A method for realizing high-precision underwater integrated navigation by fusing AUV kinetic velocity and single-beacon acoustic data

Abstract

The application provides a method for realizing high-precision underwater integrated navigation by fusing AUV kinetic velocity and single-beacon acoustic data, and belongs to the technical field of underwater navigation and positioning. The method comprises the following steps: estimating the speed against water in the carrier coordinate system by using the propeller rotating speed and the motor current, decomposing the speed against water to the navigation coordinate system by fusing the SINS attitude, obtaining the three-dimensional motion velocity of the AUV, and obtaining the preliminary position based on the dead reckoning; adaptively selecting the navigation strategy according to the operation time length and acoustic availability: the pure kinetic dead reckoning is adopted for the medium-short time task, and the single-beacon distance information is introduced for the long time operation, the navigation position after correction is obtained by periodically constraining the dead reckoning result through the geometric projection filtering algorithm of resetting the starting point. Compared with a single data source, the application significantly improves the navigation precision and reliability, and effectively solves the technical problem that the application of the acoustic sensor is limited in the deep sea environment.

Description

Technical Field

[0001] This invention relates to the field of underwater navigation technology, and in particular to a method for achieving high-precision underwater integrated navigation by integrating AUV dynamic velocity and acoustic data from a single beacon. Background Technology

[0002] Autonomous underwater vehicles (AUVs), as core equipment for marine exploration and development, are widely used in seabed topography mapping, resource exploration, and underwater reconnaissance, placing higher demands on the performance of their navigation systems. Currently, underwater speed and position correction mainly relies on acoustic methods.

[0003] Doppler velocimeters can provide high-precision seabed tracking speeds, but their effective operating range is limited by their height above the seabed, making them suitable only for near-shore or near-bottom operations. Acoustic Doppler current profilers primarily measure velocity relative to water, not velocity relative to the ground, and face signal attenuation issues in deep-sea environments. Electromagnetic current meters can only measure velocity relative to water, are susceptible to the influence of seawater conductivity and geomagnetic field distribution, and are prone to zero-point and measurement drift, requiring frequent calibration. In deep-sea environments beyond their measurement range, all of the above sensors fail because they cannot acquire bottom-tracking signals. Therefore, how to obtain reliable velocity information in complex acoustic environments or deep-sea conditions has become a critical challenge that urgently needs to be solved in the field of underwater navigation.

[0004] To address this issue, utilizing the AUV's own dynamic model for velocity estimation offers a feasible alternative. Unlike sensors that rely on external acoustic signals, dynamic velocity originates from internal information such as the propeller speed within the propulsion system, offering significant advantages such as strong autonomy and resistance to environmental interference. This idea was first proposed by Koifman and Bar-Itzhack in 1999, using Kalman filtering combined with an aircraft dynamic model to assist inertial navigation. Subsequently, this approach was extended to the field of underwater vehicles. Research, exemplified by the HUGIN AUV series, demonstrates a high degree of consistency between the velocity output by the dynamic model constructed based on sea trial data and the measured DVL (Driving Volume Limit) values, fully validating the feasibility and effectiveness of the dynamic model as a velocity assistance method during DVL failure.

[0005] However, although dynamic model assistance can provide velocity information when DVL (Depth-to-Low) navigation fails, its use alone still suffers from cumulative drift, making it difficult to meet the needs of long-duration, high-precision navigation tasks. In deep-sea operations, due to deployment limitations, AUVs often can only receive acoustic signals from a single beacon for periodic position correction, making constrained positioning methods based on single-beacon ranging a hot research topic. It should be noted that most existing methods treat the dynamic model and acoustic positioning methods separately, failing to deeply integrate the AUV's own dynamic information to provide continuous motion constraints. This results in insufficient overall robustness of the navigation system during long-duration missions and an inability to effectively suppress error accumulation.

