Radar and AIS track association method based on adaptive dynamic threshold

Abstract

The invention particularly relates to a radar and AIS track association method based on an adaptive dynamic threshold. The method comprises the following steps: acquiring radar and AIS data and analyzing target information; based on the radar target information, least square fitting of a sliding window is adopted for a radar track, and the radar course is solved based on the radar track; calculating an adaptive dynamic threshold based on the distance of the radar target, the size of the AIS target and the measurement error of the sensor; determining an initial correlation quality factor based on the time initial correlation threshold value and the adaptive dynamic threshold value, and when the initial correlation quality factor meets the initial correlation quality threshold value, performing track initial correlation on the radar target and the AIS target; and establishing a fuzzy factor set based on the Euclidean distance, the navigational speed, the course and the orientation between the radar target and the AIS target, determining a fuzzy correlation quality factor based on the fuzzy factor set, and performing track correlation on the radar target and the AIS target when the fuzzy correlation quality factor meets a fuzzy correlation quality threshold. According to the method, the problems of missed association and wrong association caused by a fixed distance threshold are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of multi-sensor track association, and particularly to a radar and AIS track association method based on an adaptive dynamic threshold. Background Art

[0002] In the field of maritime traffic management, marine radars and the Automatic Identification System (AIS) are two key sensors for obtaining ship target information. Radars can provide all-day and all-weather surveillance at long distances, but their detection accuracy is relatively low, the amount of information is limited, and they are vulnerable to terrain and complex sea conditions. In contrast, AIS operates in the very high frequency band and is not restricted by factors such as position, distance, and weather, and can provide richer ship information.

[0003] Radars can obtain information such as the longitude, latitude, distance, angle, radial velocity, and timestamp of a target, while the target information obtained by AIS includes Maritime Mobile Service Identity (MMSI), longitude and latitude, course, speed, and timestamp. The data update frequency of radars is usually fixed, for example, updated every 5 seconds. The data update frequency of AIS depends on the ship's movement speed and course change rate, usually between 2 seconds and 180 seconds.

[0004] In order to give full play to the advantages of the two sensors and make up for their respective deficiencies, it is very necessary to fuse radar and AIS data. This fusion can provide more accurate and comprehensive ship target information, which is of great significance for maritime traffic management. However, the prerequisite for realizing data fusion is to accurately associate the ship tracks detected by radar and AIS.

[0005] When associating the tracks of radars and the Automatic Identification System (AIS), a fixed distance threshold is usually used to determine whether the targets detected by the two systems are the same ship. However, this method with a fixed threshold has the following two main problems:

[0006] (1) Radar detection error: There is a distance error in the radar's detection of a target, and this error increases as the distance between the target and the radar increases. This means that for long-distance targets, the detection accuracy of the radar will decrease, thus increasing the risk of missed association.

[0007] (2)Differences between AIS and radar positioning: The ship positions reported by AIS are based on Global Positioning System (GPS) positioning and usually reflect the geometric center of the ship or a preset reference point. Radar detects targets through electromagnetic wave reflection and may detect strong reflection points at the physical edges of the ship (such as the bow, stern, or side). This difference may cause inconsistent positions reported by radar and AIS, thereby affecting the accuracy of track association.

[0008] Due to the above reasons, the method using a fixed distance threshold is prone to missed associations when dealing with long-distance and large ships, and may have false associations when dealing with short-distance and small ships.

[0009] It should be noted that the information disclosed in the above background art section is only used to enhance the understanding of the background of the present invention, and thus may include information that does not constitute the prior art known to those of ordinary skill in the art. Summary of the Invention

[0010] When the target distance is far, the distance measurement error of the radar will increase significantly. At the same time, the size of different ships and the measurement error of the sensor will also affect the judgment accuracy of the fixed distance threshold. If a fixed distance threshold is maintained, distant targets may not be correctly associated due to excessive measurement errors, and ships of different sizes may also have false associations. To solve the deficiencies of the prior art, the present invention provides a method for radar and AIS track association based on an adaptive dynamic threshold, which is used to solve the problems of missed associations and false associations caused by the fixed distance threshold, thereby improving the accuracy and reliability of radar and AIS track association.

[0011] Other features and advantages of the present invention will become apparent through the following detailed description, or will be learned in part through the practice of the present invention.

