A method and system for positioning a marine vessel

Abstract

The application relates to the technical field of ship positioning, and discloses a ship positioning method and system, the method introduces a landmark information acquisition and priority matching sequence generation mechanism, optimizes the selection process of target landmarks, and effectively improves the calculation accuracy of the actual position of the ship by combining radar echo with the accurate matching of target landmarks. In addition, by inputting the position deviation between the actual position and the position calculated by the inertial navigation system into a Kalman filter for correction, the cumulative error of the inertial navigation system is significantly reduced, the accuracy and reliability of the ship position information are finally ensured, and the ship position information is updated to an electronic chart system, so that the problem that the traditional positioning method is prone to interference and positioning accuracy is reduced in a complex environment is effectively solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of ship positioning, and in particular to a ship positioning method and system. BACKGROUND

[0002] In modern maritime navigation, ensuring the accuracy and reliability of the position of large ships is crucial for navigation safety and operational efficiency. Although the Global Navigation Satellite System (GNSS) is the main means of obtaining real-time position information in open waters, in some complex environments, such as narrow fjords or island-dense archipelago navigation areas, high mountains or dense islands can cause long-term and large-scale shielding and reflection of satellite signals, resulting in the satellite positioning system being unable to receive a sufficient number of valid satellite signals, thus failing to calculate accurate position information, resulting in positioning interruption or significant deviation caused by multipath effects.

[0003] To this end, ships are usually equipped with an inertial navigation system (INS) as an important auxiliary or backup positioning means. The inertial navigation system does not rely on any external signals and calculates the position, attitude, and speed of the ship through internal gyroscopes and accelerometers. However, the inertial navigation system has an inherent error accumulation problem. Over time, small measurement deviations and noise are continuously amplified in the integration process, causing the calculated position to gradually deviate from the true position of the ship, i.e., "drift." In environments where satellite signals are unreliable for a long time, the cumulative error of the inertial navigation system will pose a threat to navigation safety.

[0004] To solve the long-term drift problem of the inertial navigation system, a common approach is to use a shipborne radar for auxiliary positioning, which compares and matches the real-time echo image scanned by the radar with the corresponding target information stored on the electronic chart to calculate a relatively accurate ship position for correcting the cumulative error of the inertial navigation system. However, in actual navigation, especially in busy fishing operation areas or adverse weather conditions (such as heavy rain), the radar display screen not only has fixed echoes from shorelines and islands, but also is filled with a large number of moving target echoes (such as fishing boats, ferries) and rain echoes (rain clutter). These interference signals can severely degrade the signal quality of real landmarks, making the shoreline profile unclear, and causing the automatic matching system to incorrectly identify moving targets or rain clutter as fixed landmarks on the chart, thereby introducing greater positioning errors when correcting the inertial navigation system based on the incorrect matching results, which can directly lead to incorrect navigation decisions by the crew, causing collisions or grounding accidents.

[0005] In view of the above problems, there is an urgent need for improvement in the prior art. SUMMARY

[0006] The application provides a ship positioning method and system, aiming at solving the problems of positioning interruption or significant deviation of a global navigation satellite system in a complex environment, inherent error accumulation of an inertial navigation system, and positioning precision reduction of a shipborne radar auxiliary positioning system due to interference signals.

[0007] In a first aspect, to solve the above technical problems, the application provides a ship positioning method, comprising:

[0008] acquiring landmark information and generating a priority matching sequence;

[0009] selecting a target landmark according to the priority matching sequence and matching a radar echo;

[0010] calculating an actual position of the ship according to a relative position relationship between the radar echo and the target landmark;

[0011] inputting a position deviation between the actual position and a position calculated by an inertial navigation system into a Kalman filter to correct the inertial navigation system;

[0012] acquiring a ship position according to the modified inertial navigation system and updating the position to an electronic chart system.

[0013] Preferably, the acquiring landmark information and generating a priority matching sequence comprises:

[0014] acquiring fixed landmark information from the electronic chart system;

[0015] calculating a geometric uniqueness score for each landmark;

[0016] generating the priority matching sequence according to the geometric uniqueness score.

[0017] Preferably, the selecting a target landmark according to the priority matching sequence and matching a radar echo comprises:

[0018] determining a limited search area according to a position calculated by the inertial navigation system;

[0019] selecting a target landmark from the priority matching sequence;

[0020] preprocessing the radar echo and extracting a radar echo contour in the limited search area;

[0021] comparing the radar echo contour with a geometric contour of the target landmark, the comparison comprising shape comparison, size comparison and relative position comparison;

[0022] if a comparison result reaches a preset similarity standard, confirming a successful match; otherwise, selecting a next target landmark from the priority matching sequence and repeating the matching process.

[0023] Preferably, the calculating the geometric uniqueness score for each landmark comprises:

[0024] Obtaining ship motion data;

[0025] Generating an expected distortion profile of the landmark according to the ship motion data;

[0026] Extracting radar echo features;

[0027] Comparing the radar echo features with features of the expected distortion profile;

[0028] Verifying consistency of distortion features of the radar echo with distortion features predicted by the ship motion data;

[0029] Calculating the geometric uniqueness score of the landmark according to the comparison result and the verification result.

[0030] Preferably, the generating an expected distortion profile of the landmark according to the ship motion data comprises:

[0031] Obtaining ship multi-dimensional motion data;

[0032] Decomposing a geometric profile of the landmark into at least one geometric primitive;

[0033] Predicting an instantaneous displacement trajectory of each geometric primitive within a radar scanning period according to the ship multi-dimensional motion data;

[0034] Superimposing the geometric primitive and the instantaneous displacement trajectory to generate the expected distortion profile of the landmark.

[0035] Preferably, the verifying consistency of distortion features of the radar echo with distortion features predicted by the ship motion data comprises:

[0036] Performing multi-scale feature extraction on the radar echo to obtain energy distribution, texture features and local geometric structure information of the radar echo;

[0037] Generating a plurality of expected distortion profiles of different degrees according to the ship motion data;

[0038] Comparing the energy distribution, the texture features and the local geometric structure information of the radar echo with corresponding features of the plurality of expected distortion profiles of different degrees to obtain a plurality of similarity scores;

[0039] Analyzing distribution trends of the plurality of similarity scores on the plurality of expected distortion profiles of different degrees to identify a similarity peak value with the ship motion data;

[0040] Identifying abnormal echoes in the radar echo that do not match features of known interference sources;

[0041] According to the similarity peak value and the abnormal echo, it is confirmed that the distortion feature of the radar echo is consistent with the distortion feature predicted by the ship movement data.