[0006] In summary, how to integrate dynamic velocity with limited external acoustic observation depth to achieve high-precision and high-reliability navigation of AUVs in acoustically deficient and deep-sea environments is a technical problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0007] To address the technical limitations of acoustic sensors in deep-sea environments, this invention proposes a high-precision underwater integrated navigation method that integrates AUV dynamic velocity and single beacon acoustic data. This method aims to overcome the limitations of traditional velocity sensors in deep-sea operations. Its core innovation lies in proposing a dual-mode navigation strategy that adaptively selects the navigation mode based on the operation duration and acoustic data availability, thereby ensuring both accuracy and system robustness.

[0008] Specifically, this invention first obtains the water speed in the carrier coordinate system by real-time calculation of the coupling relationship between the AUV propeller speed and the motor drive current; then, it integrates the attitude data provided by the strapdown inertial navigation system, decomposes the speed into the navigation coordinate system, and calculates the three-dimensional motion speed of the AUV in real time, providing continuous speed information for subsequent navigation.

[0009] Based on this, to address the problem of the accumulation of position error over time in single dynamic dead reckoning, this invention designs two complementary navigation modes:

[0010] Mode A: For short- to medium-duration missions, a pure dead reckoning mode based on dynamic velocity is adopted, which takes advantage of its simple open-loop integral structure and direct error propagation path to avoid the coupling instability introduced by closed-loop filtering.

[0011] Mode B: For long-endurance operations, distance information provided by a single beacon is introduced, and a geometric projection filtering algorithm based on a reset starting point is used to periodically constrain the dynamic dead reckoning results, effectively suppressing error divergence.

[0012] Through the above dual-mode strategy, the method of the present invention significantly improves navigation accuracy and reliability compared with a single data source, and effectively solves the technical problem of limited application of acoustic sensors in deep-sea environments.

[0013] The present invention adopts the following solution:

[0014] A method for achieving high-precision underwater integrated navigation by integrating AUV dynamic velocity and acoustic data from a single beacon includes:

[0015] S1: Real-time calculation of the coupling relationship between AUV propeller speed and motor drive current to obtain the water speed in the carrier coordinate system;

[0016] S2: By integrating the attitude data provided by the strapdown inertial navigation system (SINS), the surface speed in the carrier coordinate system is decomposed into the navigation coordinate system to obtain the three-dimensional motion speed of the AUV.

[0017] S3: Based on the aforementioned three-dimensional motion velocity, perform dead reckoning to obtain a preliminary position estimate for the AUV;

[0018] S4: A filtering algorithm that resets the starting point is used to periodically constrain the navigation results calculated by dynamic dead reckoning using the distance information provided by a single beacon, so as to obtain the corrected navigation position.

[0019] As a further improvement of the present invention, the specific implementation process of step S1 is as follows:

[0020] S11: Establish a quasi-steady-state model of propeller thrust T and torque Q, based on the thrust coefficient K. T (J) and torque coefficient K Q The linear relationship between (J) and the advance coefficient J is used to determine the propeller performance curve:

[0021] ,

[0022] ,

[0023] In the formula, D is the propeller diameter. Where is the density of seawater, n is the propeller speed, T is the propeller thrust, Q is the propeller torque, and J is the advance coefficient.

[0024] S12: Collect the drive current of the AUV propulsion motor, and determine the torque Q under the current operating condition based on the correspondence between motor current and torque;

[0025] S13: The advance coefficient J quantifies the geometric relationship controlling the hydrodynamic angle of attack of the propeller blades. The specific formula is as follows:

[0026] ,

[0027] In the formula, w is the wake fraction, D is the propeller diameter, n is the propeller speed, and v is the vehicle speed.

[0028] S14: Integrating S11 over a finite operating range, thrust coefficient K T (J) and propeller torque coefficient K Q (J) can be represented as:

[0029] ,

[0030] ,

[0031] In the formula, K T (0) and K Q (0) is the zero flow coefficient determined by experiments, and α and β represent dimensionless coefficients.

