CN117198042A - Track prediction method, device, equipment and storage medium - Google Patents
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Abstract
The application discloses a track prediction method, a device, equipment and a storage medium, wherein the method comprises the following steps: creating a first track prediction model based on a distributed algorithm; correcting the first track prediction model by utilizing a speed vector included angle of a target at a sample point of an adjacent sensor to obtain a second track prediction model; and distributing computing power nodes matched with the current computing type to the second track prediction model to conduct track prediction. According to the application, the prediction effect of the track prediction model is optimized by creating the distributed track prediction model, denoising processing is performed by utilizing the speed vector included angle, and possible overfitting errors of the track prediction model are reduced based on the ridge regression method, and the operation efficiency of the distributed logic algorithm is improved by distributing adaptive calculation nodes for the track prediction model.
Description
Technical Field
The present application relates to the field of data processing technologies, and in particular, to a track prediction method, apparatus, device, and storage medium.
Background
Along with the rapid development of socioeconomic, radar monitoring technology gradually enters the field of view of the public, and the technical means of tracking targets by using radar can be applied to traffic services. Tracking refers to the radar processing of target measurements to achieve an estimate of the target state. Measurement refers to an observed value related to the state of a target, so measurement may also be referred to as measurement or observation. A track is a motion trace, i.e., a tracking trace, formed by the state of a target estimated from a set of measurements from the same target.
However, some current track prediction methods are not suitable for scenes with strong noise and high track density because of track splitting on all measurement points of an associated gate, have large data storage capacity, are unfavorable for rapid initial calculation, and have a plurality of false tracks, so that not only the accuracy of a track prediction result is affected, but also the efficiency of track prediction is affected.
Disclosure of Invention
In order to solve the problems, the embodiment of the application provides a track prediction method, a device, equipment and a storage medium.
In a first aspect, an embodiment of the present application provides a track prediction method, where the method includes:
creating a first track prediction model based on a distributed algorithm;
correcting the first track prediction model by utilizing a speed vector included angle of a target at a sample point of an adjacent sensor to obtain a second track prediction model;
and distributing computing power nodes matched with the current computing type to the second track prediction model to conduct track prediction.
Preferably, the first track prediction model is:
；
wherein,a temporary track state estimation result obtained through polynomial fitting; />The measurement result of the kth moment in the ith track sequence; r (k) is the track covariance matrix and H (k) is the measurement matrix.
Preferably, before correcting the first track prediction model by using the speed vector included angle of the target at the adjacent sensor sample points, the method further comprises:
acquiring a speed vector included angle of a target at a sample point of an adjacent sensor; the velocity vector angle θ satisfies the following equation:
；
where v (k) and v (k+1) are velocity vectors of the target estimated with neighboring sensor samples, respectively.
Preferably, the correcting the first track prediction model by using the speed vector included angle of the target at the sample point of the adjacent sensor to obtain a second track prediction model includes:
and if the speed vector included angle theta is larger than a preset threshold value, stopping the track starting process of the current sensor sample point.
Preferably, after obtaining the second track prediction model, the method further comprises:
regularization processing is carried out on the second track prediction model based on a ridge regression method, and a data prediction fitting result is obtained;
if the fitting result represents that the second track prediction model is fitted, screening sensor sample points;
and correcting the second track prediction model based on the screened sensor sample points.
Preferably, before assigning the computing power node adapted to the current operation type to the second track prediction model for track prediction, the method further includes:
acquiring the current operation type of the second track prediction model; the operation types include logic operation, parallel computation and neural network computation.
Preferably, the assigning the computing power node adapted to the current operation type to the second track prediction model to perform track prediction includes:
acquiring idle computing forces respectively corresponding to the computing force nodes of each type of the server at present;
and distributing computing power nodes matched with the current computing type to the second track prediction model based on the idle computing power to perform track prediction.
