Intelligent amphibious ship navigation positioning method and system and storage medium

Abstract

The invention relates to the technical field of ship radio positioning, in particular to an intelligent amphibious ship navigation positioning method and system and a storage medium. The method comprises the following steps: carrying out amphibious ship inertial position analysis on an amphibious ship to obtain amphibious ship inertial position data; performing Doppler effect navigation speed compensation on the amphibious ship by using a Doppler log to generate ship Doppler compensation speed data; performing radio positioning processing on the amphibious ship to generate radio positioning data of the amphibious ship; performing navigation parameter dynamic correction according to the amphibious ship inertial position data, the ship Doppler compensation speed data and the amphibious ship radio positioning data to obtain intelligent ship position positioning data; and displaying the intelligent ship position positioning data by a navigation display terminal to obtain amphibious ship navigation display information. According to the invention, high-precision and high-reliability amphibious ship navigation positioning is realized through dynamic correction of navigation parameters.

Description

Technical Field

[0001] The present invention relates to the technical field of ship radio positioning, and in particular to an intelligent amphibious ship navigation and positioning method, system and storage medium. Background Art

[0002] Amphibious ships have the ability to sail on water and on land, and their navigation and positioning requirements are more special. They need to adapt to two completely different environments on the water and on land. Traditional ship navigation and positioning methods mainly rely on global navigation satellite systems (GNSS) such as GPS and Beidou. GNSS calculates the position and speed information of the ship by receiving signals from multiple satellites. However, GNSS signals are easily blocked by obstacles such as buildings and trees, especially in areas such as nearshore, ports, and inland rivers, the accuracy and reliability of the signals will be significantly reduced. In addition, the positioning accuracy of GNSS in land environments is also affected by factors such as terrain and vegetation, making it difficult to meet the needs of high-precision navigation and positioning of amphibious ships. However, traditional navigation and positioning methods are prone to lagging the position when dealing with rapid changes in direction of ships, resulting in navigation delay problems. These methods are usually based on the current position and speed of the ship for calculation, without fully considering the dynamic information such as the acceleration and turning rate of the ship. When a ship changes direction rapidly, its acceleration and turning rate will change dramatically, resulting in navigation delays, which are particularly evident during the water-land conversion process of amphibious ships. Summary of the invention

[0003] Based on this, the present invention provides an intelligent amphibious ship navigation and positioning method, system and storage medium to solve at least one of the above technical problems.

[0004] To achieve the above object, an intelligent amphibious ship navigation and positioning method comprises the following steps: Step S1: performing real-time ship inertial measurement on the amphibious ship to generate original ship inertial measurement data; performing real-time ship attitude mode annotation according to the original ship inertial measurement data to obtain real-time ship attitude angle data; performing inertial position analysis on the amphibious ship based on the real-time ship attitude angle data to obtain inertial position data of the amphibious ship; Step S2: Acquire the navigation environment type data of the amphibious ship; perform Doppler effect navigation speed compensation on the amphibious ship using a Doppler log based on the navigation environment type data of the amphibious ship, and generate Doppler compensation speed data of the ship; Step S3: Perform radio positioning processing on the amphibious ship to generate amphibious ship radio positioning data; perform time-space reference alignment processing based on the amphibious ship inertial position data, ship Doppler compensation speed data and amphibious ship radio positioning data to generate time-space synchronous positioning monitoring data; perform dynamic correction of navigation parameters based on the time-space synchronous positioning monitoring data to obtain intelligent ship position positioning data; Step S4: Perform navigation map matching on the smart ship position data and display it on the navigation display terminal to obtain amphibious ship navigation display information.

[0005] The present invention obtains accurate ship attitude angle data through real-time ship inertial measurement and attitude mode annotation, and uses this to perform inertial position analysis to obtain inertial position data of amphibious ships. Compared with the traditional method that relies solely on GNSS, the INS has strong autonomy and is not interfered by the external environment, which effectively makes up for the defect of GNSS in terms of reduced accuracy when the signal is blocked or interfered, especially in areas where GNSS signals are easily affected, such as nearshore, ports, inland rivers and land environments, which can provide more reliable position information and ensure the continuous navigation capability of amphibious ships in various complex environments. According to different navigation environment type data, the Doppler speed meter is used to compensate for the navigation speed (step S2). Due to the huge differences in the resistance, friction and other characteristics of the water and land environment, the speed of the ship changes dramatically during the water-land conversion process. The traditional navigation method based on constant speed or simple speed model is difficult to accurately estimate the real-time speed of the ship, resulting in positioning errors. However, this method can correct the ship speed in real time through the Doppler effect navigation speed compensation, improve the accuracy of navigation positioning, especially in the dynamic environment of water-land conversion, effectively solve the problem of inaccurate speed estimation of the traditional method, and ensure the accuracy of navigation information. Through the alignment of time and space references, the time difference and space difference between different data sources are eliminated, ensuring the consistency and reliability of the data. Based on the positioning monitoring data synchronized in time and space, the method can correct navigation parameters such as position, speed, attitude, etc. in real time, further improving the accuracy and real-time performance of navigation positioning, and effectively solving the problems of position lag and navigation delay in traditional methods when the ship changes direction quickly. Especially when amphibious ships perform complex operations such as land-water conversion, rapid turning or acceleration and deceleration, the motion state of the ship can be captured more accurately, and more timely navigation information can be provided, thereby enhancing the reliability and safety of navigation. Therefore, the intelligent amphibious ship navigation and positioning method of the present invention effectively solves the problems of low positioning accuracy and large delay of traditional navigation methods in amphibious environments by fusing multi-source navigation information, dynamically correcting navigation parameters and matching electronic maps, and realizes high-precision, high-reliability and high-real-time navigation and positioning of amphibious ships, which significantly improves the navigation safety and operating efficiency of amphibious ships.

[0006] Preferably, step S1 comprises the following steps: Step S11: An inertial measurement unit is fixedly installed on the hull of the amphibious ship, and the inertial measurement unit is started to perform real-time ship inertial measurement to generate original ship inertial measurement data; Step S12: performing data preprocessing on the original ship inertial measurement data to obtain preprocessed ship inertial measurement data; Step S13: extracting the angular velocity according to the pre-processed ship inertial measurement data to generate the ship angular velocity; Step S14: performing time integration according to the ship angular velocity, and performing real-time ship attitude mode annotation through a preset ship attitude mode library to obtain real-time ship attitude angle data; Step S15: performing attitude angle increment calculation based on the real-time ship attitude angle data, and performing linear velocity integration to generate preliminary ship linear velocity data; Step S16: Perform inertial position analysis of the amphibious ship based on the real-time ship attitude angle data and the preliminary ship linear velocity data to obtain inertial position data of the amphibious ship.

[0007] The present invention obtains the original ship inertial measurement data by fixing an inertial measurement unit on the hull and starting real-time measurement, laying the foundation for subsequent high-precision inertial navigation. Preprocessing the original data can effectively remove noise and interference, improve data quality, and provide more reliable input for subsequent attitude solution and position analysis. Through angular velocity extraction and time integration, and combining the preset ship attitude mode library to perform real-time attitude mode annotation, the attitude angle data of the ship can be accurately obtained, which is a key link in inertial navigation. The introduction of the attitude mode library can effectively identify the movement mode of the ship, such as straight-line navigation, turning, land-water conversion, etc., thereby improving the accuracy and robustness of attitude solution, especially in the case of amphibious ships performing complex maneuvers, it can more accurately reflect the attitude changes of the ship. Based on the real-time attitude angle data, the attitude angle increment calculation and linear velocity integration can be performed to obtain more accurate ship linear velocity data, avoiding the positioning error caused by inaccurate velocity estimation in the traditional method. Finally, the inertial position analysis of the amphibious ship is performed in combination with the real-time attitude angle data and the accurate linear velocity data, and the high-precision inertial position data of the amphibious ship can be obtained.

[0008] Preferably, step S16 comprises the following steps: Step S161: using the GNSS navigation system to obtain a real-time rough position of the amphibious ship to obtain rough position data of the amphibious ship; Step S162: using the rough position data of the amphibious ship as the calibration position, and calculating the ship's inertial movement position according to the real-time ship attitude angle data and the preliminary ship linear speed data, to generate the ship's inertial movement node data; Step S163: performing navigation coordinate conversion on the ship inertial movement node data, and drawing a ship movement curve according to the rough position data of the amphibious ship to obtain the ship inertial drift curve data; Step S164: performing ship static state recognition on the ship inertial drift curve data based on the real-time ship attitude angle data and the preliminary ship linear velocity data to obtain ship static state data; Step S165: performing zero speed judgment according to the ship stationary state data, and performing node zero speed correction marking on the ship inertia drift curve data to obtain a node zero speed correction mark; Step S166: The ship inertial drift curve data is set to zero speed through the node zero speed correction mark, and the zero speed position error is corrected to generate the amphibious ship inertial position data.

[0009] The present invention uses GNSS to obtain the rough position data of amphibious ships, providing an initial reference point for subsequent inertial position solution. The rough position data is used as the calibration positioning position, and the inertial movement position is calculated in combination with the real-time attitude angle data and the preliminary linear velocity data, so that a series of ship inertial movement node data can be obtained, and these node data constitute the motion trajectory of the ship. Through navigation coordinate conversion and ship movement curve drawing, the inertial drift of the ship can be intuitively displayed. The key to achieving zero speed correction is to identify the stationary state of the ship on the inertial drift curve data and make zero speed judgment based on the stationary state data. When the ship is in a stationary state, its speed is theoretically zero, but due to the error and drift of the inertial measurement unit, the actual measured speed is often not zero. By identifying the stationary state of the ship, the node zero speed correction mark can be made on the inertial drift curve, and the speed is set to zero at the marked node, thereby correcting the cumulative error of inertial navigation.