[0042] Preferably,

[0043] The identification of the abnormal echo in the radar echo that does not match the feature of the known interference source comprises:

[0044] Obtaining feature data of the radar echo, the feature data comprising instantaneous intensity distribution, local shape feature and movement trajectory feature;

[0045] According to the real-time movement data of the ship and the geometric profile of the target landmark, an expected distortion feature set of the landmark under different movement intensities is generated;

[0046] According to the feature data and the expected distortion feature set, a similarity is calculated;

[0047] When the similarity is lower than a preset threshold, the feature data is verified with dynamic features of the known interference source respectively;

[0048] If the feature data matches the dynamic features of the known interference source, the radar echo is marked as the abnormal echo;

[0049] If the feature data has dynamic overlap or imitation with one of the expected distortion features in the set, the continuity and stability of the movement trajectory of the radar echo, and the relevance of the movement trajectory to the ship's own movement are judged;

[0050] If the movement trajectory of the radar echo does not have stability and the relevance is lower than a preset threshold, the radar echo is identified as the abnormal echo.

[0051] Preferably, the judgment of the continuity and stability of the movement trajectory of the radar echo, and the relevance of the movement trajectory to the ship's own movement comprises:

[0052] The instantaneous position sequence of the radar echo is segmented;

[0053] The position change rate and direction change rate in each segment after segmentation are fitted;

[0054] According to the fitting result, the continuity and stability in each segment are judged;

[0055] Obtaining real-time attitude data and instantaneous movement parameters of the ship;

[0056] predicting an expected motion trajectory of the fixed landmark in a radar scanning cycle according to the real-time attitude data and the instantaneous motion parameters;

[0057] comparing the motion trajectory of the radar echo with the expected motion trajectory in terms of similarity;

[0058] judging the correlation according to the similarity.

[0059] Preferably, the fitting of the rate of change of position and the rate of change of direction in each segment after the segment processing comprises:

[0060] acquiring ship attitude sensor data;

[0061] attitude compensating the instantaneous position sequence according to the ship attitude sensor data to obtain an attitude-compensated radar echo instantaneous position sequence;

[0062] performing frequency domain analysis on the attitude-compensated radar echo instantaneous position sequence, identifying and filtering out high-frequency noise components, obtaining a radar echo instantaneous position sequence after filtering out high-frequency noise components and performing polynomial fitting to obtain a rate of change of position;

[0063] differencing the rate of change of position to obtain a rate of change of direction.

[0064] In a second aspect, the present application provides a ship positioning system, comprising:

[0065] a detection end for acquiring landmark information and generating a priority matching sequence;

[0066] a processing end for selecting a target landmark according to the priority matching sequence and matching a radar echo, calculating an actual position of the ship according to the relative position relationship between the radar echo and the target landmark, and inputting a position deviation between the actual position and a position calculated by an inertial navigation system into a Kalman filter to correct the inertial navigation system;

[0067] an output end for acquiring a ship position according to the modified inertial navigation system and updating to an electronic chart system.

[0068] The application can effectively screen out landmarks with higher recognition by acquiring landmark information and generating a priority matching sequence, avoiding false matching caused by blurred or interfered landmarks in complex environments. When matching radar echoes, the method selects target landmarks according to the priority matching sequence, pre-processes and extracts contours of the radar echoes, and then compares the shape, size and relative position in multiple dimensions to ensure the accuracy of matching. In addition, the position deviation between the calculated actual position of the ship and the position calculated by the inertial navigation system is input into the Kalman filter for correction, effectively solving the inherent error accumulation problem of the inertial navigation system, and significantly improving the long-term stability and accuracy of positioning. Finally, the ship position obtained by the modified inertial navigation system is updated to the electronic chart system, ensuring the real-time and reliability of the ship position information. Compared with the prior art, the application can effectively overcome the signal interruption and multipath effect of the global navigation satellite system in complex environments, and the problem that the radar aided positioning is easily disturbed by moving targets and rain clutter, significantly improving the positioning accuracy and anti-interference ability of the ship in complex navigation areas (such as narrow straits, island-dense areas or busy fishing operation areas), thereby ensuring the safety of navigation and operation efficiency. BRIEF DESCRIPTION OF DRAWINGS

[0069] Figure 1 is a ship positioning method flowchart provided by an embodiment of the application;

[0070] Figure 2 is a ship positioning system structure diagram provided by an embodiment of the application. DETAILED DESCRIPTION

[0071] The technical solutions in the embodiments of the application will be clearly and completely described below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, rather than all the embodiments of the application. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor fall within the protection scope of the application.

[0072] With reference to Figure 1 , an embodiment of the application provides a ship positioning method flowchart, including the following steps:

[0073] S11, acquiring landmark information and generating a priority matching sequence;

[0074] S12, selecting a target landmark according to the priority matching sequence and matching radar echoes;

[0075] S13, calculating the actual position of the ship according to the relative position relationship between the radar echoes and the target landmark;

[0076] S14, input the deviation between the actual position and the position calculated by the inertial navigation system into a Kalman filter to correct the inertial navigation system;

[0077] S15, obtain the ship position according to the modified inertial navigation system and update to the electronic chart system.

[0078] The "landmark information" referred to in the present application refers to fixed landmark data with clear geographic coordinates and geometric contour characteristics pre-stored on the electronic chart system, such as islands, coastlines, lighthouses, fixed platforms, etc. These landmark information is the basis for the ship to carry out radar-assisted positioning. The "priority matching sequence" refers to the sequence formed after the landmark information is sorted according to a certain strategy, which aims to give priority to those landmarks that are more likely to be accurately identified and matched in the subsequent matching process, in order to improve the matching efficiency and accuracy. "Radar echo" refers to the signal received by the ship radar system after transmitting electromagnetic waves, which is reflected back by the surrounding objects. After processing, these signals can form a radar image showing the distance, direction and shape of the objects. "Inertial navigation system" is a self-contained navigation system that does not rely on external signals. It measures the angular velocity and linear acceleration of the ship through internal gyroscopes and accelerometers, and calculates the position, velocity and attitude of the ship through integration operations. "Kalman filter" is a high-efficiency recursive filter that can estimate the state of a dynamic system from a series of incomplete or noisy measurements. It is commonly used in navigation systems to fuse information from different sensors to obtain more accurate estimates. "Electronic chart system" is the core equipment of modern ship navigation, which displays chart information in digital form and can superimpose dynamic information such as ship position, route, speed, etc. in real time, providing intuitive situational awareness for the crew.

[0079] The ship positioning method of the present application generates a priority matching sequence in advance, enabling the system to strategically select target landmarks and preferentially match those with high geometric uniqueness and less interference, significantly improving the accuracy and efficiency of matching.

