Unmanned ship navigation radar echo processing method, device and equipment

Abstract

The invention provides an unmanned ship navigation radar echo processing method, device and equipment, belongs to the technical field of computer information processing, and solves the problems that the current navigation radar echo processing efficiency is not high, a target echo signal is unstable and is easily influenced by sea clutters. The method comprises the following steps: acquiring an unmanned ship navigation radar echo set at a plurality of moments; clustering processing is carried out on the unmanned ship navigation radar echo sets at the multiple moments to obtain a line segment set; determining echo clustering data at a plurality of moments according to the line segment set; mapping the echo clustering data at the multiple moments into a grid map to obtain grid clustering data at the multiple moments; and carrying out clustering merging processing on the grid clustering data at the multiple moments to obtain radar echo processing data. According to the scheme, grid clustering is formed through historical information accumulation, the clustering efficiency is effectively improved, the influence of interference factors is reduced, and the robustness of track processing is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of computer information processing, and particularly to a method, device, and equipment for processing the echo of an unmanned boat navigation radar. Background Art

[0002] The operation of an unmanned boat depends on the autonomous sensing ability of the equipment on the boat. The navigation radar is the main sensing means for an unmanned boat to sail at sea. Its main advantages are as follows: First, the navigation radar is less affected by weather conditions and can maintain effective detection in bad weather such as fog, rain, and snow, providing continuous surrounding situation awareness information for the unmanned boat; Second, the navigation radar can work stably in a complex electromagnetic environment and has good anti-interference ability to ensure reliable sensing information in most environments; Finally, the detection range of the navigation radar is between 100m and 15km, which can meet the distance requirements for safe navigation. However, the existing navigation radars are generally monitored and operated by personnel, and the ability to automatically detect and track navigation obstacles is insufficient. The current navigation radar can output all echo information, and it is necessary to be able to complete echo clustering based on the echo information, and then build a navigation and track targets on the basis of clustering.

[0003] The existing clustering methods are widely applicable to various scenarios such as lidar processing and image processing, and achieve good processing effects. However, compared with the echo of the navigation radar, these methods have the following problems: First, they do not make full use of the characteristics of the navigation radar echo output according to the beam angle, and the efficiency of processing the navigation radar echo is not high; Second, they do not consider the problem that the navigation radar is easily affected by the marine electromagnetic environment, and the target echo intensity and size are unstable; Third, they do not consider the influence of sea waves on the navigation radar, and the sea clutter often flashes in the navigation radar echo. Summary of the Invention

[0004] The present invention provides a method, device, and equipment for processing the echo of an unmanned boat navigation radar, which solves the problems of low efficiency in processing the echo of the current navigation radar, unstable target echo signals, and being easily affected by sea clutter.

[0005] To solve the above technical problems, the technical solution of the present invention is as follows:

[0006] An embodiment of the present invention provides a method for processing the echo of an unmanned boat navigation radar, including:

[0007] Obtaining a set of echoes of an unmanned boat navigation radar at multiple moments;

[0008] Performing clustering processing on the set of echoes of the unmanned boat navigation radar at the multiple moments to obtain a set of line segments;

[0009] Determining echo clustering data at multiple moments according to the set of line segments;

[0010] Map the echo clustering data at the multiple moments to a grid map to obtain grid clustering data at the multiple moments;

[0011] Perform clustering and merging processing on the grid clustering data at the multiple moments to obtain radar echo processing data.

[0012] Optionally, perform clustering processing on the unmanned boat navigation radar echo sets at the multiple moments to obtain a line segment set, including:

[0013] Perform parsing processing on the unmanned boat navigation radar echo sets at the multiple moments to obtain navigation radar echo sequences at multiple beam angles;

[0014] Perform traversal processing on the navigation radar echo sequences at the multiple beam angles according to preset conditions to obtain multiple single echo information;

[0015] Perform merging processing on the multiple single echo information to obtain a line segment set.

[0016] Optionally, determine the echo clustering data at the multiple moments according to the line segment set, including:

[0017] Determine multiple line segment data at multiple beam angles according to the line segment set;

[0018] Determine multiple distance maximum and minimum values according to the multiple line segment data at the multiple beam angles;

[0019] Determine the coincidence degree of multiple distance intervals according to the multiple distance maximum and minimum values;

[0020] Determine the echo clustering data at the multiple moments according to the coincidence degree of the multiple distance intervals.

