A method and system for multi-base station collaborative sensing of offshore vessel positioning

Abstract

The present application discloses a method and system for multi-base station collaborative sensing of offshore vessel positioning. The method includes: obtaining the base station coordinates of M base stations and the distance estimation sets of each base station and multiple target ships respectively; preliminarily screening the distance estimation set according to the screening condition that the data association satisfies the triangle inequality, and obtaining a set of data association schemes; using the Gauss-Newton algorithm and the Hungarian algorithm, calculating each data association scheme in turn, obtaining the target estimated position of the target ship and the corresponding minimum distance error of each data association scheme; comparing each minimum distance error to obtain the optimal distance error; using the optimal target estimated position corresponding to the optimal distance error as the final positioning result of multiple target ships. The present application can realize fast and high-precision offshore vessel positioning under the conditions of increasing number of targets and changing signal-to-noise ratio, effectively reducing errors, improving positioning accuracy, and ensuring real-time multi-target tracking.

Description

Technical Field

[0001] The present invention relates to the technical field of multi-target positioning, and in particular to a method and system for collaboratively sensing offshore vessel positioning by multiple base stations. Background Art

[0002] Due to load limitations, it is difficult for small ships at sea to be equipped with traditional large multi-target detection radars, so they cannot transmit the ship's position in real time and accurately. Therefore, it is particularly important to efficiently use multiple shore base stations and modern lighthouse resources for collaborative work. Summary of the invention

[0003] The object of the present invention is to provide a method and system for cooperatively sensing offshore vessel positioning by multiple base stations to solve the technical problems in the prior art.

[0004] The various technical effects that can be produced by the optional technical solutions among the various technical solutions provided by the present invention are described in detail below.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] In a first aspect, the present application provides a method for multiple base stations to collaboratively sense offshore vessel positioning, comprising:

[0007] Obtaining the base station coordinates of M base stations and a distance estimation set between each base station and a plurality of target ships;

[0008] According to the screening condition that the data association satisfies the triangle inequality, the distance estimation set is preliminarily screened to obtain a set of data association solutions that meet the screening condition;

[0009] Using the Gauss-Newton algorithm and the Hungarian algorithm, each data association scheme in the data association scheme set is calculated in turn to obtain the target estimated position of the target ship and the corresponding minimum distance error of each data association scheme;

[0010] Compare the minimum distance errors to obtain the optimal distance error;

[0011] The optimal target estimated position corresponding to the optimal distance error is used as the final positioning result of the multiple target ships.

[0012] In some embodiments, the distance estimation set D m represents the k estimated distances between the mth base station among M base stations and the k target ships The formed collection.

[0013] In some embodiments, the preliminarily screening the distance estimation set according to the screening condition that the data association satisfies the triangle inequality to obtain the data association set that satisfies the screening condition includes:

[0014] Based on the triangle inequality, using the distance estimation set D m Constructing the equation for estimating the target position Among them, g m,k The distance estimation set D m The mapping relationship between the kth target ship and the mth base station is as follows: m Lidi m,k The large element is used as the distance estimate between the base station m and the target ship k, denoted as

[0015]

[0016] Based on the estimated target position equation, all data association schemes that meet the screening conditions are calculated, and all data association schemes satisfy the triangle inequality, which is expressed by the following formula:

[0017]

[0018]

[0019] Obtain the data association solution set of all data association solutions that satisfy the triangle inequality, wherein the data association solution set is represented by G, and

[0020] In some embodiments, the screening condition is And the number of data association schemes is (K!) 2 indivual.

