Dynamic long baseline underwater topographic measurement system based on ins, dvl and rtK buoy array

Abstract

The application discloses a kind of based on INS, DVL and RTK buoy array dynamic long baseline underwater topographic survey system, it is related to computer processing technical field, the system operation includes the following steps: S01, the absolute position of the multiple beacon nodes arranged on the water surface is obtained;S02, through the acoustic communication between the beacon node and underwater vehicle, record the beacon node position and acoustic signal propagation time when communication time, and screen effective ranging data;S03, underwater vehicle is along preset survey line navigation, based on the navigation sensor carried by itself, carries out independent navigation and attitude solution, synchronously gathers underwater topographic data, and with fixed period trigger and acoustic ranging of beacon node, simultaneously obtain sound velocity data and carry out distance correction.The application solves the problem that traditional shipborne depth measuring equipment is limited in operation due to bad sea conditions and navigation difficult area in underwater topographic survey, and greatly enhances the operation ability under complex water conditions.

Description

Technical Field

[0001] This invention relates to the field of computer processing technology, and in particular to a dynamic long baseline underwater topographic surveying system based on INS, DVL and RTK buoy arrays. Background Technology

[0002] Underwater topographic surveying is a fundamental task in many fields, including marine resource development, water conservancy engineering construction, channel dredging, and marine scientific research. Currently, the mainstream underwater topographic surveying methods mainly include shipborne acoustic bathymetry and airborne laser bathymetry. However, both methods have certain limitations. Shipborne acoustic bathymetry relies on a survey vessel as a platform, which is highly dependent on the weather and sea conditions of the operating area. In adverse sea conditions, not only is operation difficult, but the quality of the measurement data is also hard to guarantee. More importantly, in areas with difficult navigation, such as aquaculture areas, reef areas, and shoals, survey vessels cannot enter or face extremely high navigation risks, making these areas measurement blind spots. While airborne laser bathymetry can overcome some of the limitations of ship navigation, its measurement depth is severely limited by water transparency, and it is only applicable to clear nearshore waters and shallow lake areas, thus having a narrow range of applications.

[0003] To address these issues, the industry has begun exploring the use of autonomous underwater vehicles (AUVs) equipped with depth sounding equipment for underwater topographic surveying. Navigation and positioning of the AUV are core technologies determining measurement accuracy. Among common solutions, while pure inertial navigation systems (INS) provide high-frequency, continuous navigation data, their errors accumulate rapidly over time, making them unsuitable for long-duration, high-precision measurements alone. Combining INS with a Doppler log (DVL) can utilize the precise velocity information provided by the DVL to suppress INS drift; however, this approach lacks an absolute position reference, and long-term positioning accuracy still decreases with increasing range, essentially representing relative positioning. Another approach involves introducing a long baseline positioning system (LBL), which provides high-precision absolute position calibration for the AUV by pre-deploying transponder arrays on the seabed. However, the deployment and retrieval of seabed LBL arrays are costly, complex, and time-consuming, and their effective positioning range is limited to a finite area covered by the array, making it difficult to meet the demands for large-scale, high-efficiency, and low-cost underwater topographic surveying.

[0004] Therefore, how to provide an underwater topographic surveying solution that can achieve large-scale, high-precision absolute positioning, overcome the shortcomings of traditional shipborne surveying and seabed LBL technology, and has the advantages of high environmental adaptability and low cost is a technical problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0005] To address the aforementioned technical problems, the present invention employs a dynamic long baseline underwater topographic surveying system based on INS, DVL, and RTK buoy arrays. The system operation includes the following steps:

[0006] S01. Obtain the absolute positions of multiple beacon nodes deployed on the water surface;

[0007] S02. Through acoustic communication between the beacon node and the underwater vehicle, record the beacon node position and acoustic signal propagation time at the time of communication, and filter the effective ranging data.

[0008] S03. The underwater vehicle navigates along the preset survey line, performs autonomous navigation and attitude calculation based on its own navigation sensors, collects underwater terrain data simultaneously, and triggers acoustic ranging with beacon nodes at fixed intervals, while acquiring sound speed data for distance correction.

[0009] S04. Based on the beacon node position and acoustic propagation time in the effective ranging data, the weighted intersection positioning method is used to calculate the discrete absolute position of the underwater vehicle at the communication time, and the discrete absolute position is used as the observation value and input into the combined navigation filter of the underwater vehicle to correct the cumulative error of the navigation sensor in real time and smooth the state, thereby generating continuous, high-frequency navigation and positioning data.

[0010] S05. Perform spatiotemporal registration of the continuous, high-frequency navigation and positioning data with the underwater topographic data, and output underwater topographic results including geographic coordinates and water depth values.

[0011] Preferably, step S01, obtaining the absolute positions of the multiple beacon nodes arranged on the water surface, includes:

[0012] Each surface buoy acquires its own real-time dynamic differential positioning absolute coordinates through an RTK receiver and records the attitude data of each buoy.

[0013] Based on the buoy attitude data, the center position of the first acoustic transponder installed on the corresponding surface buoy is eccentrically corrected to obtain the coordinates of the ranging reference point.

[0014] Preferably, the acoustic communication between the beacon node and the underwater vehicle in step S02 includes periodic data communication between the surface buoy and the underwater vehicle through their respective first and second acoustic transponders, recording the absolute positioning coordinates of the surface buoy and the timestamps of the acoustic signal transmission and reception at the time of communication, and simultaneously evaluating the quality of the acoustic signal and filtering out effective ranging data with a signal-to-noise ratio higher than a preset threshold.

[0015] Preferably, in step S03, the underwater vehicle performs autonomous navigation and attitude calculation based on its own navigation sensors, and simultaneously collects underwater terrain data. This includes the underwater vehicle navigating along a preset survey line, performing autonomous navigation and attitude calculation based on an inertial navigation system and a Doppler log, simultaneously collecting underwater terrain data through a multibeam echo sounder, and triggering acoustic ranging with surface buoys at fixed intervals during navigation, while simultaneously recording the sound speed data measured in real time by a sound speed profiler for distance correction.

