A radar plot and AIS track adaptive parallel matching method and system

Abstract

The application discloses a radar plot and AIS track adaptive parallel matching method and system, and belongs to the technical field of radar and navigation information. In order to reduce the algorithm complexity and the dependence of AIS data, the application comprises the following steps: identifying the database arrival data, performing voting on the projected radar plot in a parameter space by a system error extraction module based on an error distribution function to obtain a plurality of counters and filtering the counters by a filter, finally accumulating the results of all the filters and performing peak value detection to extract the system error of the radar plot; performing speed correlation checking on the searched radar track point by a radar plot index module, and searching the radar track point with a speed parameter meeting the requirements; performing sum and optimization on an error function according to a PARZEN window based on the radar track point with the speed parameter meeting the requirements by an error function optimization module; and updating the membership ship number in the AIS table of the database by using the radar track point with the speed parameter meeting the requirements.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of radar and navigation information, and particularly relates to a radar plot and AIS track adaptive parallel matching method and system. BACKGROUND

[0002] With the increasing and complex of marine traffic, the ship traffic density of coastal, port and inland waterways is increasing, and the single reliance on AIS ship management system exists in high load.

[0003] The traditional ship traffic management system mainly relies on two subsystems: AIS and radar system.

[0004] AIS data has the characteristics of high precision and wide coverage; however, the arrival of AIS data is unpredictable and random, especially in high-density traffic areas, especially the AIS data near the shore usually uses self-organized time division multiple access (SODTMA), and when the time slot resource is insufficient or the time slot resource is insufficient, it may cause the data of the ship to be missing, in addition, the revisit interval of AIS data can be very large.

[0005] Correspondingly, the radar is actively detected, and the radar plot data obtained therefrom has a fixed and controllable time interval; however, the radar system is obviously affected by the environment, and the position error is larger than that of the AIS data, and is usually used in combination with AIS data to provide redundant detection capability to supplement in the case of AIS data default or AIS data error.

[0006] The traditional correlation method, such as the track-to-track method, depends on first building a track for the radar plot data and then matching with the AIS data, which can effectively avoid the situation of reporting AIS data and the situation of reporting false AIS data, and early literature also gives a large number of mature schemes, which can realize the optimal fusion mode under any communication mode (such as periodic communication mode, event triggered communication mode, full rate communication) and has mature performance boundary estimation to involve fusion correction, but this kind of method has the problem of repeated use of heading and speed parameters in the process of building a track and matching, in addition, this kind of algorithm depends on time correlation, is difficult to parallel, and repeated matching of part of the information, which will naturally lead to the slow operation time of this kind of algorithm.

[0007] Another correlation method relies on measurement level fusion algorithm, which depends on joint probability data association (JPDA) and can effectively associate data in high noise, handle complex scenes when targets cross or approach, effectively use multi-source information and complete matching in complex scenes. However, this method is very sensitive to false negatives and false positives, and the hypothesis space will expand dramatically with the increase of the number, which is not suitable for areas with high ship density such as coastal and port areas. In addition, although the algorithm of JPDA can be parallel, the premise of parallelism is that the motion of targets is independent of each other, which is also not suitable for matching and fusion in port areas.

[0008] In summary, the two main matching methods in the prior art both pay a computational cost to achieve optimal matching, are not suitable for complex environments in coastal areas, and are too dependent on AIS data, which can easily cause a series of false matches when AIS data is missing. SUMMARY

[0009] The problem to be solved by the present application is to reduce the algorithm complexity and the dependence on AIS data, and a radar plot and AIS track adaptive parallel matching method and system are proposed.

