Ship navigation positioning method based on radar chart matching

Abstract

The invention relates to the technical field of ship navigation, in particular to a ship navigation positioning method based on radar chart matching, which comprises the following steps of: preprocessing radar echoes; radar echo shoreline extraction: detecting a radar echo contour, calculating a local gradient and an edge direction at each pixel point, outputting a shoreline contour of the radar echo, and extracting radar shoreline feature points; matching the radar image with the electronic chart scale; electronic chart shoreline data reading: according to a chart storage data structure, obtaining shoreline feature point data by analyzing data records of a shoreline layer; image matching: matching the radar shoreline feature points with the electronic chart shoreline feature points by calculating the distance between the feature points; and confirming the ship position. According to the method, the influence of echo noise on an image matching result can be reduced, the radar echo contour is extracted, and the influence of image noise is overcome.

Description

Technical Field

[0001] This invention relates to the field of ship navigation technology, and specifically to a ship navigation and positioning method based on radar chart matching. Background Technology

[0002] Safe navigation of ships relies heavily on ship navigation and positioning technology. Obtaining accurate, real-time, and reliable ship position information is a prerequisite for the normal operation of ships. Therefore, ship navigation and positioning technology is one of the core elements of safe navigation for ships today.

[0003] Currently, the technologies used for ship navigation and positioning mainly include Global Navigation Satellite System (GNSS), inertial navigation system / log combination, and visual navigation.

[0004] 1) The most widely used ship navigation and positioning technology is the Global Navigation Satellite System (GNSS). Currently, major GNSS systems include my country's BeiDou Navigation Satellite System (BDS), the United States' GPS (Global Positioning System), Russia's GLONASS (GLOBALNAVIGATION SATELLITE SYSTEM), and the European Union's GALILEO (Galileo) satellite navigation system. When GNSS signals are good, they can provide accurate navigation information for ships. However, when navigating in obstructed areas or when electromagnetic waves are interfered with, the GNSS reception signal deteriorates or even drops off, causing the ship to lose positioning information and become unable to navigate.

[0005] 2) IMU / DVL integrated navigation is employed. An IMU (Inertial Measurement Unit) consists of a three-axis accelerometer and a three-axis gyroscope, measuring linear and angular velocities and integrating them to obtain the vehicle's attitude. However, attitude diverges over time, causing cumulative errors in the system. A DVL (Doppler Velocity Measure) is a navigation device that measures the vehicle's velocity acoustically. By calculating the Doppler frequency shift, the vehicle's velocity relative to the seabed can be obtained. However, in deep waters and sea conditions with significant wind, waves, and currents, DVL velocity measurements relative to the ground have errors, affecting the accuracy of integrated navigation. When GNSS signal lock is lost, relying solely on IMU / DVL integrated navigation is problematic. For ships, IMUs are limited by cost, have poor accuracy, are prone to divergence, and cannot maintain high-precision navigation for extended periods. The navigation and positioning system cannot provide accurate navigation information.

[0006] 3) Visual navigation uses cameras as the core sensor to acquire environmental information. Visual navigation utilizes cameras to observe the surrounding environment and has advantages such as no drift, good stability, high resolution, low price, and easy installation. However, different cameras bring different problems. Monocular cameras lack depth information, the synchronization of binocular cameras is difficult to guarantee and requires a huge amount of computation, and depth cameras are easily affected by lighting and have a small field of view. These problems will cause the positioning accuracy of visual navigation systems to decrease in scenarios with fast camera movement or weak texture.

[0007] In conclusion, given the challenges of GNSS signal loss or low positioning accuracy using other methods during actual ship navigation, it is essential to research a ship navigation and positioning method suitable for complex sea conditions. Summary of the Invention

[0008] The purpose of this invention is to provide a ship navigation and positioning method based on radar chart matching, so as to solve the technical problem of low positioning accuracy in existing navigation systems during ship navigation. The specific technical solution is as follows:

[0009] This invention provides a ship navigation and positioning method based on radar chart matching, comprising the following steps: acquiring radar data of the ship; radar echo preprocessing: filtering out clutter from the radar data using a region growing method; radar echo shoreline extraction: detecting the radar echo contour, calculating the local gradient and edge direction at each pixel, outputting the shoreline contour of the radar echo, and extracting radar shoreline feature points; radar image and electronic chart scale matching: matching the electronic chart with extracted coastal edge features and the real-time radar image with extracted coastal edge features scaled to the chart scale; electronic chart shoreline data reading: obtaining shoreline feature point data by parsing the data records of the shoreline layer according to the chart storage data structure; image matching: matching radar shoreline feature points with electronic chart shoreline feature points by calculating the distance between feature points; ship position confirmation: calculating the chart coordinates corresponding to the radar image scanning center and obtaining the ship's position.