[0021] Optionally, map the echo clustering data at the multiple moments to a grid map to obtain grid clustering data at the multiple moments, including:

[0022] Perform coordinate transformation processing on the echo clustering data at the multiple moments to obtain the global coordinate position data of the unmanned boat;

[0023] Map the global coordinate position data of the unmanned boat to a grid map to obtain grid data;

[0024] Determine the echo intensity data according to the grid data;

[0025] Determine the grid clustering data at the multiple moments according to the grid data and the echo intensity data.

[0026] Optionally, determine the grid clustering data at the multiple moments according to the grid data and the echo intensity data, including:

[0027] According to:

[0028] ,

[0029] Determine the grid clustering data at multiple moments;

[0030] Among them, is the grid clustering data at the k-th moment, is the grid position on the x-axis in the global coordinate system, is the grid position on the y-axis in the global coordinate system, is the grid echo intensity data, is the j-th echo distance of the m-th beam angle at the k-th moment, is the j-th echo intensity of the m-th beam angle at the k-th moment, where k, m, and j are natural numbers, is the echo clustering data, represents the echo mapped to the grid .

[0031] Optionally, perform clustering and merging processing on the grid clustering data at the multiple moments to obtain radar echo processing data, including:

[0032] Obtain element occupancy probability data based on the grid clustering data at the multiple moments;

[0033] Perform binarization processing on the element occupancy probability data to obtain the binarized element occupancy probability data;

[0034] Perform screening processing on the binarized element occupancy probability data to obtain multiple screened grid clustering data;

[0035] Perform merging processing on the multiple screened grid clustering data to obtain radar echo processing data.

[0036] Optionally, obtain element occupancy probability data based on the grid clustering data at the multiple moments, including:

[0037] According to:

[0038] ,

[0039] Obtain element occupancy probability data;

[0040] Among them, is the element occupancy probability data of each element in the grid clustering from 0 to the k-th moment, is the element occupancy probability data of each element in the grid clustering from 0 to the (k - 1)-th moment, is the maximum echo intensity, η 1 is the unoccupied probability, is the grid position on the x-axis in the global coordinate system from time 0 to time k. is the grid position on the y-axis in the global coordinate system from time 0 to time k. is the grid clustering data from time 0 to time k - 1.

[0041] An embodiment of the present invention also provides an unmanned boat navigation radar echo processing device, including:

[0042] An acquisition module, configured to acquire a set of unmanned boat navigation radar echoes at multiple times;

[0043] A processing module, configured to perform clustering processing on the set of unmanned boat navigation radar echoes at the multiple times to obtain a set of line segments; determine echo clustering data at multiple times according to the set of line segments; map the echo clustering data at the multiple times into a grid map to obtain grid clustering data at multiple times; perform clustering and merging processing on the grid clustering data at the multiple times to obtain radar echo processing data.

[0044] An embodiment of the present invention also provides a computing device, including: a processor and a memory storing a computer program, and when the computer program is run by the processor, the above method is executed.

[0045] An embodiment of the present invention also provides a computer-readable storage medium, storing an instruction, and when the instruction runs on a computer, the computer is enabled to execute the above method.

[0046] The technical solution of the present invention at least includes the following effects:

[0047] The above solution of the present invention acquires a set of unmanned boat navigation radar echoes at multiple times; performs clustering processing on the set of unmanned boat navigation radar echoes at the multiple times to obtain a set of line segments; determines echo clustering data at multiple times according to the set of line segments; maps the echo clustering data at the multiple times into a grid map to obtain grid clustering data at multiple times; performs clustering and merging processing on the grid clustering data at the multiple times to obtain radar echo processing data. This solution utilizes the characteristic that the navigation radar echoes are arranged in ascending order of distance for each azimuth angle, improving the processing efficiency; converting to grid clustering and merging multiple echo information reduces the subsequent data volume without reducing the information volume, which is helpful for subsequent real-time processing of navigation tracks such as route building and tracking; effectively combines historical information and current detection information, reducing the influence caused by changes in the electromagnetic environment, sea clutter, and the quality change of navigation radar echoes such as the detection angle of ships. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 is a flowchart of the unmanned boat navigation radar echo processing method provided by an embodiment of the present invention;

[0049] Figure 2 It is a structural diagram of an unmanned boat navigation radar echo processing device provided by an embodiment of the present invention;

[0050] Figure 3 It is a schematic structural diagram of a computing device provided by an embodiment of the present invention. Specific embodiments

[0051] Hereinafter, exemplary embodiments of the present invention will be described in more detail with reference to the accompanying drawings. Although the exemplary embodiments of the present invention are shown in the drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided so that the present invention can be more thoroughly understood and the scope of the present invention can be completely conveyed to those skilled in the art.