[0012] According to a first aspect of the present invention, there is provided a method for radar and AIS track association based on an adaptive dynamic threshold, the method comprising:

[0013] Obtain radar data and parse radar target information, wherein the radar target information includes longitude, latitude, azimuth, and timestamp;

[0014] Obtain AIS data and parse AIS target information, wherein the AIS target information includes longitude, latitude, speed, course, and timestamp; preprocess the AIS target information;

[0015] Use a sliding window least squares fitting to fit the radar track based on the radar target information, and solve the radar course based on the radar track;

[0016] Calculate an adaptive dynamic threshold based on the distance from the radar to the target, the size of the AIS target, and the measurement error of the sensor;

[0017] Determine an initial association quality factor based on the initial association threshold value of time and the adaptive dynamic threshold. When the initial association quality factor meets the initial association quality threshold, perform an initial track association on the radar target and the AIS target;

[0018] Establish a fuzzy factor set based on the Euclidean distance, speed, course, and azimuth between the radar target and the AIS target. Determine a fuzzy association quality factor based on the fuzzy factor set. When the fuzzy association quality factor meets the fuzzy association quality threshold, perform a track association on the radar target and the AIS target.

[0019] In some exemplary embodiments, the preprocessing of the AIS target information includes:

[0020] Eliminate targets with longitude and latitude exceeding the maximum value;

[0021] Convert longitude and latitude to rectangular coordinates;

[0022] Use a method combining speed and course to interpolate the rectangular coordinates to the time of the radar for time alignment.

[0023] In some exemplary embodiments, the calculating of the adaptive dynamic threshold based on the distance from the radar to the target, the size of the AIS target, and the measurement error of the sensor includes:

[0024] Determine a corresponding adjustment coefficient according to the distance interval to which the distance from the radar to the target belongs, and determine the radar ranging error according to the product of the distance from the radar to the target and the corresponding adjustment coefficient;

[0025] Calculate a basic distance threshold according to the size of the AIS target;

[0026] Calculate the measurement error of the sensor according to the measurement variance of the radar and the measurement variance of the AIS;

[0027] Calculate the adaptive dynamic threshold based on the radar ranging error, the basic distance threshold, and the measurement error of the sensor.

[0028] In some exemplary embodiments, the calculating of the basic distance threshold according to the size of the AIS target includes:

[0029] Extract the size of the target from the AIS data, including the length and width of the target;

[0030] Calculate the basic distance threshold based on the length and width of the target. The formula used is:

[0031]

[0032] Among them, Th base is the base distance threshold, and L and W are the length and width of the target respectively.

[0033] In some exemplary embodiments, calculating the adaptive dynamic threshold based on the radar ranging error, the base distance threshold, and the measurement error of the sensor includes:

[0034] Multiplying the radar ranging error by the measurement error of the sensor and then adding the result to the base distance threshold to obtain the adaptive dynamic threshold.

[0035] In some exemplary embodiments, determining the initial association quality factor based on the initial time correlation threshold value and the adaptive dynamic threshold includes:

[0036] Calculating the initial association quality factor by a scoring method:

[0037] When both conditions are met: the absolute value of the time difference between the radar and AIS is not greater than the initial time correlation threshold value, and the absolute value of the distance difference between the radar and AIS is not greater than the adaptive dynamic threshold, the score of the initial association quality factor is incremented by 1; otherwise, it is decremented by 1.

[0038] In some exemplary embodiments, determining the fuzzy association quality factor based on the fuzzy factor set includes:

[0039] Calculating the membership degree of association at time k based on the fuzzy factor set and the weights of the fuzzy factor set;

[0040] Constructing a membership degree matrix of the radar and AIS tracks at time k based on the membership degree of association at time k;

[0041] Judging whether the maximum value in each row of the track association membership degree matrix is greater than the association membership degree threshold. If it is greater than the membership degree threshold, it is considered that the i-th AIS target and the j-th radar target are associated at time k;

[0042] Whenever the i-th AIS target and the j-th radar target are associated at time k, the corresponding fuzzy association quality factor MAFM AiRj is incremented by 1.

[0043] According to the second aspect of the present invention, there is provided a storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the method for associating radar and AIS tracks based on an adaptive dynamic threshold described in the first aspect above is implemented.

[0044] According to the third aspect of the present invention, there is provided a computer program product having a computer program stored thereon, and when the computer program is executed by a processor, the method for associating radar and AIS tracks based on an adaptive dynamic threshold described in the first aspect above is implemented.

[0045] According to a fourth aspect of the present invention, there is provided an electronic device, comprising:

[0046] a processor; and

[0047] a memory for storing executable instructions of the processor;

[0048] wherein the processor is configured to implement the radar and AIS track association method based on an adaptive dynamic threshold described in the first aspect above when executing the executable instructions.

[0049] The radar and AIS track association method based on an adaptive dynamic threshold provided by the embodiments of the present invention improves the limitations of the fixed distance threshold in the prior art. By combining the size of the ship and the measurement error of the sensor, a dynamic distance threshold is set to effectively address this situation. This method can dynamically adjust the distance threshold according to the specific size of the ship, avoiding missed associations and false associations caused by unreasonable setting of the fixed threshold. By processing the target information obtained from the radar and AIS, the information of the same target from the two sensors is found and associated, thereby improving the efficiency and safety of maritime traffic management.

