CN111967196A - Multi-measuring-station layout method and system based on genetic algorithm - Google Patents
Multi-measuring-station layout method and system based on genetic algorithm Download PDFInfo
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- CN111967196A CN111967196A CN202010877396.3A CN202010877396A CN111967196A CN 111967196 A CN111967196 A CN 111967196A CN 202010877396 A CN202010877396 A CN 202010877396A CN 111967196 A CN111967196 A CN 111967196A
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- 230000002068 genetic effect Effects 0.000 title claims abstract description 28
- 238000005259 measurement Methods 0.000 claims abstract description 26
- 230000005855 radiation Effects 0.000 claims abstract description 19
- 230000006870 function Effects 0.000 claims description 12
- 230000001154 acute effect Effects 0.000 claims description 5
- 230000035945 sensitivity Effects 0.000 claims description 3
- 230000004807 localization Effects 0.000 claims 2
- 238000005457 optimization Methods 0.000 abstract description 8
- 239000011159 matrix material Substances 0.000 abstract description 7
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- G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
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- G06N3/126—Evolutionary algorithms, e.g. genetic algorithms or genetic programming
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- G06F2111/00—Details relating to CAD techniques
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Abstract
The invention discloses a multi-measuring-station layout method and a system based on a genetic algorithm, wherein the method comprises the following steps: in a radiation source positioning area, acquiring an included angle of tangent lines at the intersection point of hyperbolas according to a hyperbola formed by taking any two measuring stations as focuses; and solving the layout objective function by adopting a genetic algorithm by taking the maximum sum of the included angles as a layout objective function and the layout area of the measuring station as a constraint condition to obtain the position of each corresponding measuring station, so as to layout the plurality of measuring stations. The sum of included angles of tangents of hyperbolas formed by every two measuring stations at intersection points in a specified radiation source positioning area is used as a layout optimization index, and a covariance matrix of measurement errors does not need to be considered, so that the optimized layout of multiple measuring stations is realized.
Description
Technical Field
The invention relates to the technical field of measuring station layout, in particular to a multi-measuring-station layout method and system based on a genetic algorithm.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
The time difference positioning TDOA technology has the advantages of good concealment, low requirement on an antenna and the like, and is widely researched and applied in the field of radio monitoring, and the time difference positioning precision is not only related to the time difference estimation precision and the positioning calculation algorithm, but also influenced by the number and the geometric layout of measuring stations. The existing optimized layout scheme of the measuring station generally adopts a geometric precision factor (GDOP) or a Cramer-Lo boundary (CRLB) as an optimization index to establish an optimized model, and then adopts a gridding or genetic algorithm to solve; the measuring stations form an optimal distribution station aiming at the positioned target area, and the principle is that the average value of the Clarmerico bound of the positioning error generated in the target positioning area or the GDOP value in the positioning area is minimum, and the average value and the GDOP value are equivalent in nature.
Therefore, the existing method needs to calculate the CRLB boundary or GDOP value of the designated area, and needs to consider the covariance matrix of the measurement error, while most methods assume that the measurement error is a normal distribution obeying a zero mean and a known variance, however, in practical application, the error variance is often unknown or approximate, so in practical engineering application, if the covariance matrix of the measurement error needs to be used, estimation needs to be performed according to a practical scenario, and the measurement process is complex, and it is not easy to obtain the covariance matrix in real time.
Disclosure of Invention
In order to solve the problems, the invention provides a multi-measuring-station layout method and a multi-measuring-station layout system based on a genetic algorithm, the sum of included angles of tangents of hyperbolas formed by two measuring stations at intersection points in a specified radiation source positioning area is used as a layout optimization index, and a covariance matrix of measuring errors is not required to be considered, so that the optimized layout of the multi-measuring-stations is realized.
In order to achieve the purpose, the invention adopts the following technical scheme:
in a first aspect, the present invention provides a genetic algorithm-based multi-measurement station layout method, including:
in a radiation source positioning area, acquiring an included angle of tangent lines at the intersection point of hyperbolas according to a hyperbola formed by taking any two measuring stations as focuses;
and solving the layout objective function by adopting a genetic algorithm by taking the maximum sum of the included angles as a layout objective function and the layout area of the measuring station as a constraint condition to obtain the position of each corresponding measuring station, so as to layout the plurality of measuring stations.
In a second aspect, the present invention provides a genetic algorithm based multi-measurement station placement system, comprising:
the positioning module is used for acquiring an included angle of a tangent line at an intersection point of hyperbolas according to a hyperbola formed by taking any two measuring stations as focuses in a radiation source positioning area;
and the layout module is used for solving the layout objective function by using the maximum sum of the included angles and the layout area of the measuring station as the constraint condition and adopting a genetic algorithm to obtain the position of each corresponding measuring station so as to layout the measuring stations.