[0052] As Figure 1 shown, an embodiment of the present invention proposes an unmanned boat navigation radar echo processing method, including:

[0053] Step 11, obtaining a set of unmanned boat navigation radar echoes at multiple moments;

[0054] Step 12, performing clustering processing on the set of unmanned boat navigation radar echoes at the multiple moments to obtain a set of line segments;

[0055] Step 13, determining echo clustering data at multiple moments according to the set of line segments;

[0056] Step 14, mapping the echo clustering data at the multiple moments into a grid map to obtain grid clustering data at multiple moments;

[0057] Step 15, performing clustering and merging processing on the grid clustering data at the multiple moments to obtain radar echo processing data.

[0058] In this embodiment, the navigation radar equipped on the unmanned boat continuously emits electromagnetic waves and receives the echo signals reflected by the targets. These echo signals contain information such as the position, distance, and speed of the target objects. Over a period of time, the radar echo data is recorded at regular time intervals (such as every 1 s, every 0.5 s, etc.). The echo data at each moment can be represented as a set of data points, where each data point contains the relevant information of the targets detected by the radar at that moment, such as the polar coordinates (distance, azimuth angle) of the targets, etc. Integrating the echo data at all the recorded moments together forms a set containing the unmanned boat navigation radar echoes at multiple moments. This set can be regarded as a three-dimensional data structure, where two dimensions represent the polar coordinates of the targets and the other dimension represents time.

[0059] For each echo data point, extract its key features, such as position (distance and azimuth), time, etc. These features will be used for subsequent clustering analysis. Input the extracted features into the clustering processing model to cluster the echo data points at multiple moments. During the clustering process, the clustering processing model will assign data points to different clusters according to the similarity between data points (such as distance, angle, etc.). For the data points in each clustering cluster, use a line segment fitting algorithm (such as the least squares method) to fit them into a line segment. This line segment can approximately represent the distribution trend of the data points in this cluster, reflecting the movement trajectory or characteristics of the target over a period of time. Finally, integrate the line segments obtained by fitting all clustering clusters together to form a set of line segments.

[0060] For each line segment in the set of line segments, analyze its features, such as the starting point, ending point, length, direction, etc. of the line segment. These features can reflect the movement state and distribution of the target at different moments. According to the relationship between the line segment and time, determine the line segment corresponding to each moment. For example, the time stamp of the data points on the line segment can be used to determine which moment's echo clustering this line segment belongs to. For each moment, integrate the features of the line segments associated with it to form the echo clustering data for this moment. The echo clustering data can include information such as the number of line segments and the characteristic parameters of each line segment, and these information can comprehensively describe the clustering situation of the echo data at this moment.

[0061] Define a two-dimensional grid space. According to the detection range and accuracy requirements of the unmanned boat navigation radar, divide the space into several grids of equal size. Each grid can be represented by a unique coordinate. For the echo clustering data at each moment, map the line segment features in it into the grid space. Specifically, determine which grids each line segment passes through, and assign a weight value to each passed grid according to the features of the line segment (such as length, density, etc.). The weight value can reflect the intensity or importance of the echo clustering within this grid. After mapping the echo clustering data at all moments into the grid space, obtain the grid clustering data at multiple moments. The grid clustering data can be represented as a two-dimensional matrix, where each element corresponds to the weight value of a grid, and the rows and columns of the matrix correspond to the coordinates of the grids respectively.

[0062] Fuse the raster clustering data at multiple moments, and comprehensively consider the changes in the raster weight values at different moments. For example, the weighted average method can be used to perform weighted summation on the raster weight values at different moments to obtain a comprehensive raster weight distribution. Extract features from the fused raster clustering data, such as the position, shape, size, and motion trend of the target area. Image processing techniques (such as edge detection, morphological analysis, etc.) can be used to identify the target area and analyze its characteristic parameters. According to the extracted features and analysis results, generate radar echo processing data. The radar echo processing data can include the position information of the target, motion state information, target type judgment, etc., and this information can be directly used for the navigation decision-making of the unmanned boat, such as obstacle avoidance, path planning, etc.

[0063] This technical solution is oriented to the needs of autonomous perception of unmanned boats. Using the navigation radar as the main perception means, first utilize the characteristics of navigation radar echo information to complete echo clustering; then map the echo clustering to the raster map, and form raster clustering through the accumulation of historical information, effectively improving the clustering efficiency, and effectively utilizing the raster characteristics and historical information, reducing the influence of environmental factors such as the marine electromagnetic environment and sea clutter, and improving the robustness of subsequent track processing.