[0016] Preferably, step S04, which involves using a weighted intersection positioning method to calculate the discrete absolute position of the underwater vehicle at the time of communication, includes:

[0017] S41. Construct the spherical observation equation, and let the position of the underwater vehicle be P. u =(x,y,z), and the center position of the i-th buoy transducer is P. i =(x i ,y i ,z i The measured distance is R. i= c⋅Δt i Where: c is the equivalent sound velocity after sound velocity profile correction, Δt i Let be the acoustic signal propagation time, and the equation is: ;

[0018] S42. Perform a Taylor expansion of the nonlinear equation at the approximate location P0 to construct the linearized error equation: v i =a i δx+b i δy+c i δz−(R i -R 0i ), where: (a i ,b i ,c i R is the direction cosine. 0i The calculated distance from the approximate location to the buoy;

[0019] S43. Construct a weight matrix P based on the signal-to-noise ratio and real-time geometric accuracy factor of each surface buoy. Iteratively solve for the position correction (δx, δy, δz) using the weighted least squares method, and update the underwater vehicle's position until the correction is less than a set threshold to obtain the absolute position P. u .

[0020] Preferably, in step S43, the diagonal element p of the weight matrix P is... i Based on the ranging noise variance of the i-th buoy The geometric precision factor is determined in combination, where: Based on real-time signal quality estimation, the geometric precision factor is calculated in real-time from the relative geometric relationship between the buoy and the underwater vehicle. The weighting formula is as follows: This suppresses the influence of buoys with poor geometry or high ranging noise during the solution process, where GDOP is the geometric accuracy factor.

[0021] Preferably, in step S04, inputting the discrete absolute position as an observation value into the combined navigation filter of the underwater vehicle includes using the absolute position calculated by the weighted spherical intersection as the observation value z of the extended Kalman filter. k The state vector of the extended Kalman filter includes three-dimensional position, three-dimensional velocity, three-dimensional attitude, three-axis zero bias of the gyroscope, three-axis zero bias of the accelerometer, and DVL scale factor error;

[0022] The time update of the extended Kalman filter is based on INS mechanics orchestration and DVL velocity information. The measurement update uses the difference between the calculated position and the predicted position as information, and dynamically adjusts the observation noise covariance matrix R through an adaptive Kalman filter algorithm. k The process noise covariance matrix Q k To achieve data fusion.

[0023] Preferably, in the adaptive Kalman filter, the observation noise covariance matrix R k Based on the real-time estimation of the post-hoc unit weight variance of the weighted spherical intersection solution at the current epoch, when the internal consistency accuracy of the solution is high, R... k Smaller values ​​enhance confidence in external observations; when the solution results are affected by abnormal interference, leading to a decrease in internal consistency accuracy, R... k Adaptive scaling reduces the contamination of navigation results by abnormal observations, while utilizing the short-term characteristics of INS to maintain trajectory smoothness.

[0024] Preferably, in step S05, the continuous, high-frequency underwater vehicle navigation and positioning data is time-aligned with each transmission ping of the multibeam echo sounder. The navigation position is interpolated to the acquisition time of each echo sounder point using an interpolation method. At the same time, the beam footprint is corrected for refraction using real-time sound velocity profile data to generate the geodetic coordinates and water depth value of each echo sounder point, thus forming an underwater digital terrain model.

[0025] The present invention has at least the following beneficial effects:

[0026] 1. By using an underwater vehicle as a measurement platform, autonomous measurements can be taken in complex waters such as aquaculture areas and reef areas that are inaccessible or pose extremely high risks to traditional shipborne equipment. This significantly reduces dependence on sea conditions and weather, greatly improving the environmental adaptability and data acquisition flexibility of underwater topographic surveying. Simultaneously, the use of acoustic depth sounding overcomes the limitations of airborne laser depth sounding on water transparency.

[0027] 2. By combining RTK buoy arrays with the long baseline positioning principle, surface buoys are used as dynamic, high-precision absolute position beacons, replacing traditional seabed LBL arrays. This not only eliminates the high costs and complex procedures of deploying and recovering seabed arrays, but also allows the positioning system to be flexibly moved and rapidly deployed along the survey area.

[0028] 3. By employing a tightly coupled data fusion strategy, the advantages of each sensor are fully utilized. The INS / DVL combination provides high-frequency, smooth relative positioning information, compensating for the low frequency and susceptibility to interference inherent in pure underwater acoustic positioning. Simultaneously, the absolute position calculated by the RTK buoy array is used as the observation value to periodically calibrate and correct the accumulated errors of the INS / DVL system. This complementary approach effectively mitigates the limitations of a single sensor, ultimately outputting continuous, smooth, high-precision, and highly reliable underwater vehicle trajectories, laying a solid foundation for high-quality underwater topographic mapping. Attached Figure Description

[0029] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying 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.

[0030] Figure 1 This invention provides an implementation diagram for Embodiment 1.

[0031] Figure 2 The flowchart provided is for Embodiment 2 of the present invention. Detailed Implementation

[0032] 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, and 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.

[0033] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or devices.

[0034] This embodiment provides a dynamic long baseline underwater topographic measurement system based on INS, DVL, and RTK buoy arrays, such as... Figure 1 As shown, the system includes a buoy module matrix and an underwater vehicle, detailed below:

[0035] The buoy module matrix consists of at least three surface buoys deployed on the water surface of the survey area. Each surface buoy is equipped with at least an RTK receiver and a first acoustic transponder, wherein:

[0036] RTK receivers are used to acquire the absolute positioning coordinates of surface buoys in real time;

[0037] The first acoustic transponder is used for acoustic communication with underwater vehicles;

[0038] An underwater vehicle, which includes at least:

[0039] An inertial navigation system is used to provide attitude and acceleration information for underwater vehicles;

[0040] Doppler logs are used to provide speed information for underwater vehicles through bottom tracking; depth sounders are used to collect underwater topographic data.