[0032] S15: Based on the variables obtained from the above steps, the speed u of the aircraft was further derived:

[0033] .

[0034] As a further improvement of the present invention, in step S14:

[0035] Since the dimensionless coefficient β was not obtained in open water testing, the DVL measurement value obtained in shallow sea using an AUV was used as the true value, and the coefficient β was obtained using the least squares method.

[0036] ,

[0037] In the formula, v d The velocity v is measured by DVL. i The velocity is obtained using dynamics.

[0038] As a further improvement of the present invention, the specific implementation process of step S2 is as follows:

[0039] S21: Acquire attitude data output in real time by the strapdown inertial navigation system (SINS), including roll angle. Pitch angle θ and yaw angle ;

[0040] S22: Use the obtained attitude data to obtain the direction cosine matrix, and transform the obtained velocity in the vehicle coordinate system to the navigation coordinate system.

[0041] As a further improvement of the present invention, the specific implementation process of step S3 is as follows:

[0042] S31: Initialize dead reckoning parameters. Let the position at the initial time k=0 be P0. The initial position is obtained from the GNSS positioning before the AUV enters the water. Let the position after the last acoustic calibration be P. last = P0, index k of the last acoustic calibration time. last =0;

[0043] S32: Define the time series for dead reckoning, with a sampling interval of Δt and time indices of k=1,2,3,...,N; define the time series for acoustic ranging as t j , j=1,2,...,M, where M is the total number of acoustic ranging attempts;

[0044] S33: Establish a dead reckoning position update formula based on the three-dimensional motion velocity V obtained in step S2. i = [V E-i V N-i V U-i ] T Perform recursion:

[0045] ,

[0046] In the formula, P DR (k) represents the predicted position at time k based on dead reckoning, P last The position after the last sound school correction, k last For the time index of the last acoustic correction, Δt DR The sampling time interval for dynamic velocity;

[0047] S34: If no new acoustic distance information is received, continue using step S33 to perform dead reckoning and output a preliminary position estimate P. DR (k) is the current navigation location of the AUV;

[0048] S35: For vertical position estimation, the depth value h(k) measured by the pressure sensor is directly used to replace P. DR The celestial component in (k), namely:

[0049] .

[0050] As a further improvement of the present invention, the specific implementation process of step S4 is as follows:

[0051] S41: Preset acoustic ranging period T acoustic When the AUV sails to the preset ranging time or receives a ranging request triggered by an acoustic beacon, the acoustic ranging process is initiated.

[0052] S42: The AUV uses an acoustic transducer to measure the distance to a single beacon deployed on the seabed, obtaining the slant range measurement Z at the current time k. k Simultaneously record the precise position of the beacon in the navigation coordinate system B = [x B y B z B ] T The distance measurement model is as follows:

[0053] ,

[0054] in, Noise for distance measurement;

[0055] S43: Calculation of dead reckoning position P in step S3 DR (k) Geometric distance d to acoustic beacon B DR :

[0056] ;

[0057] S44: Calculate the geometry scaling factor α k :

[0058] ;

[0059] S45: Perform geometric projection correction on the dead reckoning position based on the scaling factor to obtain the corrected fused position P. fused :

[0060] ,

[0061] Where, if α k > 1 indicates that the dead reckoning distance is underestimated, and the estimated position needs to be expanded outwards; if α k < 1 indicates that the dead reckoning distance is overestimated, and the estimated position needs to be narrowed inward; if α k = 1, then the dead reckoning position is consistent with the distance measurement value, and no correction is required;

[0062] S46: Adjust the corrected position P fused As the new starting point for dead reckoning, reset the dead reckoning parameters:

[0063] ;

[0064] S47: Return to step S3 and continue dead reckoning for subsequent moments starting from the corrected position until the next acoustic ranging information is received. Repeat steps S41-S46 to achieve periodic constraint on dynamic dead reckoning error.