[0064] In an optional embodiment of the present invention, step 12 may include:

[0065] Step 121, perform parsing processing on the set of navigation radar echoes of the unmanned boat at the multiple moments to obtain navigation radar echo sequences at multiple beam angles;

[0066] Step 122, perform traversal processing on the navigation radar echo sequences at the multiple beam angles according to preset conditions to obtain multiple single echo information;

[0067] Step 123, perform merging processing on the multiple single echo information to obtain a set of line segments.

[0068] In this embodiment, it is assumed that At time, the set of navigation radar echoes of the unmanned boat is where is the beam angle, is the maximum number of beam angles, is the set of echoes in the direction of this beam angle. It is expressed as the echo sequence , is the single echo information, is the echo distance, is the echo intensity, is the number of echoes; in the echo sequence among them is arranged in ascending order according to . The position of the unmanned boat at a certain moment in the global coordinate system is , which are the x-axis coordinate position , the y-axis coordinate position , and the heading angle . The grid map , where is the grid position of the x-axis in the global coordinate system, is the grid position of the y-axis in the global coordinate system, and the grid size is a predetermined constant .

[0069] The calculation process of the line segment set is specifically as follows: Analyze the navigation radar echo set of the unmanned boat at the multiple moments to obtain the navigation radar echo sequences of multiple beam angles; for the echo sequence of each beam angle , cluster each echo point in the sequence into a line segment set .

[0070] S21, set .

[0071] S22, set , is an empty set.

[0072] S23, traverse the navigation radar echo sequences of the multiple beam angles according to preset conditions, that is, sequentially obtain the subsequent first from such that , where is the echo intensity threshold, which is a predetermined constant. Add this echo to .

[0073] S24, add the subsequent until , then the aggregation of the line segment is completed, and set

[0074] S25, repeat S22 to S24 until all echo points in the echo sequence have been processed, then the line segment set is obtained.

[0075] In an alternative embodiment of the present invention, step 13 may include:

[0076] Step 131, determine multiple line segment data of multiple beam angles according to the line segment set;

[0077] Step 132, determine multiple distance maximum and minimum values according to the multiple line segment data of the multiple beam angles;

[0078] Step 133: Determine the coincidence degree of multiple distance intervals according to the multiple distance maximum values.

[0079] Step 134: Determine the echo clustering data at multiple moments according to the coincidence degree of the multiple distance intervals.

[0080] In this embodiment, the calculation process of the echo clustering data at multiple moments is specifically as follows:

[0081] S301: Set ;

[0082] S302: Set ;

[0083] S303: Set ;

[0084] S304: Take out the first in that has not been marked as a processed beam angle . Calculate the maximum and minimum distances in , , . The arithmetic expressions are as follows:

[0085] ,

[0086] ;

[0087] S305: Calculate the maximum and minimum distances in , , ;

[0088] S306: Determine whether the distance interval of coincides with the distance interval of . If there is an overlapping part, add to , that is:

[0089]

[0090] S307: Set , and repeat S305 to S306 until in all the distance interval coincidences have been judged;

[0091] S308: Mark this as processed; in take another line segment with the same beam angle , set as the label of this line segment, and repeat S304 to S307 until in the same beam angle All line segments have been processed;

[0092] S309, set If then set ; Repeat S304 to S308. Until All marks in

[0093] S310, set ; Take the line segments in that are not marked as processed, set as the label of this line segment, and repeat S302 to S309; until all line segments in have been processed and are assigned to each echo cluster in.

[0094] In an alternative embodiment of the present invention, step 14 may include:

[0095] Step 141, perform coordinate transformation processing on the echo cluster data at the multiple moments to obtain the global coordinate position data of the unmanned boat;

[0096] Step 142, map the global coordinate position data of the unmanned boat into a grid map to obtain grid data;

[0097] Step 143, determine the echo intensity data according to the grid data;

[0098] Step 144, determine the grid cluster data at multiple moments according to the grid data and the echo intensity data.

[0099] In this embodiment, the calculation process of the grid cluster data at multiple moments is specifically as follows:

[0100] S41, for any , convert the position of the unmanned boat in the global coordinate system to the position in the global coordinate system, and the calculation formula is:

[0101] ;

[0102] S42, map to the grid in the grid map, that is:

[0103] , ;

[0104] Among them, represents rounding down;

[0105] S43, Arithmetically average all the echoes mapped to the grid of the previous echoes to obtain the echo intensity of this grid echo intensity , and the calculation formula is as follows:

[0106] ;

[0107] wherein, represents the echo that can be mapped to the grid , represents the number of elements in the set.