[0010] A further improvement of the ship navigation and positioning method based on radar chart matching in this invention lies in the use of a region growing method to filter out clutter, including pixel merging. Specifically, seed pixels are first determined, and one or a group of valid echo regions are identified and selected as the starting point of the seed pixels on the radar image. Seed pixels are automatically selected by setting a threshold, and pixels above the threshold are considered as potential seed pixels. Similarity judgment and region growing are performed. Starting from the seed pixel, pixels with the same properties are searched in its surrounding neighborhood and merged into the current region. These newly merged pixels are then used as new seed pixels to continue the search and merging process.

[0011] A further improvement of the ship navigation and positioning method based on radar chart matching in this invention lies in that, when automatically selecting seed pixels by setting a threshold, similarity criteria for grayscale difference threshold and similarity criteria for reflectivity difference threshold are set to determine whether two pixels meet the similarity criteria for grayscale difference threshold or reflectivity difference threshold. A stack or queue is used to manage the seed pixels to be processed. The initial seed pixel is added to the stack or queue, and then the steps of determining whether two pixels meet the similarity criteria for grayscale difference threshold or reflectivity difference threshold are executed cyclically: a seed pixel is taken from the stack or queue; the pixels in its surrounding neighborhood are checked, and it is determined whether they can be merged according to the similarity criteria for grayscale difference threshold or reflectivity difference threshold; if so, the pixel is added to the current area and added to the stack or queue as a new seed pixel; if not, the next pixel is checked; the steps of determining whether two pixels meet the similarity criteria for grayscale difference threshold or reflectivity difference threshold are repeated until the stack or queue is empty.

[0012] A further improvement of the ship navigation and positioning method based on radar chart matching in this invention is that the clutter filtering method using the region growing method also includes region merging and clutter filtering. As region growing proceeds, pixels that were originally scattered and met the similarity criteria of gray value difference threshold or reflectivity difference threshold will gradually merge into connected regions. Pixels that do not meet the similarity criteria of gray value difference threshold or reflectivity difference threshold are excluded from the merged regions. Different labels are used to mark different connected regions. Pixels that are not marked as connected regions are clutter and are filtered out from the radar image.

[0013] A further improvement of the ship navigation and positioning method based on radar chart matching in this invention is that, when detecting the radar echo profile, a median filtering method is used to smooth the radar echo image, and the contrast of the smoothed radar echo image is enhanced.

[0014] A further improvement of the ship navigation and positioning method based on radar chart matching in this invention is that, when calculating the local gradient at each pixel, the gradient components in the horizontal and vertical directions of each pixel in the radar echo image after contrast enhancement are calculated, and the gradient direction and gradient intensity of each pixel are calculated based on the gradient components.

[0015] A further improvement of the ship navigation and positioning method based on radar chart matching in this invention is that, when calculating the edge direction at each pixel, pixels with gradient strength greater than the upper threshold are considered strong boundaries, pixels with gradient strength between the upper and lower thresholds are considered weak boundaries, and pixels with gradient strength less than the lower threshold are considered non-boundaries. All strong boundary points are retained as part of the edge. For each weak boundary point, it is traversed, and it is checked whether there is a strong boundary point in its eight neighborhood. If so, the weak boundary point is connected to the nearest strong boundary point and retained as part of the edge. If not, but other weak boundary points are connected to form a continuous path, and the path connects to at least one strong boundary point, the weak boundary point is retained. Weak boundary points that are not connected to strong boundaries and do not form a valid path are considered non-boundaries and deleted from the edge image. The extracted edges are smoothed, and the final edge image is output.

[0016] A further improvement of the ship navigation and positioning method based on radar chart matching in this invention lies in the relationship between the electronic chart and the scaling factor when matching the radar image with the electronic chart scale:

[0017] N m =S d / S i 1);

[0018] Wherein: S i Original scale of electronic nautical chart; S d To display the scale; N m The scaling factor for the electronic nautical chart;

[0019] Let S d =S l The required scaling factor N for the electronic nautical chart is... m as follows:

[0020]

[0021] Where: DPI is the number of pixels in the radar image; R is the radar operating range; r is the radius of the radar echo; S l This is the scale of the radar image.

