Target position prediction method and device based on Kalman filtering, equipment and medium

Abstract

The invention provides a target position prediction method and device based on Kalman filtering, equipment and a medium, and relates to the technical field of space optical communication. The method comprises the following steps: setting an initial state of a Kalman filter according to an initial position of a target light spot; the predicted value of the target light spot at the T moment is calculated by using the predicted value of the Kalman filter at the T-1 moment state; obtaining an observation value of the target light spot at the T moment; and correcting the predicted value at the T moment according to the observed value at the T moment to obtain the position information of the target light spot at the T moment.

Description

Technical Field

[0001] The present invention relates to the technical field of space optical communication, and particularly relates to a method, device, equipment and medium for predicting the target position based on Kalman filtering. Background Art

[0002] As the core subsystem of a free space optical communication system, the APT (acquisition, pointing and tracking) acquisition, pointing and tracking system, its tracking and aiming accuracy is one of the important indicators of the system. In actual situations, data filtering methods can be used to process errors such as sensor errors and dynamic hysteresis errors, thereby reducing data fluctuations and dynamic hysteresis. Methods such as the least squares method, Wiener filtering, and Kalman filtering are often used for data processing. In recent years, as a classic optimal estimation, Kalman filtering has been greatly applied and promoted in both theory and engineering. Although Kalman filtering can achieve very good prediction and optimal estimation effects during the tracking and aiming process, the utilization rate of data is not high, and there are problems with difficult tracking and aiming in the space optical communication system. Summary of the Invention

[0003] In view of the above problems, the present invention provides a method, device, equipment and medium for predicting the target position based on Kalman filtering.

[0004] According to the first aspect of the present invention, a method for predicting the target position based on Kalman filtering is provided, including: setting the initial state of the Kalman filter according to the initial position of the target spot; calculating the predicted value of the target spot at time T using the predicted value of the Kalman filter at time T - 1; obtaining the observed value of the target spot at time T; and correcting the predicted value at time T according to the observed value at time T to obtain the position information of the target spot at time T.

[0005] According to an embodiment of the present invention, setting the initial state of the Kalman filter according to the initial position of the target spot includes: setting the state vector and covariance matrix of the Kalman filter according to the initial position of the target spot, where the state vector includes the position information and velocity information of the target spot, and the covariance matrix represents the uncertainty of the state vector.

[0006] According to an embodiment of the present invention, calculating the predicted value of the target spot at time T includes: calculating the predicted value of the target spot at time T using the state transition matrix and the control vector.

[0007] According to an embodiment of the present invention, the state transition matrix is:

[0008]

[0009] Wherein, Φ represents the state transition matrix, and T represents the time interval between the target light spot at the previous moment and the target light spot at the next moment.

[0010] According to an embodiment of the present invention, the predicted value at time T is corrected according to the observed value at time T to obtain the position information of the target light spot at time T, including: calculating the measurement residual between the observed value at time T and the predicted value at time T according to the measurement matrix; calculating the Kalman gain according to the measurement residual and the covariance matrix; updating the position information of the target light spot at time T according to the Kalman gain.

[0011] According to an embodiment of the present invention, the covariance matrix is:

[0012]

[0013] Wherein, Q represents the covariance matrix, and δ a 2 represents the random acceleration variance.

[0014] According to an embodiment of the present invention, obtaining the observed value of the target light spot at time T includes: obtaining the observed value of the target light spot at time T by using an optical sensor.

[0015] The second aspect of the present invention provides a target position prediction device based on Kalman filtering, including: a setting module for setting the initial state of the Kalman filter according to the initial position of the target light spot; a prediction module for predicting the predicted value of the target light spot at time T by using the predicted value in the state at time T - 1 of the Kalman filter; an obtaining module for obtaining the observed value of the target light spot at time T; a correction module for correcting the predicted value at time T according to the observed value at time T to obtain the position information of the target light spot at time T.