[0080] In addition, the application inputs the deviation between the actual position of the ship obtained by radar-assisted positioning and the position calculated by the inertial navigation system into the Kalman filter for correction. Compared with the traditional simple position correction method, the application has significant advantages. The traditional simple correction may only pull the position of the inertial navigation system directly back to the radar positioning result. Such a hard correction may cause position jumping and fail to fully utilize the smoothness and high dynamic response capability of the inertial navigation system in a short time. The Kalman filter can fuse the advantages of the two positioning sources, correct the long-term drift of the inertial navigation system by using the long-term accuracy of radar positioning, and smooth the possible instantaneous noise of radar positioning by using the high update rate and short-term stability of the inertial navigation system, thereby outputting a more smooth, accurate and reliable ship position estimation. Such a fusion mechanism not only improves the positioning accuracy, but also enhances the robustness of the positioning system. Especially in complex navigation environments where satellite signals are limited or radar echo quality is poor, the application can continuously provide high-precision ship position information, thereby providing a solid guarantee for safe navigation of the ship.

[0081] Specifically, in the above ship positioning method, the step of obtaining landmark information and generating a priority matching sequence can be further refined.

[0082] Preferably, the step of obtaining landmark information and generating a priority matching sequence comprises:

[0083] obtaining fixed landmark information from the electronic chart system;

[0084] calculating a geometric uniqueness score for each landmark;

[0085] generating the priority matching sequence according to the geometric uniqueness score;

[0086] In the step of obtaining fixed landmark information from the electronic chart system, the system extracts reference point or area data with stable geographic position characteristics from the electronic chart system through data interaction. These fixed landmark information can include coastlines, islands, beacons, bridges, buildings, etc., whose geometric contours and geographic coordinates are known and relatively stable during ship navigation. Obtaining these information is the basis for subsequent landmark matching and positioning.

[0087] Further, the step of calculating a geometric uniqueness score for each landmark means that the system evaluates the recognizability and uniqueness of the geometric shape of each fixed landmark in the radar echo. The geometric uniqueness score aims to quantify the complexity of the landmark contour, the richness of the edge features and the degree of differentiation from other landmarks. For example, an island or coastline with a complex and unique contour has a higher geometric uniqueness score because it is easier to be accurately recognized and matched in the radar image and is less likely to be confused with the surrounding environment. The calculation of this score helps to screen out landmarks that are more suitable for positioning reference.

[0088] Accordingly, generating the priority matching sequence according to the geometric uniqueness scores refers to, after calculating the geometric uniqueness scores of all landmarks, the system sorts the landmarks according to these scores to form a priority matching sequence. Generally, the higher the geometric uniqueness score of a landmark, the higher its priority in the sequence. This means that in the subsequent radar echo matching process, the system will preferentially attempt to match with those landmarks that have more unique geometric features and are easier to identify.

[0089] Through the above technical solutions, the application can ensure that the selected landmarks have high recognition and differentiation, thereby effectively improving the accuracy and efficiency of radar echo and landmark matching. This priority matching mechanism based on geometric uniqueness scores enables the system to quickly lock reliable positioning references, reduces invalid matching attempts, and thus improves the real-time performance and reliability of ship positioning, especially in complex or more disturbed sea environments, where its advantages are more obvious.

[0090] Specifically, the above step of selecting a target landmark according to the priority matching sequence and matching the radar echo can be elaborated as follows.

[0091] First, a limited search area is determined according to the position calculated by the inertial navigation system. The setting of this limited search area aims to narrow the range of radar echo and landmark matching, thereby improving matching efficiency and reducing the probability of false matching. Specifically, this area can be dynamically adjusted based on the current ship position provided by the inertial navigation system and its inherent positioning error range, for example, a circular or rectangular area with a radius of a preset value centered on the calculated position can be set.

[0092] Second, a target landmark is selected from the priority matching sequence. The priority matching sequence is generated according to the geometric uniqueness scores of the landmarks, and the higher the score, the higher the reliability of the landmark in being identified and matched in the radar image. Therefore, the selection is usually made in the order of priority of the landmarks in the sequence, with higher-score landmarks being preferentially selected for matching attempts.

[0093] Further, within the limited search area, the radar echo is preprocessed and the radar echo contour is extracted. The preprocessing of the radar echo aims to eliminate noise, clutter and other interference to obtain clear and accurate radar echo data. The preprocessing methods can include but are not limited to filtering (such as median filtering, Gaussian filtering), threshold segmentation, morphological operations (such as erosion, dilation), etc. After preprocessing, the radar echo contour is extracted from the processed radar image by edge detection algorithms (such as Canny, Sobel operator) or contour extraction algorithms (such as Marching Squares). The contour is usually represented as a series of ordered pixel points or geometric curves.

[0094] Subsequently, the radar echo profile is compared with the geometric profile of the target landmark, including shape comparison, size comparison, and relative position comparison. Shape comparison can employ methods such as Shape Context, Fourier Descriptors, or Hu Invariant Moments to evaluate the geometric similarity of the two profiles. Size comparison compares parameters such as the area, perimeter, or bounding box size of the two profiles to ensure their consistency in scale. Relative position comparison refers to evaluating the consistency between the actual position of the radar echo profile relative to the ship and the expected position of the target landmark geometric profile relative to the ship, considering the ship's own position and attitude. This can be achieved by calculating the distance and angular deviation between the centroids or specific feature points of the two profiles.

[0095] Finally, if the comparison result meets the preset similarity criterion, it is confirmed that the matching is successful; otherwise, the next target landmark is selected from the priority matching sequence and the matching process is repeated. The preset similarity criterion is a threshold for judging whether the matching is successful. This criterion can be set according to the actual application scenario and the required matching accuracy, for example, it can be set to a weighted average of shape similarity, size similarity, and relative position similarity reaching a certain specific value. If the current landmark fails to match successfully, the system will automatically select the next landmark according to the order of the priority matching sequence, and repeat the preprocessing, profile extraction, and comparison process described above until a matching landmark is found or all landmarks are traversed.

[0096] Through the above technical solutions, the ship positioning method can achieve higher matching accuracy and reliability in the process of selecting target landmarks and matching radar echoes. The introduction of the limited search area effectively reduces the computational complexity and reduces the possibility of false matching. The use of the priority matching sequence ensures the efficiency of the matching process, enabling the system to quickly locate the most likely matching landmark. Multi-dimensional profile comparison, including shape, size, and relative position comparison, greatly improves the accuracy and robustness of matching, even in the presence of some distortion or noise in the radar echo, the target landmark can be effectively identified. In addition, the iterative matching mechanism ensures that even if the preferred landmark fails to match successfully, the system can continue to try other high-priority landmarks, thereby significantly improving the overall matching success rate and the continuity of positioning.

[0097] In the above ship positioning method, calculating the geometric uniqueness score for each landmark can include the following steps:

[0098] Obtaining ship motion data;

[0099] Generating an expected distorted profile of the landmark based on the ship motion data;

[0100] Extracting radar echo features;

[0101] comparing the features of the radar echo with the features of the expected distortion profile;

[0102] verifying consistency of the distorted features of the radar echo with the distorted features predicted by the ship motion data;

[0103] calculating a geometric uniqueness score of the landmark based on the comparison result and the verification result;

[0104] wherein obtaining ship motion data refers to obtaining real-time dynamic information of the ship during navigation, such as the ship's heading, speed, roll, pitch, heave, and other attitude and motion parameters. These data can be obtained through sensors such as inertial measurement units (IMU), global positioning system (GPS) receivers, loggers, etc. mounted on the ship.