[0022] A further improvement of the ship navigation and positioning method based on radar chart matching in this invention lies in that, when calculating the distance between feature points, M and N are the sets of shoreline feature points from the radar echo and the electronic chart, respectively, and the distance H(M,N) between the feature points is defined as follows:

[0023] H(M,N)=max(h(M,N),h(N,M))3);

[0024] in,

[0025]

[0026] Where: m and n are points in M ​​and N respectively; h(M,N) is the one-way distance from feature point M to feature point N; h(N,M) is the one-way distance from feature point N to feature point M.

[0027] A further improvement of the ship navigation and positioning method based on radar chart matching in this invention lies in transforming the coordinates of radar feature point position parameters into the corresponding pixel coordinates in the radar image when calculating the chart coordinates corresponding to the radar image scanning center. This transformation is done by converting the polar coordinates to rectangular coordinates, and the calculation formula is as follows:

[0028]

[0029] Where: P is the number of pixels in each row of the image; Q is the number of pixels in each column of the image; x0 and y0 are the coordinates of the radar scan center; a and b are the coordinates of the radar feature point target on the nautical chart; S is the distance in polar coordinates; α is the bearing in polar coordinates; x is the column of the pixel in rectangular coordinates; y is the row of the pixel in rectangular coordinates.

[0030] The coordinates of the radar scan center on the nautical chart are obtained using formula 5).

[0031]

[0032] The radar scan center location information is converted into latitude and longitude information using the following formula:

[0033]

[0034] in: λ represents the longitude of the radar scan center; λ represents the latitude of the radar scan center. λ0 is the longitude of the chart reference point; K is the coordinate angle conversion factor; S0 is the distance in the chart coordinate form.

[0035] The application of the technical solution of the present invention has the following beneficial effects:

[0036] This invention relates to a ship navigation and positioning method based on radar chart matching. By preprocessing the radar echoes, the impact of echo noise on the image matching results is reduced. By performing edge detection on the radar image, the radar echo contour can be extracted effectively. By calculating the minimum distance, the radar shoreline and the electronic nautical chart shoreline are matched, ensuring the robustness and effectiveness of the matching and effectively overcoming the influence of image noise. This solves the technical problem of low positioning accuracy in existing navigation systems during ship navigation. This invention provides a solution for ship navigation and positioning when satellite navigation and positioning methods fail, and can meet the requirements for accurate navigation and positioning between the ship and the shoreline.

[0037] In addition to the objectives, features, and advantages described above, the present invention has other objectives, features, and advantages. The invention will now be described in further detail with reference to the figures. Attached Figure Description

[0038] The accompanying drawings, which form part of this application, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings:

[0039] Figure 1 This is a flowchart of the ship navigation and positioning method based on radar chart matching of the present invention. Detailed Implementation

[0040] The embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0041] See Figure 1 As shown, a ship navigation and positioning method based on radar chart matching includes the following steps:

[0042] (1) First, acquire the radar data of the ship; then perform radar echo preprocessing: specifically, filter out various clutters from the radar data based on the region growing method.

[0043] The following is a detailed description of the filtering steps and algorithm: First, seed pixels are determined by identifying and selecting one or a set of useful echo regions on the radar image as the starting point for seed pixels. These regions are then used as a reference for subsequent growth based on reflectivity characteristics or specific shapes and sizes. Seed pixels are automatically selected by setting a threshold (reflectivity threshold). Pixels above the threshold are considered potential seed pixels. In some cases, manual intervention may be required to select seed pixels to ensure accuracy. Next, similarity judgment and region growth are performed. Starting from the seed pixel, pixels with similar or identical reflectivity, grayscale values, and texture characteristics are searched within its surrounding neighborhood (e.g., eight-neighborhood or four-neighborhood). Pixels that meet the criteria are merged into the current region, and these newly merged pixels are used as new seed pixels for further searching and merging. Similarity criteria such as grayscale difference threshold and reflectivity difference threshold are set according to actual needs to determine whether two pixels are similar. A stack or queue is used to manage the seed pixels to be processed. Initial seed pixels are added to the stack or queue, and then the following operations are performed repeatedly:

[0044] ① Retrieve a seed pixel from the stack or queue;

[0045] ② Examine the pixels in its surrounding neighborhood and determine whether they can be merged based on similarity criteria;

[0046] ③ If so, add the pixel to the current region and add it as a new seed pixel to the stack or queue; if not, determine the next pixel.