In a third aspect, the present invention provides an electronic device comprising a memory and a processor, and computer instructions stored on the memory and executed on the processor, wherein when the computer instructions are executed by the processor, the method of the first aspect is performed.
In a fourth aspect, the present invention provides a computer readable storage medium for storing computer instructions which, when executed by a processor, perform the method of the first aspect.
Compared with the prior art, the invention has the beneficial effects that:
according to the invention, the designated radiation source positioning area is gridded, T discrete points are obtained by division, the sum of included angles of tangents of hyperbolas formed by two measurement stations at the intersection point in the designated radiation source positioning area is used as a layout optimization index according to the positions of the discrete points, a covariance matrix of measurement errors does not need to be calculated according to an actual scene, and the calculation complexity is reduced.
When the included angle of tangent lines of the hyperbola is smaller, a tiny time delay estimation error can cause great positioning disturbance to form a great positioning error, and when the included angle of the tangent lines at the intersection point is 90 degrees, the sensitivity of the positioning precision to the time delay estimation error is the lowest, so that the included angle formed by the tangent lines is used as an optimization index to realize the optimized layout of the multiple measuring stations.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, are included to provide a further understanding of the invention, and are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the invention and together with the description serve to explain the invention and not to limit the invention.
FIG. 1 is a schematic diagram of a genetic algorithm-based multi-measurement station layout method according to embodiment 1 of the present invention;
fig. 2(a) -2 (b) are schematic diagrams of included angles between tangents at the intersection of two hyperbolas provided in example 1 of the present invention.
The specific implementation mode is as follows:
the invention is further described with reference to the following figures and examples.
It is to be understood that the following detailed description is exemplary and is intended to provide further explanation of the invention as claimed. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the invention. As used herein, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise, and it should be understood that the terms "comprises" and "comprising", and any variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
The embodiments and features of the embodiments of the present invention may be combined with each other without conflict.
Example 1
As shown in fig. 1, the present embodiment provides a multi-measurement station layout method based on a genetic algorithm, including:
s1: in a radiation source positioning area, acquiring an included angle of tangent lines at the intersection point of hyperbolas according to a hyperbola formed by taking any two measuring stations as focuses;
s2: and solving the layout objective function by adopting a genetic algorithm by taking the maximum sum of the included angles as a layout objective function and the layout area of the measuring station as a constraint condition to obtain the position of each corresponding measuring station, so as to layout the plurality of measuring stations.
In this embodiment, a layout area and a radiation source positioning area of the measurement station are pre-designated, and the radiation source positioning area is gridded and divided into T discrete points with coordinates of (x)t,yt)，t＝1,2,...T；
The geometric constraint area where the radiation source target is located is regarded as a measuring station layout area, N measuring stations are arranged in the measuring station layout area, and the coordinates of the N measuring stations are (x)n,yn) N, selecting two measuring stations as two focuses of a hyperbola, and positioning discrete point positions (x) in the area according to the radiation sourcet,yt) Obtaining a hyperbolic tangent line;
in this embodiment, one of the measurement stations is selected as the primary measurement station, the other N-1 measurement stations are selected as the secondary measurement stations, and the primary measurement station (x) is selected1,y1) And any one of the secondary measurement stations (x)n,yn) Obtaining N-1 tangent lines as the focus, calculating the included angle between every two tangent lines, and taking the sum of acute angles as thetat；
As shown in fig. 2(a) -2 (b), when three stations are used for time difference positioning, two hyperbolas are formed, and the positions of the targets are determined by the intersection of the hyperbolas, wherein the included angle formed by the tangents of the two hyperbolas at the intersection points is an acute angle. When the included angle is smaller, a tiny time delay estimation error can cause great positioning disturbance, and a great positioning error is formed; when the included angle of the tangent lines at the intersection point is 90 degrees, the sensitivity of the positioning accuracy to the delay estimation error is the lowest, so the included angle formed by the tangent lines is used as an optimization index in the embodiment.
In step S2, T acute angle sums are obtained in sequence for T discrete points in the radiation source positioning region, and then the acute angle sums are summed to obtain the sum
The embodiment adopts a genetic algorithm to carry out optimization solution, and the sum of included angles
Maximum layout objective function, using the layout area of the measuring station as constraint condition, adopting genetic algorithm to solve the layout objective function, and selecting to make it possible to obtain the maximum layout objective function
And in the layout area of the measuring stations, the measuring stations are arranged at the positions of the obtained measuring stations, and the optimal layout scheme of the multiple measuring stations is output.
In the embodiment, the sum of the included angles of tangents of hyperbolas formed by two measuring stations at the intersection point in the designated area is used as an optimization index, and the covariance matrix of the measuring errors does not need to be considered, so that the optimized layout of the measuring stations is realized.