[0050] First, dynamically adjust the distance threshold to adapt to the target distance. When the radar detects a long-distance target, the measurement error will increase significantly. If a fixed distance threshold is used, the long-distance target may not be correctly associated due to excessive error. To solve this problem, the present invention dynamically adjusts the distance threshold according to the actual distance between the target and the radar. Specifically, for a short-distance target, a smaller threshold value is selected to ensure the accuracy of the association; while for a long-distance target, a larger threshold value is selected to avoid missed associations caused by measurement errors. This adaptive threshold setting can significantly improve the accuracy of track association.

[0051] Second, an adaptive threshold considering the ship size and sensor error. The size differences of different ships and the measurement errors of sensors will also affect track association. The present invention comprehensively considers the length and width of the ship and the measurement error of the sensor to set an adaptive dynamic threshold. In this way, the problems of missed associations and false associations caused by ship size differences can be effectively avoided, further improving the reliability of track association.

[0052] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit the present invention. Brief Description of the Drawings

[0053] The accompanying drawings here are incorporated into and form a part of this specification, showing embodiments consistent with the present invention, and are used together with the specification to explain the principles of the present invention. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention, and those of ordinary skill in the art can obtain other drawings based on these drawings without creative efforts.

[0054] Figure 1 Flowchart of the radar and AIS track association method based on adaptive dynamic threshold for an exemplary embodiment of the present invention;

[0055] Figure 2 Schematic diagram of using speed, course, and time interpolation for AIS tracks in an exemplary embodiment of the present invention;

[0056] Figure 3 Schematic diagram of calculating the course by least - squares fitting straight line in an exemplary embodiment of the present invention;

[0057] Figure 4 Flowchart of calculating the course by least - squares fitting track in an exemplary embodiment of the present invention;

[0058] Figure 5 Comparison diagram of calculating the course by least - squares fitting and the ordinary method in an exemplary embodiment of the present invention. Detailed implementation manners

[0059] Now, the exemplary embodiments will be described more comprehensively with reference to the accompanying drawings. However, the exemplary embodiments can be implemented in various forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that the present invention will be more comprehensive and complete, and the concept of the exemplary embodiments will be fully conveyed to those skilled in the art. The described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments.

[0060] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus their repeated description will be omitted. Some of the block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0061] In the related art, radar measures the distance to a target by receiving the reflected electromagnetic wave. Distance measurement is one of the basic tasks of radar. The distance R from the target to the radar is R=(ct R ) / 2, where c is the propagation speed of electromagnetic waves in the atmosphere and t Ris the time delay between the echo signal and the transmitted signal. Taking the total differential of the ranging formula, the ranging error ΔR = cΔt R / 2 + t R Δc / 2. It can be seen that the ranging error ΔR is mainly affected by the change in echo time delay Δt R and the change in the propagation speed of electromagnetic waves in space Δc. The specific analysis is as follows:

[0062] (1) Error caused by the propagation speed of electromagnetic waves: The propagation speed c of electromagnetic waves in the atmosphere changes due to random changes in parameters such as atmospheric density, humidity, and temperature, resulting in ranging errors. The ranging error ΔR c = R·Δc / c, where Δc is the change in the propagation speed of electromagnetic waves in space, and c is the propagation speed of electromagnetic waves in the atmosphere. The farther the target distance R is, the larger the ranging error ΔR c will be.

[0063] (2) Error caused by echo time delay: Early pulse - type radars measured the target distance from the display. When using the method of electronic automatic ranging, the estimation error of the echo center (proportional to the pulse width τ and inversely proportional to the signal - to - noise ratio) will cause the ranging error ΔR τ = Δt·c / 2. The farther the target distance R is, the weaker the echo energy, the lower the signal - to - noise ratio, and the larger the time - delay error, thus resulting in a larger ranging error ΔR τ will be.

[0064] (3) Error caused by atmospheric refraction: When electromagnetic waves propagate in the atmosphere, due to the uneven distribution of atmospheric media, the propagation path of electromagnetic waves is not a straight line but a curved arc, which leads to ranging errors. The ranging error ΔR 大气 = R·(1 / (1 - k)-1), where k is the atmospheric refractive index. It can be seen that the farther the target is, the larger the ranging error caused by atmospheric refraction.

[0065] Through the above analysis, the relationship between the ranging error of the radar and the target distance can be found: The farther the target is from the radar, the larger the ranging error. However, in the existing technology for dealing with the association between radar and AIS tracks, the method of using a fixed distance threshold is generally adopted. This method has obvious defects: If the distance threshold is set too small, the tracks of distant targets may not be successfully associated; if the distance threshold is set too large, although the association problem of distant targets is solved, the association accuracy of near - distance targets will be seriously affected, and false associations are likely to occur.