[0021] In some embodiments, the use of the Gauss-Newton algorithm and the Hungarian algorithm to calculate each data association scheme in the data association scheme set to obtain the target estimated position and minimum distance error of the target vessel by each data association scheme includes:

[0022] S301, for the first data association scheme in the data association scheme set, using the Gauss-Newton algorithm, calculating the positioning position results of the target ship based on the 1st to nth base stations;

[0023] S302, calculating a first data association scheme for the M base stations except for n base stations through the Hungarian algorithm;

[0024] S303, based on the first data association scheme and the positioning position results of the target ship by the first to nth base stations, calculating the target estimated position of the target ship by the first data association scheme to calculate a first minimum distance error;

[0025] S304, for the second data association scheme in the data association scheme set, using the Gauss-Newton algorithm, calculating the positioning results of the second to nth base stations for the target vessel;

[0026] S305, repeating steps S302 to S303, calculating the target estimated position of the target vessel using a second data association scheme to calculate a second minimum distance error;

[0027] For the (n-2)th data association scheme in the data association scheme set, steps S301 to S303 are repeated until the target estimated position of the target ship and the corresponding minimum distance error of all data association schemes in the data association scheme set are calculated.

[0028] In some embodiments, step S302 and step S303 include:

[0029] Using the formula Calculate distance estimation error

[0030] By using the Hungarian algorithm, the distance estimation error is based on Matching indicator function Calculate the first data association scheme of (Mn) base stations, expressed by the following formula:

[0031]

[0032]

[0033]

[0034] in, represents the matching index function,

[0035] Β m Defined as all A collection of: Defined as the distance estimation error; t represents the tth data association scheme, where t = 1, and the first data association scheme is

[0036] Based on the first data association scheme and the positioning position results of the target ship from the 1st to the nth base stations, and calculating the target estimated position of the target ship by the first data association scheme And the first minimum distance error is calculated according to the following formula:

[0037]

[0038] in, represents the first minimum distance error, Represents variance.

[0039] In some embodiments, the matching index function It means that for the mth (m>n) base station, if the distance estimation set D m Middle elements as the distance to the kth target, then the matching index function is 1.

[0040] In some embodiments, the following formula is used to compare the minimum distance errors to obtain the optimal distance error:

[0041]

[0042] in, represents the optimal distance error.

[0043] In a second aspect, the present application provides a system for cooperatively sensing offshore vessel positioning by multiple base stations, including a data processing center and M base stations, each of which is communicatively connected to the data processing center;

[0044] The base station is used to receive and process the radio signal of the target vessel to form a distance estimation set;

[0045] The data processing center is used to obtain the base station coordinates of the M base stations and the estimated distance sets between each base station and a plurality of target ships;

[0046] The data processing center is further used to perform preliminary screening on the distance estimation set according to the screening condition that the data association satisfies the triangle inequality, so as to obtain a set of data association solutions that meet the screening condition;

[0047] The data processing center is further used to use the Gauss-Newton algorithm and the Hungarian algorithm to sequentially calculate each data association scheme in the data association scheme set to obtain the target estimated position of the target ship and the corresponding minimum distance error of each data association scheme;

[0048] The data processing center is also used to compare the various minimum distance errors to obtain the optimal distance error;

[0049] The data processing center is further used to use the optimal target estimated position corresponding to the optimal distance error as the final positioning result of the multiple target ships.

[0050] Implementing one of the above technical solutions of the present invention has the following advantages or beneficial effects:

[0051] The method and system for multi-base station collaborative sensing of offshore vessel positioning of the present application obtains a data association scheme set by preliminary screening of a distance estimation set, and then uses a Gauss-Newton algorithm and a Hungarian algorithm to sequentially calculate each data association scheme in the data association scheme set to obtain a target estimated position of the target vessel and a corresponding minimum distance error for each data association scheme; compares each minimum distance error to obtain an optimal distance error; and uses the optimal target estimated position corresponding to the optimal distance error as the final positioning result of multiple target vessels, which can achieve fast and high-precision offshore vessel positioning under the conditions of an increase in the number of targets and a change in the signal-to-noise ratio, effectively reduce errors, improve positioning accuracy, and ensure real-time multi-target tracking.