[0105] Further, generating an expected distortion profile of the landmark based on the ship motion data refers to taking into account the geometric deformation of the landmark that may occur when the ship is in motion. For example, when the ship rolls or pitches, the angle at which the radar beam sweeps across the landmark changes, resulting in a deviation between the shape of the radar echo and the actual geometric profile of the landmark. The generation of the expected distortion profile aims to simulate this distortion caused by the ship's own motion, so as to accurately compare with the actual radar echo later.

[0106] Specifically, extracting radar echo features refers to processing the original echo signal received by the radar and extracting key information for identification and matching, such as the intensity distribution, shape, size, texture, and local geometric structure of the echo. These features are the basis for landmark identification and matching.

[0107] Thus, comparing the features of the radar echo with the features of the expected distortion profile refers to comparing the features extracted from the actual received radar echo with the features of the landmark distortion profile predicted based on the ship motion data. This comparison can use various image processing or pattern recognition algorithms, such as shape matching algorithms, feature point matching algorithms, etc., to quantify the similarity between the two.

[0108] As a preferred embodiment, verifying consistency of the distorted features of the radar echo with the distorted features predicted by the ship motion data aims to confirm whether the distortion of the radar echo is mainly caused by the ship's own motion, rather than by environmental noise, other ships, or unknown interference sources. This verification process helps to exclude abnormal echoes and improves the accuracy and reliability of landmark identification.

[0109] Finally, according to the comparison result and the verification result, the geometric uniqueness score of the landmark is calculated. The score is a quantitative evaluation of the reliability and recognizability of the landmark as a positioning reference under the current ship motion state. The higher the score, the higher the matching degree of the radar echo feature of the landmark with the expected distortion profile, and the stronger the consistency of the distortion feature with the ship motion prediction, so the value of the landmark as a positioning reference is greater.

[0110] Through the above technical solution, the geometric uniqueness of each landmark can be more accurately evaluated, especially in a dynamic ship motion environment. By considering the expected distortion of the radar echo caused by ship motion and comparing and verifying the actual echo, the accuracy and robustness of landmark identification can be significantly improved, and false matching or missed matching caused by ship motion can be reduced. Therefore, the generated priority matching sequence will contain higher quality and more reliable landmarks, thereby providing a more solid foundation for subsequent radar echo matching and ship position calculation, and ultimately improving the overall accuracy and reliability of ship positioning.

[0111] Specifically, the above step of generating an expected distortion profile of a landmark according to ship motion data can include the following contents:

[0112] Obtaining ship multi-dimensional motion data;

[0113] Decomposing the geometric profile of the landmark into at least one geometric primitive;

[0114] According to the ship multi-dimensional motion data, predicting the instantaneous displacement trajectory of each geometric primitive within the radar scanning period;

[0115] Superimposing the geometric primitive and the instantaneous displacement trajectory to generate the expected distortion profile of the landmark;

[0116] Wherein, obtaining ship multi-dimensional motion data refers to obtaining various motion parameters generated by the ship during navigation, such as ship heading, speed, roll, pitch, heave, yaw, and other attitude and motion information. These data can be obtained in real time by various sensors configured on the ship, such as Global Positioning System (GPS), Inertial Measurement Unit (IMU), gyroscope, accelerometer, etc. These multi-dimensional motion data can fully reflect the dynamic characteristics of the ship within the radar scanning period.

[0117] Further, decomposing the geometric profile of the landmark into at least one geometric primitive refers to structurally decomposing the ideal geometric shape of the fixed landmark stored in the electronic chart system, such as the coastline, island, bridge, building, etc. Specifically, the complex landmark profile can be decomposed into a series of basic geometric elements, such as straight line segments, circular arcs, elliptical arcs, polygon vertex sequences, etc. Such decomposition helps to simplify the subsequent mathematical modeling and motion prediction process, making the simulation of the distorted form of the landmark under the radar view more accurate and efficient.

[0118] On this basis, according to the multi-dimensional motion data of the ship, the instantaneous displacement trajectory of each geometric primitive in the radar scanning period is predicted. Specifically, due to the rolling, yawing and other motions of the ship during navigation, the instantaneous position of the landmark relative to the radar antenna will change during the radar scanning process. By utilizing the obtained multi-dimensional motion data of the ship, the relative displacement path of each geometric primitive during one scanning of the radar antenna (i.e. one radar scanning period) can be accurately calculated. For example, according to the instantaneous speed, angular velocity of the ship and the scanning rate of the radar antenna, the expected motion trajectory of each geometric primitive on the radar image can be calculated.

[0119] Finally, the geometric primitive and the instantaneous displacement trajectory are superimposed to generate the expected distorted profile of the landmark. Specifically, each decomposed geometric primitive is combined with its predicted instantaneous displacement trajectory in the radar scanning period. This superimposition operation can be understood as reconstructing the distorted or deformed form of the original geometric shape of the landmark on the radar image after considering the influence of ship motion. Thus, the generated expected distorted profile can more realistically reflect the shape change of the landmark that may occur in the actual radar echo, providing a more accurate reference for subsequent radar echo feature comparison.

[0120] Through the above technical solution, the influence of ship motion on the shape of radar echo can be more accurately simulated, and a more actual expected distorted profile can be generated. Thus, when comparing the features of the radar echo and the expected distorted profile in the subsequent, the accuracy and reliability of the comparison can be significantly improved, and thus the calculated geometric uniqueness score of the landmark is more accurate. Such accuracy is crucial for subsequent target landmark selection and radar echo matching, which helps to improve the overall accuracy and robustness of ship positioning, thereby effectively avoiding the problem of increased positioning error due to landmark distortion.

[0121] The existing ship positioning method may only rely on single-dimensional feature comparison or simple threshold judgment when verifying the consistency of the distortion characteristics of the radar echo and the distortion characteristics predicted by the ship motion data. However, in the actual complex marine environment, the radar echo may be affected by multiple factors such as sea clutter, rain and snow, reflections of other ships, and electronic interference, which may cause the radar echo to appear abnormal or artifacts, thereby interfering with the accurate judgment of the landmark distortion characteristics. If the above problems are not solved, it may cause deviation in the calculation of the landmark geometric uniqueness score, thereby affecting the accuracy and reliability of ship positioning. To this end, the present application proposes a more robust and accurate verification method, which aims to ensure that the consistency between the distortion characteristics of the radar echo and the distortion characteristics predicted by the ship motion data is more reliable through multi-dimensional analysis and abnormal echo identification.