[0010] To achieve the above purpose, the present application realizes the following technical scheme:

[0011] A radar plot and AIS track adaptive parallel matching method, comprising the following steps:

[0012] S1. The database arrival data is identified, the AIS data is saved to the database AIS table and data preprocessing is performed if it is identified as AIS data, and the radar data is saved to the database radar table if it is identified as radar data. When the saved radar data is greater than a certain batch, the next step is performed;

[0013] S2. The system error extraction module performs interpolation on the AIS data obtained in step S1 to align the time of the AIS data with the time of the radar data, then projects the radar plot into the parameter space through the difference transformation method according to the longitude and latitude of the AIS data, then votes the projected radar plot in the parameter space based on the error distribution function to obtain a plurality of counters, filters the obtained plurality of counters through a filter, and finally accumulates the results of all filters and then performs peak detection to extract the system error of the radar plot;

[0014] S3. The radar track index module retrieves radar track points based on the system error obtained in step S2, performs a speed correlation check on the retrieved radar track points, and proceeds to the next step for radar track points whose speed parameters meet the requirements. For radar track points whose speed parameters do not meet the requirements, the retrieval is repeated until a specified number of times is reached or a radar track point whose speed parameters meet the requirements is found.

[0015] S4. The error function optimization module optimizes the error function based on the radar track points whose velocity parameters meet the requirements obtained in step S3, according to the PARZEN window.

[0016] S5. Based on the radar track points whose speed parameters meet the requirements obtained in step S3, update the ship numbers in the AIS table of the database.

[0017] Furthermore, the specific implementation method of step S1 includes the following steps:

[0018] S1.1. Set the data extracted from the AIS table in the database to include Maritime Mobile Identifier (MMSI), time, longitude, latitude, speed, navigation, and vessel number. Construct a unique constraint on the composite key of MMSI and time in the AIS data.

[0019] S1.2. Set the data extracted from the database radar table to include primary key, time, longitude, latitude, and radial velocity;

[0020] S1.3. If more than three batches of existing radar data are detected, proceed to the next step.

[0021] Furthermore, the specific implementation method of step S2 includes the following steps:

[0022] S2.1. Interpolate the AIS data obtained in step S1 to align its time with the radar data time. Use the second-order spline interpolation method to interpolate the AIS data to obtain the position of the AIS data at the radar scanning time.

[0023] S2.2. Based on the longitude and latitude of the AIS data, the radar points are projected into the parameter space using a differential transformation method. The expression is:

[0024]

[0025] Among them, P' radar,t P is the position of the radar point at time t after differential transformation. ais,t P is the position of the AIS data at time t. radar,t T is the position of the radar point at time t. radar It is the radar scan time, T ais,start T is the time of the first point in the AIS data currently used to perform the match.ais,end is the time at which the last point of the AIS data used to perform the matching is located; the projected radar track is represented by (x radar,t ,y radar,t );

[0026] S2.3. For each projected radar track, a vote is performed based on an error distribution function according to a Bayesian estimation framework, as follows:

[0027] f(x-x radar,t ,y-y radar,t )

[0028] where f(x,y) is the error distribution function, and x, y are the variables of the error distribution function;

[0029] The same vote is performed for each projected radar track, and the result is represented as:

[0030]

[0031] where D t is the set of projected points in the parameter space of all radar tracks at time t, and C is a counter at time t, representing the vote result at time t;

[0032] S2.4. For each counter C , a filter is used as follows:

[0033]

[0034] where H n,t represents the filtered result at time t, n represents the serial number of the filter, and N * represents a set of positive integers;

[0035] Based on the result of filtering using a threshold of 0.05-n, the results of all filters are accumulated, and the accumulated result is represented as:

[0036]

[0037] A peak detection is performed for each H , and the deviation of the point with the highest value from the original point is set as the system error, and the detected peak is represented as (x peak ,y peak ).

[0038] Further, the specific implementation method of step S3 includes the following steps:

[0039] ​S3.1. Radar Track Index: The systematic error obtained in step S2 is transformed as follows:

[0040] P' ais,t =P ais,t +(x peak ,y peak )

[0041] Among them, P' ais,t It is the reference point used to index radar points at time t;

[0042] The nearest neighbor method is used to select the closest point for each radar track point to obtain the radar track point P. radar,t,0 The expression is:

[0043]

[0044] Where argmin represents the search for the radar point set D t Chinese Ambassador || P radar,t -P' ais,t The smallest point P ||2 radar,t The expression |||2 represents the L2 norm of the vectors. Specifically, it represents the L2 norm of the vectors obtained by subtracting two vectors (x1, x2, ..., x). n This symbol means to perform the calculation using the following formula.