[0052] This application is particularly suitable for small civilian vessels that are not equipped with navigation systems, and can significantly improve the efficiency and safety of maritime traffic management, environmental monitoring and rescue operations. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. It is obvious that the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work. In the drawings:

[0054] Figure 1 is a schematic diagram of a system for cooperatively sensing offshore vessel positioning by multiple base stations according to an embodiment of the present application;

[0055] Figure 2 It is a flowchart of a method for cooperatively sensing offshore vessel positioning by multiple base stations according to an embodiment of the present application;

[0056] Figure 3 is a schematic diagram of a multi-target positioning result of an embodiment of the present application;

[0057] Figure 4 is a schematic diagram of the effect of the signal-to-noise ratio on positioning accuracy in an embodiment of the present application;

[0058] Figure 5 It is a schematic diagram of the influence of the signal-to-noise ratio on the positioning accuracy in an embodiment of the present application. DETAILED DESCRIPTION

[0059] In order to make the purpose, technical solutions and advantages of the present invention clearer, the various exemplary embodiments to be described below will refer to the corresponding drawings, which constitute a part of the exemplary embodiments, wherein various exemplary embodiments that may be used to implement the present invention are described. Unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The implementation methods described in the following exemplary embodiments do not represent all implementation methods consistent with the present disclosure. It should be understood that they are only examples of processes, methods, devices, etc. that are consistent with some aspects of the present disclosure as detailed in the attached claims, and other embodiments may also be used, or the embodiments listed herein may be modified in structure and function without departing from the scope and essence of the present invention.

[0060] In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", etc. indicate the orientation or positional relationship based on the drawings, which is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the elements referred to must have a specific orientation, be constructed and operated in a specific orientation. The terms "first", "second", etc. are only used for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. The term "multiple" means two or more. The terms "connected" and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, an integral connection, a mechanical connection, an electrical connection, a communication connection, a direct connection, an indirect connection through an intermediate medium, and can be the internal connection of two elements or the interaction relationship between two elements. The term "and / or" includes any and all combinations of one or more related listed items. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to the specific circumstances.

[0061] In order to illustrate the technical solution of the present invention, a specific embodiment is used below for description, and only the parts related to the embodiment of the present invention are shown.

[0062] Embodiment 1:

[0063] The present application provides a method for multiple base stations to collaboratively sense offshore vessel positioning. The method for multiple base stations to collaboratively sense offshore vessel positioning is applied to a system for multiple base stations to collaboratively sense offshore vessel positioning, such as Figure 1As shown, the system includes a data processing center 10 and M base stations 20. The M base stations 20 are deployed in coastal areas, taking three base stations as an example. Each base station 20 is connected to the data processing center 10 via an optical fiber; the base station 20 receives and processes the radio signal of the ship to form a distance estimation set and a radial velocity estimation set. Each base station 20 has a base station 20 coordinate. The data processing center 10 stores the base station 20 coordinates of each base station 20, and obtains the distance estimation set and radial velocity estimation set between each base station 20 and each ship. The data processing center 10 processes the base station 20 coordinates, the distance estimation set, and the radial velocity estimation set, and receives and processes the radio signal of the ship through the collaborative work of multiple base stations 20, and adopts a multi-target positioning method based on data association, thereby calculating the accurate position of the ship.

[0064] When the data processing center 10 processes the base station 20 coordinates, the distance estimation set and the radial velocity estimation set, the base station 20 coordinates of each base station 20 and the distance estimation set between each base station 20 and multiple target ships are obtained; according to the screening condition that the data association satisfies the triangle inequality, the distance estimation set is preliminarily screened to obtain a data association scheme set that satisfies the screening condition; using the Gauss-Newton algorithm and the Hungarian algorithm, each data association scheme in the data association scheme set is calculated in turn to obtain the target estimated position of the target ship and the corresponding minimum distance error of each data association scheme; comparing the various minimum distance errors to obtain the optimal distance error; and taking the optimal target estimated position corresponding to the optimal distance error as the final positioning result of the multiple target ships.

[0065] In some embodiments, the base station 20 can be an ISAC base station, which can be installed on a modern lighthouse. The modern lighthouse has radio communication capabilities, so that multiple base stations 20 can be connected to the data processing center 10 via optical fiber. The data processing center 10 can be a cloud server or other equipment, which is not limited here.