[0065] In step S44 of the method of this invention, the principle of geometric projection is innovatively introduced into the underwater single beacon navigation constraint, avoiding the complex process of filter parameter tuning, and directly using the spatial geometric projection relationship for position correction.

[0066] In step S45 of this invention, the position after each acoustic correction is innovatively used as a new starting point to reset the integration origin for subsequent dead reckoning. This strategy effectively avoids the problem of errors accumulating infinitely with integration time in traditional dead reckoning, controlling the positioning error within each correction cycle and achieving bounded error under long-duration conditions.

[0067] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0068] (1) To address the DVL failure problem in deep-sea environments, this invention proposes a navigation method based on the deep fusion of AUV dynamic velocity and single beacon acoustic data, overcoming the technical bottlenecks of poor positioning accuracy and low continuity of traditional navigation methods under acoustic signal constraints. Compared with existing solutions that rely on acoustic velocity sensors such as DVL, this invention eliminates the need for external velocity measurement equipment, achieving high-precision positioning solely through the vehicle's own dynamic model and sparse acoustic ranging information, significantly improving the autonomous navigation capability and system robustness of AUVs in acoustically blind areas such as deep and ultra-deep water.

[0069] (2) This invention introduces a geometric scaling factor filtering algorithm based on a reset starting point, which effectively solves the divergence problem of error accumulation over time in pure dynamic dead reckoning. Compared with traditional nonlinear estimation methods such as extended Kalman filtering in the prior art, this algorithm does not require accurate prior noise statistics, has low computational complexity, is easy to implement in engineering, and can stably suppress positioning drift under long-endurance conditions, providing reliable technical support for AUV long-range operations without DVL.

[0070] (3) This invention constructs a scene-adaptive multi-source fusion navigation framework, which can dynamically adjust the fusion strategy according to the AUV operating environment (such as deep-sea cruising, near-bottom operations, shallow water interference, etc.), overcoming the inherent limitation of poor adaptability of single navigation methods in complex sea conditions. This framework organically combines dynamic model calculation, acoustic single beacon positioning, and inertial navigation information, realizing intelligent switching of navigation modes in all sea conditions and all time domains, significantly improving the system's environmental adaptability and comprehensive positioning performance, and providing an integrated solution for long-endurance high-precision navigation of AUVs in complex marine environments. Attached Figure Description

[0071] Figure 1 This is a schematic diagram of the overall process of the present invention;

[0072] Figure 2 This is a schematic diagram of the data acquisition scenario of the present invention;

[0073] Figure 3 This is a comparison chart of ultra-short baseline dynamic dead reckoning and dynamic dead reckoning plus single beacon trajectory;

[0074] Figure 4 This is a comparison chart of the errors between dynamic dead reckoning and the dynamic dead reckoning + single beacon method. Detailed Implementation

[0075] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0076] The present invention will now be described in further detail with reference to the accompanying drawings:

[0077] A method for achieving high-precision underwater integrated navigation by integrating AUV dynamic velocity and acoustic data from a single beacon, according to... Figure 2 The working scenario shown was conducted in the vicinity of the 3000-meter waters of the South China Sea on July 21, 2024. Data collected included time delay data from a 2-4kHz low-frequency sonar and a single seabed beacon, propeller speed returned by the AUV itself, attitude data from the strapdown inertial navigation system, and pressure gauge data. Specific steps are as follows (refer to...). Figure 1 (as shown)

[0078] S1: Real-time calculation of the coupling relationship between AUV propeller speed and motor drive current to obtain the water speed in the carrier coordinate system;

[0079] S11: The AUV sets its speed according to the mission requirements, and based on the propeller speed returned by the AUV itself, and its coupling relationship, establishes a quasi-steady-state model of propeller thrust T and torque Q, based on the thrust coefficient K. T (J) and torque coefficient K Q The linear relationship between (J) and the advance coefficient J is used to determine the propeller performance curve:

[0080] ,

[0081] ,

[0082] In the formula, D is the propeller diameter, which is selected according to the actual size of the AUV. The AUV used in this experiment has a diameter of 0.5m. The density is 1.05 × 10⁻⁶, taken as the density of seawater since the experiment was conducted in the South China Sea.3 kg / m 3 n is the propeller speed, obtained in real time by the AUV, with a sampling interval of 1 second; T is the propeller thrust; Q is the propeller torque; and J is the advance coefficient.