[0047] ④ Repeat the above process until the stack or queue is empty.

[0048] Then, region merging and clutter filtering are performed. As region growth progresses, previously scattered similar pixels gradually merge into larger connected regions. Simultaneously, pixels dissimilar to the seed pixels (i.e., clutter) are excluded from the merged regions. During the merging process, different labels are used to mark different connected regions. Ultimately, pixels not marked as connected regions are considered clutter and can be filtered out of the radar image. After filtering, the filtering effect is evaluated. Signal-to-noise ratio (SNR) and clutter suppression ratio (CSRR) metrics are used to check whether the filtered radar image retains useful echo regions and effectively filters out various types of clutter.

[0049] (2) Radar echo shoreline extraction: Detect the radar echo contour, calculate the local gradient and edge direction at each point, and the point with the local maximum of the gradient is the edge point. In this invention, setting a single threshold for edge extraction often leads to the loss of some edges. Choosing a suitable threshold is difficult; therefore, two thresholds are set to ensure the accuracy of edge extraction. Pixels above the upper threshold are called strong boundaries; pixels below the lower threshold are called non-boundaries; and those in between are called weak boundaries. Only strong boundaries and their connected weak boundaries are retained as the final edges.

[0050] The following are the detailed methods for shoreline extraction:

[0051] ① Preprocessing: Median filtering is used to smooth the radar echo image to reduce the impact of noise on gradient calculation. Histogram equalization and other methods are used to enhance contrast and improve the accuracy of edge detection.

[0052] ② For each pixel in the preprocessed image, the gradient components in the horizontal and vertical directions are calculated using the Canny operator (a multi-level edge detection algorithm).

[0053] ③ Calculate the gradient direction and gradient intensity (i.e., the magnitude of the gradient) of each pixel based on the gradient components.

[0054] ④ Gradient direction approximation: The gradient direction of each pixel is approximated to one of the eight main directions, namely the four base point directions and the four corner point directions.

[0055] ⑤ Set thresholds: Based on the characteristics of the image, set an upper threshold for identifying strong boundaries with higher values ​​and a lower threshold for distinguishing non-boundaries with lower values.

[0056] ⑥ Pixel Classification: Pixels with gradient strength greater than the upper threshold are considered strong boundaries. Weak boundaries: Pixels with gradient strength between the upper and lower thresholds are considered weak boundaries. Pixels with gradient strength less than the lower threshold are treated as non-boundaries.

[0057] ⑦ Edge Connection Processing: All strong boundary points are directly retained as part of the edge. For each weak boundary point, iterate through its 8-neighborhood and check if a strong boundary point exists within it. If so, connect the weak boundary point to the nearest strong boundary point and retain it as part of the edge. If there is no strong boundary point in its 8-neighborhood, but other weak boundary points are connected to form a continuous path, and this path connects to at least one strong boundary point, then these weak boundary points are also retained. Isolated weak boundaries are removed: Weak boundary points that are not connected to strong boundaries and do not form a valid path are considered non-boundaries and removed from the edge image.

[0058] ⑧ Smoothing using the buffer circle method: Smoothing the extracted edges using inner and outer buffer circles can reduce jitter or noise caused by gradient calculation or threshold selection. The final edge image is output, which clearly represents the shoreline contour of the radar echo.

[0059] (3) Matching radar image with electronic chart scale: Let the original scale of the electronic chart be S i The display scale is S d The scaling factor of the electronic nautical chart is N. m The relationship between the three is as follows:

[0060] N m =S d / S i 1);

[0061] Let the radar operating range be R, with units in nm, and the output radar echo be an image with radius r, with units in mm. The scale of this radar image is S. l .

[0062] Let S d =S l Then we can determine the required scaling factor for the electronic nautical chart:

[0063]

[0064] Where DPI is the number of pixels in the radar image; R is the radar operating range; r is the radius of the radar echo; S l This is the scale of the radar image.

[0065] (4) Reading coastline data from electronic nautical charts: Based on the data structure of the nautical chart storage, the coastline feature point data is obtained by parsing the data records of the coastline layer.