[0016] The third aspect of the present invention provides an electronic device, including: one or more processors; a storage device for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors execute the above-mentioned method.

[0017] The fourth aspect of the present invention provides a computer-readable storage medium, on which executable instructions are stored, and when the instructions are executed by a processor, the processor executes the above-mentioned method.

[0018] According to the target position prediction method, device, equipment and medium based on Kalman filtering provided by the present invention, by using the Kalman filtering linear system state equation, through the system input and output observation data, predicting the information of the next moment from the information of the target light spot at the previous moment, and performing an algorithm for optimal estimation of the system state, the tracking accuracy and stability of the system are improved. Description of the Drawings

[0019] Through the following description of the embodiments of the present invention with reference to the accompanying drawings, the above content and other objects, features, and advantages of the present invention will become clearer. In the drawings:

[0020] Figure 1 Schematically shows a flowchart of a target position prediction method based on Kalman filtering according to an embodiment of the present invention;

[0021] Figure 2 Schematically shows a schematic diagram of multi-step Kalman filtering in a target position prediction method based on Kalman filtering according to an embodiment of the present invention;

[0022] Figure 3 Schematically shows a structural block diagram of a target position prediction device based on Kalman filtering according to an embodiment of the present invention; and

[0023] Figure 4 Schematically shows a block diagram of an electronic device suitable for implementing a target position prediction method based on Kalman filtering according to an embodiment of the present invention. Detailed Embodiments

[0024] To make the objectives, technical solutions, and advantages of the present invention clearer and more understandable, the following further details the present invention in conjunction with specific embodiments and with reference to the accompanying drawings. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the scope of protection of the present invention. The terms used herein are only for describing specific embodiments and are not intended to limit the present invention. The terms "including", "comprising", etc. used herein indicate the presence of the described features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

[0025] In the present invention, unless otherwise clearly defined and limited, the terms "installed", "connected", "connected", "fixed", etc. should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or integrated; it can be a mechanical connection, an electrical connection, or can communicate with each other; it can be a direct connection, or indirectly connected through an intermediate medium, and can be the communication inside two elements or the interaction relationship between two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0026] In the description of the present invention, it should be understood that the orientation or positional relationship indicated by the terms "longitudinal", "length", "circumferential", "front", "rear", "left", "right", "top", "bottom", "inner", "outer", etc. is based on the orientation or positional relationship shown in the drawings. These are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the subsystems or elements referred to must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation to the present invention.

[0027] Throughout the drawings, the same elements are denoted by the same or similar reference numerals. When it may cause confusion in the understanding of the present invention, conventional structures or configurations will be omitted. Also, the shapes, sizes, and positional relationships of the components in the drawings do not reflect the true sizes, proportions, and actual positional relationships.

[0028] Similarly, in order to streamline the present invention and assist in understanding one or more of the various aspects of the invention, in the above description of the exemplary embodiments of the present invention, the various features of the present invention are sometimes grouped together into a single embodiment, figure, or description thereof. The description with reference to terms such as "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples.

[0029] In addition, the terms "first" and "second" are used only for descriptive purposes and cannot be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Therefore, the features defined with "first" and "second" may explicitly or implicitly include one or more of such features. In the description of the present invention, the meaning of "a plurality" is at least two, such as two, three, etc., unless otherwise specifically defined.

[0030] In the case of using expressions similar to "at least one of A, B, and C, etc.", generally, it should be interpreted according to the meaning that those skilled in the art usually understand this expression (for example, "a system having at least one of A, B, and C" should include, but not be limited to, a system having only A, only B, only C, having A and B, having A and C, having B and C, and / or having A, B, and C, etc.).

[0031] In the technical solution of the present invention, the processing of the data involved (such as including but not limited to user personal information), such as collection, storage, use, processing, transmission, provision, disclosure, and application, etc., all comply with the provisions of relevant laws and regulations, take necessary confidentiality measures, and do not violate public order and good customs.