[0083] S12: Determine the corresponding motor current based on the propeller speed generated by the AUV itself, and determine the torque Q under the current operating condition based on the correspondence between motor current and torque.

[0084] S13: The advance coefficient J quantifies the geometric relationship controlling the hydrodynamic angle of attack of the propeller blades. The specific formula is as follows:

[0085] ,

[0086] In the formula, w is the wake fraction, which is selected as 0.3 according to the characteristics of the AUV itself, D is the propeller diameter, n is the propeller speed, and v is the vehicle speed.

[0087] S14: Integrating S11 over a finite operating range, thrust coefficient K T (J) and propeller torque coefficient K Q (J) can be represented as:

[0088] ,

[0089] ,

[0090] In the formula, K T (0) and K Q (0) is the zero flow coefficient determined by experiments, and α and β represent dimensionless coefficients.

[0091] Since the dimensionless coefficient β was not obtained in the open water test, the DVL measurement value was obtained in shallow sea using an AUV as the true value, and the coefficient β was obtained using the least squares method.

[0092] ,

[0093] In the formula, v d The velocity v is measured by DVL. i The velocity is obtained using dynamics.

[0094] S15: Based on the variables obtained from the above steps, the speed u of the aircraft was further derived:

[0095] .

[0096] S2: By integrating the attitude data provided by the strapdown inertial navigation system (SINS), the surface speed in the vehicle coordinate system is decomposed into the navigation coordinate system to obtain the three-dimensional motion speed of the AUV. The specific implementation process is as follows:

[0097] S21: Acquire attitude data output in real time by the strapdown inertial navigation system (SINS), including roll angle. Pitch angle θ and yaw angle ;

[0098] S22: Obtain the direction cosine matrix using the obtained attitude data. The velocity obtained from the vehicle coordinate system is converted to the navigation coordinate system.

[0099] .

[0100] S3: Based on the aforementioned three-dimensional motion velocity, dead reckoning is performed to obtain a preliminary position estimate of the AUV. The specific implementation process is as follows:

[0101] S31: Initialize dead reckoning parameters. Let the position at the initial time k=0 be P0. The initial position is obtained from the GNSS positioning before the AUV enters the water. Let the position after the last acoustic calibration be P. last = P0, index k of the last acoustic calibration time. last =0;

[0102] S32: Define the time series for dead reckoning, with a sampling interval of Δt and time indices of k=1,2,3,...,N; define the time series for acoustic ranging as t j , j=1,2,...,M, where M is the total number of acoustic ranging attempts;

[0103] S33: Establish a dead reckoning position update formula based on the three-dimensional motion velocity V obtained in step S2. i = [V E-i V N-i V U-i ] T Perform recursion:

[0104] ,

[0105] In the formula, P DR (k) represents the predicted position at time k based on dead reckoning, P last The position after the last sound school correction, k last Δt is the index of the last sound correction time. DR The sampling time interval for dynamic velocity;

[0106] S34: If no new acoustic distance information is received, continue using step S33 to perform dead reckoning and output a preliminary position estimate P. DR (k) is the current navigation location of the AUV;

[0107] S35: For vertical position estimation, the depth value h(k) measured by the pressure sensor is directly used to replace P. DR The celestial component in (k), namely:

[0108] ,

[0109] S4: A filtering algorithm that resets the starting point is adopted. The distance information provided by a single beacon is used to periodically constrain the navigation results calculated by dynamic dead reckoning, and the corrected navigation position is obtained. The specific implementation process is as follows:

[0110] S41: The ranging period T of this acoustic equipment acoustic The time is 20 seconds. When the AUV sails to the preset ranging time or receives a ranging request triggered by an acoustic beacon, the acoustic ranging process is started.