[0122] The above verification of the consistency of the distortion characteristics of the radar echo and the distortion characteristics predicted by the ship motion data comprises:

[0123] Multi-scale feature extraction is performed on the radar echo to obtain energy distribution, texture features, and local geometric structure information of the radar echo;

[0124] According to the ship motion data, a plurality of different degrees of expected distortion profiles are generated;

[0125] The energy distribution, texture features, and local geometric structure information of the radar echo are compared;

[0126] The corresponding features of the plurality of different degrees of expected distortion profiles are obtained to obtain a plurality of similarity scores;

[0127] The distribution trend of the plurality of similarity scores on the plurality of different degrees of expected distortion profiles is analyzed to identify a similarity peak value with the ship motion data;

[0128] Abnormal echoes in the radar echo that do not match the characteristics of known interference sources are identified;

[0129] According to the similarity peak value and the abnormal echo, it is confirmed that the distortion characteristics of the radar echo and the distortion characteristics predicted by the ship motion data have consistency.

[0130] Specifically, multi-scale feature extraction of radar echoes refers to processing radar echo data by applying different analysis scales or resolutions to obtain feature information at different granularities. For example, techniques such as wavelet transform, multi-resolution analysis, or Gaussian pyramid can be used to extract energy distribution, texture features, and local geometric structure information from radar echoes. Among them, energy distribution can reflect the intensity and range of echoes; texture features can describe the surface details and uniformity of echoes; and local geometric structure information can reveal the shape, edge, and corner point features of echoes. Through multi-scale extraction, the internal characteristics of radar echoes can be more comprehensively and meticulously captured, providing a rich data basis for subsequent comparison and analysis.

[0131] Further, generating a plurality of different degrees of expected distortion profiles according to the ship motion data refers to simulating the various deformation patterns that the landmark may exhibit within the radar scanning period based on the real-time motion state of the ship (such as heading, speed, roll, pitch, yaw, etc.). For example, a series of expected distortion profiles of the landmark at different roll, pitch, or yaw angles can be generated according to the range of changes in the ship's attitude. These profiles can cover various distortion conditions that the landmark may encounter in actual motion, thereby constructing an expected distortion model library.

[0132] Thus, comparing the energy distribution, texture features, and local geometric structure information of the radar echoes with the corresponding features of the plurality of different degrees of expected distortion profiles results in a plurality of similarity scores. Specifically, various feature matching algorithms can be used, such as correlation-based, Euclidean distance, structural similarity index (SSIM), or deep learning feature matching methods, to compare the actual extracted radar echo features with the corresponding features of each expected distortion profile in the model library one by one and calculate the corresponding similarity scores. Each score reflects the matching degree of the actual echo with a specific degree of expected distortion profile.

[0133] Among them, analyzing the distribution trend of a plurality of similarity scores over a plurality of different degrees of expected distortion profiles and identifying the similarity peak value with the ship motion data refers to finding the distortion degree corresponding to the highest similarity score by statistically analyzing or curve fitting these similarity scores. This similarity peak value represents the distortion state closest to the actual radar echo predicted by the ship motion data, thereby enabling more accurate judgment of the true distortion condition of the landmark echo.

[0134] In addition, abnormal echoes in the radar echoes that do not match the characteristics of known interference sources are identified, aiming to exclude interference of non-beacon echoes. Known interference sources can include sea clutter, rain and snow echoes, radar reflections of other ships or electronic interference, etc. Through preset interference feature models or machine learning classifiers, real-time analysis is performed on the radar echoes to identify those echoes that do not match the expected distortion characteristics of beacons and match the characteristics of known interference sources, and mark them as abnormal echoes. The purpose of this step is to ensure that the subsequent consistency confirmation is based on real beacon echoes.

[0135] Finally, according to the similarity peak and the abnormal echoes, it is confirmed that the distortion characteristics of the radar echoes are consistent with the distortion characteristics predicted by the ship motion data. This means that when a high similarity peak is identified with the distortion profile predicted by the ship motion data, and at the same time no interference is detected that is marked as an abnormal echo, it is determined that the distortion characteristics of the current radar echoes are highly consistent with the distortion characteristics predicted by the ship motion data.

[0136] Through the above technical solutions, the present application can significantly improve the accuracy and reliability of the verification of the distortion characteristics of beacon echoes in the ship positioning process. Compared with the method of only simple comparison, the present application can more comprehensively understand the complex characteristics of radar echoes through multi-scale feature extraction; it can more accurately match the distortion state of the actual echoes by generating and comparing multiple expected distortion profiles of different degrees; especially importantly, by identifying and excluding abnormal echoes, the negative impact of environmental noise and interference on the verification result is effectively avoided. These improvements together ensure that the calculation of the beacon geometric uniqueness score is more accurate, thereby providing more reliable input for the subsequent calculation of the actual position of the ship, and ultimately improving the accuracy and adaptability of the entire ship positioning system in complex environments.

[0137] Specifically, the step of identifying abnormal echoes in the radar echoes that do not match the characteristics of known interference sources can be further refined.

[0138] The identification of abnormal echoes in the radar echoes that do not match the characteristics of known interference sources includes:

[0139] Obtaining feature data of the radar echoes, the feature data including instantaneous intensity distribution, local shape features and motion trajectory features;

[0140] Generating a set of expected distortion characteristics of the beacon under different motion intensities according to real-time motion data of the ship and a geometric profile of the target beacon;

[0141] Calculating a similarity according to the feature data and the set of expected distortion characteristics;

[0142] When the similarity is lower than a preset threshold, verifying the feature data and dynamic characteristics of the known interference sources, respectively;

[0143] If the feature data matches the dynamic feature of the known interference source, the radar echo is marked as the abnormal echo;

[0144] If the feature data has dynamic overlap or imitation with one of the expected distortion feature sets, the continuity and stability of the motion trajectory of the radar echo are determined, as well as the relevance of the motion trajectory to the ship's own motion;

[0145] If the motion trajectory of the radar echo does not have stability and the relevance is weak, the radar echo is identified as the abnormal echo.

[0146] Specifically, the feature data of the radar echo is obtained to provide basic information for subsequent abnormal echo identification. The feature data can include the intensity distribution of the radar echo at a certain moment, such as its peak intensity, average intensity, and gradient information of intensity change with space. Local shape features can refer to the geometric profile, aspect ratio, area, and perimeter of the echo, which help to distinguish different types of targets. Motion trajectory features can refer to the position change sequence of the echo in consecutive scanning periods, including its speed, acceleration, and direction change. These feature data can be extracted and quantified by a radar signal processing module.

[0147] Further, according to the real-time motion data of the ship and the geometric profile of the target landmark, an expected distortion feature set of the landmark under different motion intensities is generated. The real-time motion data of the ship can include the ship's speed, heading, roll, pitch, heave, and other attitude and motion parameters. The geometric profile of the target landmark refers to its shape information in the ideal state. Since the ship's motion will cause distortion of the radar echo, it is necessary to predict the distorted form of the landmark echo that may occur according to different motion intensities (e.g., different roll angles or speeds) to form a set of expected distortion features for subsequent comparison.