[0108] S44, Through the above steps, all the echoes in are mapped to the grid map in, and is converted into grid clustering under the grid map , and the calculation formula is as follows:

[0109] ,

[0110] wherein, is the grid clustering data at the k-th moment, is the grid position on the x-axis under the global coordinate system, is the grid position on the y-axis under the global coordinate system, is the j-th echo distance of the m-th beam angle at the k-th moment, is the j-th echo intensity of the m-th beam angle at the k-th moment, and k, m, and j are natural numbers, is the echo clustering data.

[0111] In an optional embodiment of the present invention, step 15 may include:

[0112] Step 151, Obtain element occupancy probability data according to the grid clustering data of the multiple moments;

[0113] Step 152, Perform binarization processing on the element occupancy probability data to obtain the binarized element occupancy probability data;

[0114] Step 153, Perform screening processing on the binarized element occupancy probability data to obtain multiple screened grid clustering data;

[0115] Step 154, Perform merging processing on the multiple screened grid clustering data to obtain radar echo processing data.

[0116] In this embodiment, to Grid clustering at a moment and Grid clustering at a moment are merged to obtain a grid clustering result containing to echo information at a moment , specifically as follows:

[0117] S51. For the grid clustering at a moment , it is directly obtained from the grid clustering at the 0th moment. Denote , as the grid position on the x-axis in the global coordinate system, , as the grid position on the y-axis in the global coordinate system, and as the occupancy probability of the grid. Then the obtaining method is as follows:

[0118]

[0119] wherein, is the maximum echo intensity, which is a predetermined constant;

[0120] S52. Using the grid clustering from to at a moment and the grid clustering at a moment , calculate the occupancy probability of each element in the grid clustering at a moment . The calculation formula is: .

[0121] ,

[0122] wherein, is the occupancy probability data of each element in the grid clustering from 0 to the kth moment, is the occupancy probability data of each element in the grid clustering from 0 to the (k - 1)th moment, is the maximum echo intensity, η 1 is the unoccupied probability, is the grid position on the x-axis in the global coordinate system from 0 to the kth moment, is the grid position on the y-axis in the global coordinate system from 0 to the kth moment, is the grid clustering data from 0 to the (k - 1)th moment;

[0123] S53. Set the occupancy probability of the elements in the grid clustering less than the occupancy probability deletion threshold to 0, greater than Set as , where is a predefined constant;

[0124] S54. For the elements with a occupancy probability of 0 in, delete this element from the grid clustering . For multiple with intersections, merge them into one clustering, and finally obtain the grid clustering at time , that is, obtain the radar echo processing data.

[0125] A specific embodiment of the unmanned boat navigation radar echo processing method provided by the embodiment of the present invention is as follows:

[0126] Suppose at the unmanned boat navigation radar echo set at time is , where is the beam angle, is the maximum number of beam angles, is the echo set in the direction of this beam angle. is represented as the echo sequence , is the single echo information, is the echo distance, is the echo intensity, is the number of echoes; in the echo sequence is arranged in ascending order according to . The position of the unmanned boat in the global coordinate system at time is , which are the x-axis coordinate position , the y-axis coordinate position , and the heading angle . The grid map , where is the x-axis grid position in the global coordinate system, is the y-axis grid position in the global coordinate system, and the grid size is the predefined constant .

[0127] Step S10. For the echo sequence of each beam angle, cluster each echo point in the sequence into a line segment set .

[0128] Step S11. Set .

[0129] Step S12. Set .

[0130] Step S13. Sequentially obtain the first subsequent one from such that​ of , where is the echo intensity threshold, which is a predetermined constant. Add this echo to .

[0131] Step S14. For subsequent , until , the aggregation of the line segment is completed, and set

[0132] Step S15. Repeat Step S12 to Step S14 until all echo points in the echo sequence have been processed. Then, the line segment set is obtained.

[0133] Step S20. Aggregate the line segment sets for each beam angle into echo clusters according to the distance interval overlap degree.

[0134] Step S21. Set .

[0135] Step S22. Set .

[0136] Step S23. Set .

[0137] Step S24. Take out the first beam angle that is not marked as processed . Calculate the maximum and minimum distances in , .

[0138]

[0139]

[0140] Step S25. Calculate the maximum and minimum distances in , .

[0141] Step S26. Determine whether the distance interval of overlaps with the distance interval of . If there is an overlapping part, add to .

[0142]

[0143] Step S27, set , and repeat steps S25 to S26 until all in have judged the distance interval overlap.