[0010] Preferably, step S2 comprises the following steps: Step S21: using a Doppler log to monitor the real-time navigation speed and obtain the initial navigation speed of the amphibious ship; Step S22: Acquire the navigation environment type data of the amphibious ship; Step S23: selecting a Doppler log speed measurement mode according to the amphibious ship navigation environment type data to obtain a Doppler log speed measurement mode; Step S24: Perform Doppler effect compensation on the initial amphibious ship navigation speed through the Doppler odometer speed measurement mode to obtain ship Doppler compensated speed data.

[0011] Due to the huge differences in the physical properties of the water and land environments, the speed measurement mode and accuracy of the Doppler odometer in different environments are also different. By selecting the appropriate speed measurement mode according to the navigation environment type data, the performance of the Doppler odometer can be maximized and the accuracy of speed measurement can be improved. For example, when sailing in water, the bottom tracking mode can be selected; when driving on land, the land mode can be selected. This method of adaptively selecting the speed measurement mode according to the environment effectively improves the adaptability and robustness of speed measurement and ensures that accurate speed information can be obtained in different environments. Finally, the initial navigation speed is compensated for the Doppler effect by the selected Doppler odometer speed measurement mode to obtain the Doppler compensated speed data of the ship. The Doppler effect compensation takes into account the influence of the relative motion of the ship motion and the medium (water or land) on the speed measurement, thereby eliminating the error caused by the Doppler effect and further improving the accuracy of speed measurement.

[0012] Preferably, step S3 comprises the following steps: Step S31: using an amphibious vessel to receive a radio positioning signal to obtain a radio frequency signal; Step S32: performing radio positioning processing according to the radio frequency signal to generate radio positioning data of the amphibious ship; Step S33: performing time-space reference alignment processing on the amphibious ship inertial position data, the ship Doppler compensated speed data and the amphibious ship radio positioning data to generate time-space synchronous positioning monitoring data; Step S34: establishing an inertial navigation error model according to the inertial position data of the amphibious ship in the time-space synchronous positioning monitoring data; Step S35: using the Doppler compensation velocity data of the ship in the time-space synchronous positioning monitoring data to calculate the velocity vector difference of the inertial navigation error model, and performing navigation axial coordinate projection to generate inertial navigation velocity data; Step S36: Establish position constraints based on the amphibious ship radio positioning data in the time-space synchronous positioning monitoring data, and use the inertial navigation speed data to dynamically correct the navigation parameters to obtain the intelligent ship position positioning data.

[0013] The time-space synchronization of the present invention ensures the time and space consistency between different data sources, and improves the accuracy and reliability of data fusion. By establishing an inertial navigation error model based on the time-space synchronized inertial position data, the error of the inertial navigation system can be effectively analyzed and quantified, providing a basis for subsequent error correction. By using Doppler compensation velocity data to perform velocity vector difference calculation and navigation axial coordinate projection on the inertial navigation error model, more accurate inertial navigation velocity data can be generated. Doppler velocity data provides correction information for the inertial navigation velocity, thereby reducing the error of the inertial navigation velocity and improving navigation accuracy. By using radio positioning data to establish position constraints, and combining accurate inertial navigation velocity data to dynamically correct navigation parameters, more accurate and reliable intelligent ship position positioning data can be obtained.

[0014] Preferably, step S32 includes the following steps: Step S321: enhancing the signal-to-noise ratio of the radio frequency signal and capturing the satellite signal to obtain capture result data; the satellite signal capture searches for the pseudo code phase and carrier frequency of the radio frequency signal and determines whether there is a valid positioning signal; Step S322: When there is a valid signal in the captured result data, the satellite signal is tracked to obtain tracking loop signal data; Step S323: performing pseudo code demodulation based on the tracking loop signal data to obtain pseudo range data; Step S324: performing carrier phase smoothing on the pseudorange data to obtain smoothed pseudorange data; Step S325: multipath error suppression is performed on the smoothed pseudorange data by using the amphibious ship navigation environment type data to obtain multipath suppressed pseudorange data; Step S326: Utilize the multipath suppression pseudo-range data to calculate the position of the amphibious ship and generate radio positioning data of the amphibious ship.

[0015] The present invention can improve signal quality and identify effective positioning signals by enhancing the signal-to-noise ratio of radio frequency signals and capturing satellite signals, thereby laying a foundation for subsequent signal tracking and demodulation. By tracking the captured satellite signals, more accurate signal parameters, such as pseudocode phase and carrier frequency, can be obtained, providing more reliable data for pseudorange measurement. Pseudocode demodulation based on tracking loop signal data can obtain pseudorange data, which is a key step in radio positioning. Carrier phase smoothing of pseudorange data can effectively reduce the influence of noise and multipath errors in pseudorange measurement and improve the accuracy of pseudorange measurement. Multipath error suppression of smoothed pseudorange data using amphibious ship navigation environment type data further improves the reliability of pseudorange data. Due to the complex and changeable environment of amphibious ships, the influence of multipath effect is particularly significant. According to different navigation environment types, adopting corresponding error suppression strategies can effectively reduce the influence of multipath errors and improve positioning accuracy. For example, in areas near the coast or with dense buildings, a stronger multipath suppression algorithm can be used. Finally, by using the pseudo-range data with multipath error suppression to solve the position of the amphibious ship, more accurate and reliable radio positioning data of the amphibious ship can be obtained.

[0016] Preferably, step S325 includes the following steps: The multipath effect occurrence probability is queried according to the amphibious ship navigation environment type data to obtain the multipath occurrence probability data; The multipath effect analysis is performed based on the multipath occurrence probability data, and the multipath signal model is established using the smoothed pseudorange data to obtain the multipath signal model; The multipath signal model is used to identify the multipath signal of the radio frequency signal, and the antenna array fixedly installed on the amphibious ship is used to estimate the multipath signal error to obtain the multipath signal error data; Using the multipath signal error data to perform multipath error compensation on the smoothed pseudorange data to obtain compensated pseudorange data; The compensated pseudo-range data is subjected to narrow-band filtering suppression to generate ship multipath suppression pseudo-range data.

[0017] The present invention can effectively evaluate the risk of multipath error by querying the probability of multipath effect according to the navigation environment type data of amphibious ships and performing multipath effect analysis, and provide guidance for subsequent multipath error suppression. A multipath signal model is established based on multipath occurrence probability data and smoothed pseudorange data, which can more accurately describe the characteristics of multipath signals and provide a basis for multipath signal identification and error estimation. The multipath signal model established is used to identify the radio frequency signal, and the multipath signal error is estimated in combination with the antenna array, so that the size and direction of the multipath error can be more accurately quantified. The introduction of the antenna array can effectively distinguish between direct signals and multipath signals, and improve the accuracy of multipath error estimation. Multipath error compensation is performed on the smoothed pseudorange data using the estimated multipath signal error data, which can effectively reduce the influence of multipath error and improve the accuracy of pseudorange measurement. The compensated pseudorange data is subjected to narrowband filtering suppression, which can further remove residual multipath error and noise, improve the signal-to-noise ratio of the pseudorange data, and thus generate purer and more reliable ship multipath suppression pseudorange data.

[0018] Preferably, step S4 comprises the following steps: Step S41: performing geographic coordinate conversion on the intelligent ship position positioning data to obtain geographic coordinate data; Step S42: performing navigation coordinate processing according to the geographic coordinate data, and performing navigation map matching through preset electronic nautical chart data to obtain matching navigation data; Step S43: Display the matching navigation data on the navigation display terminal to obtain the amphibious ship navigation display information.

[0019] The present invention converts the position data of intelligent ships into geographic coordinate data, making the position information easier to understand and use, and convenient for integration with other geographic information systems. By performing navigation coordinate processing based on geographic coordinate data and performing navigation map matching through preset electronic chart data, the positioning information of the ship can be associated with the geographic information on the electronic chart, thereby providing richer navigation information. Map matching technology can effectively correct positioning errors, improve navigation accuracy, and provide a more intuitive navigation display. For example, the position of the ship can be accurately displayed on the electronic chart, and the surrounding terrain, waterways, obstacles and other information can be displayed. Displaying the matched navigation data on the navigation display terminal can provide users with clear and intuitive navigation information, such as the current position, heading, speed, destination, etc. of the ship, as well as auxiliary information such as electronic charts, route planning, and obstacle alarms.

[0020] Preferably, the present invention also provides an intelligent amphibious ship navigation and positioning system, which executes the intelligent amphibious ship navigation and positioning method as described above, and the intelligent amphibious ship navigation and positioning system comprises: The ship inertial measurement module is used to perform real-time ship inertial measurement on amphibious ships and generate original ship inertial measurement data; perform real-time ship attitude mode annotation based on the original ship inertial measurement data to obtain real-time ship attitude angle data; perform inertial position analysis on amphibious ships based on the real-time ship attitude angle data to obtain inertial position data on amphibious ships; The Doppler log compensation module is used to obtain the navigation environment type data of the amphibious ship; based on the navigation environment type data of the amphibious ship, the Doppler log is used to perform Doppler effect navigation speed compensation on the amphibious ship to generate Doppler compensation speed data of the ship; The time-space navigation positioning correction module is used to perform radio positioning processing on amphibious ships and generate amphibious ship radio positioning data; perform time-space reference alignment processing based on amphibious ship inertial position data, ship Doppler compensation speed data and amphibious ship radio positioning data to generate time-space synchronous positioning monitoring data; perform dynamic correction of navigation parameters based on time-space synchronous positioning monitoring data to obtain intelligent ship position positioning data; The intelligent navigation display module is used to match the intelligent ship position positioning data with the navigation map and display it on the navigation display terminal to obtain the amphibious ship navigation display information.

