Method and system for simulating target random trajectory based on Gaussian noise interference

Abstract

The present invention discloses a method and system for simulating random trajectory of a target based on Gaussian noise interference, which relates to the technical field of trajectory simulation of mobile targets at sea, including: obtaining geographic data of a target water area and parameter information of a mobile target; the target water area is a closed water area; based on preset motion rules and irregular algorithms of the mobile target, a plurality of initial simulation trajectory data are generated in the target water area; Gaussian noise interference is added to the plurality of initial simulation trajectory data to obtain target simulation trajectory data. The present invention alleviates the technical problem of difficulty in obtaining random motion trajectory data of a target at sea in the prior art.

Description

Technical Field

[0001] The present invention relates to the technical field of marine mobile target trajectory simulation, and in particular to a target random trajectory simulation method and system based on Gaussian noise interference. Background Art

[0002] With the current social development, the improvement of the global positioning system, the increasing popularity of positioning equipment, and the increasing amount of geographic location information, it is difficult to obtain real data for the processing and training of trajectory location information, or to verify whether the functions of corresponding modules are correct as a data basis, especially for moving targets at sea. Therefore, how to generate random trajectory data, reasonable target movement direction and speed, and provide them to system training has become a difficult point. Summary of the invention

[0003] The purpose of the present invention is to provide a method and system for simulating target random trajectory based on Gaussian noise interference in order to solve at least one of the above technical problems.

[0004] In the first aspect, an embodiment of the present invention provides a method for simulating target random trajectories based on Gaussian noise interference, comprising: acquiring geographic data of a target water area and parameter information of a mobile target; the target water area is a closed water area; based on preset motion rules and irregular algorithms of the mobile target, generating a plurality of initial simulation trajectory data within the target water area; adding Gaussian noise interference to the plurality of initial simulation trajectory data to obtain target simulation trajectory data.

[0005] Furthermore, the method also includes: fusing the target simulation trajectory data with the target public attribute information, and sending the data to a message queue to form real-time streaming data.

[0006] Furthermore, the geographic data of the target water area includes: the latitude and longitude information of the trajectory points on the trajectory line that closes the target water area; the parameter information of the mobile target includes: the number of simulated trajectories of the mobile target, the latitude and longitude information of the starting point of the mobile target, the time interval for generating simulated trajectory points, and the data protocol code.

[0007] Further, the preset motion rule includes a range of moving speed of the moving target; based on the preset motion rule and the irregular algorithm of the moving target, a plurality of initial simulated trajectory data are generated in the target water area, including: based on the preset motion rule, determining the current moving speed of the moving target at the current trajectory point; based on the current moving speed, the current position of the moving target, the time interval for generating simulated trajectory points and the irregular algorithm, calculating the longitude and latitude information of the next trajectory point of the moving target; including:

[0008] θ=Min(N,Random.nextDouble(0,90)*0.02)

[0009] f(x)=x+stcosθ

[0010] f(y)=y+stsinθ

[0011] Wherein, N is the maximum angle of random simulation, θ is the moving angle of the next trajectory point, Random.nextDouble(0,90) means taking a random value between 0 and 90, s is the current moving speed, t is the time interval for generating the simulated trajectory points, (x, y) is the current position of the moving target, and (f(x), f(y)) is the latitude and longitude information of the next trajectory point.

[0012] Further, adding Gaussian noise interference to the multiple initial simulation trajectory data to obtain target simulation trajectory data includes: randomly selecting at least one trajectory point to be adjusted in each of the initial simulation trajectory data; adding Gaussian noise interference to the at least one trajectory point to be adjusted to obtain the target simulation trajectory data; wherein adding Gaussian noise interference to the at least one trajectory point to be adjusted includes:

[0013]

[0014] Among them, (f′(x t ),f′(y t )) is the latitude and longitude information after adding Gaussian noise interference to the trajectory point to be adjusted, (x t ,y t ) is the latitude and longitude information of the trajectory point to be adjusted, a and b are preset constants, and c is the standard deviation.

[0015] In the second aspect, an embodiment of the present invention also provides a target random trajectory simulation system based on Gaussian noise interference, comprising: an acquisition module, a generation module and a noise module; wherein the acquisition module is used to acquire the geographical data of the target water area and the parameter information of the mobile target; the target water area is a closed water area; the generation module is used to generate a plurality of initial simulation trajectory data in the target water area based on the preset motion rules and irregular algorithm of the mobile target; the noise module is used to add Gaussian noise interference to the plurality of initial simulation trajectory data to obtain the target simulation trajectory data.

[0016] Furthermore, the system also includes: a fusion module, which is used to fuse the target simulation trajectory data with the target public attribute information, and send them to a message queue to form real-time streaming data.