[0032] Figure 1 Schematically shown is a flowchart of a target position prediction method based on Kalman filtering according to an embodiment of the present invention.

[0033] As Figure 1 shown, the target position prediction method based on Kalman filtering in this embodiment includes operations S1 to S4.

[0034] In operation S1, according to the initial position of the target light spot, set the initial state of the Kalman filter.

[0035] In this embodiment, first obtain the initial position of the target light spot, and the initial position of the target light spot is:

[0036]

[0037] where X represents that the initial position of the target light spot is (0, 0), the velocities in the x and y directions are (0, 0), and the accelerations in the x and y directions are (0, 0).

[0038] According to the initial position of the target light spot, it is necessary to initialize the state vector and covariance matrix of the Kalman filter. Among them, the state vector includes the position information and velocity information of the target light spot, and the covariance matrix represents the uncertainty of the state vector.

[0039] In operation S2, use the predicted value of the Kalman filter at time T - 1 to calculate the predicted value of the target light spot at time T.

[0040] In this embodiment, obtain the predicted value of the Kalman filter at time T - 1, and then use the state transition matrix and control vector to calculate the predicted value of the target light spot at time T.

[0041] The state transition matrix is:

[0042]

[0043] where Φ represents the state transition matrix, and T represents the time interval between the target light spot at the previous moment and the target light spot at the next moment. This matrix represents the relationship between position, velocity, and acceleration.

[0044] In operation S3, obtain the observed value of the target light spot at time T.

[0045] In this embodiment, use an optical sensor or other means to obtain the position observation data of the target light spot. These data can be collected in real time or periodically.

[0046] In operation S4, correct the predicted value at time T according to the observed value at time T to obtain the position information of the target light spot at time T.

[0047] In this embodiment, according to the measurement matrix, the measurement residual between the observed value at time T and the predicted value at time T is calculated. According to the measurement residual and the covariance matrix, the Kalman gain is calculated. According to the Kalman gain, the position information of the target light spot at time T is updated.

[0048] The measurement matrix is:

[0049]

[0050] where H represents the measurement matrix, and the measurement matrix only observes the position information of the target light spot.

[0051] The covariance matrix is:

[0052]

[0053] where Q represents the covariance matrix, and δ a 2 represents the random acceleration variance.

[0054] Kalman filtering is an algorithm that uses the linear system state equation to optimally estimate the system state through the system input-output observation data. The algorithms involved are:

[0055] One-step state prediction of the target light spot:

[0056] State update of the target light spot:

[0057] where,

[0058] Filter gain matrix: K(k + 1) = P(k + 1)H T [HP(k + 1|k)H T + R] -1

[0059] where P represents the state covariance.

[0060] One-step prediction covariance: P(k + 1|k) = ΦP(k|k)Φ T + ΓQr T ,

[0061] where Γ represents the noise drive matrix,

[0062] Covariance update: (k + 1|k + 1) = [I n - K(k + 1)H]P(k + 1|k)

[0063]

[0064] On the other hand, for the two actuators of the space optical communication system, the coarse alignment structure has a large field of view but moves slowly, while the fine alignment structure has a small field of view but moves quickly. Therefore, how to effectively utilize the data at a high sampling rate and accurately predict the spot position after multiple sampling periods is very important for the tracking accuracy.

[0065] Based on this, in the embodiments of the present invention, by setting reasonable sampling time, control time, and prediction time for multiple Kalman filters, the data utilization rate is improved based on multi-step Kalman filtering, specifically as Figure 2 shown.

[0066] Figure 2 Schematically shows the principle diagram of multi-step Kalman filtering in the target position prediction method based on Kalman filtering according to the embodiments of the present invention.