[0021] Preferably, the present invention also provides a computer-readable storage medium storing a computer program, wherein the computer program, when executed, implements the intelligent amphibious ship navigation and positioning method as described in any one of the above. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 It is a schematic diagram of the steps of the intelligent amphibious ship navigation and positioning method of the present invention; Figure 2 for Figure 1 Detailed implementation steps of step S1 in FIG. Figure 3 for Figure 1 Detailed implementation steps of step S3 in FIG. The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0023] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0024] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0025] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0026] To achieve this, please refer to Figures 1 to 3 The present invention provides an intelligent amphibious ship navigation and positioning method, comprising the following steps: Step S1: performing real-time ship inertial measurement on the amphibious ship to generate original ship inertial measurement data; performing real-time ship attitude mode annotation according to the original ship inertial measurement data to obtain real-time ship attitude angle data; performing inertial position analysis on the amphibious ship based on the real-time ship attitude angle data to obtain inertial position data of the amphibious ship; Step S2: Acquire the navigation environment type data of the amphibious ship; perform Doppler effect navigation speed compensation on the amphibious ship using a Doppler log based on the navigation environment type data of the amphibious ship, and generate Doppler compensation speed data of the ship; Step S3: Perform radio positioning processing on the amphibious ship to generate amphibious ship radio positioning data; perform time-space reference alignment processing based on the amphibious ship inertial position data, ship Doppler compensation speed data and amphibious ship radio positioning data to generate time-space synchronous positioning monitoring data; perform dynamic correction of navigation parameters based on the time-space synchronous positioning monitoring data to obtain intelligent ship position positioning data; Step S4: Perform navigation map matching on the smart ship position data and display it on the navigation display terminal to obtain amphibious ship navigation display information.

[0027] In an embodiment of the present invention, the intelligent amphibious ship navigation and positioning method comprises the following steps: Step S1: performing real-time ship inertial measurement on the amphibious ship to generate original ship inertial measurement data; performing real-time ship attitude mode annotation according to the original ship inertial measurement data to obtain real-time ship attitude angle data; performing inertial position analysis on the amphibious ship based on the real-time ship attitude angle data to obtain inertial position data of the amphibious ship; In an embodiment of the present invention, an inertial measurement unit (IMU) of model Xsens MTi-G-710 is fixedly installed at the center of the hull of an amphibious ship to ensure that the measurement axis of the IMU is aligned with the hull coordinate system. The three-axis acceleration and three-axis angular velocity data are collected at a frequency of 200Hz to generate raw ship inertial measurement data, and transmitted to the navigation computer through the data bus, and the data format is IEEE 754 floating point number. In the navigation computer, temperature compensation is performed using the pre-determined IMU temperature coefficient, and the zero bias value obtained by static measurement is subtracted to pre-process the raw data. The error state Kalman filter is used to perform attitude solution on the pre-processed inertial data, and the state vector includes attitude error, gyroscope zero bias and accelerometer zero bias, and the measurement vector is the measurement value of the accelerometer and gyroscope. The state equation of the Kalman filter is based on the IMU kinematic model, and the measurement equation is based on the IMU measurement model. The attitude error is estimated by Kalman filtering, and the initial attitude is corrected to obtain real-time roll angle, pitch angle and heading angle (in the form of Euler angles), with an update frequency of 200Hz, forming real-time ship attitude angle data. The attitude angle data is combined with the initial position information provided by GNSS, and the linear velocity and position are integrated using the fourth-order Runge-Kutta method to obtain the inertial position data (longitude, latitude and altitude) of the amphibious ship, with an update frequency of 200Hz.

[0028] Step S2: Acquire the navigation environment type data of the amphibious ship; perform Doppler effect navigation speed compensation on the amphibious ship using a Doppler log based on the navigation environment type data of the amphibious ship, and generate Doppler compensation speed data of the ship; In the embodiment of the present invention, the environmental data is collected in real time by installing a water level sensor (e.g., an ultrasonic water level sensor with a measurement range of 0-5 meters and an accuracy of 1 cm), a GPS positioning module (e.g., u-blox ZED-F9P with a positioning accuracy of 1 meter) and an attitude sensor (e.g., Xsens MTi-G-710) on an amphibious ship, and the data transmission frequency is 10Hz. The navigation computer determines whether the ship is in a surface navigation state based on the water level height threshold (e.g., 0.5 meters), determines whether the ship is in a land navigation state based on the GPS position information, and determines the other cases as a tidal flat navigation state, and generates the navigation environment type data of the amphibious ship. A Teledyne Workhorse Navigator DVL Doppler log is installed on the bottom of the amphibious ship to measure the speed of the ship relative to the bottom of the water or the ground, and the data update frequency is 10Hz. When sailing on the water surface, start the "bottom tracking mode" of the DVL, and use the water flow sensor installed on the ship (for example, Valeport Midas ECM, measuring range 0-5m / s, accuracy 1cm / s) to measure the water flow speed and direction, subtract the water flow speed vector from the speed vector measured by the DVL, and perform Doppler effect navigation speed compensation. When sailing on the beach and land, start the "bottom tracking mode" and "ground tracking mode" of the DVL respectively, without Doppler effect compensation. The processed speed data is used as the ship's Doppler compensated speed data.

[0029] Step S3: Perform radio positioning processing on the amphibious ship to generate amphibious ship radio positioning data; perform time-space reference alignment processing based on the amphibious ship inertial position data, ship Doppler compensation speed data and amphibious ship radio positioning data to generate time-space synchronous positioning monitoring data; perform dynamic correction of navigation parameters based on the time-space synchronous positioning monitoring data to obtain intelligent ship position positioning data; In an embodiment of the present invention, a Trimble MB-Two multi-beam receiver is installed on an amphibious ship to receive radio signals from a shore-based ultra-short baseline (USBL) positioning system, and the data update frequency is 1Hz. The USBL system consists of a main base station and three auxiliary base stations, and the base station positions are known and fixed. The navigation computer receives radio frequency signals and calculates the distance between the ship and each base station by measuring the time difference between the signal reaching the ship-borne receiver and each auxiliary base station. Using these distance information and the known base station positions, three-dimensional spatial positioning is performed by the least squares method to generate amphibious ship radio positioning data (longitude, latitude and altitude). The navigation computer receives amphibious ship inertial position data (200Hz), ship Doppler compensated speed data (10Hz) and amphibious ship radio positioning data (1Hz). The three data are unified to 10Hz using a linear interpolation method, and time synchronization is performed using the precision time protocol (PTP) to generate time-space synchronous positioning monitoring data. Based on this data, the error state Kalman filter is used to dynamically correct the navigation parameters. The state vector of the Kalman filter includes position error, velocity error, attitude error, IMU zero bias and clock error, and the measurement vector is USBL position and DVL velocity. The state error is estimated by Kalman filtering, and the inertial navigation solution result is corrected to obtain the position positioning data (longitude, latitude, altitude, speed and attitude) of the intelligent ship, with an update frequency of 10Hz.

[0030] Step S4: Perform navigation map matching on the smart ship position data and display it on the navigation display terminal to obtain amphibious ship navigation display information.

[0031] In an embodiment of the present invention, the position data of the intelligent ship is converted from the WGS84 coordinate system to the UTM coordinate system to obtain geographic coordinate data. The geographic coordinate data is smoothed using a moving average filter, and the window size is set to 5. The smoothed geographic coordinate data is matched with the preset S-57 standard electronic chart data. A point-to-line segment distance matching algorithm is used, and the search radius is set to 10 meters to find the nearest waterway or coastline to obtain matching navigation data, including the position, heading and speed of the ship on the electronic chart. The matching navigation data is displayed on a navigation display terminal (e.g., MaxSeaTimeZero ECDIS). The real-time position, heading, speed, surrounding environment (e.g., water depth, obstacles, waterways, etc.) and preset routes of amphibious ships are displayed on the electronic chart.

[0032] Preferably, step S1 comprises the following steps: Step S11: An inertial measurement unit is fixedly installed on the hull of the amphibious ship, and the inertial measurement unit is started to perform real-time ship inertial measurement to generate original ship inertial measurement data; Step S12: performing data preprocessing on the original ship inertial measurement data to obtain preprocessed ship inertial measurement data; Step S13: extracting the angular velocity according to the pre-processed ship inertial measurement data to generate the ship angular velocity; Step S14: performing time integration according to the ship angular velocity, and performing real-time ship attitude mode annotation through a preset ship attitude mode library to obtain real-time ship attitude angle data; Step S15: performing attitude angle increment calculation based on the real-time ship attitude angle data, and performing linear velocity integration to generate preliminary ship linear velocity data; Step S16: Perform inertial position analysis of the amphibious ship based on the real-time ship attitude angle data and the preliminary ship linear velocity data to obtain inertial position data of the amphibious ship.