[0066] To solve this problem, the present invention dynamically adjusts the distance threshold according to the actual distance between the target and the radar. Specifically, for distant targets, the distance threshold value is appropriately increased to ensure that their tracks can be accurately associated; for close targets, the distance threshold value is decreased to improve the accuracy of association. Through this dynamic adjustment mechanism, the present invention can effectively avoid the problems of missed association and false association caused by unreasonable setting of a fixed distance threshold.

[0067] In addition, the longitude and latitude positions of ships reported by AIS are usually based on Global Positioning System (GPS) positioning, and the reported positions may be the geometric center of the ship or a preset reference point. While the radar detects targets through electromagnetic wave reflection, so what may be detected is the strong reflection point on the physical edge of the ship, such as the bow, stern or side of the ship. This position difference causes the situations of missed association and false association to easily occur when using a fixed distance threshold for ships of different sizes. For example, for large ships, due to their large size, the deviation between the edge position detected by the radar and the center position reported by AIS may be large, and the fixed threshold may result in missed association; for small ships, the small deviation may result in false association.

[0068] To solve this problem, it is necessary to calculate a maximum possible position deviation based on the length and width of the ship and set the distance threshold based on this. Assuming that the position reported by AIS is the center of the ship, the position detected by the radar may be any edge of the ship, so the distance threshold can be set as a function of the length and width of the ship. However, the disadvantage of the prior art is that it does not consider the size of the ship when setting the distance threshold, and uses the same distance threshold regardless of the size of the target, which easily leads to incorrect association.

[0069] In view of the above-mentioned disadvantages and deficiencies of the prior art, in this exemplary embodiment, a method for associating radar and AIS tracks based on an adaptive dynamic threshold is provided. By combining the distance of the radar target, the size of the target, and the measurement error of the sensor to set a dynamic distance threshold, this situation can be effectively dealt with. This method can dynamically adjust the distance threshold according to the specific size of the ship, avoiding the problems of missed association and false association caused by unreasonable setting of a fixed threshold.

[0070] Reference Figure 1 As shown, the method for associating radar and AIS tracks based on an adaptive dynamic threshold may specifically include the following steps:

[0071] Step S1, obtain radar data and parse radar target information, where the radar target information includes longitude and latitude, azimuth, and timestamp;

[0072] Step S2, obtain AIS data and parse AIS target information, where the AIS target information includes longitude and latitude, speed, course, and timestamp; perform preprocessing on the AIS target information.

[0073] Step S3, use a sliding window least squares fitting for the radar track based on the radar target information, and solve the radar course based on the radar track.

[0074] Step S4, calculate an adaptive dynamic threshold based on the distance from the radar to the target, the size of the AIS target, and the measurement error of the sensor.

[0075] Step S5, determine the initial association quality factor based on the initial association threshold and the adaptive dynamic threshold. When the initial association quality factor meets the initial association quality threshold, perform initial track association on the radar target and the AIS target.

[0076] Step S6, establish a fuzzy factor set based on the Euclidean distance, speed, course, and azimuth between the radar target and the AIS target, and determine the fuzzy association quality factor based on the fuzzy factor set. When the fuzzy association quality factor meets the fuzzy association quality threshold, perform track association on the radar target and the AIS target.

[0077] Next, each step of the radar and AIS track association method based on the adaptive dynamic threshold in this exemplary embodiment will be described in more detail with reference to the accompanying drawings and embodiments.

[0078] In step S1, obtain radar data and parse radar target information.

[0079] Exemplarily, receive data transmitted from the radar through a network port via the User Datagram Protocol (UDP), and parse the information of the target according to the radar data protocol. Information such as the track number, longitude and latitude position information, and timestamp of the target can be parsed from the data transmitted from the radar. The speed of the target can be obtained based on the position information and time information.

[0080] In step S2, obtain AIS data and parse AIS target information.

[0081] Exemplarily, receive data transmitted from the AIS through a network port via the User Datagram Protocol (UDP), and parse the information of the target according to the AIS message protocol. Information such as the MMSI number, longitude and latitude, speed, course, and timestamp of the target can be parsed from the received AIS message. Among them, the MMSI number is used to form a team with the track number in the radar target information during subsequent track association.

[0082] In step S2, perform preprocessing on the AIS target information.

[0083] Specifically, it includes steps S21 - S23:

[0084] Step S21, eliminating the targets whose longitude and latitude parsed from the AIS message exceed the maximum values; among them, the range of longitude is between -180 degrees and 180 degrees, and the range of latitude is between -90 degrees and 90 degrees.

[0085] Step S22, setting the projected coordinates as (X, Y), and converting the longitude and latitude into rectangular coordinates using the Gauss-Kruger projection transformation formula. As Figure 2 shown, it is the coordinate system of the target after Gauss-Kruger projection, where the coordinate origin O is located at the intersection of the zero-degree meridian and the equator, the positive direction of the x-axis points to the due north, and the positive direction of the y-axis points to the due east. The conversion formula is as follows:

[0086]

[0087] In the formula, X and Y are the horizontal and vertical coordinates (m) projected onto the direct coordinate system. λ is the longitude and latitude in the WGS-84 coordinate system; a and b are the major and minor semi-axes (m) of the earth ellipsoid; S is the meridian arc length from the equator to the latitude; N is the radius of curvature of the prime vertical at the latitude (m); η is the second eccentricity of the earth.