[0067] As Figure 2 shown, the multi-step Kalman filtering improves the data utilization rate and accuracy by changing the prediction step length. Four Kalman filters work sequentially at intervals of 5 ms. When the coarse alignment prediction step length is 20 ms, the first Kalman filter starts working from the zero moment and gives the prediction value at 20 ms; when the next sampled data arrives, the second Kalman filter gives the value at 25 ms; and so on. When sampling reaches 20 ms, the first Kalman filter combines the current observed value and the prediction value 20 ms ago to give the optimal estimate of the current position and gives the prediction value at 40 ms. At this time, the coarse alignment control instruction outputs the current optimal position information and the prediction value at 40 ms to the coarse alignment control module, which is used to adjust the current position and predict the coordinates 20 ms later, completing one cycle. However, only one of the data of the four Kalman filters is used for position correction and prediction, and the data utilization rate is 25%.

[0068] When the coarse alignment prediction step length is 25 ms, the same four filters work sequentially at intervals of 5 ms. When sampling reaches 20 ms, the coarse alignment control instruction outputs the current optimal position information and the prediction value at 40 ms to the coarse alignment control module. However, the prediction value at 45 ms has been obtained at this time. Therefore, the data smoothing method can be used to smooth the prediction values at 35 ms, 40 ms, and 45 ms, so that the data at 40 ms has a better prediction result. As a result, three of the data of the four Kalman filters are used for data prediction, and the data utilization rate is 75%.

[0069] And so on, when the prediction step length is 30 ms, the data utilization rate reaches 100% as shown in Table 1.

[0070]

[0071] Table 1 Data utilization rate

[0072] The simulation results show that the data utilization rate and accuracy of multi-step Kalman filtering are higher than those of single-step.

[0073] Figure 3 The structural block diagram of the target position prediction device based on Kalman filtering according to an embodiment of the present invention is schematically shown.

[0074] As Figure 3 shown, the target position prediction device based on Kalman filtering in this embodiment includes: a setting module 301, a prediction module 302, an acquisition module 303, and a correction module 304.

[0075] The setting module 301 is configured to set the initial state of the Kalman filter according to the initial position of the target light spot.

[0076] The prediction module 302 is configured to predict the predicted value of the target light spot at time T by using the predicted value of the Kalman filter at time T-1.

[0077] The acquisition module 303 is configured to acquire the observed value of the target light spot at time T.

[0078] The correction module 304 is configured to correct the predicted value at time T according to the observed value at time T to obtain the position information of the target light spot at time T.

[0079] According to an embodiment of the present invention, any multiple of the setting module 301, the prediction module 302, the acquisition module 303, and the correction module 304 may be combined and implemented in one module, or any one of them may be split into multiple modules. Or, at least part of the functions of one or more of these modules may be combined with at least part of the functions of other modules and implemented in one module. According to an embodiment of the present invention, at least one of the setting module 301, the prediction module 302, the acquisition module 303, and the correction module 304 may be at least partially implemented as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), a system on chip, a system on substrate, a system on package, an application specific integrated circuit (ASIC), or any other reasonable way of integrating or packaging circuits, etc., implemented by hardware or firmware, or implemented in any one of the three implementation manners of software, hardware, and firmware, or in an appropriate combination of any several of them. Or, at least one of the setting module 301, the prediction module 302, the acquisition module 303, and the correction module 304 may be at least partially implemented as a computer program module, which can execute the corresponding functions when the computer program module is run.

[0080] It should be noted that the target position prediction device based on Kalman filtering in the embodiments of the present invention corresponds to the target position prediction method based on Kalman filtering in the embodiments of the present invention. The specific implementation details and the technical effects brought are the same, and will not be elaborated here.

[0081] Figure 4 Schematically shows a block diagram of an electronic device suitable for implementing a target position prediction method based on Kalman filtering according to an embodiment of the present invention.