[0066] (5) Image matching: Matching radar shoreline feature points with electronic nautical chart shoreline feature points is achieved by calculating the distance between feature points. The smaller the distance, the more accurate the positioning and the higher the matching degree. M and N are the sets of shoreline feature points of the radar echo and the electronic nautical chart, respectively, and the distance between them is defined as follows:

[0067] H(M,N)=max(h(M,N),h(N,M))3);

[0068] in,

[0069]

[0070] In the formula, m and n are points in M ​​and N, respectively; h(M,N) is the one-way distance from feature point M to N; h(N,M) is the one-way distance from feature point N to feature point M. First, find the minimum value of the distance from each point in M ​​to all points in N, and then find the maximum value among all the minimum values.

[0071] (6) Ship position confirmation: Determine the nautical chart coordinates corresponding to the radar scanning center. First, transform the coordinates of the radar feature point position parameters into the corresponding pixel coordinates in the radar image, changing from polar coordinates (distance S, bearing α) to rectangular coordinates (pixel column x, pixel row y). The calculation formula is as follows:

[0072]

[0073] In the formula: P is the number of pixels in each row of the image; Q is the number of pixels in each column of the image; x0 and y0 are the coordinates of the radar scan center; a and b are the coordinates of the radar feature point target on the nautical chart; S is the distance in polar coordinates; α is the bearing in polar coordinates; x is the column of the pixel in rectangular coordinates; y is the row of the pixel in rectangular coordinates.

[0074] The coordinates of the radar scan center on the nautical chart can be obtained using formula 5).

[0075]

[0076] The radar scan center location information is converted into latitude and longitude information using the following formula:

[0077]

[0078] in: λ represents the longitude of the radar scan center; λ represents the latitude of the radar scan center. λ0 is the longitude of the chart reference point; K is the coordinate angle conversion factor; S0 is the distance in the chart coordinate form.

[0079] Finally, the ship's position information can be determined based on the relative positional relationship between the radar scanning center and the ship, thus enabling ship navigation and positioning.

[0080] This invention relates to a ship navigation and positioning method based on radar chart matching. By preprocessing the radar echoes, the impact of echo noise on the image matching results is reduced. By performing edge detection on the radar image, the radar echo contour can be extracted effectively. By calculating the minimum distance, the radar shoreline and the electronic nautical chart shoreline are matched, ensuring the robustness and effectiveness of the matching and effectively overcoming the influence of image noise. This solves the technical problem of low positioning accuracy in existing navigation systems during ship navigation. This invention provides a solution for ship navigation and positioning when satellite navigation and positioning methods fail, and can meet the requirements for accurate navigation and positioning between the ship and the shoreline.

[0081] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A ship navigation positioning method based on radar chart matching, characterized in that, The method comprises the following steps: Obtaining radar data of a ship; Radar echo preprocessing: filtering out clutter by using region growing method; Radar echo coastline extraction: detecting radar echo contour, calculating local gradient and edge direction at each pixel point, outputting radar echo coastline contour, and extracting radar coastline feature points; Radar image and electronic chart scale matching: matching the electronic chart with extracted coastline features and the real-time radar image with extracted coastline features and scaled to the scale of the chart; Electronic chart coastline data reading: obtaining coastline feature point data by analyzing data records of the coastline layer according to the chart storage data structure; Image matching: matching radar coastline feature points and electronic chart coastline feature points by calculating the distance between the feature points; Ship position confirmation: calculating the chart coordinates corresponding to the scanning center of the radar image, and obtaining the ship position.

2. The radar chart matching based ship navigation positioning method according to claim 1, characterized in that, The region growing method for filtering out clutter includes merging pixels, specifically: first, determine the seed pixel, identify and select one or a group of effective echo regions on the radar image as the starting point of the seed pixel, automatically select the seed pixel by setting a threshold, the pixel points higher than the threshold are regarded as potential seed pixels, similarity judgment and region growing are performed, starting from the seed pixel, search for pixels with the same properties in its neighborhood, merge them into the current region, and continue to search and merge using the newly merged pixels as new seed pixels.

3. The radar chart matching based ship navigation positioning method according to claim 2, wherein, When automatically selecting the seed pixel by setting the threshold, set the similarity criterion of the gray value difference threshold and the similarity criterion of the reflectivity difference threshold to judge whether two pixels meet the similarity criterion of the gray value difference threshold or the similarity criterion of the reflectivity difference threshold, use a stack or queue to manage the seed pixels to be processed, add the initial seed pixel to the stack or queue, and then loop to execute the steps of judging whether two pixels meet the similarity criterion of the gray value difference threshold or the similarity criterion of the reflectivity difference threshold: Take out a seed pixel from the stack or queue; Check the pixels in its neighborhood, and judge whether they can be merged according to the similarity criterion of the gray value difference threshold or the similarity criterion of the reflectivity difference threshold; If yes, add the pixel to the current region and add it as a new seed pixel to the stack or queue, if not, judge the next pixel; Repeat the step of judging whether two pixels meet the similarity criterion of the gray value difference threshold or the similarity criterion of the reflectivity difference threshold until the stack or queue is empty.