[0017] Furthermore, the preset motion rule includes a range of moving speeds of the moving target; the generating module is further used to: determine the current moving speed of the moving target at the current trajectory point based on the preset motion rule; calculate the longitude and latitude information of the next trajectory point of the moving target based on the current moving speed, the current position of the moving target, the time interval for simulating trajectory point generation and the irregular algorithm; including:

[0018] θ=Min(N,Random.nextDouble(0,90)*0.02)

[0019] f(x)=x+stcosθ

[0020] f(y)=y+stsinθ

[0021] Wherein, N is the maximum angle of random simulation, θ is the moving angle of the next trajectory point, Random.nextDouble(0,90) means taking a random value between 0 and 90, s is the current moving speed, t is the time interval for generating the simulated trajectory points, (x, y) is the current position of the moving target, and (f(x), f(y)) is the latitude and longitude information of the next trajectory point.

[0022] In a third aspect, an embodiment of the present invention further provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method provided in the embodiment of the present invention when executing the computer program.

[0023] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer instructions, and when the computer instructions are executed by a processor, the method provided in the embodiment of the present invention is implemented.

[0024] The present invention provides a method and system for simulating random trajectory of a target based on Gaussian noise interference, which can generate mobile random trajectory data at sea according to the set trajectory attributes, and can encode according to the encoding requirements; the present invention adds trajectory point data based on Gaussian noise to perform noise influence on a large number of data, which can better reflect the authenticity of the data; the present invention can generate a large number of trajectory point data and provide them for data training and processing, thereby alleviating the technical problem of difficulty in obtaining random moving trajectory data of targets at sea in the prior art. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the implementation methods of the present application or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0026] Figure 1 A flowchart of a method for simulating a target random trajectory based on Gaussian noise interference provided by an embodiment of the present invention;

[0027] Figure 2 A schematic diagram of a visualization result of a moving target simulation trajectory data provided by an embodiment of the present invention;

[0028] Figure 3 A schematic diagram of a target random trajectory simulation system based on Gaussian noise interference provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0029] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0030] Embodiment 1

[0031] Figure 1 FIG. 1 is a flow chart of a method for simulating a target random trajectory based on Gaussian noise interference according to an embodiment of the present invention. Figure 1 As shown, the method specifically comprises the following steps:

[0032] Step S102, obtaining geographic data of the target water area and parameter information of the mobile target; the target water area is a closed water area.

[0033] In an optional implementation provided by an embodiment of the present invention, the geographic data of the target water area includes: longitude and latitude information of trajectory points on a trajectory line that closes the target water area.

[0034] Specifically, under a unified coordinate system, a closed area is selected on a visual map; then the locations of land and islands are identified, the closed area is checked, land, islands and other areas are removed, a closed trajectory line is generated, and finally the latitude and longitude information of the trajectory points on the closed trajectory points is obtained.

[0035] In an optional implementation provided by an embodiment of the present invention, the parameter information of the mobile target includes: the number of simulated trajectories of the mobile target, the longitude and latitude information of the starting point of the mobile target, the time interval for generating simulated trajectory points, and the data protocol code.

[0036] Specifically, in the target water area, a point is randomly selected as the starting point of the moving target, and when the operation is not stopped, the trajectory of the moving target will continue to run randomly in the target water area. Optionally, the parameter information also includes the number of moving targets, and the number of moving targets is consistent with the number of simulated trajectories of the moving targets.

[0037] In the embodiment of the present invention, the data protocol encoding is a format for encoding the generated target simulation trajectory data, and the encoding supports a built-in encoding format.

[0038] Step S104, based on the preset motion rules and irregular algorithms of the mobile target, a plurality of initial simulation trajectory data are generated in the target water area.

[0039] Specifically, the preset motion rule includes a moving speed range of the moving target, such as 0-100 km / h.

[0040] Specifically, step S104 also includes the following steps:

[0041] Step S1041, determining the current moving speed of the moving target at the current trajectory point based on a preset motion rule;

[0042] Step S1042, based on the current moving speed, the current position of the moving target, the time interval for generating simulated trajectory points and the irregular algorithm, calculate the longitude and latitude information of the next trajectory point of the moving target.

[0043] Specifically, they include:

[0044] θ=Min(N,Random.nextDouble(0,90)*0.02)

[0045] f(x)=x+stcosθ

[0046] f(y)=y+stsinθ

[0047] Where N is the maximum angle of random simulation, θ is the moving angle of the next trajectory point, Random.nextDouble(0,90) means taking a random value between 0 and 90, s is the current moving speed, t is the time interval for generating simulated trajectory points, (x, y) is the current position of the moving target, and (f(x), f(y)) is the latitude and longitude information of the next trajectory point.