[0082] As Figure 4 shown, the electronic device 400 according to an embodiment of the present invention includes a processor 401, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 402 or a program loaded from a storage section 408 into a random access memory (RAM) 403. The processor 401 may include, for example, a general microprocessor (such as a CPU), an instruction set processor, and / or a related chipset, and / or a dedicated microprocessor (such as an application specific integrated circuit (ASIC)), etc. The processor 401 may also include on-board memory for caching purposes. The processor 401 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of the present invention.

[0083] In the RAM 403, various programs and data required for the operation of the electronic device 400 are stored. The processor 401, the ROM 402, and the RAM 403 are connected to each other through a bus 404. The processor 401 performs various operations of the method flow according to an embodiment of the present invention by executing the programs in the ROM 402 and / or the RAM 403. It should be noted that the program may also be stored in one or more memories other than the ROM 402 and the RAM 403. The processor 401 may also perform various operations of the method flow according to an embodiment of the present invention by executing the programs stored in one or more memories.

[0084] According to an embodiment of the present invention, the electronic device 400 may further include an input / output (I / O) interface 405, and the input / output (I / O) interface 405 is also connected to the bus 404. The electronic device 400 may further include one or more of the following components connected to the I / O interface 405: an input portion 406 including a keyboard, a mouse, etc.; an output portion 407 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc. and a speaker, etc.; a storage portion 408 including a hard disk, etc.; and a communication portion 404 including a network interface card such as a LAN card, a modem, etc. The communication portion 404 performs communication processing via a network such as the Internet. The drive 410 is also connected to the I / O interface 405 as needed. A removable medium 411, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 410 as needed so that a computer program read therefrom can be installed into the storage portion 408 as needed.

[0085] The present invention also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments; or may exist separately without being assembled into the device / apparatus / system. The above computer-readable storage medium carries one or more programs, and when the one or more programs are executed, the method provided according to the embodiments of the present invention is implemented.

[0086] According to an embodiment of the present invention, the computer-readable storage medium may be a non-volatile computer-readable storage medium, for example, it may include but is not limited to: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present invention, the computer-readable storage medium may be any tangible medium that contains or stores a program, and the program can be used by or in combination with an instruction execution system, apparatus, or device. For example, according to an embodiment of the present invention, the computer-readable storage medium may include the ROM 402 and / or the RAM 403 described above and / or one or more memories other than the ROM 402 and the RAM 403.

[0087] An embodiment of the present invention further includes a computer program product, which includes a computer program, and the computer program includes program codes for executing the method shown in the flowchart. When the computer program product runs in a computer system, the program codes are used to cause the computer system to implement the method provided by the embodiments of the present invention.

[0088] When the computer program is executed by the processor 401, the above functions defined in the system / apparatus of the embodiments of the present invention are executed. According to an embodiment of the present invention, the above-described systems, apparatuses, modules, units, etc. can be implemented by computer program modules.

[0089] In one embodiment, the computer program can rely on tangible storage media such as optical storage devices, magnetic storage devices, etc. In another embodiment, the computer program can also be transmitted and distributed in the form of signals on a network medium, and be downloaded and installed through the communication part 404, and / or be installed from the removable medium 411. The program code included in the computer program can be transmitted by any suitable network medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0090] In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 404, and / or be installed from the removable medium 411. When the computer program is executed by the processor 401, the above functions defined in the system of the embodiments of the present invention are executed. According to an embodiment of the present invention, the above-described systems, devices, apparatuses, modules, units, etc. can be implemented by computer program modules.

[0091] According to an embodiment of the present invention, the program code for executing the computer program provided by the embodiments of the present invention can be written in any combination of one or more programming languages. Specifically, these computing programs can be implemented using high-level procedural and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages include but are not limited to, such as Java, C++, python, the "C" language or similar programming languages. The program code can be executed entirely on the user computing device, partially on the user device, partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device can be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device (for example, by using an Internet service provider to connect through the Internet).