[0045] S3.2. Velocity Correlation Check: For the radar track point P output in step S3.1 radar,t,0 Perform a radial velocity correlation check. If the radial velocity is within three times the radar velocity resolution range, the radar track point selection is considered correct. Output the matched radar track point and execute step S4; otherwise, proceed to step S3.3.

[0046] S3.3. For radar track points that do not meet the requirements, remove the point. For the remaining radar track point set D t \{P radar,t,0}, find the new closest point P radar,t,1 ,as follows:

[0047]

[0048] Then re-perform the speed correlation check. If it still does not meet the requirements, continue in P... radar,t ∈D t {P radar,t,0 ,P radar,t,1 The search continues within the set until the number of repetitions reaches K. After this, the search stops, all radar track points obtained from the index are output, and step S4 is executed to obtain the optimal radar track point P.radar,t,max .

[0049] Further, the specific implementation method of step S4 comprises the following steps:

[0050] S4.1. Based on the radar block where the radar track point obtained by step S3 is located, the error function of the corresponding radar block is optimized, and the expression is as follows:

[0051]

[0052] Wherein, D k is all the indexed radar track points in block i, and there are k+1 in total, f i,old is the old error distribution function in block i, f i,new is the updated error distribution function in block i, (lon P ,lat P ) is the offset of the indexed radar track point P compared with the system error:

[0053] (lon P ,lat P )=P radar,t,max -P ais,t -(x peak ,y peak );

[0054] S4.2. The updated error distribution function of step S4.1 is stored in the form of a data table in the hard disk.

[0055] A radar point track and AIS track adaptive parallel matching system, relying on the radar point track and AIS track adaptive parallel matching method, comprises a database, a system error extraction module, a radar point track indexing module and an error function optimization module.

[0056] The database is connected with the system error extraction module, the system error extraction module is connected with the radar point track indexing module and the error function optimization module, and the radar point track indexing module is further connected with the database.

[0057] Advantages of the present application:

[0058] The radar point track and AIS track adaptive parallel matching method provided by the present application projects the radar point track to be matched into a parameter space, eliminates the time sequence of the radar point track, and converts the traditional build-navigation-matching problem into a parameter scanning problem. Therefore, the method has the ability of parallel execution and does not depend on the time sequence, thereby greatly improving the processing efficiency.

[0059] The radar plot and AIS track adaptive parallel matching method can continuously optimize error distribution during system operation, and realizes more accurate and robust radar data processing by iteratively using error function construction and peak index technology.

[0060] The radar plot and AIS track adaptive parallel matching method combines parameter space mapping and peak detection technology, realizes efficient extraction of radar system errors in parallel, and can accurately index the track matched with the real track from the radar plot. In addition, the application can dynamically optimize the error distribution according to the indexed track during operation, realize adaptive matching optimization, and significantly improve the robustness and real-time performance of radar data processing. BRIEF DESCRIPTION OF DRAWINGS

[0061] Figure 1 The flowchart of the radar plot and AIS track adaptive parallel matching method of the application;

[0062] Figure 2 The structure block diagram of the radar plot and AIS track adaptive parallel matching method of the application;

[0063] Figure 3 The schematic diagram of the radar plot projection into the parameter space of the application;

[0064] Figure 4 The schematic diagram of the parameter space voting result according to the error distribution of the application;

[0065] Figure 5 The schematic diagram of the error distribution function optimized by the PARZEN window in the embodiment of the application. DETAILED DESCRIPTION

[0066] In order to make the purpose, technical scheme and advantages of the application clearer, the application is further described in detail below in combination with the drawings and specific embodiments. It should be understood that the specific embodiments described herein are only used to explain the application, and are not used to limit the application, that is, the described specific embodiments are only a part of the embodiments of the application, but not all the specific embodiments. The components of the specific embodiments of the application described and shown in the drawings herein can be arranged and designed in various different configurations, and the application can have other embodiments.