[0033] As an example of the present invention, refer to Figure 2 As shown, Figure 1 Detailed implementation steps of step S1 in the flowchart, in this example, step S1 includes: Step S11: An inertial measurement unit is fixedly installed on the hull of the amphibious ship, and the inertial measurement unit is started to perform real-time ship inertial measurement to generate original ship inertial measurement data; In an embodiment of the present invention, a rigid, flat and horizontal mounting base is selected. The IMU is firmly fixed to the base using high-strength bolts to ensure that the IMU does not undergo relative displacement or vibration during the navigation of the ship. The X-axis, Y-axis and Z-axis of the IMU need to be aligned with the longitudinal, transverse and vertical directions of the ship, respectively, and the allowable deviation angle is less than 0.1 degrees. Use a precision level and an angle ruler for measurement and adjustment. After the installation is completed, make electrical connections to the IMU. Connect the power cord of the IMU to the power supply system of the ship (e.g., a 24V DC power supply) and ensure that the power supply is stable and reliable. Connect the data output line of the IMU (e.g., RS422 or CAN bus) to the data processing unit. Then, start the IMU. The IMU usually has a self-test function, which automatically performs an internal circuit check and sensor preheating after startup. The warm-up time usually takes 30 minutes to 1 hour to ensure that the sensor reaches a stable operating temperature. After the warm-up is completed, the IMU starts to output raw ship inertial measurement data at a set sampling frequency (e.g. 200Hz), including the linear acceleration measured by the three-axis accelerometer (unit: m / s²) and the angular velocity measured by the three-axis gyroscope (unit: ° / s).

[0034] Step S12: performing data preprocessing on the original ship inertial measurement data to obtain preprocessed ship inertial measurement data; In an embodiment of the present invention, the original ship inertial measurement data is filtered. Due to factors such as ship vibration and wave impact, the original data usually contains a large amount of high-frequency noise. A low-pass filter is used to filter out these noises. For example, a Butterworth low-pass filter with a cutoff frequency of 20 Hz can be used. The specific implementation method is: the original data is passed through a digital filter, and the transfer function of the filter is determined according to the design formula of the Butterworth filter. The order of the filter (for example, 4th order or 8th order) needs to be adjusted according to the actual situation. The higher the order, the better the filtering effect, but the greater the delay. The filtered data will become smoother. Then, zero bias correction is performed. IMU sensors have inherent zero bias errors, that is, they will output non-zero values ​​in a stationary state. The zero bias error will drift slowly over time. The zero bias is estimated by static data acquisition. Specific operation: When the ship is stationary, collect IMU data for a period of time (for example, 10 minutes) and calculate the average value. The average value is used as the estimated value of the zero bias error.

[0035] Step S13: extracting the angular velocity according to the pre-processed ship inertial measurement data to generate the ship angular velocity; In an embodiment of the present invention, the angular velocity information of the ship is extracted from the preprocessed IMU data. The preprocessed ship inertial measurement data includes three-axis accelerometer data and three-axis gyroscope data. The output value of the three-axis gyroscope is directly extracted from the preprocessed data, which is the angular velocity of the ship. These angular velocity data correspond to the rotation rate of the ship around its own three axes (longitudinal axis, transverse axis and vertical axis). For example, if the gyroscope X-axis output is 0.5° / s, it means that the ship is rotating around its longitudinal axis (i.e. rolling) at a speed of 0.5 degrees per second. The three-axis angular velocity data is output in digital form, for example, transmitted in a specific data format (for example, including a frame header, a data type identifier, a data value, a checksum, etc.) through an RS422 interface. After receiving these data, the data processing unit parses them according to the data format, extracts the angular velocity values ​​in three directions, and generates the ship angular velocity data. The unit is degree / second (° / s).

[0036] Step S14: performing time integration according to the ship angular velocity, and performing real-time ship attitude mode annotation through a preset ship attitude mode library to obtain real-time ship attitude angle data; In an embodiment of the present invention, the angular velocity data is integrated to obtain the attitude angle, and the attitude mode is marked. First, the ship angular velocity data is time-integrated. The universal joint lock problem in the Euler angle integration can be avoided by using the quaternion method for integration. Specific operation: Substitute the angular velocity data at the current moment and the quaternion at the previous moment into the quaternion differential equation to calculate the quaternion at the current moment. The quaternion represents the attitude of the ship relative to the reference coordinate system (for example, the navigation coordinate system). Then, the quaternion is converted into Euler angles (roll angle, pitch angle and heading angle) to obtain the real-time attitude angle data of the ship. In order to improve the integration accuracy, a high-order integration algorithm, such as the Runge-Kutta method, can be used. Then, the attitude mode is marked. Establish a ship attitude mode library, which contains predefined attitude modes and their corresponding attitude angle ranges.

[0037] Step S15: performing attitude angle increment calculation based on the real-time ship attitude angle data, and performing linear velocity integration to generate preliminary ship linear velocity data; In an embodiment of the present invention, the attitude angle increment is calculated based on the attitude angle, and the attitude angle increment refers to the change in the attitude angle between two adjacent sampling moments. The attitude angle increment is obtained by subtracting the attitude angle at the previous moment from the attitude angle at the current moment. For example, if the roll angle at the current moment is 15.2 degrees and the roll angle at the previous moment is 15.0 degrees, the roll angle increment is 0.2 degrees. Then, the linear acceleration is integrated. Before integration, the linear acceleration measured by the accelerometer needs to be converted from the hull coordinate system to the navigation coordinate system. A rotation matrix is ​​constructed using the attitude angle data, and the acceleration vector in the hull coordinate system is converted to the navigation coordinate system. Then, the acceleration in the navigation coordinate system is integrated to obtain preliminary linear velocity data. The trapezoidal integration method or the Simpson integration method is used. For example, if the X-axis acceleration in the navigation coordinate system at the current moment is 0.1m / s², the X-axis velocity at the previous moment is 0m / s, and the sampling time interval is 0.01 seconds, the X-axis velocity at the current moment calculated by the trapezoidal integration method is Due to the error of the accelerometer, the integrated linear velocity will drift over time.

[0038] Step S16: Perform inertial position analysis of the amphibious ship based on the real-time ship attitude angle data and the preliminary ship linear velocity data to obtain inertial position data of the amphibious ship.

[0039] In the embodiment of the present invention, the preliminary linear velocity data is integrated twice to obtain the position increment. The trapezoidal integration method or the Simpson integration method is also used. For example, if the X-axis velocity in the navigation coordinate system at the current moment is 0.001m / s, the X-axis position at the previous moment is 0m, and the sampling time interval is 0.01 seconds, the X-axis position increment at the current moment calculated by the trapezoidal integration method is Then, the position increment is added to the position at the previous moment to get the position at the current moment. In order to obtain the absolute position, an initial position needs to be input as a reference. For example, when a ship starts the navigation system, an initial position (longitude, latitude and altitude) is obtained through GPS or other positioning methods as the starting point of inertial navigation.

[0040] Preferably, step S16 comprises the following steps: Step S161: using the GNSS navigation system to obtain a real-time rough position of the amphibious ship to obtain rough position data of the amphibious ship; Step S162: using the rough position data of the amphibious ship as the calibration position, and calculating the ship's inertial movement position according to the real-time ship attitude angle data and the preliminary ship linear speed data, to generate the ship's inertial movement node data; Step S163: performing navigation coordinate conversion on the ship inertial movement node data, and drawing a ship movement curve according to the rough position data of the amphibious ship to obtain the ship inertial drift curve data; Step S164: performing ship static state recognition on the ship inertial drift curve data based on the real-time ship attitude angle data and the preliminary ship linear velocity data to obtain ship static state data; Step S165: performing zero speed judgment according to the ship stationary state data, and performing node zero speed correction marking on the ship inertia drift curve data to obtain a node zero speed correction mark; Step S166: The ship inertial drift curve data is set to zero speed through the node zero speed correction mark, and the zero speed position error is corrected to generate the amphibious ship inertial position data.

[0041] In an embodiment of the present invention, a GNSS (Global Navigation Satellite System, such as GPS, Beidou, GLONASS or Galileo) receiver is used to obtain the initial position and rough position information of an amphibious ship. A GNSS antenna is installed on an amphibious ship, and the antenna position should be as open as possible to avoid obstruction to ensure that a sufficient number of satellite signals (at least 4) are received. The GNSS antenna is connected to a GNSS receiver. The GNSS receiver receives satellite signals, measures the propagation time of the signals, and calculates the distance between the ship and the satellite using the triangulation positioning principle. The three-dimensional position (longitude, latitude and altitude) and time information of the ship can be solved by the distance information of at least four satellites. The GNSS receiver outputs the rough position data of the amphibious ship at a set frequency (e.g., 1Hz or 5Hz). The format of the data usually follows the NMEA 0183 standard or the binary format defined by the receiver manufacturer. The data is transmitted to the data processing unit via a serial port (e.g., RS232) or a network interface (e.g., UDP). After receiving the data, the data processing unit parses it according to the data format to extract the longitude, latitude and altitude information. Combine the GNSS rough position, attitude angle and preliminary linear velocity data to calculate the inertial movement position. First, use the GNSS rough position data as the initial position of the inertial navigation. When the inertial navigation system is started, the first valid position data (longitude, latitude and altitude) output by the GNSS receiver is used as the starting position of the inertial navigation. This position will be used as a reference point for subsequent inertial position calculations. Then, based on the real-time ship attitude angle data and preliminary ship linear velocity data obtained in step S15, the ship's inertial movement position is calculated. The specific calculation method is: based on the inertial position, attitude angle and linear velocity at the previous moment, calculate the position increment at the current moment. The position increment calculation needs to take into account the attitude change of the ship, convert the velocity in the hull coordinate system to the navigation coordinate system, and then integrate it. For example, the following formula is used to calculate the position increment: Among them, ΔE, ΔN and ΔU represent the position increments in the east, north and celestial directions, respectively; Vt is the preliminary linear velocity; φ, θ and ψ are the roll angle, pitch angle and heading angle, respectively; Δt is the sampling time interval. The calculated position increment is added to the inertial position of the previous moment to obtain the ship's inertial mobile node data at the current moment. The ship's inertial mobile node data is usually expressed in a relative coordinate system (for example, with the position at the start time as the origin). In order to compare and fuse with other navigation data (such as GNSS data), it needs to be converted to a unified navigation coordinate system, such as the WGS-84 geographic coordinate system (longitude, latitude and altitude). This can be achieved through coordinate translation and rotation transformation. The translation amount is determined by the initial GNSS position, and the rotation matrix is ​​determined by the initial attitude angle. Then, the ship movement curve is drawn based on the converted inertial mobile node data.