[0088] Step S23, using the method combining speed and heading to interpolate the AIS data to the radar time and perform time alignment; setting the interpolation points as (x i , y i ), the previous coordinates (x 1 , y 1 ), and the subsequent coordinates (x 2 , y 2 ) corresponding to the times t 1 and t 2 respectively, with the speeds v 1 and v 2 respectively, and the headings θ 1 and θ 2 respectively, and interpolating the time t i . The formula is as follows:

[0089]

[0090] According to the time differences between the radar time point and the times t 1 and t 2 , calculate the weights at both ends A and B.

[0091]

[0092] Solve the AIS coordinates corresponding to the radar time point.

[0093]

[0094] As shown Figure 3 in the figure, the blue dots are the original positions of the AIS target data, and the orange data is the position information of the target obtained after interpolation using the combined speed and heading.

[0095] Furthermore, it also includes calculating the azimuth of the AIS target based on the AIS receiving rack and the longitude and latitude of the target position. Among them, the radar and the AIS receiving rack are set at the same position, and the longitude and latitude position information is known, denoted as A(lat1, lon1), and the longitude and latitude of the target position can be obtained by parsing the AIS message, denoted as B(lat2, lon2).

[0096] Convert the longitude and latitude to radians. The conversion formula is radians = angle x (pi / 180). After conversion, let the latitude and longitude of point A be and λ 1 , and let the latitude and longitude of point B be and λ 2 .

[0097] According to spherical geometry:

[0098]

[0099] The calculated θ angle ranges from -180 degrees to 180 degrees.

[0100] So the final azimuth angle

[0101] In step S3, based on the radar target information, the least squares fitting of the radar track is performed using a sliding window, and the radar heading is solved based on the radar track.

[0102] As Figure 4 shown, it specifically includes steps S31 - S36. Through steps S31, S32, S33, S34, S35, and S36, the heading of the target movement can be solved; the distance and azimuth information of the target detected by the radar are transformed to the rectangular coordinate system through the Gauss-Krüger projection. The x-axis is in the direction of the central meridian of the projection area, the positive direction points to the north, and the coordinate origin is at the intersection of the central meridian and the equator. The y-axis points to the east.

[0103] As Figure 4 shown, the target moves from one end point A(R A ,θ A ) of the window to the other end point B(R B ,θ B ).

[0104] Step S31, convert the distance and azimuth to the rectangular coordinate system:

[0105] x A = R A cos(θ A ),y A = R A sin(θ A )

[0106] x B = R B cos(θ B ),y B = R B sin(θ B )

[0107] Step S32, calculate the displacement vector from point A to point B:

[0108] Δx = x B - x A , Δy = y B - y A

[0109] Step S33, calculate the azimuth angle of the displacement vector,

[0110] bearing = arctan(Δy / Δx)

[0111] Step S34, if the azimuth angle bearing of the displacement vector is less than 0 degrees, add 360 degrees to convert it to the range of 0 to 360 degrees,

[0112] bearing = bearing + 360° if bearing < 0°

[0113] Step S35, the least squares method is a mathematical optimization technique that finds the best function match for data by minimizing the sum of the squares of the errors. Let the slope of the fitted line be k, then

[0114]

[0115] According to k = tan(a), the slope angle a ranges from 0 to 180 degrees and is the angle between the x-axis and the line clockwise in the figure. By inverse solution, the slope angle a = arctan(k).

[0116] Step S36, combine the azimuth angle bearing in S33 and the slope angle a in S35.

[0117] When k > 0 and 0° < bearing < 90°, the course = arctan(k).

[0118] When k > 0 and 180° < bearing < 270°, the course = arctan(k) + 180°.

[0119] When k < 0 and 90° < bearing < 180°, the course = 180° - |arctan(k)|.

[0120] When k < 0 and 270° < bearing < 360°, the course = 360° - |arctan(k)|.

[0121] As Figure 5 shown, it is the relationship between the window length for calculating the motion course of the target and the standard deviation of the target course. It can be seen that under the same window length, the standard deviation of the result obtained by using the least squares fitting algorithm to calculate the course is smaller than that obtained by directly selecting the two window endpoints to solve the course.

[0122] In step S4, an adaptive dynamic threshold is calculated based on the distance from the radar to the target, the size of the AIS target, and the measurement error of the sensor.