[0067] Therefore, the following detailed description of the specific embodiments of the application provided in the attached drawings is not intended to limit the scope of the application claimed, but merely represents selected specific embodiments of the application. Based on the specific embodiments of the application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of the application.

[0068] In order to further understand the inventive content, characteristics and effects of the present application, the following specific embodiments are exemplified, and the attached drawings are Figure 1 -attached Figure 5 The detailed description is as follows:

[0069] Example 1:

[0070] A radar plot and AIS track adaptive parallel matching method, comprising the following steps:

[0071] S1. Identify the database arrival data, save the AIS data identified as AIS data to the database AIS table and perform data preprocessing, save the radar data identified as radar data to the database radar table, and when the saved radar data is greater than a certain batch, proceed to the next step;

[0072] Further, the specific implementation method of step S1 comprises the following steps:

[0073] S1.1. Set the data extracted from the database AIS table to include Maritime Mobile Service Identity (MMSI), time, longitude, latitude, speed, navigation, and ship number, and construct a unique constraint on the composite key of MMSI and time in the AIS data;

[0074] The database radar table format is shown in Table 1:

[0075] Table 1 Database Radar Table

[0076]

[0077] The ship number is allowed to be empty, but for AIS that is empty for a long time, it should be marked as -1;

[0078] S1.2. Set the data extracted from the database radar table to include the primary key, time, longitude, latitude, and radial speed;

[0079] The database radar table format is shown in Table 2:

[0080] Table 2 Database Radar Table

[0081]

[0082] S1.3. When the existing radar data is greater than three batches, proceed to the next step;

[0083] Further, the method needs to run a certain number of rounds in advance by the latest radar data and the corresponding AIS data before outputting the correct results, and the specific number of rounds is recommended to be greater than twenty rounds to ensure that the error distribution function is fully updated.

[0084] S2. The system error extraction module interpolates the AIS data obtained in step S1 to align the time with the time of the radar data, then projects the radar track into the parameter space through the difference transformation method according to the longitude and latitude of the AIS data, then votes the projected radar track in the parameter space based on the error distribution function to obtain a plurality of counters, filters the plurality of counters obtained through the filter, and finally accumulates the results of all filters and performs peak detection to extract the system error of the radar track;

[0085] Further, the specific implementation method of step S2 includes the following steps:

[0086] S2.1. Interpolate the AIS data obtained in step S1 to align the time with the time of the radar data, use the second-order spline interpolation method to interpolate the AIS data, and obtain the position of the AIS data at the radar scanning time;

[0087] S2.2. Project the radar track into the parameter space through the difference transformation method according to the longitude and latitude of the AIS data, the expression is:

[0088]

[0089] Where, P' radar,t is the position of the radar track at time t after difference transformation, P ais,t is the position of the AIS data at time t, P radar,t is the position of the radar track at time t, T radar is the time of radar scanning, T ais,start is the time at which the first point of the AIS data used for performing matching is located, T ais,end is the time at which the last point of the AIS data used for performing matching is located; the projected radar track is represented by (x radar,t , y radar,t ); the result after step processing is shown in Figure 3 ;

[0090] S2.3. According to the Bayesian estimation framework, vote based on the error distribution function for the projected radar track obtained in step S2.2, as shown in the following formula:

[0091] f(x-x radar,t ,y-y radar,t )

[0092] Where f(x, y) is the error distribution function, x, y are the variables of the error distribution function.