In a second aspect, an embodiment of the present application provides a track prediction apparatus, including:
the creation module is used for creating a first track prediction model based on a distributed algorithm;
the correction module is used for correcting the first track prediction model by utilizing the speed vector included angle of the target at the sample point of the adjacent sensor to obtain a second track prediction model;
and the prediction module is used for distributing computing power nodes matched with the current operation type to the second track prediction model to conduct track prediction.
In a third aspect, an embodiment of the present application provides an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of the method as provided in the first aspect or any one of the possible implementations of the first aspect when the computer program is executed.
In a fourth aspect, embodiments of the present application provide a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements a method as provided by the first aspect or any one of the possible implementations of the first aspect.
The beneficial effects of the application are as follows: the method comprises the steps of creating a distributed track prediction model, denoising by utilizing a speed vector included angle, reducing possible overfitting errors of the track prediction model based on a ridge regression method, optimizing the prediction effect of the track prediction model, and improving the operation efficiency of a distributed logic algorithm by distributing adaptive computing nodes for the track prediction model.
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In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of a track prediction method according to an embodiment of the present application;
FIG. 2 is a schematic structural diagram of a track prediction apparatus according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application.
In the following description, the terms "first," "second," and "first," are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. The following description provides various embodiments of the application that may be substituted or combined between different embodiments, and thus the application is also to be considered as embracing all possible combinations of the same and/or different embodiments described. Thus, if one embodiment includes feature A, B, C and another embodiment includes feature B, D, then the present application should also be considered to include embodiments that include one or more of all other possible combinations including A, B, C, D, although such an embodiment may not be explicitly recited in the following.
The following description provides examples and does not limit the scope, applicability, or examples set forth in the claims. Changes may be made in the function and arrangement of elements described without departing from the scope of the application. Various examples may omit, replace, or add various procedures or components as appropriate. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Furthermore, features described with respect to some examples may be combined into other examples.
Referring to fig. 1, fig. 1 is a schematic flow chart of a track prediction method according to an embodiment of the present application. In an embodiment of the present application, the method includes:
step S110, a first track prediction model is created based on a distributed algorithm.
The distributed algorithm is an operation method for realizing multiplication operation. When the multiplication and addition function is completed, the distributed algorithm adds the partial products generated by each corresponding bit of each input data in advance to form corresponding partial products, and then adds the partial products to form a final result. The distributed algorithm can greatly reduce the scale of a hardware circuit, is easy to realize pipeline processing, and improves the execution speed of the circuit.
In one example, a plurality of sensor samples may be provided, each connected to a tracker, and the plurality of trackers are connected to the same central fusion device, and the central fusion device performs unified processing on the data collected by the sensor samples.
In one embodiment, the first track prediction model is:
；
wherein,a temporary track state estimation result obtained through polynomial fitting; />The measurement result of the kth moment in the ith track sequence; r (k) is the track covariance matrix and H (k) is the measurement matrix. Where L in the first is a lower case letter corresponding to the letter L, representing a plurality of sequencesSequence number l in the column.
In this embodiment, the main idea of the algorithm is to form an association gate by predicting the state of the next moment, reserve the points in the association gate, then perform logic judgment on the acceleration and the speed of the points in the association gate, reserve the points meeting the conditions, and after the continuous test for a preset number of moments succeeds, start successfully, otherwise, start fail. The first track prediction model reserves tracks smaller than a certain threshold value by judging accumulated information of tracks, so that the track detection probability is improved, false tracks are reduced, meanwhile, the data storage quantity is reduced, and the quick start calculation of the tracks is facilitated.
And step S120, correcting the first track prediction model by utilizing the speed vector included angle of the target at the sample point of the adjacent sensor to obtain a second track prediction model.
In this embodiment, the directional constraint of speed is increased to reduce noise interference with the onset of the voyage. Here, the speed vector included angle may include a heading angle, a pitch angle, and a roll angle corresponding to the instantaneous speed vector. And establishing a speed coordinate system according to the instantaneous speed vector direction of the track target, so as to obtain the included angles of all directions.