4. The radar chart matching based ship navigation positioning method according to claim 3, wherein, The region growing method for filtering out clutter also includes merging regions and clutter filtering. As the region growing proceeds, the originally dispersed pixels that meet the similarity criterion of the gray value difference threshold or the similarity criterion of the reflectivity difference threshold will gradually merge into connected regions, the pixels that do not meet the similarity criterion of the gray value difference threshold or the similarity criterion of the reflectivity difference threshold are excluded from the merged region, different labels are used to mark different connected regions, and the pixels not marked as connected regions are clutter, which are filtered out from the radar image.

5. The radar chart matching based ship navigation positioning method according to claim 1, wherein, When detecting the radar echo contour, median filtering is used to smooth the radar echo image, and the radar echo image after smoothing is enhanced in contrast.

6. The radar chart matching based ship navigation positioning method according to claim 5, wherein, In the calculation of the local gradient at each pixel point, the gradient components of each pixel point in the radar echo image after contrast enhancement in horizontal and vertical directions are calculated, and the gradient direction and gradient strength of each pixel point are calculated according to the gradient components.

7. The radar chart matching based ship navigation positioning method according to claim 6, characterized in that, In the calculation of the edge direction at each pixel point, the pixel points with gradient strength greater than the upper threshold are treated as strong boundaries, the pixel points with gradient strength between the upper and lower thresholds are treated as weak boundaries, and the pixel points with gradient strength less than the lower threshold are treated as non-boundaries, all strong boundary points are retained as part of the edge, each weak boundary point is traversed, and it is checked whether there is a strong boundary point in its eight-neighbor domain, if yes, the weak boundary point is connected with the nearest strong boundary point and retained as part of the edge; if not, but there is another weak boundary point connected to form a continuous path, and the path is connected to at least one strong boundary point, the weak boundary point is retained, the weak boundary point not connected to the strong boundary and not forming an effective path is regarded as a non-boundary and deleted from the edge image, the extracted edge is smoothed, and the final edge image is output.

8. The radar chart matching based ship navigation positioning method according to claim 1, wherein, In the matching of the radar image and the electronic chart scale, the relationship between the electronic chart and the scaling factor is: N m = S d / S i 1); Wherein: Si is the original scale of the electronic chart; S d is the display scale; N m is the zoom factor of the electronic chart; Let S d = S l Then the required scale factor N m for the electronic chart is as follows: Wherein: DPI is the number of pixels of the radar image; R is the working range of the radar; r is the radius of the radar echo; S l is the scale of the radar image.

9. The radar chart matching based ship navigation positioning method according to claim 1, wherein, In the calculation of the distance between the feature points, M and N are respectively the feature point sets of the radar echo and the coastline of the electronic chart, and the distance H(M, N) between the feature points is defined as follows: H(M, N) = max(h(M, N), h(N, M)) 3); Wherein, Wherein: m and n are points in M and N respectively; h(M, N) is the one-way distance from feature point M to feature point N; h(N, M) is the one-way distance from feature point N to feature point M.

10. The radar chart matching based ship navigation positioning method according to claim 1, wherein, In the calculation of the chart coordinates corresponding to the radar image scanning center, the radar feature point position parameter coordinates are converted into the corresponding pixel point coordinates in the radar image, and the polar coordinate form is converted into the rectangular coordinate form, and the calculation formula is: Wherein: P is the number of pixel points in each row of the image; Q is the number of pixel points in each column of the image; x0 and y0 are the coordinate values of the radar scanning center; a and b are the coordinate values of the radar feature point target on the chart; S is the distance in polar coordinate form; α is the azimuth in polar coordinate form; x is the column of the pixel point in rectangular coordinate form; y is the row of the pixel point in rectangular coordinate form; The coordinate values of the radar scanning center on the chart are obtained from the calculation formula 5): The radar scanning center position information is converted into latitude and longitude information by the following formula: wherein: is the longitude of the center of the radar scan; λ is the latitude of the center of the radar scan; is the longitude of the chart reference point; λ0 is the latitude of the chart reference point; K is the coordinate angle conversion coefficient; S0 is the distance in chart coordinate form.

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