[0093] The same voting is performed for each projected radar track, and the result is represented as:

[0094]

[0095] Where D t is the set of projected points in the parameter space for all radar tracks at time t, is the counter at time t, which represents the voting result at time t; a schematic diagram of the voting is shown in Figure 4 ;

[0096] Further, when the steps are performed for the first time, the error distribution function is assumed to be:

[0097]

[0098] Where f i (lon, lat) is the error distribution function. Since the area scanned by the radar is large, the error distribution function for the entire radar scanning area is often different, so the radar scanning area needs to be divided and numbered, and the error distribution function for different blocks is independently dynamically optimized. lon and lat are the longitude and latitude, respectively. lon , and lat are the covariances of the longitude and latitude, respectively, which can be obtained by the following formula:

[0099] , lon , dis · sin θ |

[0100] Where dis is the distance resolution of the radar, and θ is the angle of the radar's normal north offset east. Similarly, lat can be represented by the following formula, with the same parameter meanings:

[0101] , lat , dis · cos θ |

[0102] In the second and subsequent iterations, the error distribution function will change. In the second and subsequent iterations, the error distribution function will change, and the error distribution function will be recorded in the hard disk. However, when the program unexpectedly terminates and starts running, it will still be considered as the first time to perform steps 1-3, in order to avoid the early error error distribution function causing a series of subsequent errors in matching;

[0103] S2.4. For the multiple counters obtained in step S2.3, filtering is performed using the following filter:

[0104]

[0105] where H n,t represents the filtering result at time t, n represents (please give the meaning of the letter), N * represents (please give the meaning of the letter);

[0106] Based on the result of filtering using a threshold of 0.05·n, the results of all filters are accumulated, and the accumulated result is represented as:

[0107]

[0108] For peak detection, set the deviation of the point with the highest value from the origin as the system error, and obtain the detected peak as (x peak ,y peak ).

[0109] S3. The radar track index module searches the radar track point according to the system error obtained in step S2, checks the speed correlation of the searched radar track point, and proceeds to the next step for the radar track point with a speed parameter meeting the requirements, and re-searches the radar track point with a speed parameter not meeting the requirements until a specified number of times or a radar track point with a speed parameter meeting the requirements is searched;

[0110] Further, the specific implementation method of step S3 includes the following steps:

[0111] S3.1. Radar track indexing: the system error obtained in step S2 is converted as follows:

[0112] P' ais,t = P ais,t +(x peak ,y peak )

[0113] where P' ais,t is the reference point for indexing the radar track at time t;

[0114] The nearest neighbor method is used to select the closest point for each radar track, and the radar track point P radar,t,0 is obtained, and the expression is:

[0115]

[0116] where argmin represents finding the point P t in the radar track set D radar,t that makes ||P ais,t -P' radar,t ||2 minimum, and || ||2 here represents performing where P radar,t has coordinates (lon radar,t , lat radar,t ), and the corresponding P ais,t has coordinates (lon ais,t , lat ais,t );

[0117] S3.2. Velocity association check: for the radar track point P radar,t,0 outputted in step S3.1, a radial velocity association check is performed, if the radial velocity is within three times of the radar's velocity resolution, it is considered that the radar track point selection is correct, the matched radar track point is outputted, and step S4 is executed, otherwise step S3.3 is executed;

[0118] S3.3. For the radar track point that does not meet the requirements, it is removed from the searched point set, and the following operations are re-executed:

[0119]

[0120] Then the velocity association check is re-performed, if it still does not meet the requirements, further search is performed in the set of P radar,t ∈D t {P radar,t,0 , P radar,t,1} until the number of repetitions reaches K times, no further search is performed, all radar track points obtained by the index are outputted, and step S4 is executed, and the optimal radar track point obtained by the index is P radar,t,max .

[0121] S4. Error function optimization module optimizes the error function according to the PARZEN window based on the radar track point whose velocity parameter meets the requirements obtained in step S3;

[0122] Further, the specific implementation method of step S4 includes the following steps:

[0123] S4.1. The radar track point obtained by the index in step S3 is used to optimize the error function of the corresponding radar block based on the radar block, and the expression is:

[0124]

[0125] where D k is all the indexed radar track points in the block i, and there are k+1 in total, f i,old is the old error distribution function in the block i, and f i,new is the updated error distribution function in the block i, (lon P , lat P) is the offset of the indexed radar track point P compared to the system error:

[0126] (lon P ,lat P )=P radar,t,max -P ais,t -(x peak ,y peak );

[0127] S4.2. The updated error distribution function of step S4.1 is re-stored in the hard disk in the form of a data table.