[0123] Two tracks with a large difference in time and distance are generally not the same target, so statistical methods can be used for initial track association. When the time relationship between the radar data and the AIS data of a certain target meets the conditions, it can be preliminarily determined that the two can be associated. The conditions are expressed as follows:

[0124] |t Ai -t Rj | ≤ Th t i = 1, 2, …, M; j = 1, 2, …, N

[0125] where t Ai is the timestamp of the AIS, t Ri is the timestamp of the radar, Th t represents the initial time association threshold, N represents the number of AIS targets, and M represents the number of radar targets. Additionally, in space, a similar association judgment can be made as in time, calculating the Euclidean distance between two distance points, and the conditional expression is as follows

[0126] |R Ai -R Rj | ≤ Th D i = 1, 2, …, M; j = 1, 2, …, N

[0127] where |R Ai -R Rj | represents the distance between the radar target and the AIS target, N represents the number of AIS targets, M represents the number of radar targets, and Th D represents the distance association threshold.

[0128] In the analysis of the prior art, we learned that radar ranging is affected by various factors, including the electromagnetic wave propagation speed, echo time delay, and atmospheric refraction. The errors caused by these factors are proportional to the target distance, that is, the farther the target distance, the greater the ranging error. The association method based on a fixed distance threshold has obvious defects: if the distance threshold is set too large, close-range targets are prone to mis-association; if the distance threshold is set too small, long-range targets may not be successfully associated. In addition, the fixed distance threshold cannot adapt to the differences in different ship sizes and sensor measurement errors, which further reduces the correct rate of association.

[0129] To address the above problems, the advantage of the present invention is that the distance threshold can be dynamically adjusted according to factors such as the distance between the target and the radar, the size of the ship, and the measurement error of the sensor. Specifically, as the target distance changes, the distance threshold will change accordingly, thus effectively solving the problems brought by the fixed threshold. This dynamic adjustment mechanism can ensure that the accuracy and reliability of the radar-AIS track association are significantly improved under different distances and different ship sizes. The specific analysis is as follows:

[0130] The relationship between the radar ranging error and the target distance R can be expressed as: ΔR = k·R, where k is the proportionality coefficient, which is affected by factors such as the change in the electromagnetic wave propagation speed, atmospheric refraction, and echo time delay error. In practical applications, the target distance R can be divided into multiple intervals, and different intervals correspond to different threshold values. That is

[0131]

[0132] where k 1 , k 2 , …, k n are the adjustment coefficients for different intervals and can be adjusted according to actual test data.

[0133] In addition, the length L and width W of the target ship extracted and parsed from the AIS data are used to calculate the basic distance threshold (maximum offset) Th base :

[0134]

[0135] The measurement error of the sensor is where and are the measurement variances of the radar and AIS respectively. Therefore, considering the distance from the ship to the radar and the size of the ship, the finally set distance threshold

[0136] Th D = Th·Δ sensor + Thbase 。

[0137] In step S5, an initial association quality factor is determined based on the initial time correlation threshold value and the adaptive dynamic threshold value. When the initial association quality factor meets the initial association quality threshold, an initial track association is performed between the radar target and the AIS target.

[0138] Specifically, let the initial association quality factor The initial association quality is calculated using a scoring method. When the initial association threshold is met, the quality factor score is incremented by 1; otherwise, it is decremented by 1, as expressed below

[0139]

[0140] When the initial correlation quality factor meets the condition

[0141] CAFM AiRj (k) ≥ Th cafm

[0142] it can indicate that the i-th AIS target is initially associated with the j-th radar target, where Th cafm represents the initial association quality threshold.

[0143] In step S6, a fuzzy factor set is established based on the Euclidean distance, speed, course, and bearing between the radar target and the AIS target. Based on the fuzzy factor set, a fuzzy association quality factor is determined. When the fuzzy association quality factor meets the fuzzy association quality threshold, a track association is performed between the radar target and the AIS target.

[0144] Select the Euclidean distance, speed, course, and bearing between the radar and AIS targets as the fuzzy factor set. The universe of discourse U = {u 1 , u 2 , u 3 , u 4}, and the fuzzy factor set is as follows:

[0145]

[0146] where D Ai (k) is the position of AIS target i at time k; D Rj (k) is the position of radar target j at time k; SOG Ai (k) is the speed of AIS target i at time k; SOG Rj (k) is the speed of radar target j at time k; COG Ai (k) is the course of AIS target i at time k; COG Rj (k) is the course of radar target j at time k; DIR Ai (k) is the bearing of AIS target i at time k; DIR Rj(k) is the azimuth of radar target j at time k.