[0041] A sound velocity measurement sensor is used to collect real-time sound velocity data to correct for water depth.

[0042] The second acoustic transponder is used to transmit and receive acoustic signals with the first acoustic transponder of the surface buoy in order to measure the distance between the underwater vehicle and each surface buoy.

[0043] Specifically, all surface buoys and underwater vehicles undergo strict time synchronization via GPS PPS pulses to ensure consistent time references across all devices. Subsequently, at least three surface buoys are deployed in the survey area, roughly surrounding the designated operational zone. The buoys can be maintained in relative stability using anchor chains or precisely positioned using dynamic positioning. After deployment, each buoy acquires its absolute coordinates in real time via an RTK receiver. Combined with data from attitude sensors on the buoy, a dynamic coordinate transformation is performed on the eccentric vector from the RTK antenna phase center to the acoustic transducer center, thus obtaining the instantaneous precise position of the acoustic transducer center.

[0044] After entering the water, the underwater vehicle navigates autonomously along a pre-set survey line. During navigation, the inertial navigation system and Doppler logger collect and fuse data at high frequencies (e.g., 100Hz), providing continuous information on the underwater vehicle's position, velocity, and attitude. Simultaneously, a multibeam echo sounder emits sound beams at a certain frequency (e.g., 20-50Hz) to collect underwater topographic data, and a sound velocity sensor measures the water's sound velocity profile in real time, which is used to correct the depth and acoustic ranging values.

[0045] During navigation, the underwater vehicle triggers acoustic positioning measurements with surface buoys at fixed intervals (e.g., once per second). Specifically, the underwater vehicle's second acoustic transponder transmits an interrogation signal; the first acoustic transponders of each surface buoy receive this signal and record its arrival time. Simultaneously, each buoy transmits its current RTK position and signal arrival timestamp back to the underwater vehicle acoustically, or via a wireless communication module to a shore-based control center, which then transmits the data to the underwater vehicle. Upon receiving the data from each buoy, the underwater vehicle calculates the one-way propagation time of the acoustic signal by combining it with the recorded signal transmission times, and converts it into a distance observation value using real-time sound speed data.

[0046] After obtaining distance observations from at least three buoys and their corresponding buoy positions, the underwater vehicle (or shore-based processing system) uses the weighted spherical intersection method to calculate the absolute position at the current moment. During the calculation process, the weights of each buoy are dynamically adjusted based on the signal-to-noise ratio of each buoy signal and the geometrical precision factor (GDOP) at the current moment. This suppresses the influence of buoys with poor geometry or high ranging noise on the calculation results, thereby obtaining a highly reliable discrete absolute position.

[0047] Because acoustic positioning operates at a low frequency (typically 0.5-2Hz), while the output frequencies of inertial navigation systems and Doppler logs are higher (50-200Hz), the system employs an extended Kalman filter to deeply fuse the two. The discrete absolute position calculated by the weighted spherical intersection is used as the observation value and input into a combined navigation filter centered on INS / DVL. The filter state vector includes three-dimensional position, three-dimensional velocity, three-dimensional attitude, gyroscope bias, accelerometer bias, and DVL scale factor error, etc. Through time updates (based on INS mechanical orchestration and DVL velocity information) and measurement updates (based on acoustic positioning observations), real-time correction of INS / DVL accumulated errors and state smoothing are achieved, ultimately outputting continuous, high-frequency underwater vehicle navigation and positioning data.

[0048] After data acquisition is completed, the post-processing stage begins. The high-frequency navigation and positioning data is time-aligned with each transmission ping of the multibeam echo sounder. Interpolation is used to accurately match the navigation position to the acquisition time of each echo sounder point. Simultaneously, real-time sound velocity profile data is used to correct the refraction of the beam footprints. Finally, the geodetic coordinates and water depth values ​​of each echo sounder point are generated, forming a standard format underwater digital terrain model.

[0049] Secondly, the multiple surface buoys that make up the buoy module matrix are deployed by anchor chain mooring and the attitude data is measured in real time based on the attitude sensors mounted on the buoys. The attitude data includes at least the current roll, pitch and bow angle of the surface buoy.

[0050] Based on the above attitude data, a dynamic coordinate transformation is performed on the eccentric vector from the phase center of the RTK antenna on the water surface buoy to the center of the first acoustic transponder to obtain the instantaneous position of the center of the first acoustic transponder.

[0051] Specifically, during the buoy manufacturing or installation phase, a fixed eccentricity vector of the RTK antenna phase center relative to the acoustic transponder center in the buoy carrier coordinate system is determined through precise measurement. The carrier coordinate system is typically defined as follows: with the buoy's center of gravity or transponder center as the origin, the X-axis points towards the buoy's bow, the Y-axis is perpendicular to the X-axis and points towards the starboard side, and the Z-axis is perpendicular to the XOY plane and points upwards. This fixed eccentricity vector can be obtained using measuring tools such as a total station. When the buoy is operating on the surface, attitude sensors collect the buoy's roll, pitch, and bow angles in real time at higher frequencies (e.g., 10Hz-20Hz). These angles describe the rotational relationship of the buoy carrier coordinate system relative to the local horizontal coordinate system (NE-T or NE-UH). The system uses these attitude angles to construct a rotation matrix, transforming the fixed eccentricity vector in the carrier coordinate system to the local horizontal coordinate system, thus obtaining the dynamically changing components of the eccentricity vector in the local horizontal coordinate system. Finally, by subtracting or adding the dynamic eccentricity vector component to the phase center coordinates (usually geodetic coordinates) measured by the RTK antenna, the precise geographic coordinates of the acoustic transponder center at the current moment can be calculated. For example, assuming the phase center of a buoy's RTK antenna is located 2 meters directly above the transponder center, the transponder center coordinates can be obtained directly by subtracting 2 meters vertically from the antenna coordinates without swaying. However, when the buoy rolls 10 degrees, the line connecting the antenna and the transponder is no longer perpendicular, and a simple vertical subtraction will produce a horizontal positional deviation. By measuring the 10-degree roll angle using an attitude sensor, the system can calculate the projection components of the eccentricity vector in the horizontal and vertical directions, thereby accurately correcting the antenna coordinates and obtaining the accurate position of the transponder center in the geodetic coordinate system.

