vehicle in real time, and to estimate the motion state of the underwater vehicle through an adaptive capacitive Kalman filter algorithm.

6. An electronic device, characterized in that, include: At least one processor, at least one memory, a communication interface, and a bus; The processor, memory, and communication interface communicate with each other through the bus. The memory stores program instructions that can be executed by the processor, which invokes the program instructions to implement the method as described in any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause the computer to perform the method as described in any one of claims 1 to 4.

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