[0148] On this basis, the feature data of the radar echo obtained is compared with the generated expected distortion feature set. Similarity calculation can use various methods, such as based on Euclidean distance, cosine similarity, or correlation coefficient, etc. The purpose of this step is to determine whether the current radar echo matches the landmark distortion feature caused by ship motion.

[0149] When the calculated similarity is lower than the preset threshold, it indicates that the current radar echo does not match the expected landmark distortion feature, and further verification is needed to determine whether the feature data matches the dynamic feature of the known interference source. Known interference sources can include sea clutter, rain and snow clutter, bird flocks, radar signal interference from other ships, etc. By comparison, it can be determined whether the echo is caused by these known interference sources.

[0150] If the feature data matches the dynamic characteristics of a known interference source, the radar echo is marked as an abnormal echo. This means that the echo is not from a target landmark, but is caused by interference.

[0151] In addition, if the feature data has dynamic overlap or imitation with one of the expected distortion feature sets, further judgment is needed on the continuity and stability of the motion trajectory of the radar echo, and the relevance of the motion trajectory to the ship's own motion. This situation may occur when the interference source has similar characteristics to the landmark echo, or the motion pattern of the interference source has some relevance to the ship's motion.

[0152] Specifically, judging the continuity and stability of the motion trajectory of the radar echo aims to assess whether the echo's behavior pattern over time is consistent with physical laws and expectations. Continuity refers to the smoothness of the echo's position change over time, and stability refers to the fluctuation range of its motion parameters such as speed and direction. The relevance of the motion trajectory to the ship's own motion refers to whether the echo's motion is closely related to the ship's own attitude change, course change, and other motion states. For example, if the ship rolls, the distorted trajectory of the landmark echo should correspond to the periodic change of the roll.

[0153] If the motion trajectory of the radar echo does not have stability and the relevance is weak, the echo is identified as an abnormal echo. This indicates that the echo's behavior pattern does not conform to the expected performance of landmark echoes under the influence of the ship's motion, and lacks reasonable relevance to the ship's own motion, so it is likely to be an abnormal signal that is not a landmark.

[0154] Through the above technical solutions, the application can significantly improve the recognition accuracy and robustness of abnormal signals in radar echoes. Traditional abnormal echo recognition methods may only rely on simple threshold judgment or direct comparison with known interference sources, making it difficult to effectively handle abnormal signals with complex dynamic characteristics or similarities to landmark echoes. However, by introducing the judgment of the continuity, stability, and relevance to the ship's own motion of the radar echo's motion trajectory, the application can deeply analyze the dynamic behavior pattern of the echo, effectively distinguishing between landmark distorted echoes caused by ship motion and real abnormal echoes. This allows the system to accurately eliminate interference in complex sea conditions and multiple interference source environments, ensuring the accuracy of subsequent landmark matching and improving the reliability and precision of ship positioning.

[0155] Specifically, the above judgment of the continuity and stability of the motion trajectory of the radar echo, and the relevance of the motion trajectory to the ship's own motion includes:

[0156] Segmenting the instantaneous position sequence of the radar echo;

[0157] fitting the position change rate and the direction change rate in each segment after the segment processing;

[0158] judging the continuity and stability in each segment according to the fitting result;

[0159] obtaining real-time attitude data and instantaneous motion parameters of the ship;

[0160] predicting an expected motion trajectory of the fixed landmark in a radar scanning period according to the real-time attitude data and the instantaneous motion parameters;

[0161] comparing the similarity degree of the motion trajectory of the radar echo and the expected motion trajectory;

[0162] judging the correlation according to the similarity degree;

[0163] Wherein, the segment processing of the instantaneous position sequence of the radar echo refers to dividing the radar echo position data continuously obtained within a period of time into several shorter time periods or space segments. This is aimed at dealing with the possible nonlinear or complex changes of the radar echo trajectory. Through segment processing, local features can be more effectively captured, the complexity of subsequent fitting can be reduced, and the sensitivity to local abnormalities of the trajectory can be improved. Segment processing can be based on fixed time intervals, position change thresholds or adaptive algorithms.

[0164] Further, the fitting of the position change rate and the direction change rate in each segment after the segment processing refers to applying mathematical fitting methods such as polynomial fitting, spline fitting or Kalman filtering to the instantaneous position data in each segment to smooth the data and extract its motion trend. The position change rate can be understood as the displacement speed of the radar echo in unit time, and the direction change rate reflects the degree of change of its motion direction. Through fitting, the influence of measurement noise can be eliminated, and more accurate and stable motion parameters can be obtained.

[0165] Thus, according to the fitting result, the continuity and stability in each segment are judged. For example, if the fitting residual is small, and the position change rate and the direction change rate remain relatively stable or show a predictable trend within the segment, it can be judged that the radar echo trajectory in the segment has good continuity and stability. On the contrary, if the fitting effect is poor, or the change rate fluctuates sharply, it may indicate that the trajectory has interruptions, jumps or instability.

[0166] At the same time, the real-time attitude data and the instantaneous motion parameters of the ship are obtained, including but not limited to the ship's heading, roll, pitch, yaw angular velocity, speed and acceleration, etc. These data are usually provided by the ship's own inertial navigation system, global positioning system (GPS) or other sensors, and they are the basis for predicting the expected motion trajectory of the fixed landmark.

[0167] On this basis, the expected motion trajectory of the fixed landmark in the radar scanning period is predicted according to the real-time attitude data and the instantaneous motion parameters. Since the ship is in motion, the relative position of the fixed landmark scanned by the radar will change over time. Through the ship's own motion data, the relative motion trajectory that a theoretical fixed landmark should present on the radar screen in a specific radar scanning period can be accurately calculated. This provides a reliable benchmark for subsequent comparison.

[0168] Subsequently, the similarity of the motion trajectory of the radar echo and the expected motion trajectory is compared. This can be achieved through various similarity measurement methods, such as Euclidean distance, correlation coefficient, dynamic time warping (DTW) algorithm, etc. By quantifying the difference between the two, it can objectively evaluate whether the radar echo conforms to the expected performance of the fixed landmark under the ship's motion.

[0169] Finally, the correlation is determined according to the similarity. If the similarity of the motion trajectory of the radar echo and the expected motion trajectory is high, and the difference is within an acceptable range, it indicates that the radar echo is highly correlated with the ship's own motion, and is most likely a real echo from the fixed landmark. Conversely, if the similarity is low and the difference is significant, it indicates that the echo has weak correlation with the ship's motion, and may be an abnormal echo or interference.

[0170] Through the above technical solution, the accuracy and robustness of abnormal radar echo recognition in the ship positioning method can be significantly improved. This scheme effectively reduces the misjudgment rate caused by radar echo data noise, environmental interference or non-fixed targets by deeply and quantitatively judging the continuity, stability of the radar echo trajectory and its correlation with the ship's own motion. This enables the ship positioning system to still reliably filter out real landmark echoes for positioning in complex and variable sea conditions, thereby improving the overall positioning accuracy and safety.