[0128] S5. The membership ship number in the AIS table of the database is updated according to the radar track point whose speed parameter meets the requirements obtained in step S3.

[0129] The method based on example 1 is illustrated as follows:

[0130] Step S1: Perform initialization:

[0131] Based on mysql5.7, use mysql connector / cpp for management, create AIS table for database A in terminal, and the table properties are shown in Table 1; at the same time, create radar track table for database A, and the table properties are shown in Table 3.

[0132] During the initialization process, the pre-stored data in the AIS table will also be stored, and the assumed data is shown in Table 1.

[0133] Table 3 Data pre-stored in AIS table during initialization

[0134]

[0135] At the same time, at least three batches of radar track points need to be pre-stored during the initialization process. In order to facilitate visualization, only three random points are generated for each batch.

[0136] Table 4 Data pre-stored in radar table during initialization

[0137] Primary key 0 1 2 3 4 5 6 7 8 Time 15 15 15 30 30 30 45 45 45 Longitude 0.003 0.0017 0.0 0.001 0.004 0.0032 0.004 0.0055 0.0053 Latitude 0.0014 0.0018 0.0 0.003 0.0037 0.0025 0.0012 0.0057 0.0044 Radial velocity 2.0 12.7 16.0 3.0 12.9 22.0 11.0 4.0 13.0

[0138] Step S1 is executed: when the data arrives, it is judged whether the data is radar data. It is assumed that the first data is radar track data and the second data is AIS data, and the two data arrive at the same time.

[0139] When the radar data arrives, the radar track data is submitted to the corresponding table, and then the signal of the execution program is triggered, and step S2 is prepared to be executed.

[0140] At the same time, the AIS data also performs a submission task, and the data entry is submitted to the corresponding table.

[0141] The submitted AIS data is shown in Table 5:

[0142] Table 5 Newly submitted AIS data

[0143]

[0144] The submitted radar data is shown in Table 6:

[0145] Table 6 Newly submitted radar data

[0146] Primary key 9 10 11 Time 60 60 60 Longitude 0.0015 0.007 0.0 Latitude 0.007 0.007 0.0043 Radial velocity 17.0 13.0 13.0

[0147] Step S2 is executed,

[0148] First, all MMSI numbers in the database are obtained: 000000000, 000000001. Then, all entries are extracted for different MMSI numbers. It is detected that the number of entries corresponding to 00000001 is too small to perform second-order spline interpolation, and is not executed. For the AIS data corresponding to 000000000, the interpolation function of the gsl library is used for interpolation, and the longitude sequence of the interpolated AIS data is [0.00055, 0.0022, 0.00445, 0.0056] and the latitude sequence is [0.00055, 0.0022, 0.00445, 0.0056] on the time sequence of [15, 30, 45, 60]. Assuming that the radar normal direction is north, then the speed sequence of the AIS data is [9.19, 9.19, 9.19, 9.05].

[0149] Then, the radar data is subjected to difference transformation based on the aligned AIS data, and the radar track in the parameter space after difference transformation is:

[0150] Table 7 Radar track in the parameter space after projection

[0151]

[0152] In the first run, according to step S2, assuming that the radar station is at longitude -1 and latitude -1, the error 3σ of the radar station is 1100m, and the variance corresponding to the longitude is and the latitude is

[0153] Since there is only one track, it is assumed that all points in this track are in block number 0, and thus, according to f0(z,y), i.e., f(lon-lon ais ,lat-lat ais ) in the foregoing, voting can be performed to obtain

[0154] Generally, to simplify the calculation, a discrete space is used to record Here, it is assumed that the search longitude is 11 m, i.e., 0.0001 degrees, so as to obtain 4 counters.

[0155] The filter is used to perform peak detection on the voting space, and the maximum value is located at the longitude and latitude [0.001, 0.001]. According to the detected point trace, step S3 is performed.