In one embodiment, before the first track prediction model is modified by using the velocity vector included angle of the target at the adjacent sensor sample in step S120, the method further includes:
acquiring a speed vector included angle of a target at a sample point of an adjacent sensor; the velocity vector angle θ satisfies the following equation:
；
where v (k) and v (k+1) are velocity vectors of the target estimated with neighboring sensor samples, respectively.
In this embodiment, the target speed vectors estimated by using the neighboring sensor sample points are v (k) and v (k+1), respectively, and on the premise of establishing the speed coordinate system, the relationship between the speed coordinate system and the ground rectangular coordinate system can be established by using the heading angle, the pitch angle and the roll angle corresponding to the instantaneous speed vector, so as to obtain the speed vector included angle of the target at the neighboring sensor sample points.
In one embodiment, step S120, the correcting the first track prediction model by using the velocity vector included angle of the target at the sample point of the adjacent sensor to obtain the second track prediction model includes:
and if the speed vector included angle theta is larger than a preset threshold value, stopping the track starting process of the current sensor sample point.
The speed vector included angle represents the speed direction change value of the target, and the larger the speed vector included angle is, the faster the steering of the target is indicated. If the velocity vector angle exceeds the cornering ability of the target, the track initiation process of the current sensor sample needs to be stopped. The first track prediction model is corrected through the speed vector included angle, noise data can be filtered, the data storage quantity is reduced, and the quick initial calculation of the track is facilitated.
In one embodiment, after obtaining the second track prediction model, the method further comprises:
regularization treatment is carried out on the second track prediction model based on a ridge regression method, and a data prediction fitting result is obtained;
if the fitting result represents that the second track prediction model is fitted, screening sensor sample points;
and correcting the second track prediction model based on the screened sensor sample points.
When there are multiple related variables in the linear regression model, their coefficient certainty becomes worse and exhibits high variance. For example, a large positive coefficient on one variable may be offset by a similarly sized negative coefficient on its associated variable. Ridge regression is the prevention of this by imposing constraints on the coefficients, which can reduce the probability of overfitting.
Common linear regression uses the least squares method:
;
wherein X is a characteristic value matrix, y is a target value matrix, and T is a weight constant.
The ridge regression is thatIs added with a normal number matrix->The expression is:
；
where w is the ridge regression estimate and k is the ridge parameter.
The probability of data overfitting can be effectively reduced through the constraint of ridge regression, and the prediction accuracy of the second track prediction model is improved.
And step S130, distributing computing power nodes matched with the current operation type to the second track prediction model to perform track prediction.
The computing power is a key core capability of processing service information of a device or a platform for completing a certain service, and relates to the computing capability of the device or the platform, including logic computing capability, parallel computing capability, neural network acceleration and the like. Depending on the algorithm run and the type of data computation involved, the computing power can be divided into logic computing power, parallel computing power and neural network computing power.
In one embodiment, before assigning the second track prediction model to the computing power node adapted to the current operation type for track prediction in step S130, the method further includes:
acquiring the current operation type of the second track prediction model; the operation types include logic operation, parallel computation, and neural network computation.
In this embodiment, the current operation type of the second track prediction model is obtained in advance. Correspondingly, the idle computing power of each computing power node can be calculated through the related model.
It should be noted that, for the computing chips in the same device or platform, the computing forces provided by different types of computing chips may be mapped to a unified dimension through a metric function. For heterogeneous computing power equipment and platforms, assuming that n logic operation chips, m parallel computing chips and p neural network acceleration chips exist, the computing power requirement of a service can be uniformly described as [ computing power quantization model ]:
in the method, in the process of the application,is the total calculation force requirement; f (x) is a mapping function; />、/>And->Mapping a proportionality coefficient; q1 (TOPS), q2 (FLOPS) and q3 (FLOPS) are redundant algorithms. I. j and k are the corresponding chip sequences in the plurality of chips respectively.