[0111] S42: The AUV uses an acoustic transducer to measure the distance to a single beacon deployed on the seabed, obtaining the slant range Z at the current time k. k Simultaneously record the precise position of the beacon in the navigation coordinate system B = [x B , y B , z B ] T The distance measurement model is as follows:

[0112] ,

[0113] in To minimize distance measurement noise, the ranging accuracy is better than 0.1% of the slant distance.

[0114] S43: Calculation of dead reckoning position P in step S3 DR (k) Geometric distance d to acoustic beacon B DR :

[0115] ;

[0116] S44: Calculate the geometry scaling factor α k :

[0117] ;

[0118] S45: Perform geometric projection correction on the dead reckoning position based on the scaling factor to obtain the corrected fused position P. fused :

[0119] ,

[0120] Where, if α k > 1 indicates that the dead reckoning distance is underestimated, and the estimated position needs to be expanded outwards; if α k < 1 indicates that the dead reckoning distance is overestimated, and the estimated position needs to be narrowed inward; if α k = 1, then the dead reckoning position is consistent with the distance measurement value, and no correction is required;

[0121] S46: Adjust the corrected position P fused As the new starting point for dead reckoning, reset the dead reckoning parameters:

[0122] ;

[0123] S47: Return to step S3 and continue dead reckoning for subsequent moments starting from the corrected position until the next acoustic ranging information is received. Repeat steps S41-S46 to achieve periodic constraint on dynamic dead reckoning error.

[0124] The effectiveness of this method was verified through a long-duration deep-sea experiment. The specific experiments and results are as follows:

[0125] (1): In July 2024, a deep-sea long-endurance experiment was conducted in a certain sea area of ​​the South China Sea (water depth of about 2400 m). After the AUV operated continuously for 5 hours, a data segment of about 48 minutes was selected for verification. The experimental area was located about 270 km east of Sanya. The USBL positioning results were used as the true value reference in the experiment.

[0126] (2): Navigation solutions were performed using pure dynamic dead reckoning (Method A) and dynamic dead reckoning with single beacon constraints (Method B), respectively. The trajectory pairs were as follows: Figure 3 As shown.

[0127] (3): The positional errors of the two methods relative to the USBL reference were calculated, and the root mean square error (RMS) was statistically analyzed. The results are shown in Table 1.

[0128] Analysis of the navigation method established through long-duration deep-sea experiments verifies the effectiveness and superiority of the method of the present invention in long-duration deep-sea scenarios. Figure 3This paper presents a comparison between navigation trajectories derived from pure dynamic dead reckoning and those with single beacon constraints. In the figure, squares represent the USBL reference trajectory, hexagons represent the pure dynamic dead reckoning results, and circles represent the navigation trajectory using the method of this invention (dynamic dead reckoning + single beacon constraint). The results show that after 5 hours of continuous operation, pure dynamic dead reckoning, due to SINS attitude divergence, could no longer accurately describe the actual motion trajectory of the AUV in curved sections, exhibiting significant deviations from the reference trajectory. However, after introducing single beacon constraints, the navigation trajectory closely matches the USBL reference trajectory, demonstrating excellent tracking performance, especially in curved sections.

[0129] As shown in Table 1, after 5 hours of pure dynamic dead reckoning, the eastward error reached 443.768 m and the northward error was 77.005 m. After introducing single beacon constraints, the eastward error was reduced to 18.998 m (a reduction of 95.7%) and the northward error was reduced to 42.864 m (a reduction of 44.3%). Figure 4 The error variation curves over time are shown, demonstrating that the single-beacon constraint corrects the position at each ranging moment, effectively suppressing long-term drift and keeping the error within a limited range. The reduction in eastward error is more significant because the correction magnitude of the geometric projection filtering algorithm is proportional to the initial error; directions with larger initial errors receive greater correction.