[0042] Connect the inertial position points at each moment in a two-dimensional plane (e.g., longitude-latitude plane) or three-dimensional space to form a continuous curve. Use attitude angle and linear velocity data to identify the stationary state of the ship. The stationary state of a ship refers to the state in which the ship remains stationary or moves at a very low speed for a period of time. In the stationary state, the speed error and position error of the inertial navigation system will be significantly reduced. Therefore, accurate identification of the stationary state is crucial for subsequent zero-speed correction. The specific identification method is: set a time window (e.g., 10 seconds) and a speed threshold (e.g., 0.1m / s) and an attitude angle change rate threshold (e.g., 0.5 degrees / second). If within the time window, the absolute value of the initial ship linear velocity is always less than the speed threshold, and the absolute value of the change rate of the three attitude angles (roll, pitch, heading) is always less than the attitude angle change rate threshold, the ship is considered to be in a stationary state. Once the ship is detected to enter a stationary state, the ship stationary state data is generated, which contains the start time and end time of the stationary state. Zero speed judgment and correction marking are performed based on the stationary state data. First, zero speed judgment is performed. According to the ship stationary state data obtained in step S164, it is determined whether the ship is in a zero-speed state. If the ship is in a stationary state and the speed data output by the GNSS receiver is also close to zero (for example, less than 0.05 m / s), the ship is considered to be in a zero-speed state. Then, the ship's inertial drift curve data is marked for node zero-speed correction. On the ship's inertial drift curve, find the node corresponding to the start time of the zero-speed state, mark the node, and generate a node zero-speed correction mark. Use the zero-speed correction mark to set the speed to zero and correct the position error. First, set the speed to zero. On the ship's inertial drift curve, find a node with a node zero-speed correction mark. Force the speed calculated by the inertial navigation system corresponding to the node to zero. This can eliminate the speed drift error of the inertial navigation system in a stationary state. Then, perform zero-speed position error correction. Since the integration of the speed error will cause a position error, the position error needs to be corrected after the speed is set to zero. A simple method is to compare the inertial position corresponding to the zero-speed correction mark with the GNSS rough position to calculate the position error. The position error is evenly distributed throughout the zero speed interval, and the inertial position is corrected at each moment. A more complex method is to use a Kalman filter, take zero speed as observation information, and estimate and correct the state of the inertial navigation system (position, speed, attitude, and sensor error). After the speed is set to zero and the position error is corrected, the final inertial position data of the amphibious ship is obtained.

[0043] Preferably, step S2 comprises the following steps: Step S21: using a Doppler log to monitor the real-time navigation speed and obtain the initial navigation speed of the amphibious ship; Step S22: Acquire the navigation environment type data of the amphibious ship; Step S23: selecting a Doppler log speed measurement mode according to the amphibious ship navigation environment type data to obtain a Doppler log speed measurement mode; Step S24: Perform Doppler effect compensation on the initial amphibious ship navigation speed through the Doppler odometer speed measurement mode to obtain ship Doppler compensated speed data.

[0044] As an example of the present invention, refer to Figure 3 As shown, Figure 1 Detailed implementation steps of step S2 in the embodiment are shown in the flowchart. In this embodiment, step S2 includes: Step S21: using a Doppler log to monitor the real-time navigation speed and obtain the initial navigation speed of the amphibious ship; In an embodiment of the present invention, the transducer of the Doppler log is installed on the bottom of an amphibious ship. The installation position of the transducer should be as far away from the propeller and protrusions on the surface of the hull as possible to reduce the interference of bubbles and turbulence. The transducer usually includes multiple acoustic array elements (for example, four), each pointing in a different direction (for example, at an angle of 30 degrees or 45 degrees to the longitudinal axis of the hull). The transducer is connected to the host of the Doppler log via a cable. Then, the Doppler log is started. The host of the Doppler log controls the transducer to periodically emit sound wave pulses. The frequency of the sound wave pulse depends on different Doppler log models and application scenarios, and is usually between tens of kHz and several MHz. For example, for shallow water applications, 600kHz or 1200kHz sound waves can be used; for deep water applications, 75kHz or 150kHz sound waves can be used. The transducer simultaneously receives echo signals reflected from the bottom of the water or suspended objects in the water. The host of the Doppler log processes the received echo signal and uses the Doppler effect to calculate the speed of the ship relative to the bottom (bottom tracking mode) or the water layer (water tracking mode). The Doppler effect means that when there is relative motion between the sound source (transducer) and the reflector (bottom or suspended object), the frequency of the received echo signal will change. The magnitude of the frequency change is proportional to the relative speed. The Doppler log calculates the speed components of the ship in all directions by measuring the frequency change of the echo signal. The Doppler log outputs the initial amphibious ship navigation speed data at a set frequency (such as 1Hz or 10Hz), including longitudinal speed, lateral speed and vertical speed.

[0045] Step S22: Acquire the navigation environment type data of the amphibious ship; In an embodiment of the present invention, the current position (longitude and latitude) of the ship is obtained by a GNSS receiver, and then the pre-loaded chart database is queried to determine the type of sea area to which the position belongs. For example, the sea area type can be divided into "inland river", "nearshore shallow water area", "deep sea area", "port area", etc. Another way is to measure the current water depth through a ship-borne sensor (such as a water depth sounder) and judge the sea area type according to the water depth range. For example, an area with a water depth of less than 50 meters can be divided into "nearshore shallow water area", and an area with a water depth of more than 2000 meters can be divided into "deep sea area". The navigation environment type data can also be obtained by manual input. For example, the driver can manually select the corresponding environment type according to the current navigation area and sea conditions. The obtained navigation environment type data is transmitted to the data processing unit in a coded form (for example, the number 1 represents "inland river", the number 2 represents "nearshore shallow water area", etc.).

[0046] Step S23: selecting a Doppler log speed measurement mode according to the amphibious ship navigation environment type data to obtain a Doppler log speed measurement mode; In an embodiment of the present invention, a suitable Doppler speed meter speed measurement mode is selected. Different navigation environments have different effects on the speed measurement performance of the Doppler speed meter. For example, in shallow water areas, sound waves can easily reach the bottom of the water and reflect, so the bottom tracking mode can be selected; in deep water areas, sound waves are difficult to reach the bottom of the water, or the reflected signal is very weak, so the water tracking mode can be selected. In areas with strong water flow, it is necessary to consider the impact of water flow on speed measurement and select a suitable compensation algorithm. The specific selection method is: establish a mapping table between the navigation environment type and the Doppler speed measurement mode. The mapping table is pre-formulated according to the Doppler speed measurement characteristics under different environments. According to the navigation environment type data obtained in step S22, the mapping table is queried to select the corresponding Doppler speed measurement mode, sound wave frequency and other parameters (such as pulse length, transmission power, etc.). The data processing unit sends the selected speed measurement mode and parameters to the host of the Doppler speed meter.

[0047] Step S24: Perform Doppler effect compensation on the initial amphibious ship navigation speed through the Doppler odometer speed measurement mode to obtain ship Doppler compensated speed data.

[0048] In an embodiment of the present invention, the initial navigation speed is compensated for the Doppler effect to obtain the final speed data. The speed measured by the Doppler log is the speed relative to the sound wave reflector, not the absolute speed relative to the ground. In the bottom tracking mode, the reflector is the water bottom, and the speed relative to the bottom is measured; in the water tracking mode, the reflector is the suspended matter in the water, and the speed relative to different water layers is measured. For example, in the bottom tracking mode, it is mainly necessary to compensate for the sound speed error. The sound speed in water changes with the changes in temperature, salinity and pressure. The Doppler log is usually equipped with a temperature sensor to measure the water temperature. According to the empirical formula or table lookup, the sound speed at the current water temperature can be calculated. The calculated sound speed is used to correct the Doppler speed measurement result. In the water tracking mode, in addition to the sound speed error, the influence of the water flow needs to be considered. The Doppler log can measure the speed of multiple water layers to form a water flow profile. According to the water flow profile, the average flow velocity of the entire water column can be estimated, and the average flow velocity is deducted from the Doppler speed measurement result to obtain the speed relative to the bottom of the water. If the ship's heading is known, the water velocity can be decomposed into the longitudinal and transverse directions of the ship and compensated separately. After Doppler effect compensation, the ship's Doppler compensated velocity data is obtained, including the longitudinal, transverse and vertical ground speeds.