[0066] The vessel can be a small civilian vessel that is not equipped with a navigation system. Through the method of the multi-base station collaborative sensing of offshore vessel positioning of the present application, the position of the vessel can be quickly determined and multi-target tracking can be achieved. In addition, the present application can not only improve the real-time and accuracy of vessel positioning, but also provide reliable data support for maritime traffic management, environmental monitoring and rescue operations, etc., greatly enhancing the safety and efficiency of maritime activities. Through the collaborative work of multiple base stations, the received signals can be quickly processed, the ship position can be updated in real time, and the timeliness of positioning information can be ensured; by comprehensively processing the signals of the target ship, the error can be effectively reduced and the positioning accuracy can be improved; and the system of the multi-base station collaborative sensing of offshore vessel positioning can adapt to a variety of environments and conditions, especially for small ships and ships not equipped with advanced navigation equipment. In addition, the system design takes into account redundancy and fault-tolerant mechanisms, and even if some base stations fail, the stable operation of the positioning system can still be guaranteed; real-time and accurate positioning information can effectively prevent and reduce maritime accidents and improve the safety of maritime activities. In summary, through the collaborative work of multiple base stations on the shore and modern lighthouses, an efficient and reliable offshore vessel positioning system can be provided. This will not only help improve the efficiency of maritime traffic management, but also provide strong support for environmental monitoring and rescue operations, significantly enhancing the safety and efficiency of maritime activities.

[0067] In an embodiment of the present application, a data association scheme set is obtained by preliminary screening of a distance estimation set, and then each data association scheme in the data association scheme set is calculated in turn using a Gauss-Newton algorithm and a Hungarian algorithm to obtain a target estimated position of the target vessel and a corresponding minimum distance error for each data association scheme; each minimum distance error is compared to obtain an optimal distance error; and the optimal target estimated position corresponding to the optimal distance error is used as a final positioning result of multiple target vessels. This enables fast and high-precision offshore vessel positioning to be achieved under conditions of an increase in the number of targets and a change in the signal-to-noise ratio, effectively reducing errors, improving positioning accuracy, and ensuring real-time multi-target tracking.

[0068] The present application also provides a method for the multi-base station cooperative sensing of offshore vessel positioning, which is executed by the data processing center of the system, such as Figure 2 As shown, the method for multiple base stations to cooperatively sense offshore vessel positioning includes the following steps S10 to S40.

[0069] S10: Obtain the base station coordinates of M base stations and the distance estimation sets of multiple target ships detected by each base station.

[0070] Specifically, each base station has its own base station coordinates, which are stored in the data processing center. When the base station receives and processes the radio signal sent by the ship, an estimated distance value and radial velocity estimated value of the base station to the ship are formed.

[0071] When locating the target ship, the data processing center obtains the base station coordinates of each base station and the estimated distance set between each base station and the target ship.

[0072] The embodiment of the present application takes M base stations and K target ships as an example.

[0073] In some embodiments, the distance estimation set D m represents the k estimated distances between the mth base station among M base stations and the k target ships The formed set is expressed by the following formula 1:

[0074]

[0075] in, Represents the estimated distance between the mth base station and the kth target.

[0076] The radial estimation set V m represents the k radial velocity estimates of the mth base station among M base stations and the k target ships The formed set is expressed by the following formula 2:

[0077]

[0078] in, represents the estimated radial velocity between the mth base station and the kth target.

[0079] S20: According to the screening condition that the data association satisfies the triangle inequality, the distance estimation set is preliminarily screened to obtain a set of data association solutions that meet the screening condition.

[0080] In some embodiments, step S20 may include:

[0081] Based on the triangle inequality, using the distance estimation set D m The target position estimation equation is constructed and expressed as the following formula 3:

[0082]

[0083] and,

[0084] Among them, g m,k The distance estimation set D m Mapping with the kth target ship, for the mth base station, the distance estimation set D m Lidi m,k The largest element is the estimated distance between the base station m and the target ship k, denoted as

[0085] In some embodiments, the screening condition is expressed by Formula 5:

[0086]

[0087] Among all g that meet the screening condition (Formula 5) m,k In the allocation scheme, the number of data association schemes is (K!) 2 indivual.