[0052] Furthermore, in combination Figure 2 As shown, the system described above is configured to perform the following steps:

[0053] S01. Obtain the absolute positions of multiple beacon nodes deployed on the water surface.

[0054] Furthermore, in dynamic long-baseline underwater topographic surveying systems based on INS, DVL, and RTK buoy arrays, surface buoys serve as dynamic reference stations, with their core function being to provide high-precision absolute position references for underwater vehicles. However, surface buoys are not stationary in actual operating environments; they are subject to complex six-degree-of-freedom motion due to wind, waves, and currents, including roll, pitch, heading, and heave. Simultaneously, the phase center of the RTK receiver antenna on the buoy and the transducer center of the acoustic transponder used for underwater acoustic ranging have a fixed spatial offset in their physical structure and do not coincide. Therefore, directly using the coordinates measured by the RTK antenna phase center as the ranging reference point will introduce systematic positioning errors due to changes in buoy attitude. To solve this problem, this invention employs eccentricity correction technology. Its core lies in accurately recalculating the high-precision absolute coordinates of the RTK antenna phase center to the acoustic transducer center through real-time attitude measurement and spatial coordinate transformation, thereby obtaining the coordinates of the ranging reference point that truly participates in distance calculation.

[0055] In practice, each surface buoy is equipped with an RTK receiver, a high-precision attitude sensor (such as a fiber optic gyroscope or inertial measurement unit), and an acoustic transponder. The RTK receiver outputs the geodetic coordinates (usually latitude and longitude or projected plane coordinates in the WGS-84 coordinate system) of the antenna phase center at a fixed frequency (e.g., 10Hz to 20Hz). The attitude sensor simultaneously measures the roll, pitch, and heading angles of the buoy body in real time. Before the buoy leaves the factory or is deployed, the three-dimensional eccentricity vector (Δx, Δy, Δz) of the RTK antenna phase center relative to the acoustic transducer center in the buoy carrier coordinate system is precisely measured and calibrated. This eccentricity vector is a fixed value that describes the relative positional relationship between the two on the rigid structure of the buoy.

[0056] As the buoy moves across the water, the attitude sensor outputs real-time attitude angles at each moment. The system uses these attitude angles to construct a rotation matrix from the carrier coordinate system to the local horizontal coordinate system. This fixed eccentric vector is then transformed in real-time using the rotation matrix to calculate the dynamic projection components of the eccentric vector on each axis of the local horizontal coordinate system, caused by the buoy's tilt and rotation. Subsequently, by adding these dynamic projection components to the real-time geodetic coordinates of the RTK antenna phase center, the instantaneous three-dimensional coordinates of the acoustic transducer center in the geodetic coordinate system can be accurately calculated. These coordinates serve as the ranging reference point coordinates for acoustic ranging calculations.

[0057] S02. Through acoustic communication between the beacon node and the underwater vehicle, record the beacon node position and acoustic signal propagation time at the moment of communication, and filter the effective ranging data.

[0058] Specifically, a pre-defined periodic question-and-answer communication mechanism is employed, with a typical period of 1 second (i.e., 1Hz). The underwater vehicle, acting as the communication master node, periodically transmits acoustic interrogation signals under the timing control of its internal high-stability crystal oscillator. These signals propagate through an underwater acoustic channel and are received by multiple surface buoys within the operational area. Upon detecting a valid interrogation signal, the first acoustic transponder of each buoy records the precise time of arrival (TOA), based on the buoy's local GPS PPS pulse, ensuring a timestamp accuracy down to the microsecond level. Subsequently, the buoy packages the recorded TOA information along with its RTK absolute positioning coordinates at that moment (after attitude eccentricity correction) and transmits it back to the underwater vehicle as an acknowledgment signal via the acoustic link. The underwater vehicle's second acoustic transponder receives the acknowledgment signal, records the arrival time, and thus obtains the one-way propagation time Δt between the underwater vehicle and each buoy. i Meanwhile, the underwater vehicle records its own inertial navigation system (INS) predicted position and attitude data at the moment of launch, providing prior information for subsequent data fusion.

[0059] S03. The underwater vehicle navigates along the preset survey line, performs autonomous navigation and attitude calculation based on its own navigation sensors, collects underwater terrain data simultaneously, and triggers acoustic ranging with beacon nodes at fixed intervals, while acquiring sound speed data for distance correction.

[0060] Specifically, after the underwater vehicle is launched, its onboard inertial navigation system (INS) and Doppler log (DVL) are activated. The INS measures the vehicle's three-axis angular velocity and linear acceleration in real time using gyroscopes and accelerometers, while the DVL emits sound waves into the seabed and receives the echoes, calculating the vehicle's velocity relative to the seabed using the Doppler frequency shift effect. The data from the INS and DVL are fused using a Kalman filter. The INS provides high-frequency attitude and acceleration information, while the DVL provides stable velocity observations to suppress long-term drift of the INS, thus forming a continuous and reliable autonomous navigation reference for real-time calculation of the vehicle's position, velocity, and attitude. Simultaneously, a multibeam echo sounder emits fan-shaped sound waves into the seabed at a specific frequency, receiving the echoes to form a series of depth sounding points and collecting underwater topographic data. This data is strictly synchronized with the navigation timestamps calculated by the INS / DVL. During navigation, the underwater vehicle actively triggers acoustic positioning communication at a preset fixed period (e.g., once per second): the acoustic transponder on the vehicle emits an interrogation signal, and each buoy in the surface buoy array receives the signal, records its arrival time, and transmits its RTK coordinates and that time information back acoustically; after receiving the transmitted data, the vehicle records the transmission and reception times of the signal, thus obtaining the one-way propagation time of the sound wave underwater. Simultaneously, the vehicle's onboard sound velocity profiler measures the vertical profile of the sound velocity in the water in real time, performing layer-by-layer integral correction on the ranging value based on the actual sound velocity along the propagation time path to eliminate distance errors caused by changes in sound velocity and ensure the accuracy of each acoustic ranging result. Throughout the process, continuous INS / DVL navigation, multibeam terrain acquisition, and periodic acoustic positioning proceed in parallel, with all data bearing a unified time stamp, providing a complete foundation for subsequent positioning calculations and data fusion.