[0049] Preferably, step S3 comprises the following steps: Step S31: using an amphibious vessel to receive a radio positioning signal to obtain a radio frequency signal; Step S32: performing radio positioning processing according to the radio frequency signal to generate radio positioning data of the amphibious ship; Step S33: performing time-space reference alignment processing on the amphibious ship inertial position data, the ship Doppler compensated speed data and the amphibious ship radio positioning data to generate time-space synchronous positioning monitoring data; Step S34: establishing an inertial navigation error model according to the inertial position data of the amphibious ship in the time-space synchronous positioning monitoring data; Step S35: using the Doppler compensation velocity data of the ship in the time-space synchronous positioning monitoring data to calculate the velocity vector difference of the inertial navigation error model, and performing navigation axial coordinate projection to generate inertial navigation velocity data; Step S36: Establish position constraints based on the amphibious ship radio positioning data in the time-space synchronous positioning monitoring data, and use the inertial navigation speed data to dynamically correct the navigation parameters to obtain the intelligent ship position positioning data.

[0050] In an embodiment of the present invention, a radio receiving antenna is installed on an amphibious ship. The type and installation position of the antenna depend on the radio positioning system used. For example, if a very short wave (VHF) radio positioning system is used, a whip antenna or a loop antenna can be used and installed at a higher position of the hull to obtain a better reception effect. If a Loran-C system is used, a long wire antenna can be used. If a satellite positioning system (such as Beidou short message) is used, a helical antenna or a planar antenna can be used. The antenna is connected to a radio receiver through a feeder. The radio receiver receives a radio signal from a radio positioning source (such as a shore-based base station, a navigation satellite or a differential GPS base station) according to a preset frequency and modulation method. The received radio signal usually contains ranging information (such as signal arrival time, arrival time difference or pseudorange) or angle measurement information (such as signal arrival angle) for positioning. The radio receiver amplifies, filters, demodulates and digitizes the received signal to generate a radio frequency signal. The signal is output in digital form, for example, transmitted to a data processing unit through a serial port (RS232 or RS422) or a network interface (UDP or TCP). The distance between the ship and the base station is calculated by measuring the propagation time or phase difference of the signal. Then, using this distance information and the known base station position, the three-dimensional position coordinates (longitude, latitude and altitude) of the amphibious ship are calculated through a three-dimensional spatial positioning algorithm (e.g., the least squares method). The calculated position coordinates and timestamp information are recorded to generate the radio positioning data of the amphibious ship. The navigation computer receives the inertial position data of the amphibious ship, the Doppler compensated velocity data of the ship, and the radio positioning data of the amphibious ship. The three data have different sampling frequencies, for example, 100Hz for the inertial position data, 10Hz for the Doppler velocity data, and 1Hz for the radio positioning data. A time interpolation method, such as linear interpolation, is used to unify the three data to the same sampling frequency, such as 10Hz. Then, the timestamps of the three data are aligned using the precision time protocol (PTP) or other time synchronization methods to ensure that the data is processed under the same time reference. The three data after time synchronization are integrated together to generate time-space synchronous positioning monitoring data. An inertial navigation error model is established based on the inertial position data in the time-space synchronous positioning monitoring data. Inertial navigation errors mainly include sensor errors (zero bias, scale factor error and random noise of gyroscopes and accelerometers) and initial alignment errors (initial attitude error and initial position error). The error model describes the change pattern of these errors over time. The commonly used inertial navigation error model is a state space-based model that takes the error term as a state variable and establishes the state equation and observation equation. For example, the following state equation can be used: ; Among them, δX is the state vector, including position error, velocity error, attitude error, gyroscope bias and accelerometer bias, etc.; F is the state transfer matrix, which describes the relationship between the error and time; G is the noise driving matrix, and W is the process noise vector. The state transfer matrix F is determined according to the propagation law of the inertial navigation error, and the process noise W is usually assumed to be Gaussian white noise. The Doppler compensated velocity data (in the navigation coordinate system) in the spatiotemporal synchronous positioning monitoring data is compared with the velocity data (in the navigation coordinate system) calculated by the inertial navigation system, and the velocity differences in three directions (east, north and celestial) are calculated. These velocity differences reflect the velocity error of the inertial navigation system. Then, the navigation axis coordinate projection is performed. The calculated velocity difference is projected onto the three axes of the navigation coordinate system. This can be achieved by a simple coordinate transformation. For example, if the velocity difference is ΔV, the eastward velocity error is the eastward component of ΔV, the northward velocity error is the northward component of ΔV, and the celestial velocity error is the celestial component of ΔV. After velocity vector difference calculation and navigation axial coordinate projection, inertial navigation velocity data is obtained, which contains velocity error information of the inertial navigation system in three directions. The radio positioning data (longitude, latitude and altitude) of amphibious ships in the time-space synchronous positioning monitoring data is used as a position constraint. Since the radio positioning system (such as GPS) provides absolute position information, it can be used as a true value or reference value to constrain the position error of the inertial navigation system. Then, the inertial navigation velocity data and position constraints are used to dynamically correct the navigation parameters. The Kalman filter algorithm is used for data fusion. The inertial navigation error model established in step S34 is used as the state equation of the Kalman filter, and the inertial navigation velocity data obtained in step S35 and the position constraints established in this step are used as the observation equation of the Kalman filter. The Kalman filter estimates the state vector of the inertial navigation system (including position error, velocity error, attitude error, gyroscope zero bias and accelerometer zero bias, etc.) based on the state equation and the observation equation, and the corrected output is the intelligent ship position positioning data.

[0051] Preferably, step S32 includes the following steps: Step S321: enhancing the signal-to-noise ratio of the radio frequency signal and capturing the satellite signal to obtain capture result data; the satellite signal capture searches for the pseudo code phase and carrier frequency of the radio frequency signal and determines whether there is a valid positioning signal; Step S322: When there is a valid signal in the captured result data, the satellite signal is tracked to obtain tracking loop signal data; Step S323: performing pseudo code demodulation based on the tracking loop signal data to obtain pseudo range data; Step S324: performing carrier phase smoothing on the pseudorange data to obtain smoothed pseudorange data; Step S325: multipath error suppression is performed on the smoothed pseudorange data by using the amphibious ship navigation environment type data to obtain multipath suppressed pseudorange data; Step S326: Utilize the multipath suppression pseudo-range data to calculate the position of the amphibious ship and generate radio positioning data of the amphibious ship.

[0052] In an embodiment of the present invention, the signal-to-noise ratio of the received radio frequency signal is enhanced, for example, narrowband filtering technology is used to filter out out-of-band noise and improve the signal-to-noise ratio of the signal. The enhanced signal is used to capture the satellite signal using a capture algorithm. Specifically, by performing a two-dimensional search on the pseudo code phase and carrier frequency of the radio frequency signal and comparing them with the known satellite pseudo code and carrier frequency, it is determined whether there is a valid positioning signal. The search range is set to 0-1023 code chips for the pseudo code phase, -5kHz to +5kHz for the carrier frequency, and a step size of 100Hz. The search results, including information such as the satellite PRN code, pseudo code phase, carrier frequency and signal-to-noise ratio, are recorded as capture result data. If a match between the pseudo code and carrier frequency corresponding to a certain satellite is detected, it is considered that the satellite signal is successfully captured. If there is a valid signal in the capture result data, that is, at least one satellite signal is captured, the tracking loop is started to track the captured satellite signal. The tracking loop includes a pseudo code tracking loop and a carrier tracking loop. The pseudocode tracking loop adjusts the phase of the local pseudocode to keep it synchronized with the pseudocode phase of the received satellite signal; the carrier tracking loop adjusts the frequency of the local carrier to keep it synchronized with the carrier frequency of the received satellite signal. The signal data output by the tracking loop, including information such as pseudocode phase, carrier phase and signal-to-noise ratio, is recorded as tracking loop signal data. The code phase information output by the code tracking loop in step S322 is used to control the local pseudocode generator to generate a pseudocode synchronized with the received signal. The received signal is correlated with the locally generated pseudocode to remove the pseudocode modulation in the signal. Then, the propagation time of the signal can be calculated by measuring the delay time of the bit transition edge of the received signal relative to the local clock. The propagation time is multiplied by the speed of light to obtain pseudorange data. The pseudorange data contains the geometric distance from the satellite to the receiver, the satellite clock error, the receiver clock error, the ionospheric delay, the tropospheric delay and the multipath error. The carrier phase information output by the carrier tracking loop in step S322 is used to calculate the carrier phase change between adjacent epochs. The carrier phase measurement value is much more accurate than the pseudorange measurement value. Convert the carrier phase change to the distance change (multiply by the carrier wavelength). Use the carrier phase change to correct the pseudorange change. For example, a Hatch filter can be used to smooth the pseudorange. The Hatch filter is a recursive filter that uses the pseudorange measurement value and carrier phase change at the current moment, as well as the smoothed pseudorange value at the previous moment, to calculate the smoothed pseudorange data at the current moment. According to the amphibious ship navigation environment type data, the smoothed pseudorange data is subjected to multipath error suppression. Multipath error refers to the error caused by the signal reaching the receiver through multiple paths. In the water surface or mudflat environment, the multipath error is more serious. Use multipath suppression technology, such as narrow correlation technology or multipath estimation delay locked loop (MEDLL) technology, to correct the smoothed pseudorange data and reduce the impact of multipath error.The corrected pseudorange data is recorded as multipath suppression pseudorange data. The multipath suppression pseudorange data is used to solve the position of the amphibious ship. Specifically, the multipath suppression pseudorange data, satellite position information and receiver clock error information are input into a positioning solution algorithm, such as a least squares method or a Kalman filter algorithm. By solving the positioning equation, the three-dimensional position coordinates (longitude, latitude and altitude) of the amphibious ship are calculated. The calculated position coordinates and timestamp information are recorded to generate the amphibious ship radio positioning data.