[0144] Step S28, mark this as processed. In take another line segment with the same beam angle , set as the label of this line segment, and repeat steps S24 to S27 until all line segments with the same beam angle in have been processed.

[0145] Step S29, set , if , then set . Repeat steps S24 to S28 until all marks have been processed.

[0146] Step S210, set . Take the unmarked line segments as processed in , set as the label of this line segment, and repeat steps S22 to S29 until all line segments in have been processed and assigned to each echo cluster in.

[0147] Step S30, map the echo cluster at time to the grid map to form the grid cluster at time .

[0148] Step S31, for any , convert the position of the unmanned boat in the global coordinate system to the position in the global coordinate system.

[0149]

[0150] Step S32, map to the grid in the grid map.

[0151] ,

[0152] where Denotes rounding down.

[0153] Step S33, arithmetically average all the echoes mapped to the grid to obtain the echo intensity of this grid of the echo intensity. .

[0154]

[0155] Among them denotes the echo that can be mapped to the grid according to Steps 3-1 and 3-2 , denotes the number of elements in the set. Through the above steps, all the echoes in are mapped to the grid map , and is converted into the grid clustering under the grid map .

[0156] .

[0157] Step S40, merge the grid clustering from to the time with the grid clustering at the time to obtain the grid clustering result containing the echo information from to the time;

[0158] Step S41, for the grid clustering at the time, directly obtain it from the grid clustering at the 0 time. Denote , denotes the grid position on the x-axis in the global coordinate system, denotes the grid position on the y-axis in the global coordinate system, denotes the occupancy probability of this grid. Then is obtained as follows:

[0159]

[0160] Among them is the maximum echo intensity, which is a predetermined constant.

[0161] Step S42, use the grid clustering from to the time and Moment grid clustering , calculate the grid clustering at a moment the occupancy probability of each element in .

[0162] ,

[0163] Among them, is the unoccupied probability, which is a predefined constant.

[0164] Step S43, set the occupancy probability of the elements in the grid clustering less than the occupancy probability deletion threshold to 0, and greater than set to , where is a predefined constant.

[0165] Step S44, for the elements with an occupancy probability of 0 in, delete this element from the grid clustering . Merge multiple with intersections into one cluster. Finally, obtain the grid clustering at the moment .

[0166] The unmanned boat navigation radar echo processing method proposed by the present invention is oriented to the needs of unmanned boat autonomous perception. Using the navigation radar as the main perception means, first, the echo clustering is completed by using the echo information characteristics of the navigation radar; then the echo clustering is mapped onto the grid map, and the grid clustering is formed through the accumulation of historical information, effectively improving the clustering efficiency, and effectively using the grid characteristics and historical information, reducing the influence of environmental factors such as the marine electromagnetic environment and sea clutter, and improving the robustness of subsequent track processing.

[0167] Figure 2 As

[0168] shown, the embodiment of the present invention also provides an unmanned boat navigation radar echo processing device 20, including:

[0169] An acquisition module 21, configured to acquire a set of unmanned boat navigation radar echoes at multiple moments;

[0169] A processing module 22, configured to perform clustering processing on the set of unmanned boat navigation radar echoes at the multiple moments to obtain a set of line segments; determine echo clustering data at multiple moments according to the set of line segments; map the echo clustering data at the multiple moments into a grid map to obtain grid clustering data at multiple moments; perform clustering and merging processing on the grid clustering data at the multiple moments to obtain radar echo processing data.

[0170] Optionally, the processing module 22 is specifically configured to:

[0171] Analyze and process the set of unmanned boat navigation radar echoes at the multiple moments to obtain navigation radar echo sequences at multiple beam angles;

[0172] Traverse and process the navigation radar echo sequences at the multiple beam angles according to preset conditions to obtain multiple single echo information;

[0173] Merge and process the multiple single echo information to obtain a set of line segments.

[0174] Optionally, the processing module 22 is further specifically configured to:

[0175] Determine multiple line segment data at multiple beam angles according to the set of line segments;

[0176] Determine multiple distance maximum and minimum values according to the multiple line segment data at the multiple beam angles;

[0177] Determine multiple distance interval coincidence degrees according to the multiple distance maximum and minimum values;

[0178] Determine echo clustering data at multiple moments according to the multiple distance interval coincidence degrees.

[0179] Optionally, the processing module 22 is further specifically configured to:

[0180] Perform coordinate transformation processing on the echo clustering data at the multiple moments to obtain unmanned boat global coordinate position data;

[0181] Map the unmanned boat global coordinate position data into a grid map to obtain grid data;

[0182] Determine echo intensity data according to the grid data;

[0183] Determine grid clustering data at multiple moments according to the grid data and the echo intensity data.