[0061] S04. Based on the beacon node position and acoustic propagation time in the effective ranging data, the weighted intersection positioning method is used to calculate the discrete absolute position of the underwater vehicle at the communication time. The discrete absolute position is then used as an observation value and input into the underwater vehicle's integrated navigation filter to correct the cumulative error of the navigation sensor in real time and smooth the state, generating continuous, high-frequency navigation and positioning data.

[0062] Furthermore, the weighted spherical intersection method is used to solve for the discrete absolute position of the underwater vehicle, including:

[0063] S41. Construct the spherical observation equation, and let the position of the underwater vehicle be P. u =(x,y,z), and the center position of the i-th buoy transducer is P. i =(x i ,y i ,z i The measured distance is R. i= c⋅Δt iWhere: c is the equivalent sound velocity after sound velocity profile correction, Δt i Let be the acoustic signal propagation time, and the equation is: ;

[0064] S42. Perform a Taylor expansion of the nonlinear equation at the approximate location P0 to construct the linearized error equation: v i =a i δx+b i δy+c i δz−(R i -R 0i ), where: (a i ,b i ,c i R is the direction cosine. 0i The calculated distance from the approximate location to the buoy;

[0065] S43. Construct a weight matrix P based on the signal-to-noise ratio and real-time geometric accuracy factor of each surface buoy. Iteratively solve for the position correction (δx, δy, δz) using the weighted least squares method, and update the underwater vehicle's position until the correction is less than a set threshold to obtain the absolute position P. u .

[0066] The underwater vehicle is equipped with an inertial navigation system, a Doppler log, a multibeam echo sounder, a sound velocity sensor, and a depth gauge, among which:

[0067] Doppler odometers provide high-precision velocity information through bottom-tracking and assist inertial navigation systems in suppressing drift;

[0068] The inertial navigation system provides high-frequency attitude and acceleration information, the multibeam echo sounder is used to collect wide-swath underwater topographic data, the sound velocity measurement sensor acquires sound velocity profiles in real time to correct the depth readings, and the depth gauge provides damping information for the vertical channel of the inertial navigation system.

[0069] Secondly, in step S43, the diagonal element p of the weight matrix P i Based on the ranging noise variance of the i-th buoy The geometric precision factor is determined in combination, where: Based on real-time signal quality estimation, the geometric precision factor is calculated in real-time from the relative geometric relationship between the buoy and the underwater vehicle. The weighting formula is as follows: This suppresses the influence of buoys with poor geometry or high ranging noise during the solution process, where GDOP is the geometric accuracy factor.

[0070] Furthermore, the absolute position calculated from the weighted spherical intersection is used as the observation value z of the extended Kalman filter. kThe state vector of the extended Kalman filter includes three-dimensional position, three-dimensional velocity, three-dimensional attitude, three-axis zero bias of the gyroscope, three-axis zero bias of the accelerometer, and DVL scale factor error;

[0071] The extended Kalman filter's time update is based on INS mechanics orchestration and DVL velocity information. The measurement update uses the difference between the calculated position and the predicted position as information, and dynamically adjusts the observation noise covariance matrix R through an adaptive Kalman filter algorithm. k The process noise covariance matrix Q k To achieve data fusion.

[0072] Secondly, in adaptive Kalman filtering, the observation noise covariance matrix R k Based on the real-time estimation of the post-hoc unit weight variance of the weighted spherical intersection solution at the current epoch, when the internal consistency accuracy of the solution is high, R... k Smaller values ​​enhance confidence in external observations; when the solution results are affected by abnormal interference, leading to a decrease in internal consistency accuracy, R... k Adaptive scaling reduces the contamination of navigation results by abnormal observations, while utilizing the short-term characteristics of INS to maintain trajectory smoothness.

[0073] Specifically, construct the spherical observation equation.

[0074] Let the position of the underwater vehicle be P. u =(x,y,z), where the position of the i-th buoy is P. i =(x i ,y i ,z i The distance R between the buoy and the underwater vehicle was measured. i .

[0075] The equation is: ,

[0076] Expanded to: ;

[0077] Where: c is the average sound velocity after sound velocity profile correction, Δt i The acoustic signal propagation time is denoted as .

[0078] Second stage: Linearization of equations

[0079] Since the above equations are nonlinear, they are usually linearized using Taylor series expansion and then solved iteratively.

[0080] Let the approximate coordinates of the underwater vehicle be P0=(x0,y0,z0). Expanding the equations at the approximate points yields the error equation:

[0081] ;

[0082] in:

[0083] For residuals;

[0084] To calculate the distance, i.e., the distance from the initial value P0 to the buoy P. i distance, .

[0085] This is the difference between the observed distance and the calculated distance.

[0086] The direction cosine represents the component of the unit vector pointing from the buoy to the underwater vehicle on each axis.

[0087] δx, δy, δz: Coordinate correction values.

[0088] Least squares solution and iterative update

[0089] Since there are more than three buoys, an overdetermined system of equations can be established. The coordinates of the underwater vehicle can be calculated using the least squares method. For i buoys, the matrix equation is constructed as: V = AX - L, which expands to:

[0090] ;

[0091] V: Residual vector.

[0092] A: Determined by geometric configuration, composed of direction cosines.