[0088] Based on the estimated target position equation, all data association schemes that meet the screening conditions are calculated, and all data association schemes satisfy the triangle inequality, which is expressed by the following formula 6 and formula 7:

[0089]

[0090] Obtain the data association solution set of all data association solutions that satisfy the triangle inequality, wherein the data association solution set is represented by G, and

[0091] And, the data association scheme set can be expressed by Formula 8:

[0092]

[0093] Through the triangle inequality, some impossible positioning solutions can be eliminated, thereby obtaining a set of solutions that meet the data association conditions.

[0094] S30: using the Gauss-Newton algorithm and the Hungarian algorithm, sequentially calculating each data association scheme in the data association scheme set, and obtaining the target estimated position of the target ship and the corresponding minimum distance error of each data association scheme.

[0095] In some implementations, step S30 may include:

[0096] S301. For the first data association scheme in the data association scheme set, using the Gauss-Newton algorithm, calculate the positioning results of the target ship based on the 1st to nth base stations.

[0097] The following formula 9-1 can be used to calculate the positioning results of k targets from the 1st to the nth:

[0098]

[0099] in, Represents the variance, each distance estimation error Assuming that they are independent and follow a Gaussian distribution, the distance estimation error The relationship with variance is expressed by the following formula 9-2:

[0100]

[0101] S302: Calculate a first data association scheme for the M base stations except for n base stations through the Hungarian algorithm.

[0102] S303: Based on the first data association solution The first data association scheme is used to calculate the target estimated position of the target ship based on the positioning position results of the first to nth base stations for the target ship, so as to calculate the first minimum distance error.

[0103] Specifically, step S302 and step S303 may include:

[0104] Using formula 10, calculate the distance estimation error

[0105]

[0106] By using the Hungarian algorithm, the distance estimation error is based on Matching indicator function Calculate the first data association scheme of (Mn) base stations It is expressed by the following formula:

[0107]

[0108] in, represents the matching index function,

[0109] Β m Defined as all A collection of: Defined as the distance estimation error; t represents the tth data association scheme, where t = 1, and the first data association scheme is

[0110] In some embodiments, the matching index function It means that for the mth (m>n) base station, if the distance estimation set D m Middle elements as the distance to the kth target, then the matching index function is 1.

[0111] Based on the first data association scheme and the positioning results of the target ship from the 1st to the nth base stations, and calculate the first data association scheme The target estimated position of the target vessel And the first minimum distance error is calculated according to the following formula 14:

[0112]

[0113] in, represents the first minimum distance error, Variance represents variance, which is a measure of the degree of dispersion of data or variables. In this study, it mainly refers to the values ​​of each variable (distance estimate ) and its mean is calculated as follows:

[0114]

[0115] S304: For the second data association scheme in the data association scheme set, using the Gauss-Newton algorithm, calculate the positioning results of the second to nth base stations for the target ship.

[0116] In step S304, referring to step S301, formula 9-1 is used to change m=1 in formula 9-1 to m=2, and the positioning results of the second to nth base stations for the target ship can be calculated.

[0117] S305, repeating steps S302 to S303, calculating the target estimated position of the target ship using a second data association solution to calculate a second minimum distance error.

[0118] In step S305, the second data association scheme for the M base stations excluding the n+1 base station can also be calculated by referring to steps S302 to S303.

[0119] For the (n-2)th data association scheme in the data association scheme set, steps S301 to S303 are repeated until the target estimated position of the target ship and the corresponding minimum distance error of all data association schemes in the data association scheme set are calculated.

[0120] Specifically, there are k data association schemes in the data association scheme set. For each data association scheme in the data association scheme set, the target estimated position of the target ship and the first minimum distance error of the first data association scheme are calculated with reference to the first data association scheme, and the target estimated position of the target ship and the corresponding minimum distance error of all data association schemes are calculated in sequence.

[0121] The Gauss-Newton algorithm is an effective tool for solving nonlinear least squares problems, especially for parameter estimation and data fitting. Its main advantages are fast convergence speed, high accuracy, and the ability to simplify the calculation process in some cases, especially when the objective function is approximately quadratic, it can converge quickly and provide high-precision estimates. The k-th target position estimation problem mentioned above is a nonlinear least squares problem.