[0130] This experimental example verifies that the method of the present invention can significantly suppress the cumulative error of dynamic dead reckoning under the condition of only a single beacon, and achieve high-precision navigation during long-duration deep-sea navigation. Compared with the prior art, the method of the present invention has the following beneficial effects:

[0131] (1) By fusing the dynamic velocity of the AUV with the acoustic data of a single beacon, and compared with the existing method that relies solely on pure dynamic dead reckoning, the method of this invention effectively solves the problem of long-term navigation error accumulation in the deep-sea environment. Experimental results show that after introducing the single beacon constraint, the eastward error decreased from 443.768 m to 18.998 m (a reduction of 95.7%), and the northward error decreased from 77.005 m to 42.864 m (a reduction of 44.3%), significantly improving the long-term navigation accuracy.

[0132] (2) The method of this invention introduces a geometric scaling factor filtering algorithm based on resetting the starting point. Compared with complex nonlinear methods such as traditional extended Kalman filtering, it has low computational complexity, does not require prior noise statistics, and has clear physical meaning, making it particularly suitable for real-time calculation on AUV embedded platforms. Experiments have shown that the algorithm can effectively correct the position error in each ranging cycle, and the correction magnitude adaptively matches the initial error, maintaining good tracking performance even in curved flight segments.

[0133] (3) The method of the present invention constructs a fusion framework that separates the depth constraint of the pressure sensor and the horizontal correction of the single beacon, which makes full use of the advantages of the high-precision depth measurement of the pressure sensor (error <0.01%FS), avoids the interference of the uncertainty of acoustic ranging in the vertical direction on the depth estimation, and realizes the high-precision solution of the three-dimensional position.

[0134] Table 1 Comparison of Dead Retrieval Performance between Single Beacon Assistance and Pure Dynamics

[0135]

[0136] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for achieving high-precision underwater integrated navigation by integrating AUV dynamic velocity and acoustic data from a single beacon, characterized in that, include: S1: Real-time calculation of the coupling relationship between AUV propeller speed and motor drive current to obtain the water speed in the carrier coordinate system; S2: By integrating the attitude data provided by the strapdown inertial navigation system (SINS), the surface speed in the carrier coordinate system is decomposed into the navigation coordinate system to obtain the three-dimensional motion speed of the AUV. S3: Based on the three-dimensional motion velocity, perform dead reckoning to obtain a preliminary position estimate of the AUV; S4: A filtering algorithm that resets the starting point is used to periodically constrain the navigation results calculated from dynamic dead reckoning using distance information provided by a single beacon, resulting in the corrected navigation position; the specific implementation process is as follows: S41: Preset acoustic ranging period T acoustic When the AUV sails to the preset ranging time or receives a ranging request triggered by an acoustic beacon, the acoustic ranging process is initiated. S42: The AUV uses an acoustic transducer to measure the slant range at time k by placing a single beacon on the seabed. Z k Simultaneously record the precise position of the beacon in the navigation coordinate system. B = [ x B , y B , z B ] T The distance measurement model is as follows: ， in, ν k Noise for distance measurement; S43: Calculation of dead reckoning position in step S3 P DR (k) To acoustic beacon B geometric distance d DR : ； S44: Calculate the geometric scaling factor α k : ； S45: Perform geometric projection correction on the dead reckoning position based on the scaling factor to obtain the corrected fused position. P fused : ， Among them, if α k > 1 indicates that the dead reckoning distance is underestimated, and the estimated position needs to be expanded outwards; if α k < 1 indicates that the dead reckoning distance is overestimated, and the estimated position needs to be narrowed inward; if α k = 1, then the dead reckoning position is consistent with the distance measurement value, and no correction is required; S46: Corrected position P fused As the new starting point for dead reckoning, reset the dead reckoning parameters: ， S47: Return to step S3 and continue dead reckoning for subsequent moments starting from the corrected position until the next acoustic ranging information is received. Repeat steps S41-S46 to achieve periodic constraint on dynamic dead reckoning error.