[0093] X: [δx,δy,δz] T .

[0094] L: Distance residual (observed distance minus calculated distance).

[0095] To reflect the reliability of different buoy measurement values, a weight matrix P is introduced (the weights are usually inversely proportional to the ranging error and related to the GDOP value (geometric precision factor)). The least squares method is used to solve the following formula:

[0096] ;

[0097] Where: P is the weight matrix (weighted according to the ranging accuracy).

[0098] After solving for X, update the approximate coordinates: .

[0099] Repeat the above process until δx, δy, δz are less than the set threshold (e.g., 0.01 meters).

[0100] Data Fusion: INS / DVL-Assisted Kalman Filtering

[0101] The underwater vehicle coordinates obtained by the above calculation are discrete (e.g., once per second) and are easily affected by multipath interference and jumps. In order to obtain the best results, the calculated positions are used as observations and input into the Kalman filter of INS / DVL for data fusion.

[0102] Phase 1: State Prediction

[0103] Input: INS gyroscope angular velocity ω, accelerometer specific force f.

[0104] formula:

[0105] (Predicted state)

[0106] (Predicting and estimating the covariance matrix).

[0107] Parameter description:

[0108] : This represents the predicted observation value for time k based on time k-1, i.e., the current predicted value (including predicted position, velocity, and attitude), which comes from the real-time output of INS.

[0109] : State transition matrix, constructed based on the laws of physical motion.

[0110] : Control input matrix, can be ignored.

[0111] : Control input vector, the velocity increment (acceleration f, angular velocity ω) provided by DVL.

[0112] Process noise covariance matrix, describing the random error of INS sensors (gyroscopes, accelerators), factory calibrated.

[0113] Output: A high-frequency, smooth trajectory that drifts over time.

[0114] Phase Two: Measurement Update

[0115] Input: The absolute position P calculated in step S042 u As observed value z k .

[0116] According to Kalman filtering, the update step first needs to calculate... Three quantities, and then use these three quantities to update the variable. and .

[0117] The formula is:

[0118]

[0119]

[0120]

[0121]

[0122]

[0123] The formula can be simplified as follows:

[0124] ;

[0125] Parameter description:

[0126] The optimal estimated state at time k.

[0127] : Observation vector, i.e., P u =[x,y,z] T .

[0128] The observation matrix is ​​a simple extraction matrix that extracts the positions from the state vector.

[0129] Observational noise variance is a statistical value reflecting positioning accuracy. (The specific value depends on: sound velocity profile error, multipath effect, and RTK positioning accuracy).

[0130] Kalman gain, representing the dynamic weighting coefficient, determines whether the predicted value or the observed value is more trusted.

[0131] If R k Very small (P) u (The positioning is very accurate), then K k As it grows larger, the system corrects the position of INS and moves it to P. u To move closer.

[0132] If R k Very large (P) u (position jump), then K k As the value decreases, the system trusts the INS prediction and filters out P. u Noise.

[0133] Phase 3: Output of Results

[0134] After the above operations, the final result value is obtained. (Optimal estimated state), this is a state after P uAbsolute position calibration and high-precision, high-frequency trajectory coordinates smoothed by INS / DVL. Finally, combined with bathymetry data and aligned with timestamps, underwater topographic coordinate data can be obtained.

[0135] S05. Perform spatiotemporal registration of continuous, high-frequency navigation and positioning data with underwater topographic data, and output underwater topographic results including geographic coordinates and water depth values.

[0136] Furthermore, in step S05, the continuous, high-frequency underwater vehicle navigation and positioning data is time-aligned with each transmission ping of the multibeam echo sounder. The navigation position is interpolated to the acquisition time of each echo sounder point using an interpolation method. At the same time, the beam footprint is corrected for refraction using real-time sound velocity profile data to generate the geodetic coordinates and water depth value of each echo sounder point, forming an underwater digital terrain model.

[0137] Specifically, since the navigation and positioning data output in step S04 is typically a high-frequency continuous trajectory above 50Hz, and each transmission ping (i.e., each acoustic wave transmission-reception cycle) of the multibeam echo sounder and its contained dozens to hundreds of beam footprints have independent acquisition times, the time bases of both must be strictly unified. Because the system has already performed sub-microsecond time synchronization of all devices using GPS PPS pulses in step S01, both the navigation data and the depth sounding data have timestamps accurate to the microsecond level. Based on this, for each transmission ping of the multibeam echo sounder, its precise transmission time t is extracted. ping Then search for t in the navigation and positioning data sequence. ping The vehicle's position at any given time. Since navigation data is a discrete time series, t... ping The exact moment may not fall precisely on a specific navigation data point, therefore interpolation algorithms are required for calculation. In practice, cubic spline interpolation or linear interpolation methods are typically used, utilizing t... ping Several navigation data points before and after time (e.g., t) ping -0.02 seconds and t ping (Position, velocity, and attitude information corresponding to +0.02 seconds), interpolation is used to calculate t. ping Precise position P of the underwater vehicle at any given time ping And attitude angle. For example, if the navigation data update rate is 100Hz (i.e., one point every 0.01 seconds) and the multibeam transmission frequency is 30Hz (i.e., once every 0.033 seconds), then for t ping At the time t=123456.789, cubic spline interpolation can be performed on adjacent navigation points such as t=123456.78, 123456.79, 123456.80, and 123456.81 to obtain the position coordinates at that time.

[0138] Secondly, regarding the calculation of a single beam footprint position, each transmission ping of a multibeam echo sounder generates a series of beams distributed perpendicular to the flight path, each beam corresponding to a specific transmission angle and round-trip propagation time. In obtaining t ping Precise position P of the spacecraft ping After determining the attitude (roll, pitch, bow), it is necessary to combine the eccentricity vector between the transducer and the navigation center in the hull coordinate system (obtained through cabin calibration in step one) to P. ping The coordinates are transformed to the center position of the transducer's emitting surface. Then, based on the emission angle of each beam (after attitude correction) and the round-trip propagation time, combined with the sound velocity profile data at that moment, the position vector of each beam footprint relative to the transducer is calculated using a ray tracking algorithm. Finally, this vector is transformed to the geodetic coordinate system to obtain the planar coordinates of each sounding point.