[0156] Step S3, radar point trace indexing, is performed.

[0157] According to the detected peak, the system error is extracted, and indexing is performed.

[0158] The nearest points indexed are 1, 4, 7, and 10, and the corresponding radial velocities are [12.7, 12.9, 4.0, 13.0], respectively. According to step 3-2, the radial velocity at 15 s should be between [13, 13] (m / s), the velocity at 30 s is 13.0 m / s, the velocity at 45 s should be between [13.1, 13.0] (m / s), and the velocity at 60 s should be 12.8 m / s. It is detected that the velocity of the 7th point trace is not within the range. The second nearest point is 8, and it is detected that the velocity of the 8th point trace is within the range, which meets the requirements.

[0159] The radar track points matched with the 000000000AIS track are output.

[0160] Step four is performed, and according to the indexed point traces 1, 4, 8, and 10, the blocks to which they belong are determined according to the longitude and latitude. Here, it is assumed that the block with the diagonal line (0, 0) and (0.5, 0.5) is numbered 1, and the corresponding point traces are used to optimize the error distribution function f1. According to step S4, a new error distribution function as shown in Figure 5 is obtained.

[0161] When the next batch of radar data arrives, the error distribution function in step four is used to start from step S1 again.

[0162] Embodiment 2:

[0163] A radar point trace and AIS track adaptive parallel matching system is realized based on the radar point trace and AIS track adaptive parallel matching method described in embodiment 1, and includes a database, a system error extraction module, a radar point trace indexing module, and an error function optimization module.

[0164] The database is connected to the system error extraction module, the system error extraction module is respectively connected to the radar point trace indexing module and the error function optimization module, and the radar point trace indexing module is further connected to the database.

[0165] It has to be noted that the terms "first", "second", and the like in connection with an entity or action refer to this entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises", "comprising", or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can include other elements not expressly listed or inherent to such process, method, article, or apparatus. An element proceeded by "comprises... a" does not, without further constraints, exclude the presence of additional elements of the process, method, article, or apparatus.

[0166] While the application has been described with reference to specific implementations thereof, it should be understood that various modifications and substitutions can be made by those skilled in the art without departing from the scope of the present application. Especially, features of the specific implementations disclosed herein can be combined in any manner, unless structural conflicts arise, and the combinations are not exhaustively described in the present specification only for the sake of brevity and conciseness. Therefore, the application is not limited to the specific implementations disclosed herein but includes all technical solutions falling within the scope of the claims.

Claims

1. A radar plot and AIS track adaptive parallel matching method, characterized in that, Comprise the following steps: S1. The database arrival data is identified, and the AIS data is saved to the database AIS table and the data is preprocessed, and the radar data is saved to the database radar table, and when the saved radar data is greater than a certain batch, the next step is performed; S2. The system error extraction module interpolates the AIS data obtained in step S1 to align the time with the time of the radar data, then projects the radar point track into the parameter space through the difference transformation method according to the longitude and latitude of the AIS data, and then votes the projected radar point track in the parameter space based on an error distribution function to obtain a plurality of counters, filters the obtained plurality of counters through a filter, and finally accumulates the results of all filters and then performs peak detection to extract the system error of the radar point track; The specific implementation method of step S2 comprises the following steps: S2.

1. Interpolate the AIS data obtained in step S1 to align the time with the time of the radar data, and use a second-order spline interpolation method to interpolate the AIS data to obtain the position of the AIS data at the radar scanning time; S2.

2. Project the radar point track into the parameter space through the difference transformation method according to the longitude and latitude of the AIS data, and the expression is: ； wherein, is the position of the radar track after the difference transformation at the time instant, is the position of the AIS data at the time instant, is the position of the radar track at the time instant, is the time of the radar scan, is the time at which the first point of the AIS data used for performing the matching is located, is the time at which the last point of the AIS data used for performing the matching is located; the projected radar track is denoted by ; S2.