[0048] Step S106, adding Gaussian noise interference to the multiple initial simulation trajectory data to obtain target simulation trajectory data.

[0049] Specifically, outlier data is added to the initial simulation trajectory data to form noise interference. In an embodiment of the present invention, Gaussian noise interference is added. Specifically, step S106 also includes the following steps:

[0050] Step S1061, randomly selecting at least one trajectory point to be adjusted in each initial simulation trajectory data;

[0051] Step S1062, adding Gaussian noise interference to at least one trajectory point to be adjusted to obtain target simulation trajectory data; wherein adding Gaussian noise interference to at least one trajectory point to be adjusted includes:

[0052]

[0053] Among them, (f′(x t ),f′(y t )) is the latitude and longitude information after adding Gaussian noise interference to the trajectory point to be adjusted, (x t ,y t ) is the latitude and longitude information of the trajectory point to be adjusted, a and b are preset constants, and c is the standard deviation.

[0054] Optionally, after step S106, the method provided by the embodiment of the present invention further includes: fusing the target simulation trajectory data with the target public attribute information, and sending the data to a message queue to form real-time streaming data.

[0055] In the embodiment of the present invention, in order to intuitively reflect the visualization, a simulation system corresponding to the movement of the mobile target can also be developed. By using the global map and adopting the B / S architecture, the mobile target trajectory data can be reasonably displayed on the map through real-time data processing to realize the simulation of multiple mobile target movement data. And it is persistently stored in the postgresq database. The visualization results of the mobile target simulation data are as follows: Figure 2 shown.

[0056] This embodiment provides a method for simulating target random trajectories based on Gaussian noise interference. By means of the boundary conventions of mobile targets and geographic information data, as well as the constraints of the number of targets, movement speed, and sampling period, random trajectory data can be generated on a large scale, and Gaussian noise interference is added to improve the authenticity of the obtained trajectory data.

[0057] Embodiment 2

[0058] Figure 3 Schematic diagram of a target random trajectory simulation system based on Gaussian noise interference provided according to an embodiment of the present invention. Figure 3As shown, the system includes: an acquisition module 10, a generation module 20 and a noise module 30.

[0059] Specifically, the acquisition module 10 is used to acquire geographic data of the target water area and parameter information of the mobile target; the target water area is a closed water area.

[0060] Specifically, the geographic data of the target waters include: the latitude and longitude information of the trajectory points on the trajectory line of the closed target waters; the parameter information of the mobile target includes: the number of simulated trajectories of the mobile target, the latitude and longitude information of the starting point of the mobile target, the time interval for generating simulated trajectory points, and the data protocol coding.

[0061] The generating module 20 is used to generate a plurality of initial simulation trajectory data in the target water area based on the preset motion rules and irregular algorithms of the mobile target.

[0062] The noise module 30 is used to add Gaussian noise interference to the multiple initial simulation trajectory data to obtain target simulation trajectory data.

[0063] Specifically, Figure 3 As shown, the system further includes: a fusion module 40, which is used to fuse the target simulation trajectory data with the target public attribute information, and send them to the message queue to form real-time streaming data.

[0064] Specifically, the preset motion rule includes a moving speed range of the moving target. The generating module 20 is further used for:

[0065] Based on the preset motion rules, determine the current moving speed of the moving target at the current trajectory point;

[0066] Based on the current moving speed, the current position of the moving target, the time interval for generating simulated trajectory points and the irregular algorithm, the latitude and longitude information of the next trajectory point of the moving target is calculated; including:

[0067] θ=Min(N,Random.nextDouble(0,90)*0.02)

[0068] f(x)=x+stcosθ

[0069] f(y)=y+stsinθ

[0070] Where N is the maximum angle of random simulation, θ is the moving angle of the next trajectory point, Random.nextDouble(0,90) means taking a random value between 0 and 90, s is the current moving speed, t is the time interval for generating simulated trajectory points, (x, y) is the current position of the moving target, and (f(x), f(y)) is the latitude and longitude information of the next trajectory point.

[0071] Specifically, the noise module 30 is also used for:

[0072] In each initial simulation trajectory data, at least one trajectory point to be adjusted is randomly selected;

[0073] Adding Gaussian noise interference to at least one trajectory point to be adjusted to obtain target simulation trajectory data; wherein adding Gaussian noise interference to at least one trajectory point to be adjusted includes:

[0074]

[0075] Among them, (f′(x t ),f′(y t )) is the latitude and longitude information after adding Gaussian noise interference to the trajectory point to be adjusted, (x t ,y t ) is the latitude and longitude information of the trajectory point to be adjusted, a and b are preset constants, and c is the standard deviation.