[0139] Third, sound velocity refraction correction technology is crucial for ensuring accurate depth point positioning. Because the speed of sound in seawater varies with temperature, salinity, and pressure, the sound wave propagation path is not straight but curves according to Snell's law. Using equivalent sound velocity for linear calculations would lead to systematic deviations in the depth and position of the sounding point. Therefore, in step S03, the system acquires real-time sound velocity profile data (i.e., the curve showing the change in sound velocity with depth) of the measurement area using a sound velocity measurement sensor. When calculating the beam footprint, a layered sound ray tracking method is used: the water column is divided into several layers of equal thickness, with the sound velocity within each layer considered constant or linearly changing. Based on the initial incident angle of the beam and the sound velocity value of each layer, the propagation path and horizontal displacement of the sound wave are calculated layer by layer. Finally, these are accumulated to obtain the true propagation trajectory of the beam from the transducer to the seabed, thereby accurately determining the horizontal position and vertical depth of the beam footprint. For example, assuming the initial incident angle of a certain beam is 30 degrees, in a gradient environment with a surface sound velocity of 1500 m / s and a deep sound velocity of 1480 m / s, if a linear calculation is performed using an equivalent sound velocity of 1500 m / s, the position of the sounding point may be calculated to the left by 0.5 meters and the depth to be shallower by 0.2 meters. However, by using layered sound ray tracking, this refraction effect can be accurately compensated, allowing the sounding point to return to its true position.

[0140] Finally, through the above steps, the system assigns precise geodetic coordinates (such as X and Y values ​​in the CGCS2000 coordinate system) and water depth values ​​(depths corrected for tides and sound speed) to each beam echo sounder point, forming a standard point cloud data format. The collection of all sounding points constitutes a digital terrain model covering the survey area. For example, in an actual measurement, an underwater vehicle travels along the survey line at a speed of 3 knots, and the multibeam echo sounder transmits a ping every 0.5 seconds, with each ping containing 256 beams covering a 120-degree opening angle. Through the processing in step S05, a high-density point cloud containing tens of millions of echo sounders is finally generated, with each point having centimeter-level plane coordinates and decimeter-level water depth values, which can be directly used to generate contour maps, 3D seabed topographic maps, or as basic data for projects such as port and waterway dredging and subsea pipeline laying.

[0141] Example 2

[0142] This invention provides a non-transitory computer-readable storage medium storing at least one instruction or at least one program segment, which is loaded and executed by a processor to implement the following steps:

[0143] Obtain the absolute positions of multiple beacon nodes deployed on the water surface;

[0144] By using acoustic communication between the beacon node and the underwater vehicle, the location of the beacon node and the propagation time of the acoustic signal at the moment of communication are recorded, and valid ranging data are filtered out.

[0145] The underwater vehicle navigates along a pre-set survey line, performs autonomous navigation and attitude calculation based on its own navigation sensors, collects underwater terrain data simultaneously, and triggers acoustic ranging with beacon nodes at fixed intervals, while acquiring sound speed data for distance correction.

[0146] Based on the beacon node position and acoustic propagation time in the effective ranging data, the weighted intersection positioning method is used to calculate the discrete absolute position of the underwater vehicle at the communication time. The discrete absolute position is then used as an observation value and input into the underwater vehicle's integrated navigation filter to correct the cumulative error of the navigation sensor in real time and smooth the state, generating continuous, high-frequency navigation and positioning data.

[0147] Continuous, high-frequency navigation and positioning data is spatiotemporally registered with underwater topographic data to output underwater topographic results containing geographic coordinates and water depth values.

[0148] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory 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), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0149] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0150] Example 3

[0151] This invention provides an electronic device, including a processor and a memory, wherein the memory stores at least one instruction or at least one program segment, and the at least one instruction or the at least one program segment is loaded and executed by the processor to implement the following steps:

[0152] Obtain the absolute positions of multiple beacon nodes deployed on the water surface;

[0153] By using acoustic communication between the beacon node and the underwater vehicle, the location of the beacon node and the propagation time of the acoustic signal at the moment of communication are recorded, and valid ranging data are filtered out.

[0154] The underwater vehicle navigates along a pre-set survey line, performs autonomous navigation and attitude calculation based on its own navigation sensors, collects underwater terrain data simultaneously, and triggers acoustic ranging with beacon nodes at fixed intervals, while acquiring sound speed data for distance correction.

[0155] Based on the beacon node position and acoustic propagation time in the effective ranging data, the weighted intersection positioning method is used to calculate the discrete absolute position of the underwater vehicle at the communication time. The discrete absolute position is then used as an observation value and input into the underwater vehicle's integrated navigation filter to correct the cumulative error of the navigation sensor in real time and smooth the state, generating continuous, high-frequency navigation and positioning data.

[0156] Continuous, high-frequency navigation and positioning data is spatiotemporally registered with underwater topographic data to output underwater topographic results containing geographic coordinates and water depth values.

[0157] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A dynamic long baseline underwater topographic surveying system based on INS, DVL, and RTK buoy arrays, characterized in that, The system operation includes the following steps: S01. Obtain the absolute positions of multiple beacon nodes deployed on the water surface; S02. Through acoustic communication between the beacon node and the underwater vehicle, record the beacon node position and acoustic signal propagation time at the time of communication, and filter the effective ranging data. S03. The underwater vehicle navigates along the preset survey line, performs autonomous navigation and attitude calculation based on its own navigation sensors, collects underwater terrain data simultaneously, and triggers acoustic ranging with beacon nodes at fixed intervals, while acquiring sound speed data for distance correction. S04. Based on the beacon node position and acoustic propagation time in the effective ranging data, the weighted intersection positioning method is used to calculate the discrete absolute position of the underwater vehicle at the communication time, and the discrete absolute position is used as the observation value and input into the combined navigation filter of the underwater vehicle to correct the cumulative error of the navigation sensor in real time and smooth the state, thereby generating continuous, high-frequency navigation and positioning data. S05. Perform spatiotemporal registration of the continuous, high-frequency navigation and positioning data with the underwater topographic data, and output underwater topographic results including geographic coordinates and water depth values.