In one example, assuming there are 3 different types of parallel computing chip resources, b1, b2, b3, etc., thenA mapping function representing the parallel computing power available to the jth parallel computing chip b, q2 representing the redundant computing power of the parallel computing.
In one embodiment, step S130, allocating, to the second track prediction model, an algorithm node adapted to the current operation type for track prediction, includes:
acquiring idle computing forces respectively corresponding to computing force nodes of each type of the server at present;
and distributing computing force nodes which are matched with the current computing type to the second track prediction model based on the idle computing force to carry out track prediction.
In this embodiment, based on the current operation type of the second track prediction model and the idle computing forces respectively corresponding to the computing force nodes of each type, the computing force nodes with more idle computing forces can be allocated to the second track prediction model, so that the utilization rate of the computing force nodes is improved, and the operation efficiency of the algorithm is ensured.
According to the embodiment of the application, the prediction effect of the track prediction model is optimized by creating the distributed track prediction model, denoising processing is performed by utilizing the speed vector included angle, and possible overfitting errors of the track prediction model are reduced based on the ridge regression method, and the operation efficiency of the distributed logic algorithm is improved by distributing adaptive calculation nodes for the track prediction model.
The following describes in detail the track prediction apparatus according to the embodiment of the present application with reference to fig. 2. It should be noted that fig. 2 is a schematic structural diagram of a track prediction device provided in an embodiment of the present application, which is used to execute the method of the embodiment of fig. 1 of the present application, for convenience of explanation, only a portion relevant to the embodiment of the present application is shown, and specific technical details are not disclosed, please refer to the embodiment of fig. 1 of the present application.
Referring to fig. 2, fig. 2 is a schematic structural diagram of a track prediction apparatus according to an embodiment of the present application. As shown in fig. 2, the track prediction apparatus 200 includes:
a creation module 210 for creating a first track prediction model based on a distributed algorithm;
the correction module 220 is configured to correct the first track prediction model by using a speed vector included angle of the target at the sample point of the adjacent sensor, so as to obtain a second track prediction model;
the prediction module 230 is configured to allocate, to the second track prediction model, an algorithm node adapted to the current operation type to perform track prediction.
In one embodiment, the first track prediction model is:
；
wherein,a temporary track state estimation result obtained through polynomial fitting; />The measurement result of the kth moment in the ith track sequence; r (k) is the track covariance matrix and H (k) is the measurement matrix.
In one embodiment, the track prediction apparatus 200 further includes an obtaining module, where the obtaining module is specifically configured to:
acquiring a speed vector included angle of a target at a sample point of an adjacent sensor; the velocity vector angle θ satisfies the following equation:
；
where v (k) and v (k+1) are velocity vectors of the target estimated with neighboring sensor samples, respectively.
In one embodiment, the correction module 220 is specifically configured to:
and if the speed vector included angle theta is larger than a preset threshold value, stopping the track starting process of the current sensor sample point.
In one embodiment, the track prediction apparatus 200 further includes a processing module, where the processing module is specifically configured to:
regularization treatment is carried out on the second track prediction model based on a ridge regression method, and a data prediction fitting result is obtained;
if the fitting result represents that the second track prediction model is subjected to fitting, screening sensor sample points;
and correcting the second track prediction model based on the screened sensor sample points.
In one embodiment, the obtaining module is specifically further configured to:
acquiring the current operation type of the second track prediction model; the operation types include logic operation, parallel computation, and neural network computation.
In one embodiment, the prediction module 230 is specifically configured to:
acquiring idle computing forces respectively corresponding to the computing force nodes of each type of the server at present;
and distributing computing force nodes which are matched with the current computing type to the second track prediction model based on the idle computing force to carry out track prediction.