[0171] Specifically, the fitting of the position change rate and the direction change rate in each segment after the segmentation can include the following steps:

[0172] Obtain ship attitude sensor data;

[0173] According to the ship attitude sensor data, the instantaneous position sequence is attitude compensated to obtain an attitude-compensated radar echo instantaneous position sequence;

[0174] The attitude-compensated radar echo instantaneous position sequence is analyzed in the frequency domain to identify and filter out high-frequency noise components, to obtain a radar echo instantaneous position sequence filtered of high-frequency noise components and to perform polynomial fitting to obtain a position change rate;

[0175] differencing the position change rate to obtain a direction change rate;

[0176] wherein the ship attitude sensor data is intended to obtain real-time attitude information of the ship within the radar scanning period, such as pitch angle, roll angle, yaw angle, etc., as well as instantaneous motion parameters of the ship, such as speed, acceleration, etc. These data can be provided by on-board sensors such as inertial measurement units (IMU), global positioning system (GPS) receivers, gyroscopes, accelerometers, etc., for accurately reflecting the attitude changes of the ship during the motion process.

[0177] Further, according to the ship attitude sensor data, the instantaneous position sequence is attitude-compensated, aiming to eliminate the influence of the ship's own motion (such as rolling, pitching, etc.) on the radar echo instantaneous position sequence. Specifically, when the ship sails on the sea, it will be affected by factors such as waves and wind, resulting in attitude changes, which will cause the position of the landmark echo scanned by the radar to shift instantaneously. Through attitude compensation, the instantaneous position of the radar echo can be converted from the ship coordinate system to the geodetic coordinate system or a stable reference coordinate system, thereby obtaining a more accurate and stable radar echo instantaneous position sequence.

[0178] On this basis, the radar echo instantaneous position sequence after attitude compensation is analyzed in the frequency domain to identify and filter out high-frequency noise components, aiming to improve the smoothness and accuracy of the position sequence. Specifically, the radar echo data may be affected by various random noises and environmental disturbances during the acquisition process, and these noises usually exhibit high-frequency components. Through frequency domain analysis methods such as Fourier transform, these high-frequency noise components can be identified and filtered out using low-pass filters and other techniques, thereby obtaining a more pure and more accurate radar echo instantaneous position sequence that reflects the true motion trajectory.

[0179] Subsequently, the radar echo instantaneous position sequence after filtering out high-frequency noise components is polynomial-fitted, aiming to extract continuous position change rates from discrete instantaneous position data. For example, the least squares method can be used to perform polynomial fitting on the position sequence, and the fitted polynomial function can represent the position change trend of the radar echo over time. By taking the derivative of the fitting function, the instantaneous position change rate, i.e., the speed information, can be obtained.

[0180] Finally, the position change rate is differentiated to obtain the direction change rate of the radar echo. Specifically, the direction change rate can be understood as the rate of change of the velocity vector direction over time, reflecting the degree of curvature or turning of the radar echo motion trajectory. By differentiating the position change rate (velocity), the acceleration information can be obtained, and the normal component of the acceleration is closely related to the direction change rate, so that the direction change rate can be derived.

[0181] By the technical solution, the accuracy and robustness of radar echo motion trajectory analysis can be improved significantly. The attitude compensation effectively avoids the measurement error introduced by the ship's own motion, ensuring the reliability of the original data. The frequency domain noise filtering further purifies the data and reduces the influence of random noise on the fitting result. The combination of polynomial fitting and difference processing makes the position change rate and direction change rate extracted from the discrete data more smooth and accurate, which can more truly reflect the motion characteristics of the radar echo. These improvements make the judgment of the continuity, stability and association with the ship motion of the radar echo motion trajectory more accurate, thereby effectively improving the identification ability of abnormal echoes and finally improving the overall accuracy and reliability of ship positioning.

[0182] Reference Figure 2 , Figure 2 The first embodiment of the application provides a structural schematic diagram of a ship positioning system, which comprises:

[0183] The detection end is configured to acquire landmark information and generate a priority matching sequence.

[0184] The processing end is configured to select a target landmark according to the priority matching sequence and match radar echoes, calculate the actual position of the ship according to the relative position relationship between the radar echoes and the target landmark, and input the position deviation between the actual position and the position calculated by the inertial navigation system into a Kalman filter to correct the inertial navigation system.

[0185] The output end is configured to acquire the position of the ship according to the modified inertial navigation system and update the position to an electronic chart system.

[0186] It should be noted that the ship positioning system provided by the embodiments of the application is used to execute all process steps of the ship positioning method provided by the above-mentioned embodiments, and the working principles and beneficial effects of the two are one-to-one corresponding, so the working principles and beneficial effects of the two are not repeated.

[0187] The embodiments of the application further provide a terminal device. The terminal device comprises a processor, a memory, and a computer program stored in the memory and executable on the processor. The processor implements the steps in the above-mentioned various ship positioning method embodiments when executing the computer program, for example Figure 1 The processor implements the functions of the modules / units in the above-mentioned various system embodiments when executing the computer program.

[0188] For example, the computer program can be divided into one or more modules / units, which are stored in the memory and executed by the processor to complete the present application. The one or more modules / units can be a series of computer program instruction segments capable of completing a specific function, which are used to describe the execution process of the computer program in the terminal device.

[0189] The terminal device can be a desktop computer, a notebook computer, a palm computer, a smart tablet and the like. The terminal device can include, but is not limited to, a processor, a memory. Those skilled in the art can understand that the above components are only examples of the terminal device and do not constitute a limitation on the terminal device, and can include more or fewer components than the above, or combine certain components, or different components, for example, the terminal device can also include an input / output device, a network access device, a bus and the like.

[0190] The processor can be a central processing unit (CPU), and can also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic components, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor and the like. The processor is the control center of the terminal device, and connects all parts of the terminal device through various interfaces and lines.

[0191] The memory can be used to store the computer program and / or modules, and the processor realizes various functions of the terminal device by running or executing the computer program and / or modules stored in the memory, and calling data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application program required by a function (such as a sound playing function, an image playing function, etc.), etc.; and the data storage area can store data created according to the use of the mobile phone (such as audio data, a phone book, etc.), etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, for example, a hard disk, a memory, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, at least one disk storage device, a flash memory device, or other volatile solid-state memory devices;

[0192] The modules / units integrated in the terminal device, if realized in the form of software function units and sold or used as independent products, can be stored in a computer readable storage medium. Based on this understanding, all or part of the processes in the above-mentioned embodiment methods can also be completed by a computer program instructing related hardware, and the computer program can be stored in a computer readable storage medium. When the processor executes the computer program, the steps of the above-mentioned various method embodiments can be realized. The computer program includes computer program code, which can be in the form of source code, object code, executable files or some intermediate forms, etc. The computer readable medium can include any entity or system, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc. that can carry the computer program code. It should be noted that the contents included in the computer readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction, for example, in some jurisdictions, according to legislation and patent practice, the computer readable medium does not include electric carrier signals and telecommunication signals.