2. The dynamic long baseline underwater topographic surveying system based on INS, DVL, and RTK buoy arrays according to claim 1, characterized in that, Step S01, obtaining the absolute positions of the multiple beacon nodes deployed on the water surface, includes: Each surface buoy acquires its own real-time dynamic differential positioning absolute coordinates through an RTK receiver and records the attitude data of each buoy. Based on the buoy attitude data, the center position of the first acoustic transponder installed on the corresponding surface buoy is eccentrically corrected to obtain the coordinates of the ranging reference point.

3. The dynamic long baseline underwater topographic surveying system based on INS, DVL, and RTK buoy arrays according to claim 1, characterized in that, The acoustic communication between the beacon node and the underwater vehicle in step S02 includes periodic data communication between the surface buoy and the underwater vehicle through their respective first and second acoustic transponders. The absolute positioning coordinates of the surface buoy and the timestamps of the acoustic signal transmission and reception are recorded at the time of communication. At the same time, the acoustic signal quality is evaluated, and effective ranging data with a signal-to-noise ratio higher than a preset threshold are selected.

4. The dynamic long baseline underwater topographic surveying system based on INS, DVL, and RTK buoy arrays according to claim 1, characterized in that, In step S03, the underwater vehicle performs autonomous navigation and attitude calculation based on its onboard navigation sensors, and simultaneously collects underwater terrain data. This includes the underwater vehicle navigating along a preset survey line, performing autonomous navigation and attitude calculation based on an inertial navigation system and a Doppler log, simultaneously collecting underwater terrain data through a multibeam echo sounder, and triggering acoustic ranging with surface buoys at fixed intervals during navigation, while simultaneously recording the sound speed data measured in real time by a sound speed profiler for distance correction.

5. The dynamic long baseline underwater topographic surveying system based on INS, DVL, and RTK buoy arrays according to claim 1, characterized in that, The step S04, which involves using a weighted intersection positioning method to calculate the discrete absolute position of the underwater vehicle at the time of communication, includes: S41. Construct the spherical observation equation, and let the position of the underwater vehicle be P. u =(x,y,z), and the center position of the i-th buoy transducer is P. i =(x i ,y i ,z i The measured distance is Where: c is the equivalent speed of sound after correction for the sound velocity profile. Let be the acoustic signal propagation time, and the equation is: ; S42. Perform a Taylor expansion of the nonlinear equation at the approximate location P0 to construct the linearized error equation: , where: (a i ,b i ,c i R is the direction cosine. 0i The calculated distance from the approximate location to the buoy; S43. Construct a weight matrix P based on the signal-to-noise ratio and real-time geometric accuracy factor of each surface buoy. Iteratively solve for the position correction (δx, δy, δz) using the weighted least squares method, and update the underwater vehicle's position until the correction is less than a set threshold to obtain the absolute position P. u .

6. The dynamic long baseline underwater topographic surveying system based on INS, DVL, and RTK buoy arrays according to claim 5, characterized in that, In step S43, the diagonal element p of the weight matrix P i Based on the ranging noise variance of the i-th buoy The geometric precision factor is determined in combination, where: Based on real-time signal quality estimation, the geometric precision factor is calculated in real-time from the relative geometric relationship between the buoy and the underwater vehicle. The weighting formula is as follows: This suppresses the influence of buoys with poor geometry or high ranging noise during the solution process, where GDOP is the geometric accuracy factor.

7. The dynamic long baseline underwater topographic surveying system based on INS, DVL, and RTK buoy arrays according to claim 1, characterized in that, Step S04, which involves inputting the discrete absolute position as an observation value into the underwater vehicle's integrated navigation filter, includes using the absolute position calculated by the weighted spherical intersection as the observation value z of the extended Kalman filter. k The state vector of the extended Kalman filter includes three-dimensional position, three-dimensional velocity, three-dimensional attitude, three-axis zero bias of the gyroscope, three-axis zero bias of the accelerometer, and DVL scale factor error; The time update of the extended Kalman filter is based on INS mechanics orchestration and DVL velocity information. The measurement update uses the difference between the calculated position and the predicted position as information, and dynamically adjusts the observation noise covariance matrix R through an adaptive Kalman filter algorithm. k The process noise covariance matrix Q k To achieve data fusion.

8. The dynamic long baseline underwater topographic surveying system based on INS, DVL, and RTK buoy arrays according to claim 7, characterized in that, In the adaptive Kalman filter, the observation noise covariance matrix R k Based on the real-time estimation of the post-hoc unit weight variance of the weighted spherical intersection solution at the current epoch, when the internal consistency accuracy of the solution is high, R... k Smaller values ​​enhance confidence in external observations; when the solution results are affected by abnormal interference, leading to a decrease in internal consistency accuracy, R... k Adaptive scaling reduces the contamination of navigation results by abnormal observations, while utilizing the short-term characteristics of INS to maintain trajectory smoothness.

9. The dynamic long baseline underwater topographic surveying system based on INS, DVL, and RTK buoy arrays according to claim 1, characterized in that, In step S05, the continuous, high-frequency underwater vehicle navigation and positioning data is time-aligned with each transmission ping of the multibeam echo sounder. The navigation position is interpolated to the acquisition time of each echo sounder point using an interpolation method. At the same time, the beam footprint is corrected for refraction using real-time sound velocity profile data to generate the geodetic coordinates and water depth value of each echo sounder point, thus forming an underwater digital terrain model.

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