It will be clear to those skilled in the art that the technical solutions of the embodiments of the present application may be implemented by means of software and/or hardware. "Unit" and "module" in this specification refer to software and/or hardware capable of performing a specific function, either alone or in combination with other components, such as Field programmable gate arrays (Field-Programmable Gate Array, FPGAs), integrated circuits (Integrated Circuit, ICs), etc.
The processing units and/or modules of the embodiments of the present application may be implemented by an analog circuit that implements the functions described in the embodiments of the present application, or may be implemented by software that executes the functions described in the embodiments of the present application.
Referring to fig. 3, a schematic structural diagram of an electronic device according to an embodiment of the present application is shown, where the electronic device may be used to implement the method in the embodiment shown in fig. 1. As shown in fig. 3, the electronic device 300 may include: at least one central processor 301, at least one network interface 304, a user interface 303, a memory 305, at least one communication bus 302.
Wherein the communication bus 302 is used to enable connected communication between these components.
The user interface 303 may include a Display screen (Display), a Camera (Camera), and the optional user interface 303 may further include a standard wired interface, and a wireless interface.
The network interface 304 may optionally include a standard wired interface, a wireless interface (e.g., WI-FI interface), among others.
Wherein the central processor 301 may comprise one or more processing cores. The central processor 301 connects the various parts within the overall electronic device 300 using various interfaces and lines, performs various functions of the terminal 300 and processes data by executing or executing instructions, programs, code sets, or instruction sets stored in the memory 305, and invoking data stored in the memory 305. Alternatively, the central processor 31 may be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), programmable logic array (Programmable Logic Array, PLA). The central processor 301 may integrate one or a combination of several of a central processor (Central Processing Unit, CPU), an image central processor (Graphics Processing Unit, GPU), and a modem, etc. The CPU mainly processes an operating system, a user interface, an application program and the like; the GPU is used for rendering and drawing the content required to be displayed by the display screen; the modem is used to handle wireless communications. It will be appreciated that the modem may not be integrated into the cpu 301 and may be implemented by a single chip.
The memory 305 may include a random access memory (Random Access Memory, RAM) or a Read-only memory (Read-only memory). Optionally, the memory 305 includes a non-transitory computer readable medium (non-transitory computer-readable storage medium). Memory 305 may be used to store instructions, programs, code, sets of codes, or sets of instructions. The memory 305 may include a stored program area and a stored data area, wherein the stored program area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playing function, an image playing function, etc.), instructions for implementing the above-described respective method embodiments, etc.; the storage data area may store data or the like referred to in the above respective method embodiments. The memory 305 may also optionally be at least one storage device located remotely from the aforementioned central processor 301. As shown in fig. 3, an operating system, a network communication module, a user interface module, and program instructions may be included in the memory 305, which is a type of computer storage medium.
The present application also provides a computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of the above method. The computer readable storage medium may include, among other things, any type of disk including floppy disks, optical disks, DVDs, CD-ROMs, micro-drives, and magneto-optical disks, ROM, RAM, EPROM, EEPROM, DRAM, VRAM, flash memory devices, magnetic or optical cards, nanosystems (including molecular memory ICs), or any type of media or device suitable for storing instructions and/or data.
It should be noted that, for simplicity of description, the foregoing method embodiments are all described as a series of acts, but it should be understood by those skilled in the art that the present application is not limited by the order of acts described, as some steps may be performed in other orders or concurrently in accordance with the present application. Further, those skilled in the art will also appreciate that the embodiments described in the specification are all preferred embodiments, and that the acts and modules referred to are not necessarily required for the present application.
In the foregoing embodiments, the descriptions of the embodiments are emphasized, and for parts of one embodiment that are not described in detail, reference may be made to related descriptions of other embodiments.