[0122] The Hungarian algorithm is a classic method for solving the allocation problem. It is used to find the optimal allocation solution between limited resources and tasks, such as allocating tasks under the conditions of minimizing costs or maximizing benefits. The Hungarian algorithm is used to obtain the β that minimizes the sum of the distance estimation errors of each base station to all targets. m,k,k The matching scheme can ensure that the perfect match with the best cost is found. This algorithm constructs a weight matrix and finds the match with the lowest cost based on it, ensuring that each task is uniquely and appropriately assigned. This algorithm has been optimized many times in terms of computational efficiency and can handle more complex practical problems more quickly.

[0123] S40: Compare the minimum distance errors to obtain the optimal distance error.

[0124] In some implementations, step S40 can be calculated using the following formula:

[0125]

[0126] in, represents the optimal distance error.

[0127] S50: Taking the optimal target estimated position corresponding to the optimal distance error as the final positioning result of the plurality of target ships.

[0128] Specifically, after determining the optimal distance error, the optimal target estimated position corresponding to the optimal distance error is used as the final positioning result of the multiple target ships to achieve accurate positioning of the target.

[0129] In some implementations, in order to implement a method for multi-base station collaborative sensing of offshore vessel positioning of the present application, the following algorithm is used:

[0130] Input of the algorithm: base station coordinates of each base station (a m ,b m ) and the distance estimation set Dm.

[0131] Initialization: According to formula 8: Get the data association solution set G (n) (t), define G (n)(t) is the tth data association scheme in G, initialized with t = 1;

[0132] repeat:

[0133] 1. G (n) (t) Assume the current data association scheme

[0134] 2. Solve formula 9-1 using the Gauss-Newton algorithm: Get the positioning results of k targets from the 1st to the nth

[0135] 3. According to formula 10: Calculate distance estimation error The data association scheme for the remaining (Mn) base stations is obtained by solving formula 13 through the Hungarian algorithm.

[0136] 4. Data association scheme based on the remaining (Mn) base stations Solve formula 9-1 to get a better estimate of the target position

[0137] 5. According to formula 14: Calculate the minimum distance error

[0138] 6. t = t + 1;

[0139] Until t = G (n) .

[0140] Output:

[0141] 1. By solving formula 15, we can get the optimal distance error

[0142] 2. By solving formula 9-1, the optimal target estimated position corresponding to the optimal distance error is obtained as the positioning result of the K target ships:

[0143] Figure 3 It can be seen that the method of multi-base station collaborative sensing of offshore vessel positioning adopted in the present application, although the position estimation result obtained deviates from the actual coordinates of the target due to the influence of positioning accuracy, does not result in situations where the target cannot be detected or a non-existent target is located, thereby verifying the effectiveness of the proposed algorithm.

[0144] Figure 4 As shown, from Figure 4It can be seen that as the number of targets increases, the root mean square error of the positioning error shows an upward trend, which indicates that when the number of targets increases, the positioning accuracy decreases and the error increases. At the same time, as the signal-to-noise ratio increases, the RMSE value decreases, which means that the positioning accuracy is improved. This further confirms that in signal processing and target positioning, the signal-to-noise ratio is a key factor, which plays an important role in improving system performance and reducing errors.

[0145] Figure 5 As shown in the figure, with the improvement of signal-to-noise ratio, the positioning accuracy shows an upward trend. When the signal-to-noise ratio is set to 5dB, for the positioning task of 7 targets, when the error threshold r is set to 1 meter and 1.5 meters respectively, the system achieves a positioning accuracy of 97.5% and 98.8% respectively. This result shows that by improving the signal-to-noise ratio, the system can achieve high-precision positioning of the target while maintaining a low probability of positioning error. In addition, from Figure 5 The influence of the error threshold r on the positioning accuracy can also be seen in Figure 2. When the signal-to-noise ratio remains unchanged, increasing the error threshold r can improve the positioning accuracy, because a larger error threshold provides a larger tolerance for positioning errors. However, this also means that in some application scenarios, a trade-off may need to be made between positioning accuracy and error tolerance.