3. For the projected radar point track obtained in step S2.2, vote based on the error distribution function according to the Bayesian estimation framework, as shown in the following formula: ； wherein is an error distribution function, , is a variable of the error distribution function; The same voting is performed for each projected radar point track, and the result is represented as: ； wherein, is a set of projected points in the parameter space for all radar tracks at the time instant, is a counter for the time instant, indicating the voting result for the time instant; S2.

4. For each of the plurality of counters obtained in step S2.3 filtering is performed using a filter as follows: ； wherein, represents the filtering result at time t, represents the serial number of the filter, represents the set of positive integers; based on using a threshold of The result of the filtering is accumulated for all filters, and the accumulated result is is expressed as: ； For Peak detection is performed, the deviation of the point with the highest value from the origin is set as the system error, and the detected peak value is obtained as ; S3. The radar point track index module retrieves the radar track point according to the system error obtained in step S2, checks the speed correlation of the retrieved radar track point, performs the next step on the radar track point with the required speed parameter, and re-retrieves the radar track point with the speed parameter that does not meet the requirements until a specified number of times or the radar track point with the speed parameter that meets the requirements is retrieved; S4. The error function optimization module optimizes the error function according to the PARZEN window based on the radar track point with the speed parameter that meets the requirements obtained in step S3; S5. Update the subordinate ship number in the database AIS table according to the radar track point with the speed parameter that meets the requirements obtained in step S3.

2. The radar plot and AIS track adaptive parallel matching method according to claim 1, characterized in that, The specific implementation method of step S1 comprises the following steps: S1.

1. Set the data extracted from the database AIS table to include the maritime mobile identification code MMSI, time, longitude, latitude, speed, navigation, and subordinate ship number, and construct a unique constraint on the composite key of MMSI and time in the AIS data; S1.

2. Set the data extracted from the database radar table to include the primary key, time, longitude, latitude, and radial speed; S1.

3. When the existing radar data is greater than three batches, the next step is performed.

3. The radar plot and AIS track adaptive parallel matching method according to claim 2, characterized in that, The specific implementation method of step S3 comprises the following steps: S3.

1. Radar point track indexing: convert the system error obtained in step S2 as follows: ； wherein, is a reference point used for indexing the radar track at the moment; Using the nearest neighbor method, the closest point is selected for each radar plot, resulting in radar track points The expression is: ； wherein, represents finding a set of radar tracks in such a way that the smallest point , represents taking the two-norm for the vector S3.

2. Velocity association check: for the radar track points output from step S3.1 a radial velocity association check is performed, if the radial velocity is within three times the radar's velocity resolution, the radar track point selection is considered correct, the matched radar track point is output and step S4 is performed, otherwise step S3.3; S3.

3. For radar track points that do not meet the requirements, reject the point, for the point set of the remaining radar track find the new closest point as follows: ； Then the speed correlation check is performed again, if still not meet the requirements, continue to search in the set until the number of repetitions reaches K times, no longer search, output all the radar track points obtained by the index, and perform step S4 to get the optimal radar track point of the index as . .

4. The radar plot and AIS track adaptive parallel matching method according to claim 3, characterized in that, The specific implementation method of step S4 comprises the following steps: S4.

1. Indexing the radar track points obtained in step S3, and optimizing the error function of the corresponding radar block based on the radar block where the track points are located, expressed as: ； wherein, is the set of all indexed radar track points in the zone i, where there are k+1 in total, is the old error distribution function in the zone block, is the updated error distribution function in the zone block, is the indexed radar track point compared to the bias of the systematic error: ； S4.

2. Storing the error distribution function updated in step S4.1 in the form of a data table in the hard disk.

5. A radar plot and AIS track adaptive parallel matching system, relying on a radar plot and AIS track adaptive parallel matching method according to any one of claims 1-4, characterized in that, The system comprises a database, a system error extraction module, a radar track indexing module, and an error function optimization module. The database is connected to the system error extraction module, the system error extraction module is connected to the radar track indexing module and the error function optimization module, and the radar track indexing module is also connected to the database.

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