[0092] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation that the systems and methods according to various embodiments of the present invention may achieve. In this regard, each block in the flowchart or block diagram may represent a module, a segment of a program, or a part of code, and the above-mentioned module, segment of a program, or part of code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order from that marked in the accompanying drawings. For example, two consecutive blocks shown may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram or flowchart, as well as combinations of blocks in the block diagram or flowchart, can be implemented by a dedicated hardware-based system that performs the specified function or operation, or can be implemented by a combination of dedicated hardware and computer instructions.

[0093] Those skilled in the art can understand that the features described in the various embodiments of the present invention can be combined or / and combined in various ways, even if such combinations or combinations are not explicitly described in the present invention. In particular, without departing from the spirit and teachings of the present invention, the features described in the various embodiments of the present invention can be combined and / and combined in various ways. All such combinations and / and combinations fall within the scope of the present invention.

[0094] The embodiments of the present invention have been described above. However, these embodiments are only for illustrative purposes and not for limiting the scope of the present invention. Although the embodiments have been described separately above, this does not mean that the measures in each embodiment cannot be used advantageously in combination. Without departing from the scope of the present invention, those skilled in the art can make various substitutions and modifications, and all such substitutions and modifications should fall within the scope of the present invention.

Claims

1. A method for predicting the target position based on Kalman filtering, characterized in that, comprising: Setting the initial state of the Kalman filter according to the initial position of the target spot; Calculating the predicted value of the target spot at time T by using the predicted value of the Kalman filter at the state at time T-1; Obtaining the observed value of the target spot at time T; Correcting the predicted value at time T according to the observed value at time T to obtain the position information of the target spot at time T.

2. The method for predicting the target position based on Kalman filtering according to claim 1, characterized in that, The setting the initial state of the Kalman filter according to the initial position of the target spot includes: Setting the state vector and covariance matrix of the Kalman filter according to the initial position of the target spot, wherein the state vector includes the position information and velocity information of the target spot, and the covariance matrix represents the uncertainty of the state vector.

3. The method for predicting the target position based on Kalman filtering according to claim 2, characterized in that, The calculating the predicted value of the target spot at time T includes: Calculating the predicted value of the target spot at time T by using the state transition matrix and the control vector.

4. The method for predicting the target position based on Kalman filtering according to claim 3, characterized in that, The state transition matrix is: where Φ represents the state transition matrix, and T represents the time interval between the target spot at the previous moment and the target spot at the next moment.

5. The method for predicting the target position based on Kalman filtering according to claim 2, characterized in that, The correcting the predicted value at time T according to the observed value at time T to obtain the position information of the target spot at time T includes: Calculating the measurement residual between the observed value at time T and the predicted value at time T according to the measurement matrix; Calculating the Kalman gain according to the measurement residual and the covariance matrix; Updating the position information of the target spot at time T according to the Kalman gain.

6. The method for predicting the target position based on Kalman filtering according to claim 5, characterized in that, The covariance matrix is: where Q represents the covariance matrix and δ a 2 represents the random acceleration variance.

7. The method for predicting the target position based on Kalman filtering according to claim 1, characterized in that, The obtaining the observed value of the target spot at time T includes: Obtaining the observed value of the target spot at time T by using an optical sensor.

8. A device for predicting the target position based on Kalman filtering, characterized in that, comprising: A setting module for setting the initial state of the Kalman filter according to the initial position of the target spot; A prediction module for calculating the predicted value of the target spot at time T by using the predicted value of the Kalman filter at the state at time T-1; An obtaining module for obtaining the observed value of the target spot at time T; A correcting module for correcting the predicted value at time T according to the observed value at time T to obtain the position information of the target spot at time T.

9. An electronic device, comprising: One or more processors; A storage device for storing one or more programs, Wherein, when the one or more programs are executed by the one or more processors, the one or more processors are caused to execute the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having executable instructions stored thereon, which when executed by a processor cause the processor to execute the method according to any one of claims 1 to 7.

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