In the several embodiments provided by the present application, it should be understood that the disclosed apparatus may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, such as the division of the units, merely a logical function division, and there may be additional manners of dividing the actual implementation, such as multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some service interface, device or unit indirect coupling or communication connection, electrical or otherwise.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable memory. Based on this understanding, the technical solution of the present application may be embodied essentially or partly in the form of a software product, or all or part of the technical solution, which is stored in a memory, and includes several instructions for causing a computer device (which may be a personal computer, a server, a network device, or the like) to perform all or part of the steps of the method according to the embodiments of the present application. And the aforementioned memory includes: a U-disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a removable hard disk, a magnetic disk, or an optical disk, or other various media capable of storing program codes.
Those of ordinary skill in the art will appreciate that all or a portion of the steps in the various methods of the above embodiments may be performed by hardware associated with a program that is stored in a computer readable memory, which may include: flash disk, read-Only Memory (ROM), random-access Memory (Random Access Memory, RAM), magnetic or optical disk, and the like.
The foregoing is merely exemplary embodiments of the present disclosure and is not intended to limit the scope of the present disclosure. That is, equivalent changes and modifications are contemplated by the teachings of this disclosure, which fall within the scope of the present disclosure. Embodiments of the present disclosure will be readily apparent to those skilled in the art from consideration of the specification and practice of the disclosure herein. This application is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a scope and spirit of the disclosure being indicated by the claims.
Claims (10)
1. A method of track prediction, the method comprising:
creating a first track prediction model based on a distributed algorithm;
correcting the first track prediction model by utilizing a speed vector included angle of a target at a sample point of an adjacent sensor to obtain a second track prediction model;
and distributing computing power nodes matched with the current computing type to the second track prediction model to conduct track prediction.
2. The method of claim 1, wherein the first track prediction model is:
；
wherein,a temporary track state estimation result obtained through polynomial fitting; />The measurement result of the kth moment in the ith track sequence; r (k) is the track covariance matrix and H (k) is the measurement matrix.
3. The method of claim 1, wherein prior to modifying the first track prediction model with the velocity vector angle of the target at the adjacent sensor sample, the method further comprises:
acquiring a speed vector included angle of a target at a sample point of an adjacent sensor; the included angle of the velocity vectorThe following equation is satisfied:
；
where v (k) and v (k+1) are velocity vectors of the target estimated with neighboring sensor samples, respectively.
4. A method according to claim 3, wherein said correcting the first track prediction model by using the velocity vector angle of the target at the adjacent sensor sample point to obtain the second track prediction model comprises:
if the velocity vector included angleAnd if the current sensor sample point is larger than the preset threshold value, stopping the track starting process of the current sensor sample point.
5. The method of claim 1, wherein after deriving the second track prediction model, the method further comprises:
regularization processing is carried out on the second track prediction model based on a ridge regression method, and a data prediction fitting result is obtained;
if the fitting result represents that the second track prediction model is fitted, screening sensor sample points;
and correcting the second track prediction model based on the screened sensor sample points.
6. The method of claim 1, wherein prior to assigning the second trajectory prediction model to the computing power node that is compatible with the current type of operation for trajectory prediction, the method further comprises:
acquiring the current operation type of the second track prediction model; the operation types include logic operation, parallel computation and neural network computation.
7. The method of claim 6, wherein assigning the second trajectory prediction model with an algorithm node that is compatible with the current operation type for performing a trajectory prediction, comprises:
acquiring idle computing forces respectively corresponding to the computing force nodes of each type of the server at present;
and distributing computing power nodes matched with the current computing type to the second track prediction model based on the idle computing power to perform track prediction.
8. A track prediction apparatus, the apparatus comprising:
the creation module is used for creating a first track prediction model based on a distributed algorithm;
the correction module is used for correcting the first track prediction model by utilizing the speed vector included angle of the target at the sample point of the adjacent sensor to obtain a second track prediction model;
and the prediction module is used for distributing computing power nodes matched with the current operation type to the second track prediction model to conduct track prediction.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the steps of the method according to any of claims 1-7 when the computer program is executed.
10. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method according to any of claims 1-7.
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