[0076] The present invention further provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method provided in the embodiment of the present invention when executing the computer program.

[0077] The present invention also provides a computer-readable storage medium, which stores computer instructions. When the computer instructions are executed by a processor, the method provided in the embodiment of the present invention is implemented.

[0078] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

[0079] In addition, it should be understood that although the present specification is described according to implementation modes, not every implementation mode contains only one independent technical solution. This description of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment may also be appropriately combined to form other implementation modes that can be understood by those skilled in the art.

Claims

1. A method for simulating target random trajectories based on Gaussian noise interference, characterized in that: include: Obtain geographic data of target waters and parameter information of moving targets; The target waters are closed waters; Based on the preset motion rules and irregular algorithms of the mobile target, a plurality of initial simulation trajectory data are generated in the target water area; Gaussian noise interference is added to the multiple initial simulation trajectory data to obtain target simulation trajectory data.

2. The method according to claim 1, characterized in that: The method further includes: fusing the target simulation trajectory data with target public attribute information, and sending the data to a message queue to form real-time streaming data.

3. The method according to claim 1, characterized in that: The geographical data of the target water area includes: the longitude and latitude information of the track points on the track line enclosing the target water area; The parameter information of the mobile target includes: the number of simulated trajectories of the mobile target, the longitude and latitude information of the starting point of the mobile target, the time interval for generating simulated trajectory points, and the data protocol code.

4. The method according to claim 1, characterized in that: The preset motion rule includes a moving speed range of the moving target; Based on the preset motion rules and irregular algorithms of the mobile target, a plurality of initial simulation trajectory data are generated in the target water area, including: Based on the preset motion rule, determining the current moving speed of the moving target at the current trajectory point; Based on the current moving speed, the current position of the moving target, the time interval for generating simulated track points and the irregular algorithm, the longitude and latitude information of the next track point of the moving target is calculated; including: θ=Min(N,Random.nextDouble(0,90)*0.02) f(x)=x+stcosθ f(y)=y+stsinθ Wherein, N is the maximum angle of random simulation, θ is the moving angle of the next trajectory point, Random.nextDouble(0,90) means taking a random value between 0 and 90, s is the current moving speed, t is the time interval for generating the simulated trajectory points, (x, y) is the current position of the moving target, and (f(x), f(y)) is the latitude and longitude information of the next trajectory point.

5. The method according to claim 4, characterized in that: Adding Gaussian noise interference to the multiple initial simulation trajectory data to obtain target simulation trajectory data includes: In each of the initial simulation trajectory data, randomly selecting at least one trajectory point to be adjusted; Adding Gaussian noise interference to the at least one trajectory point to be adjusted to obtain the target simulation trajectory data; wherein adding Gaussian noise interference to the at least one trajectory point to be adjusted includes: Among them, (f′(x t ),f′(y t )) is the latitude and longitude information after adding Gaussian noise interference to the trajectory point to be adjusted, (x t ,y t ) is the latitude and longitude information of the trajectory point to be adjusted, a and b are preset constants, and c is the standard deviation.

6. A target random trajectory simulation system based on Gaussian noise interference, characterized in that: include: Acquisition module, generation module and noise module; among them, The acquisition module is used to acquire geographic data of the target water area and parameter information of the mobile target; the target water area is a closed water area; The generating module is used to generate a plurality of initial simulation trajectory data in the target water area based on the preset motion rule and irregular algorithm of the mobile target; The noise module is used to add Gaussian noise interference to the multiple initial simulation trajectory data to obtain target simulation trajectory data.

7. The system according to claim 6, characterized in that: The system further comprises: a fusion module, which is used to fuse the target simulation trajectory data with the target public attribute information, and send the fusion to a message queue to form real-time streaming data.

8. The system according to claim 6, characterized in that: The preset motion rule includes a moving speed range of the moving target; the generating module is further used for: Based on the preset motion rule, determining the current moving speed of the moving target at the current trajectory point; Based on the current moving speed, the current position of the moving target, the time interval for generating simulated track points and the irregular algorithm, the longitude and latitude information of the next track point of the moving target is calculated; including: θ=Min(N,Random.nextDouble(0,90)*0.02) f(x)=x+stcosθ f(y)=y+stsinθ Wherein, N is the maximum angle of random simulation, θ is the moving angle of the next trajectory point, Random.nextDouble(0,90) means taking a random value between 0 and 90, s is the current moving speed, t is the time interval for generating the simulated trajectory points, (x, y) is the current position of the moving target, and (f(x), f(y)) is the latitude and longitude information of the next trajectory point.

9. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method according to any one of claims 1 to 5 when executing the computer program.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and when the computer instructions are executed by a processor, the method according to any one of claims 1 to 5 is implemented.

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