2. The method for achieving high-precision underwater integrated navigation by integrating AUV dynamic velocity and acoustic data from a single beacon, as described in claim 1, is characterized in that... The specific implementation process of step S1 is as follows: S11: Establish a quasi-steady-state model of propeller thrust T and torque Q, based on the thrust coefficient K. T (J) and torque coefficient K Q The linear relationship between (J) and the advance coefficient J is used to determine the propeller performance curve: ， ， In the formula, D is the propeller diameter. ρ Let be the density of seawater, n be the propeller speed, T be the propeller thrust, and Q be the propeller torque. J This is the advance coefficient; S12: Collect the drive current of the AUV propulsion motor, and determine the torque Q under the current operating condition based on the correspondence between motor current and torque; S13: Acceleration Coefficient J The geometric relationship for quantifying and controlling the hydrodynamic angle of attack of propeller blades is given by the following formula: ， In the formula, w is the wake fraction, D is the propeller diameter, n is the propeller speed, and v is the vehicle speed. S14: Integrating S11 over a finite operating range, thrust coefficient K T (J) and propeller torque coefficient K Q (J) can be represented as: ， ， In the formula, K T (0) and K Q (0) is the zero flow coefficient determined experimentally. α、β Indicates a dimensionless coefficient; S15: Based on the variables obtained from the above steps, the speed u of the vehicle can be further derived: 。 3. The method for achieving high-precision underwater integrated navigation by integrating AUV dynamic velocity and acoustic data from a single beacon, as described in claim 2, is characterized in that... In step S14: Using AUV to obtain DVL measurements in shallow waters as the true values, the coefficients are obtained using the least squares method. β : ， In the formula, v d The speed measured by DVL v i The velocity is obtained using dynamics.

4. The method for achieving high-precision underwater integrated navigation by integrating AUV dynamic velocity and single beacon acoustic data according to claim 1, characterized in that, The specific implementation process of step S2 is as follows: S21: Acquire attitude data output in real time by the strapdown inertial navigation system (SINS), including roll angle. φ Pitch angle θ and heading angle ψ ; S22: Use the attitude data to obtain the direction cosine matrix, and transform the obtained velocity in the vehicle coordinate system to the navigation coordinate system.

5. The method for achieving high-precision underwater integrated navigation by integrating AUV dynamic velocity and acoustic data from a single beacon, as described in claim 1, is characterized in that... The specific implementation process of step S3 is as follows: S31: Initialize dead reckoning parameters, assuming the initial position at time k=0 is... P 0 The initial position was obtained from GNSS positioning before the AUV entered the water, and the position after the last acoustic calibration was assumed. P last = P 0 The last time the sound school was indexed k last = 0; S32: Define the time series for dead reckoning, with a sampling interval of Δt and time indices of k=1,2,3,...,N; define the time series for acoustic ranging. t j , j=1,2,...,M, where M is the total number of acoustic ranging attempts; S33: Establish a dead reckoning position update formula based on the three-dimensional motion velocity obtained in step S2. V i = [ V E-i , V N-i , V U-i ] T Perform recursion: ， In the formula, P DR (k) Let k be the predicted position based on dead reckoning. P last This is the position after the last sound school was corrected. k last Δt is the index of the last sound correction time. DR The sampling time interval for dynamic velocity; S34: If no new acoustic distance information is received, continue using step S33 to perform dead reckoning and output a preliminary position estimate. P DR (k) As the current navigation location of the AUV; S35: For vertical position estimation, the depth value measured by the pressure sensor is used directly. h(k) replace P DR (k) The celestial component in the middle, namely: 。

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