[0147] Select the fuzzy evaluation set V = {associated, not associated}, and the mathematical expression is V = {1, 0}. Select the membership function expression of normal distribution as

[0148]

[0149] In the formula, τ q is the adjustment coefficient, r q1ij (k) represents the membership degree of association between the i-th AIS track and the j-th radar target track with the q-th factor at system time k, r q2ij (k) represents the membership degree of non-association between the i-th AIS track and the j-th radar target track with the q-th factor at system time k, σ q corresponds to the spread of the q-th factor. The spread of the fuzzy factor is related to the error distribution of the data provided by radar and AIS. Use the standard deviation of each factor as the spread of each factor, as follows

[0150]

[0151] In the formula, μ is the mean value corresponding to the factor u i and N is the length of the track sequence. Considering that the four fuzzy factors have different influences on the association between radar and AIS tracks, different weights are assigned to the four fuzzy factors, which are respectively

[0152] The comprehensive membership degree of association r 1ij (k) = α 1 r 11ij (k) + α 2 r 21ij (k) + α 3 r 31ij (k) + α 4 r 41ij (k), and the comprehensive membership degree of non-association r 2ij (k) = 1 - r 1ij (k).

[0153] The membership degree matrix of the association between radar and AIS tracks at time k can be calculated as follows.

[0154]

[0155] Find the maximum value in each row of matrix A, that is, the most likely associated i-th AIS target and j-th radar target. Then judge whether the maximum value is greater than the association membership degree threshold ε, and the value of ε ranges from 0 to 1. If it is greater than the membership degree threshold, it is considered that the i-th AIS target and the j-th radar target are associated at time k.

[0156] Whenever the i-th AIS target and the j-th radar target are associated at time k, the corresponding fuzzy association quality factor MAFM AiRj is incremented by 1. When the fuzzy association quality factor satisfies the conditions

[0157] MAFM AiRj ≥Th mafm

[0158] , it can be considered that the i-th AIS target is associated with the j-th radar target, where Th mafm represents the fuzzy association quality threshold.

[0159] Traditional track association methods usually use a fixed distance threshold for target matching, but this method has certain limitations: Since the radar ranging error is positively correlated with the target distance, it is difficult for a fixed threshold to adapt to the error characteristics of different distance segments. Specifically, when the threshold is set small, long-range targets are likely to exceed the threshold range due to the increasing cumulative error, resulting in missed associations; while when the threshold is set too large, although it can cover long-range targets, it will over-expand the association tolerance space for short-range targets, leading to false associations. In addition, the fixed threshold does not consider the influence of the physical size differences of ships. For large ships, missed judgments may occur due to the large deviation between the geometric center and the radar reflection point, and for small ships, the risk of false matching increases due to excessive threshold redundancy. To address the above problems, the present invention proposes an adaptive dynamic distance threshold technology:

[0160] (1) Establish a functional relationship between the distance threshold and the target-radar spacing, so that the threshold expands according to a preset rule (such as a linear or exponential relationship) as the target distance increases, ensuring that long-range targets have sufficient error tolerance while restricting the redundant range of short-range targets;

[0161] (2) Combine the ship length and width data provided by AIS and the sensor error model to calculate the maximum offset between the geometric center and the edge reflection point of the ship superimposed with the measurement errors of the radar and AIS to construct a dynamic reference threshold based on target characteristics. The final dynamic threshold is Th D =k·R·Δ sensor +Th base . Through the dual adaptive mechanism, the present invention not only solves the contradiction in the association of far / near targets by the fixed threshold, but also avoids the matching deviation caused by ship size differences, improving the track association accuracy and robustness in complex scenarios.

[0162] It should be noted that the above-mentioned drawings are only schematic illustrations of the processes included in the method according to the exemplary embodiments of the present invention, rather than for limiting purposes. It is easy to understand that the processes shown in the above-mentioned drawings do not indicate or limit the time sequence of these processes. Additionally, it is also easy to understand that these processes can be executed, for example, synchronously or asynchronously in multiple modules.

[0163] It should be noted that although several modules or units of a device for action execution are mentioned in the above detailed description, such division is not mandatory. In fact, according to embodiments of the present invention, the features and functions of two or more of the above-described modules or units can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0164] It should be noted that, on the other hand, the present application also provides a storage medium, which may be included in an electronic device; or may exist separately without being assembled into the electronic device. The above storage medium carries one or more programs, and when the one or more programs are executed by an electronic device, the electronic device is caused to implement the methods described in the following embodiments. For example, the electronic device may implement each step of the method as Figure 1 shown.

[0165] In one embodiment, the present application provides a computer program product, including a computer program, which when executed by a processor implements the steps in the above method embodiments.

[0166] In addition, the above drawings are only schematic illustrations of the processes included in the method according to the exemplary embodiments of the present invention, rather than for limiting purposes. It is easy to understand that the processes shown in the above drawings do not indicate or limit the chronological order of these processes. Additionally, it is also easy to understand that these processes may be executed synchronously or asynchronously in, for example, multiple modules.

[0167] Those skilled in the art will readily think of other embodiments of the present invention after considering the specification and practicing the invention herein. The present application is intended to cover any variations, uses, or adaptations of the present invention, which follow the general principles of the present invention and include common general knowledge or conventional technical means in the technical field not disclosed in the present invention. The specification and embodiments are only regarded as exemplary, and the true scope and spirit of the present invention are pointed out by the claims.