[0184] Optionally, determining grid clustering data at multiple moments according to the grid data and the echo intensity data includes:

[0185] According to:

[0186] ,

[0187] Determine grid clustering data at multiple moments;

[0188] Wherein, is the grid clustering data at the k-th moment, is the grid position on the x-axis in the global coordinate system, is the grid position on the y-axis in the global coordinate system, is the grid echo intensity data, is the j-th echo distance at the m-th aspect angle at the k-th moment, is the j-th echo intensity at the m-th aspect angle at the k-th moment, where k, m, and j are natural numbers, is the echo clustering data, represents the echo mapped to the grid .

[0189] Optionally, the processing module 22 is further specifically configured to:

[0190] Obtain element occupancy probability data according to the grid clustering data at the multiple moments;

[0191] Perform binarization processing on the element occupancy probability data to obtain the binarized element occupancy probability data;

[0192] Perform screening processing on the binarized element occupancy probability data to obtain multiple screened grid clustering data;

[0193] Perform merging processing on the multiple screened grid clustering data to obtain radar echo processing data.

[0194] Optionally, obtaining element occupancy probability data according to the grid clustering data at the multiple moments includes:

[0195] According to:

[0196] ,

[0197] Obtain element occupancy probability data;

[0198] Wherein, is the element occupancy probability data of each element in the grid clustering from 0 to the k-th moment, is the element occupancy probability data of each element in the grid clustering from 0 to the (k - 1)-th moment, is the maximum echo intensity, η 1 is the unoccupied probability, is the grid position on the x-axis in the global coordinate system from 0 to the k-th moment, is the grid position on the y-axis in the global coordinate system from 0 to the k-th moment, is the grid clustering data from 0 to the (k - 1)-th moment.

[0199] It should be noted that this device corresponds to the above method. All implementation manners in the above method embodiments are applicable to this embodiment and can achieve the same technical effects.

[0200] Such as Figure 3As shown in the figure, an embodiment of the present invention further provides a computing device 30, including a processor 31, a memory 32, a program or instruction stored on the memory 32 and executable on the processor 31. When the program or instruction is executed by the processor 31, it implements each process of the above-mentioned embodiment of the method for processing the echo of the unmanned boat navigation radar, and can achieve the same technical effect. To avoid repetition, it will not be elaborated here. It should be noted that the computing device in the embodiment of the present invention includes the above-mentioned mobile electronic device and non-mobile electronic device.

[0201] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0202] Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working processes of the above-described systems, devices, and units can refer to the corresponding processes in the foregoing method embodiments, and will not be elaborated here.

[0203] In the embodiments provided by the present invention, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces. The indirect couplings or communication connections of devices or units can be in electrical, mechanical, or other forms.

[0204] The units described as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they can be located in one place, or they can be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0205] In addition, the functional units in each embodiment of the present invention can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit.

[0206] When the above-mentioned functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on such understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods according to the various embodiments of the present invention. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, ROM, RAM, magnetic disks, or optical discs that can store program codes.

[0207] In addition, it should be noted that in the devices and methods of the present invention, obviously, each component or each step can be decomposed and / or recombined. These decompositions and / or recombinations shall be regarded as equivalent solutions of the present invention. And, the steps of performing the above series of processes can naturally be executed in chronological order according to the described order, but it is not necessary to be executed in chronological order. Some steps can be executed in parallel or independently of each other. For those of ordinary skill in the art, it can be understood that all or any steps or components of the methods and devices of the present invention can be implemented in any computing device (including processors, storage media, etc.) or a network of computing devices in the form of hardware, firmware, software, or a combination thereof, which can be achieved by those of ordinary skill in the art using their basic programming skills after reading the description of the present invention.

[0208] Therefore, the object of the present invention can also be achieved by running a program or a set of programs on any computing device. The computing device can be a well-known general-purpose device. Therefore, the object of the present invention can also be achieved only by providing a program product containing program codes for implementing the method or device. That is to say, such a program product also constitutes the present invention, and the storage medium storing such a program product also constitutes the present invention. Obviously, the storage medium can be any well-known storage medium or any storage medium developed in the future. It should also be noted that in the devices and methods of the present invention, obviously, each component or each step can be decomposed and / or recombined. These decompositions and / or recombinations shall be regarded as equivalent solutions of the present invention. And, the steps of performing the above series of processes can naturally be executed in chronological order according to the described order, but it is not necessary to be executed in chronological order. Some steps can be executed in parallel or independently of each other.