[0146] In an embodiment of the present application, a data association scheme set is obtained by preliminary screening of a distance estimation set, and then each data association scheme in the data association scheme set is calculated in turn using a Gauss-Newton algorithm and a Hungarian algorithm to obtain a target estimated position of the target vessel and a corresponding minimum distance error for each data association scheme; each minimum distance error is compared to obtain an optimal distance error; and the optimal target estimated position corresponding to the optimal distance error is used as a final positioning result of multiple target vessels. This enables fast and high-precision offshore vessel positioning to be achieved under conditions of an increase in the number of targets and a change in the signal-to-noise ratio, effectively reducing errors, improving positioning accuracy, and ensuring real-time multi-target tracking.

[0147] This application is particularly suitable for small civilian vessels that are not equipped with navigation systems, and can significantly improve the efficiency and safety of maritime traffic management, environmental monitoring and rescue operations.

[0148] The embodiment is only a special case and does not indicate that the present application is implemented in such a way.

[0149] The present application also provides a non-volatile computer-readable storage medium, which stores computer-executable instructions. When the computer-executable instructions are executed by a task control computer, the task control computer executes the method for multi-base station collaborative perception of offshore vessel positioning as described in any one of Embodiment 1.

[0150] Those skilled in the art can understand that all or part of the features / steps of the above-mentioned method embodiments can be implemented by methods, data processing systems or computer programs, and these features can be implemented without hardware, entirely by software, or by a combination of hardware and software. The aforementioned computer program can be stored in one or more computer-readable storage media, and the storage medium stores a computer program. When the computer program is executed (such as by a processor), the steps of the above-mentioned method embodiment of the cooperative sensing of offshore vessel positioning by multiple base stations are executed.

[0151] The aforementioned storage media that can store program codes include: static hard disks, solid-state hard disks, random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), optical storage devices, magnetic storage devices, flash memory, magnetic disks or optical disks and / or combinations of the above devices, that is, they can be implemented by any type of volatile or non-volatile storage device or a combination thereof.

[0152] The above description is only the preferred embodiment of the present invention. It is known to those skilled in the art that various changes or equivalent substitutions may be made to these features and embodiments without departing from the spirit and scope of the present invention. In addition, under the teachings of the present invention, these features and embodiments may be modified to adapt to specific circumstances and materials without departing from the spirit and scope of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed herein, and all embodiments falling within the scope of the claims of this application belong to the protection scope of the present invention.

Claims

1. A method for multiple base stations to collaboratively sense offshore vessel positioning, characterized in that: include: Obtaining the base station coordinates of M base stations and a distance estimation set between each base station and a plurality of target ships; According to the screening condition that the data association satisfies the triangle inequality, the distance estimation set is preliminarily screened to obtain a set of data association solutions that meet the screening condition; Using the Gauss-Newton algorithm and the Hungarian algorithm, each data association scheme in the data association scheme set is calculated in turn to obtain the target estimated position of the target ship and the corresponding minimum distance error of each data association scheme; Compare the minimum distance errors to obtain the optimal distance error; The optimal target estimated position corresponding to the optimal distance error is used as the final positioning result of the multiple target ships.

2. The method for multi-base station collaborative sensing of offshore vessel positioning according to claim 1, characterized in that: The distance estimation set D m represents the k estimated distances between the mth base station among M base stations and the k target ships The formed collection.

3. The method for multi-base station collaborative sensing of offshore vessel positioning according to claim 2, characterized in that: The step of preliminarily screening the distance estimation set according to the screening condition that the data association satisfies the triangle inequality to obtain a data association set that satisfies the screening condition includes: Based on the triangle inequality, using the distance estimation set D m Constructing the equation for estimating the target position Among them, g m,k The distance estimation set D m The mapping relationship between the kth target ship and the mth base station is as follows: m Lidi m,k The large element is used as the distance estimate between the base station m and the target ship k, denoted as Based on the estimated target position equation, all data association schemes that meet the screening conditions are calculated, and all data association schemes satisfy the triangle inequality, which is expressed by the following formula: Obtain the data association solution set of all data association solutions that satisfy the triangle inequality, wherein the data association solution set is represented by G, and 4. The method for multi-base station collaborative sensing of offshore vessel positioning according to claim 3, characterized in that: The screening conditions are And the number of data association schemes is (K!) 2 indivual.