[0168] It should be understood that the present invention is not limited to the exact structures described above and shown in the drawings, and various modifications and changes can be made without departing from its scope. The scope of the present invention is only defined by the appended claims.

Claims

1. A radar and AIS track correlation method based on adaptive dynamic threshold, characterized in that: The method comprises: Acquire radar data and parse radar target information, wherein the radar target information includes latitude and longitude, azimuth and timestamp; Acquire AIS data and parse AIS target information, where AIS target information includes latitude and longitude, speed, heading and timestamp; pre-process AIS target information; Based on the radar target information, the radar track is fitted by the least square method of sliding window, and the radar heading is solved based on the radar track; Calculates adaptive dynamic thresholds based on the radar-to-target distance, AIS target size, and sensor measurement errors; The initial association quality factor is determined based on the time initial association threshold value and the adaptive dynamic threshold. When the initial association quality factor meets the initial association quality threshold, the radar target and the AIS target are initially associated with each other. A fuzzy factor set is established based on the Euclidean distance, speed, heading and azimuth between the radar target and the AIS target. The fuzzy association quality factor is determined based on the fuzzy factor set. When the fuzzy association quality factor meets the fuzzy association quality threshold, the track association is performed on the radar target and the AIS target.

2. The radar and AIS track association method based on adaptive dynamic threshold according to claim 1 is characterized in that: The preprocessing of the AIS target information includes: Eliminate targets whose longitude and latitude exceed the maximum value; Convert longitude and latitude to rectangular coordinates; The method of combining speed and heading is used to unify the rectangular coordinates to the radar time for time alignment.

3. The radar and AIS track association method based on adaptive dynamic threshold according to claim 1 or 2, characterized in that: The adaptive dynamic threshold is calculated based on the distance between the radar and the target, the size of the AIS target and the measurement error of the sensor, including: Determine a corresponding adjustment coefficient according to the distance interval to which the distance between the radar and the target belongs, and determine the radar ranging error according to the product of the distance between the radar and the target and the corresponding adjustment coefficient; Calculate the basic distance threshold based on the size of the AIS target; Calculate the sensor measurement error based on the radar measurement variance and the AIS measurement variance; An adaptive dynamic threshold is calculated based on the radar ranging error, the basic distance threshold and the sensor measurement error.

4. The radar and AIS track association method based on adaptive dynamic threshold according to claim 3 is characterized in that: The distance threshold based on the size of the AIS target is calculated, including: Extract the size of the target from the AIS data, including the length and width of the target; The distance threshold is calculated based on the length and width of the target, using the following formula: Among them, Th base is the distance threshold based on , L and W are the length and width of the target respectively.

5. The radar and AIS track association method based on adaptive dynamic threshold according to claim 3 is characterized in that: The adaptive dynamic threshold is calculated based on the radar ranging error, the basic distance threshold and the sensor measurement error, including: The radar ranging error is multiplied by the sensor's measurement error and then added to the basic distance threshold to obtain the adaptive dynamic threshold.

6. The radar and AIS track association method based on adaptive dynamic threshold according to claim 1 is characterized in that: The determining of the initial association quality factor based on the time initial association threshold value and the adaptive dynamic threshold value includes: The initial correlation quality factor is calculated using a scoring method: When the following conditions are met at the same time: the absolute value of the time difference between the radar and AIS is not greater than the time initial association threshold, and the absolute value of the distance difference between the radar and AIS is not greater than the adaptive dynamic threshold, the initial association quality factor score is increased by 1, otherwise it is decreased by 1.

7. The radar and AIS track association method based on adaptive dynamic threshold according to claim 1 is characterized in that: The determining of the fuzzy correlation quality factor based on the fuzzy factor set includes: Calculate the k-time association membership based on the fuzzy factor set and the weights corresponding to the fuzzy factor set; Based on the k-time correlation membership, a k-time radar and AIS track correlation membership matrix is ​​constructed; Determine whether the maximum value of each row in the track association membership matrix is ​​greater than the association membership threshold. If it is greater than the membership threshold, it is considered that the i-th AIS target and the j-th radar target are associated at time k; Whenever the i-th AIS target and the j-th radar target are associated at time k, the corresponding fuzzy association quality factor MAFM is AiRj Add 1.

8. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the radar and AIS track association method based on adaptive dynamic threshold is implemented as described in any one of claims 1 to 7.

9. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the radar and AIS track association method based on adaptive dynamic threshold is implemented as described in any one of claims 1 to 7.

10. An electronic device, characterized in that: include: processor; as well as A memory, configured to store executable instructions of the processor; The processor is configured to execute the radar and AIS track association method based on adaptive dynamic threshold according to any one of claims 1 to 7 by executing the executable instructions.

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