[0053] Preferably, step S325 includes the following steps: The multipath effect occurrence probability is queried according to the amphibious ship navigation environment type data to obtain the multipath occurrence probability data; The multipath effect analysis is performed based on the multipath occurrence probability data, and the multipath signal model is established using the smoothed pseudorange data to obtain the multipath signal model; The multipath signal model is used to identify the multipath signal of the radio frequency signal, and the antenna array fixedly installed on the amphibious ship is used to estimate the multipath signal error to obtain the multipath signal error data; Using the multipath signal error data to perform multipath error compensation on the smoothed pseudorange data to obtain compensated pseudorange data; The compensated pseudo-range data is subjected to narrow-band filtering suppression to generate ship multipath suppression pseudo-range data.

[0054] In an embodiment of the present invention, the navigation computer stores a pre-established multipath effect occurrence probability database. The database is established based on a large amount of experimental data and statistical analysis, and associates different navigation environment types (water surface, tidal flat, land) with the probability of multipath effect occurrence. For example, in a surface navigation environment, due to the influence of water surface reflection, the probability of multipath effect occurrence is high and is set to 0.8; in a tidal flat navigation environment, due to the complexity of the tidal flat terrain, the probability of multipath effect occurrence is set to 0.6; in a land navigation environment, the probability of multipath effect occurrence is relatively low and is set to 0.2. The navigation computer queries the corresponding multipath effect occurrence probability from the database according to the amphibious ship navigation environment type data obtained in step S22 to obtain multipath occurrence probability data. When the multipath occurrence probability data is greater than a preset threshold (for example, 0.5), it is considered that the multipath effect is significant and multipath signal modeling is required. A multipath signal model is established using smoothed pseudorange data and known satellite position information. Specifically, it is assumed that the multipath signal consists of a direct signal and a plurality of reflected signals, and the amplitude, phase and delay of each reflected signal are unknown. Use a multipath signal parameter estimation method, such as the MUSIC algorithm or the ESPRIT algorithm, to estimate the various parameters of the multipath signal and establish a multipath signal model. This example uses a dual-path model. The received signal y(t) can be expressed as: ; Wherein, s(t) is the direct signal, α is the ratio of the amplitude of the multipath signal to the amplitude of the direct signal (0<α<1), τ is the delay of the multipath signal relative to the direct signal, and φ is the phase difference of the multipath signal relative to the direct signal. The model describes the composition and characteristics of the multipath signal, such as the number of reflected signals, the amplitude, phase and delay of each reflected signal, etc. An antenna array is fixedly installed on an amphibious ship, for example, a uniform circular array composed of 4 antenna units. The multipath signal model is used to identify the multipath signal of the received radio frequency signal. Specifically, the received signal is matched with the multipath signal model to identify the direct signal and each reflected signal. The multipath signal error is estimated by using the spatial characteristics of the antenna array. Specifically, the beamforming technology or spatial smoothing technology is used to process the signals received by each antenna unit to estimate the pseudorange error caused by the multipath signal. The estimated pseudorange error is recorded as the multipath signal error data. The multipath signal error data is subtracted from the smoothed pseudorange data to obtain the compensated pseudorange data. The purpose of multipath error compensation is to eliminate the influence of multipath effect on pseudorange measurement and improve the accuracy of pseudorange measurement. The compensated pseudorange data is suppressed by narrowband filtering to further reduce the influence of multipath error and noise. Specifically, a narrowband filter, such as a Butterworth filter, is used to filter the compensated pseudorange data, and the bandwidth of the filter is adjusted according to the characteristics of the signal and the noise level. The filtered pseudorange data is used as the final ship multipath suppression pseudorange data.

[0055] Preferably, step S4 comprises the following steps: Step S41: performing geographic coordinate conversion on the intelligent ship position positioning data to obtain geographic coordinate data; Step S42: performing navigation coordinate processing according to the geographic coordinate data, and performing navigation map matching through preset electronic nautical chart data to obtain matching navigation data; Step S43: Display the matching navigation data on the navigation display terminal to obtain the amphibious ship navigation display information.

[0056] In an embodiment of the present invention, the position data of the intelligent ship is converted from a calculation coordinate system (for example, longitude and latitude in the WGS-84 coordinate system) to a geographic coordinate system. The position data of the intelligent ship is usually expressed in the form of longitude and latitude (and altitude), which is a spherical coordinate that is not convenient for direct display on a plane map or distance and azimuth calculation. It needs to be converted into a plane rectangular coordinate system, such as a Universal Transverse Mercator Projection (UTM) coordinate system or a Gauss-Kruger projection coordinate system. The specific conversion method is: according to the selected map projection type (such as UTM or Gauss-Kruger), select the corresponding projection parameters (such as central meridian, scale factor, etc.). Then, the longitude and latitude coordinates are converted into plane rectangular coordinates (such as the easting coordinates and northing coordinates of the UTM coordinate system) using the projection formula. The geographic coordinate data is matched with the electronic chart data, and the ship position is displayed on the electronic chart. First, the navigation coordinate processing is performed. According to the geographic coordinate data (plane rectangular coordinates), the heading and speed of the ship are calculated. The heading can be obtained by calculating the azimuth between two adjacent position points, and the speed can be obtained by calculating the distance between two adjacent position points divided by the time interval. Then, navigation map matching is performed. The geographic coordinate data and the electronic chart data are superimposed. Electronic chart data are usually stored in vector format, containing geometric information and attribute information of various elements on the chart (such as coastlines, channels, lighthouses, water depth points, etc.). Using point-to-line or point-to-surface matching algorithms, the ship's position point is projected onto the nearest channel line or coastline on the electronic chart. In order to avoid mismatching, the ship's heading, speed and historical track information need to be considered. For example, a search radius can be set to only search for matching lines or surfaces within the search radius. The next moment position of the ship can also be predicted based on the ship's heading and speed, and lines or surfaces close to the predicted position are matched first. After the match is successful, the ship's position symbol (such as a triangle or a ship-shaped icon) is drawn at the corresponding position on the electronic chart to generate matching navigation data. The matching navigation data is displayed on the navigation display terminal. The navigation display terminal can be a liquid crystal display, an electronic chart display and information system (ECDIS) or an augmented reality navigation system. On the navigation display terminal, an electronic chart is displayed, and the real-time position, heading, speed and other information of the amphibious ship are graphically superimposed on the map. For example, an icon is used to represent the current position of the amphibious ship, the direction of the icon represents the heading of the ship, and the color of the icon represents the navigation status of the ship (for example, green represents normal navigation, and red represents a dangerous state). At the same time, other navigation information is displayed on the screen, such as the distance to the target point, the estimated arrival time, and the route planning.

[0057] Preferably, the present invention also provides an intelligent amphibious ship navigation and positioning system, which executes the intelligent amphibious ship navigation and positioning method as described above, and the intelligent amphibious ship navigation and positioning system comprises: The ship inertial measurement module is used to perform real-time ship inertial measurement on amphibious ships and generate original ship inertial measurement data; perform real-time ship attitude mode annotation based on the original ship inertial measurement data to obtain real-time ship attitude angle data; perform inertial position analysis on amphibious ships based on the real-time ship attitude angle data to obtain inertial position data on amphibious ships; The Doppler log compensation module is used to obtain the navigation environment type data of the amphibious ship; based on the navigation environment type data of the amphibious ship, the Doppler log is used to perform Doppler effect navigation speed compensation on the amphibious ship to generate Doppler compensation speed data of the ship; The time-space navigation positioning correction module is used to perform radio positioning processing on amphibious ships and generate amphibious ship radio positioning data; perform time-space reference alignment processing based on amphibious ship inertial position data, ship Doppler compensation speed data and amphibious ship radio positioning data to generate time-space synchronous positioning monitoring data; perform dynamic correction of navigation parameters based on time-space synchronous positioning monitoring data to obtain intelligent ship position positioning data; The intelligent navigation display module is used to match the intelligent ship position positioning data with the navigation map and display it on the navigation display terminal to obtain the amphibious ship navigation display information.

[0058] Preferably, the present invention also provides a computer-readable storage medium storing a computer program, wherein the computer program, when executed, implements the intelligent amphibious ship navigation and positioning method as described in any one of the above.