5. The method for multi-base station collaborative sensing of offshore vessel positioning according to claim 1, characterized in that: The method of calculating each data association scheme in the data association scheme set by using the Gauss-Newton algorithm and the Hungarian algorithm to obtain the target estimated position and the minimum distance error of the target vessel by each data association scheme includes: S301, for the first data association scheme in the data association scheme set, using the Gauss-Newton algorithm, calculating the positioning position results of the target ship based on the 1st to nth base stations; S302, calculating a first data association scheme for the M base stations except for n base stations through the Hungarian algorithm; S303, based on the first data association scheme and the positioning position results of the target ship by the first to nth base stations, calculating the target estimated position of the target ship by the first data association scheme to calculate a first minimum distance error; S304, for the second data association scheme in the data association scheme set, using the Gauss-Newton algorithm, calculating the positioning results of the second to nth base stations for the target vessel; S305, repeating steps S302 to S303, calculating the target estimated position of the target vessel using a second data association scheme to calculate a second minimum distance error; For the (n-2)th data association scheme in the data association scheme set, steps S301 to S303 are repeated until the target estimated position of the target ship and the corresponding minimum distance error of all data association schemes in the data association scheme set are calculated.

6. The method for multi-base station collaborative sensing of offshore vessel positioning according to claim 5, characterized in that: Step S302 and step S303 include: Using the formula Calculate distance estimation error By using the Hungarian algorithm, the distance estimation error is based on Matching indicator function Calculate the first data association scheme of (Mn) base stations, expressed by the following formula: in, represents the matching index function, Β m Defined as all A collection of: Defined as the distance estimation error; t represents the tth data association scheme, where t = 1, and the first data association scheme is Based on the first data association scheme and the positioning position results of the target ship from the 1st to the nth base stations, and calculating the target estimated position of the target ship by the first data association scheme And the first minimum distance error is calculated according to the following formula: in, represents the first minimum distance error, Represents variance.

7. The method for multi-base station collaborative sensing of offshore vessel positioning according to claim 6, characterized in that: The matching index function It means that for the mth (m>n) base station, if the distance estimation set D m Middle elements as the distance to the kth target, then the matching index function is 1.

8. The method for cooperatively sensing offshore vessel positioning by multiple base stations according to claim 1, characterized in that: Use the following formula to compare the minimum distance errors and obtain the optimal distance error: in, represents the optimal distance error.

9. A system for multiple base stations to collaboratively sense offshore vessel positioning, characterized in that: It includes a data processing center and M base stations, each of which is in communication connection with the data processing center; The base station is used to receive and process the radio signal of the target vessel to form a distance estimation set; The data processing center is used to obtain the base station coordinates of the M base stations and the estimated distance sets between each base station and a plurality of target ships; The data processing center is further used to perform preliminary screening on the distance estimation set according to the screening condition that the data association satisfies the triangle inequality, so as to obtain a set of data association solutions that meet the screening condition; The data processing center is further used to use the Gauss-Newton algorithm and the Hungarian algorithm to sequentially calculate each data association scheme in the data association scheme set to obtain the target estimated position of the target ship and the corresponding minimum distance error of each data association scheme; The data processing center is also used to compare the various minimum distance errors to obtain the optimal distance error; The data processing center is further used to use the optimal target estimated position corresponding to the optimal distance error as the final positioning result of the multiple target ships.

10. A data processing center, characterized in that: include: one or more processors; A memory for storing one or more computer programs, and one or more processors for executing one or more computer programs stored in the memory, so that one or more processors execute a method for collaboratively sensing offshore vessel positioning by multiple base stations as described in any one of claims 1-8.

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