Example 2
The embodiment of the invention relates to a multi-measurement-station layout system based on a genetic algorithm, which comprises:
the positioning module is used for acquiring an included angle of a tangent line at an intersection point of hyperbolas according to a hyperbola formed by taking any two measuring stations as focuses in a radiation source positioning area;
and the layout module is used for solving the layout objective function by using the maximum sum of the included angles and the layout area of the measuring station as the constraint condition and adopting a genetic algorithm to obtain the position of each corresponding measuring station so as to layout the measuring stations.
It should be noted that the above modules correspond to steps S1 to S2 in embodiment 1, and the above modules are the same as the examples and application scenarios realized by the corresponding steps, but are not limited to the disclosure in embodiment 1. It should be noted that the modules described above as part of a system may be implemented in a computer system such as a set of computer-executable instructions.
In further embodiments, there is also provided:
an electronic device comprising a memory and a processor and computer instructions stored on the memory and executed on the processor, the computer instructions when executed by the processor performing the method of embodiment 1. For brevity, no further description is provided herein.
It should be understood that in this embodiment, the processor may be a central processing unit CPU, and the processor may also be other general purpose processors, digital signal processors DSP, application specific integrated circuits ASIC, off-the-shelf programmable gate arrays FPGA or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, and so on. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory may include both read-only memory and random access memory, and may provide instructions and data to the processor, and a portion of the memory may also include non-volatile random access memory. For example, the memory may also store device type information.
A computer readable storage medium storing computer instructions which, when executed by a processor, perform the method described in embodiment 1.
The method in embodiment 1 may be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules in the processor. The software modules may be located in ram, flash, rom, prom, or eprom, registers, among other storage media as is well known in the art. The storage medium is located in a memory, and a processor reads information in the memory and completes the steps of the method in combination with hardware of the processor. To avoid repetition, it is not described in detail here.
Those of ordinary skill in the art will appreciate that the various illustrative elements, i.e., algorithm steps, described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
The above is only a preferred embodiment of the present invention, and is not intended to limit the present invention, and various modifications and changes will occur to those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Although the embodiments of the present invention have been described with reference to the accompanying drawings, it is not intended to limit the scope of the present invention, and it should be understood by those skilled in the art that various modifications and variations can be made without inventive efforts by those skilled in the art based on the technical solution of the present invention.
Claims (10)
1. A multi-measurement-station layout method based on genetic algorithm is characterized by comprising the following steps:
in a radiation source positioning area, acquiring an included angle of tangent lines at the intersection point of hyperbolas according to a hyperbola formed by taking any two measuring stations as focuses;
and solving the layout objective function by adopting a genetic algorithm by taking the maximum sum of the included angles as a layout objective function and the layout area of the measuring station as a constraint condition to obtain the position of each corresponding measuring station, so as to layout the plurality of measuring stations.
2. The genetic algorithm-based multi-measurement-station layout method of claim 1, wherein the radiation source localization area is gridded and divided into T discrete points.
3. A genetic algorithm based multi-station placement method as claimed in claim 2 wherein hyperbolic tangent lines are obtained from the position of each discrete point in the radiation source localization area.
4. The genetic algorithm-based multi-measurement-station layout method as claimed in claim 2, wherein any one measurement station is used as a main measurement station, the rest are auxiliary measurement stations, a hyperbola is formed by using the main measurement station and any one auxiliary measurement station as focuses, N-1 tangent lines are obtained according to the position of each discrete point in the radiation source positioning area, the sum of the included angles of every two tangent lines is taken, and N is the number of the measurement stations.
5. The genetic algorithm-based multi-measurement-station layout method according to claim 2, wherein the sum of tangent included angles is maximum layout objective function at T discrete points.
6. A genetic algorithm based multi-station layout method according to claim 1 wherein said included angles are acute.
7. The genetic algorithm-based multi-measurement-station layout method as claimed in claim 1, wherein the sensitivity of the positioning accuracy of the measurement station to the delay estimation error is the lowest when the included angle of the tangent is 90 degrees.
8. A genetic algorithm based multi-measurement station placement system, comprising:
the positioning module is used for acquiring an included angle of a tangent line at an intersection point of hyperbolas according to a hyperbola formed by taking any two measuring stations as focuses in a radiation source positioning area;
and the layout module is used for solving the layout objective function by using the maximum sum of the included angles and the layout area of the measuring station as the constraint condition and adopting a genetic algorithm to obtain the position of each corresponding measuring station so as to layout the measuring stations.
9. An electronic device comprising a memory and a processor and computer instructions stored on the memory and executed on the processor, the computer instructions when executed by the processor performing the method of any of claims 1-7.
10. A computer-readable storage medium storing computer instructions which, when executed by a processor, perform the method of any one of claims 1 to 7.
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| CN113191070A (en) * | 2021-03-24 | 2021-07-30 | 国网山东省电力公司泰安供电公司 | Particle swarm and genetic algorithm combined antenna array arrangement optimization method |
| CN114046771A (en) * | 2021-09-22 | 2022-02-15 | 福建省新天地信勘测有限公司 | Position positioning system for surveying and mapping |
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