[0059] The present application aims to obtain accurate ship attitude angle data through real-time ship inertial measurement and attitude mode annotation, and to perform inertial position analysis based on this data to obtain inertial position data of amphibious ships. Compared with the traditional method that relies solely on GNSS, the inertial navigation system (INS) has strong autonomy and is not interfered by the external environment, which effectively makes up for the defect of GNSS's reduced accuracy when the signal is blocked or interfered with, especially in areas where GNSS signals are easily affected, such as nearshore, ports, inland rivers and land environments. It can provide more reliable position information and ensure the continuous navigation capability of amphibious ships in various complex environments. According to different navigation environment type data, due to the huge differences in the resistance, friction and other characteristics of the water and land environment, the speed of the ship changes dramatically during the water-land conversion process. The traditional navigation method based on constant speed or simple speed model is difficult to accurately estimate the real-time speed of the ship, resulting in positioning errors. However, the present application can correct the ship speed in real time through Doppler effect navigation speed compensation, improve the accuracy of navigation positioning, especially in the dynamic environment of water-land conversion, effectively solve the problem of inaccurate speed estimation of traditional methods, and ensure the accuracy of navigation information. By integrating multi-source navigation information, dynamically correcting navigation parameters, and matching electronic maps, the low positioning accuracy and large delay of traditional navigation methods in amphibious environments have been effectively solved, and high-precision, high-reliability, and high-real-time navigation and positioning of amphibious ships have been achieved. Specifically, through the fusion of multi-source data from inertial navigation systems, GNSS, Doppler logs, and radio positioning systems, continuous and stable navigation services can be provided in different environments, significantly improving the navigation safety and operating efficiency of amphibious ships.

[0060] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0061] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. An intelligent amphibious ship navigation and positioning method, characterized in that: The following steps are involved: Step S1: performing real-time ship inertial measurement on the amphibious ship to generate original ship inertial measurement data; performing real-time ship attitude mode annotation according to the original ship inertial measurement data to obtain real-time ship attitude angle data; performing inertial position analysis on the amphibious ship based on the real-time ship attitude angle data to obtain inertial position data of the amphibious ship; Step S2: Acquire the navigation environment type data of the amphibious ship; perform Doppler effect navigation speed compensation on the amphibious ship using a Doppler log based on the navigation environment type data of the amphibious ship, and generate Doppler compensation speed data of the ship; Step S3: Perform radio positioning processing on the amphibious ship to generate radio positioning data of the amphibious ship; perform time-space reference alignment processing according to the inertial position data of the amphibious ship, the Doppler compensated speed data of the ship and the radio positioning data of the amphibious ship to generate time-space synchronous positioning monitoring data; Based on the time-space synchronous positioning monitoring data, the navigation parameters are dynamically corrected to obtain the position positioning data of the intelligent ship; Step S4: Perform navigation map matching on the smart ship position data and display it on the navigation display terminal to obtain amphibious ship navigation display information.

2. The intelligent amphibious ship navigation and positioning method according to claim 1 is characterized in that: Step S1 includes the following steps: Step S11: An inertial measurement unit is fixedly installed on the hull of the amphibious ship, and the inertial measurement unit is started to perform real-time ship inertial measurement to generate original ship inertial measurement data; Step S12: performing data preprocessing on the original ship inertial measurement data to obtain preprocessed ship inertial measurement data; Step S13: extracting the angular velocity according to the pre-processed ship inertial measurement data to generate the ship angular velocity; Step S14: performing time integration according to the ship angular velocity, and performing real-time ship attitude mode annotation through a preset ship attitude mode library to obtain real-time ship attitude angle data; Step S15: performing attitude angle increment calculation based on the real-time ship attitude angle data, and performing linear velocity integration to generate preliminary ship linear velocity data; Step S16: Perform inertial position analysis of the amphibious ship based on the real-time ship attitude angle data and the preliminary ship linear velocity data to obtain inertial position data of the amphibious ship.

3. The intelligent amphibious ship navigation and positioning method according to claim 2 is characterized in that: Step S16 includes the following steps: Step S161: using the GNSS navigation system to obtain a real-time rough position of the amphibious ship to obtain rough position data of the amphibious ship; Step S162: using the rough position data of the amphibious ship as the calibration position, and calculating the ship's inertial movement position according to the real-time ship attitude angle data and the preliminary ship linear speed data, to generate the ship's inertial movement node data; Step S163: performing navigation coordinate conversion on the ship inertial movement node data, and drawing a ship movement curve according to the rough position data of the amphibious ship to obtain the ship inertial drift curve data; Step S164: performing ship static state recognition on the ship inertial drift curve data based on the real-time ship attitude angle data and the preliminary ship linear velocity data to obtain ship static state data; Step S165: performing zero speed judgment according to the ship stationary state data, and performing node zero speed correction marking on the ship inertia drift curve data to obtain a node zero speed correction mark; Step S166: The ship inertial drift curve data is set to zero speed through the node zero speed correction mark, and the zero speed position error is corrected to generate the amphibious ship inertial position data.

4. The intelligent amphibious ship navigation and positioning method according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: using a Doppler log to monitor the real-time navigation speed and obtain the initial navigation speed of the amphibious ship; Step S22: Acquire the navigation environment type data of the amphibious ship; Step S23: selecting a Doppler log speed measurement mode according to the amphibious ship navigation environment type data to obtain a Doppler log speed measurement mode; Step S24: Perform Doppler effect compensation on the initial amphibious ship navigation speed through the Doppler odometer speed measurement mode to obtain ship Doppler compensated speed data.

5. The intelligent amphibious ship navigation and positioning method according to claim 1 is characterized in that: Step S3 includes the following steps: Step S31: using an amphibious vessel to receive a radio positioning signal to obtain a radio frequency signal; Step S32: performing radio positioning processing according to the radio frequency signal to generate radio positioning data of the amphibious ship; Step S33: performing time-space reference alignment processing on the amphibious ship inertial position data, the ship Doppler compensated speed data and the amphibious ship radio positioning data to generate time-space synchronous positioning monitoring data; Step S34: establishing an inertial navigation error model according to the inertial position data of the amphibious ship in the time-space synchronous positioning monitoring data; Step S35: using the Doppler compensation velocity data of the ship in the time-space synchronous positioning monitoring data to calculate the velocity vector difference of the inertial navigation error model, and performing navigation axial coordinate projection to generate inertial navigation velocity data; Step S36: Establish position constraints based on the amphibious ship radio positioning data in the time-space synchronous positioning monitoring data, and use the inertial navigation speed data to dynamically correct the navigation parameters to obtain the intelligent ship position positioning data.

6. The intelligent amphibious ship navigation and positioning method according to claim 1, characterized in that: Step S32 includes the following steps: Step S321: enhancing the signal-to-noise ratio of the radio frequency signal and capturing the satellite signal to obtain capture result data; the satellite signal capture searches for the pseudo code phase and carrier frequency of the radio frequency signal and determines whether there is a valid positioning signal; Step S322: When there is a valid signal in the captured result data, the satellite signal is tracked to obtain tracking loop signal data; Step S323: performing pseudo code demodulation based on the tracking loop signal data to obtain pseudo range data; Step S324: performing carrier phase smoothing on the pseudorange data to obtain smoothed pseudorange data; Step S325: multipath error suppression is performed on the smoothed pseudorange data by using the amphibious ship navigation environment type data to obtain multipath suppressed pseudorange data; Step S326: Utilize the multipath suppression pseudo-range data to calculate the position of the amphibious ship and generate radio positioning data of the amphibious ship.

7. The intelligent amphibious ship navigation and positioning method according to claim 6 is characterized in that: Step S325 includes the following steps: The multipath effect occurrence probability is queried according to the amphibious ship navigation environment type data to obtain the multipath occurrence probability data; The multipath effect analysis is performed based on the multipath occurrence probability data, and the multipath signal model is established using the smoothed pseudorange data to obtain the multipath signal model; The multipath signal model is used to identify the multipath signal of the radio frequency signal, and the antenna array fixedly installed on the amphibious ship is used to estimate the multipath signal error to obtain the multipath signal error data; Using the multipath signal error data to perform multipath error compensation on the smoothed pseudorange data to obtain compensated pseudorange data; The compensated pseudo-range data is subjected to narrow-band filtering suppression to generate ship multipath suppression pseudo-range data.

8. The intelligent amphibious ship navigation and positioning method according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: performing geographic coordinate conversion on the intelligent ship position positioning data to obtain geographic coordinate data; Step S42: performing navigation coordinate processing according to the geographic coordinate data, and performing navigation map matching through preset electronic nautical chart data to obtain matching navigation data; Step S43: Display the matching navigation data on the navigation display terminal to obtain the amphibious ship navigation display information.

9. An intelligent amphibious ship navigation and positioning system, characterized in that: Used to execute the intelligent amphibious ship navigation and positioning method according to claim 1, the intelligent amphibious ship navigation and positioning system comprises: The ship inertial measurement module is used to perform real-time ship inertial measurement on amphibious ships and generate original ship inertial measurement data; perform real-time ship attitude mode annotation based on the original ship inertial measurement data to obtain real-time ship attitude angle data; perform inertial position analysis on amphibious ships based on the real-time ship attitude angle data to obtain inertial position data on amphibious ships; The Doppler log compensation module is used to obtain the navigation environment type data of the amphibious ship; based on the navigation environment type data of the amphibious ship, the Doppler log is used to perform Doppler effect navigation speed compensation on the amphibious ship to generate Doppler compensation speed data of the ship; The time-space navigation positioning correction module is used to perform radio positioning processing on amphibious ships and generate amphibious ship radio positioning data; perform time-space reference alignment processing based on amphibious ship inertial position data, ship Doppler compensation speed data and amphibious ship radio positioning data to generate time-space synchronous positioning monitoring data; perform dynamic correction of navigation parameters based on time-space synchronous positioning monitoring data to obtain intelligent ship position positioning data; The intelligent navigation display module is used to match the intelligent ship position positioning data with the navigation map and display it on the navigation display terminal to obtain the amphibious ship navigation display information.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed, the intelligent amphibious ship navigation and positioning method as described in any one of claims 1 to 8 is implemented.

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