[0193] It should be noted that the system embodiments described above are merely illustrative, wherein the units described as separate components can or can not be physically separated, and the components displayed as units can or can not be physical units, i.e. can be located in one place or can be distributed to multiple network units. Part or all of the modules can be selected to achieve the purpose of the embodiment according to actual needs. In addition, the connection relationship between the modules in the system embodiment provided by the present application indicates that there is a communication connection between them, which can be implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement it without creative labor.

[0194] The above specific embodiments further illustrate the purpose, technical scheme and beneficial effects of the present application. It should be understood that the above description is only a specific embodiment of the present application and is not intended to limit the protection scope of the present application. It is particularly pointed out that any modification, equivalent replacement, improvement, etc. made by those skilled in the art within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A method of positioning a marine vessel, characterized by, The method comprises: acquiring landmark information and generating a priority matching sequence; selecting a target landmark according to the priority matching sequence and matching radar echoes; calculating the actual position of the ship according to the relative position relationship between the radar echoes and the target landmark; inputting the deviation between the actual position and the position calculated by the inertial navigation system into a Kalman filter to correct the inertial navigation system; acquiring the position of the ship according to the modified inertial navigation system and updating to the electronic chart system; the acquiring landmark information and generating a priority matching sequence comprises: acquiring fixed landmark information from the electronic chart system; calculating a geometric uniqueness score for each landmark; generating the priority matching sequence according to the geometric uniqueness score; the calculating a geometric uniqueness score for each landmark comprises: acquiring ship motion data; generating an expected distortion profile of the landmark according to the ship motion data; extracting radar echo features; comparing the radar echo features with the features of the expected distortion profile; verifying the consistency of the distortion features of the radar echoes with the distortion features predicted by the ship motion data; calculating the geometric uniqueness score of the landmark according to the comparison result and the verification result.

2. A method of positioning a marine vessel as claimed in claim 1, characterised in that, the selecting a target landmark according to the priority matching sequence and matching radar echoes comprises: determining a limited search area according to the position calculated by the inertial navigation system; selecting a target landmark from the priority matching sequence; preprocessing the radar echoes and extracting radar echo profiles in the limited search area; comparing the radar echo profiles with the geometric profile of the target landmark, which includes shape comparison, size comparison and relative position comparison; if the comparison result reaches a preset similarity standard, it is confirmed that the matching is successful; otherwise, the next target landmark is selected from the priority matching sequence and the matching process is repeated.

3. A method of positioning a marine vessel as claimed in claim 1, characterised in that, the generating an expected distortion profile of the landmark according to the ship motion data comprises: acquiring ship multi-dimensional motion data; decomposing the geometric profile of the landmark into at least one geometric primitive; predicting the instantaneous displacement trajectory of each geometric primitive within a radar scanning period according to the ship multi-dimensional motion data; superimposing the geometric primitive and the instantaneous displacement trajectory to generate the expected distortion profile of the landmark.

4. A method of positioning a marine vessel as claimed in claim 1, characterised in that, the verifying the consistency of the distortion features of the radar echoes with the distortion features predicted by the ship motion data comprises: performing multi-scale feature extraction on the radar echoes to obtain the energy distribution, texture features and local geometric structure information of the radar echoes; generating a plurality of expected distortion profiles of different degrees according to the ship motion data; comparing the energy distribution, texture features and local geometric structure information of the radar echoes with the corresponding features of the plurality of expected distortion profiles of different degrees to obtain a plurality of similarity scores; analyzing the distribution trend of the plurality of similarity scores on the plurality of expected distortion profiles of different degrees to identify the similarity peak value with the ship motion data; identifying abnormal echoes in the radar echoes that do not match the features of known interference sources; According to the similarity peak value and the abnormal echo, it is confirmed that the distortion feature of the radar echo is consistent with the distortion feature predicted by the ship movement data.

5. A method of positioning a marine vessel as claimed in claim 4, characterised in that, The identification of the abnormal echo in the radar echo that does not match the feature of the known interference source comprises: Obtaining feature data of the radar echo, the feature data comprising instantaneous intensity distribution, local shape feature and movement trajectory feature; According to real-time movement data of the ship and geometric profile of the target landmark, a set of expected distortion features of the landmark under different movement intensities is generated; According to the feature data and the set of expected distortion features, a similarity is calculated; When the similarity is lower than a preset threshold, the feature data is verified with dynamic features of the known interference source respectively; If the feature data matches the dynamic features of the known interference source, the radar echo is marked as the abnormal echo; If the feature data has dynamic overlap or imitation with one of the features in the set of expected distortion features, the continuity and stability of the movement trajectory of the radar echo, and the relevance of the movement trajectory to the ship's own movement are judged; If the movement trajectory of the radar echo does not have stability and the relevance is lower than a preset threshold, the radar echo is identified as the abnormal echo.

6. A method of positioning a marine vessel as claimed in claim 5 wherein, The judgment of the continuity and stability of the movement trajectory of the radar echo, and the relevance of the movement trajectory to the ship's own movement comprises: Segmenting the instantaneous position sequence of the radar echo; Fitting the position change rate and direction change rate in each segment after segmentation; According to the fitting result, the continuity and stability in each segment are judged; Obtaining real-time attitude data and instantaneous movement parameters of the ship; According to the real-time attitude data and the instantaneous movement parameters, an expected movement trajectory of the fixed landmark in a radar scanning period is predicted; Comparing the similarity of the movement trajectory of the radar echo and the expected movement trajectory; According to the similarity, the relevance is judged.

7. A method of positioning a vessel according to claim 6, characterised in that, The fitting of the position change rate and direction change rate in each segment after segmentation comprises: Obtaining ship attitude sensor data; According to the ship attitude sensor data, the instantaneous position sequence is attitude-compensated to obtain an attitude-compensated radar echo instantaneous position sequence; The attitude-compensated radar echo instantaneous position sequence is analyzed in frequency domain, high-frequency noise components are identified and filtered out, a radar echo instantaneous position sequence after filtering out high-frequency noise components is obtained and is polynomial-fitted to obtain the position change rate; The position change rate is differentially processed to obtain the direction change rate.

8. A vessel positioning system for performing the vessel positioning method of claim 1, characterized by Comprise: A detection end for obtaining landmark information and generating a priority matching sequence; A processing end for selecting a target landmark according to the priority matching sequence and matching a radar echo; calculating an actual position of the ship according to a relative position relationship between the radar echo and the target landmark; inputting a position deviation between the actual position and a position calculated by an inertial navigation system into a Kalman filter to correct the inertial navigation system; An output end for obtaining a ship position according to the modified inertial navigation system and updating to an electronic chart display and information system.

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