[0209] The above are the preferred embodiments of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present invention.

Claims

1. A method for processing echoes of an unmanned boat navigation radar, characterized in that: include: Obtain the unmanned boat navigation radar echo set at multiple times; Performing clustering processing on the unmanned boat navigation radar echo sets at the multiple moments to obtain a line segment set; Determining echo clustering data at multiple moments according to the line segment set; Mapping the echo clustering data at the multiple moments into a grid map to obtain grid clustering data at the multiple moments; The grid clustering data at the multiple moments are clustered and merged to obtain radar echo processing data.

2. The unmanned boat navigation radar echo processing method according to claim 1, characterized in that: Clustering is performed on the unmanned boat navigation radar echo sets at the multiple moments to obtain a line segment set, including: Analyzing and processing the navigation radar echo sets of the unmanned boat at the multiple moments to obtain navigation radar echo sequences of multiple side angles; The navigation radar echo sequences of the multiple side angles are traversed and processed according to preset conditions to obtain multiple single echo information; The plurality of single echo information are combined to obtain a line segment set.

3. The unmanned boat navigation radar echo processing method according to claim 1, characterized in that: Determining echo clustering data at multiple moments according to the line segment set includes: Determine a plurality of line segment data of a plurality of side angles according to the line segment set; Determining a plurality of distance maximum values ​​according to a plurality of line segment data of the plurality of side angles; Determining the overlap of multiple distance intervals according to the multiple distance maximum values; According to the overlap degrees of the multiple distance intervals, echo clustering data at multiple moments are determined.

4. The unmanned boat navigation radar echo processing method according to claim 1, characterized in that: Mapping the echo clustering data at the multiple moments into a grid map to obtain grid clustering data at the multiple moments includes: Performing coordinate conversion processing on the echo clustering data at the multiple moments to obtain global coordinate position data of the unmanned boat; Mapping the global coordinate position data of the unmanned boat into a grid map to obtain grid data; Determining echo intensity data according to the grid data; Grid clustering data at multiple moments are determined according to the grid data and the echo intensity data.

5. The unmanned boat navigation radar echo processing method according to claim 4 is characterized in that: Determining grid clustering data at multiple moments according to the grid data and the echo intensity data includes: according to: ， Determine raster clustering data at multiple moments; in, is the grid clustering data at the kth moment, is the x-axis grid position in the global coordinate system, is the y-axis grid position in the global coordinate system, is the grid echo intensity data, is the jth echo distance of the mth side angle at the kth moment, is the jth echo intensity at the mth side angle at the kth moment, where k, m, and j are natural numbers. is the echo clustering data, Indicates echo Map to Raster .

6. The unmanned boat navigation radar echo processing method according to claim 1, characterized in that: The grid clustering data at the multiple moments are clustered and merged to obtain radar echo processing data, including: Obtaining element occupancy probability data according to the grid clustering data at the plurality of moments; Binarizing the element occupation probability data to obtain binarized element occupation probability data; Screening the binarized element occupancy probability data to obtain a plurality of screened grid clustering data; The plurality of filtered grid cluster data are merged to obtain radar echo processing data.

7. The unmanned boat navigation radar echo processing method according to claim 6, characterized in that: According to the grid clustering data at the plurality of moments, element occupation probability data is obtained, including: according to: ， Get element occupation probability data; in, is the occupancy probability data of each element in the grid cluster from time 0 to k, is the occupancy probability data of each element in the grid cluster from 0 to k-1 time, is the maximum echo intensity, η1 is the unoccupied probability, is the x-axis grid position in the global coordinate system from time 0 to time k, is the y-axis grid position in the global coordinate system from time 0 to time k, is the grid clustering data from time 0 to k-1.

8. An unmanned boat navigation radar echo processing device, characterized in that: include: An acquisition module is used to acquire the echo sets of the navigation radar of the unmanned boat at multiple times; The processing module is used to cluster the unmanned boat navigation radar echo sets at the multiple moments to obtain a line segment set; determine the echo clustering data at the multiple moments according to the line segment set; map the echo clustering data at the multiple moments to a grid map to obtain the grid clustering data at the multiple moments; cluster and merge the grid clustering data at the multiple moments to obtain radar echo processing data.

9. A computing device, characterized in that include: A processor and a memory storing a computer program, wherein when the computer program is executed by the processor, the method according to any one of claims 1 to 7 is performed.

10. A computer-readable storage medium, characterized in that: Instructions are stored, and when the instructions are executed on a computer, the computer is caused to execute the method according to any one of claims 1 to 7.

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