CN114245316A - UWB positioning-based base station deployment optimization method and system - Google Patents
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Abstract
The application provides a base station deployment optimization method and system based on UWB positioning. Wherein, the method comprises the following steps: acquiring a deployable area of a base station in an indoor space, determining space constraint of the deployable area, performing grid division on the deployable area, and randomly generating a group of base station position combinations in each grid, wherein the base station position combinations are marked as initial base station position combinations; dividing all base station populations in a deployable area into two populations, taking the initial base station position combination as an initial value, taking the minimum GDOP, the minimum positioning root mean square error and the minimum number M of base stations deployed by the base stations as optimization targets, and respectively updating the positions of the base stations in the two populations by adopting an improved self-adaptive genetic algorithm and a Levy flight strategy algorithm, thereby carrying out base station combination optimization and obtaining the optimal base station deployment combination. The rationality of the base station deployment method can be improved through the method and the device.
Description
Technical Field
The application relates to the technical field of indoor positioning, in particular to a base station deployment optimization method and system based on UWB positioning.
Background
The existing indoor space positioning technology comprises an ultrasonic technology, a Bluetooth positioning technology, a Wi-Fi positioning technology and a Zigbee positioning technology, the technologies have good positioning performance, but are subjected to large complex indoor environment factors, cannot well meet the requirement on positioning perception in a general environment, and have the defects of low positioning precision, weak adaptability and the like. The UWB (ultra wide band) positioning technology has the advantages of strong penetrating power, strong environment adaptability, low power consumption and easy integration, and becomes the most extensive wireless positioning technology in the current indoor positioning.
At present, the deployment mode of the base station of the UWB positioning technology mainly adopts the experience of workers and the traditional layout modes such as a rectangular type, a star type, a right-angle triangular type and the like, only GDOP (geometric precision factor) is used as the evaluation index of the deployment of the base station, and the influence of the obstruction on the positioning and the problems of the cost number of the base station are not considered. More importantly, for different actual scenes including irregular spaces such as integer rules and irregular spaces, space constraints of walls and shelters exist, the deployment area of the base station cannot be determined, incomplete coverage or coverage redundancy and large positioning error can be caused if the base station is deployed improperly, although the positioning accuracy can be improved, the cost is increased, the base station is not suitable for popularization, and the base station is mechanized according to experience and a traditional layout mode, so that universality and specificity cannot be guaranteed.
Disclosure of Invention
In order to solve the problem that the existing UWB-positioning-based base station deployment work depends on experience excessively and the influence of an obstruction on positioning is caused, the embodiment of the application provides a UWB-positioning-based base station deployment optimization method and system.
In a first aspect, the present embodiment provides a method for optimizing base station deployment based on UWB positioning, where the method includes:
acquiring a deployable area of a base station in an indoor space, determining space constraint of the deployable area, performing grid division on the deployable area, and randomly generating a group of base station position combinations in each grid, and marking the base station position combinations as initial base station position combinations, wherein the space constraint is an upper bound and a lower bound of the deployable area in a direction dimension, and each grid area comprises M base stations;
dividing all base station populations in the deployable area into two populations, taking the initial base station position combination as an initial value, and taking the minimum GDOP, the minimum positioning root mean square error and the minimum number M of the base stations deployed as optimization targets, and respectively updating the positions of the base stations in the two populations by adopting an improved self-adaptive genetic algorithm and a Levy flight strategy algorithm, thereby carrying out base station combination optimization and obtaining the optimal base station deployment combination.
In some of these embodiments, acquiring a deployable area of a base station in an indoor space comprises:
and detecting whether the indoor space is a regular cuboid space structure, if not, taking two sides of the concave-convex angle side of the indoor space as extension lines, wherein the area where the extension lines intersect is a deployable area of the indoor space.
In some of these embodiments, the improved adaptive genetic algorithm comprises:
selecting random numbers of a first population, defining the random numbers as pointers P, and performing random traversal sampling selection on individuals of the first population in a pointer set [ P, P +1, …, P + L-1] at equal distances of L according to the positions of the first population;
obtaining a crossover probability of adaptively-varied non-uniform crossovers, wherein the crossover probability is
，
In order to maximize the probability of a cross-over,
in order to minimize the probability of a cross-over,
the population average fitness is the average fitness of the population,
in order to obtain the degree of fitness of the parameters,
the maximum fitness of the population;
selecting individuals of the first population as exploration individuals, wherein the expression of S variant individuals generated by each exploration individual is
，
Wherein, in the step (A),
in order to explore the individuals, the individuals are searched,
in order to generate the S variant individuals,
obeying a Gaussian normal distribution random number of (0,1), wherein R is a variation range, V is a search range,
and k is the current iteration number.
In some embodiments, all base station populations of the deployable region are sorted according to fitness and divided into two populations of different numbers, including:
randomly acquiring a numerical value from 50% to 100%, and recording the numerical value as a ranking value of population division, wherein individuals with fitness ranking before the ranking value of the population division are recorded as a first population, and the rest base stations are recorded as a second population;
the first population updates the position of the base station by adopting an improved self-adaptive genetic algorithm;
and the second population updates the position of the base station by adopting a Levy flight strategy algorithm.
In some embodiments, the base station deployment GDOP minimum, the positioning root mean square error minimum, and the number of base stations M minimum have priorities as optimization targets, and the priorities are, from high to low, GDOP, root mean square error, and the number of base stations M, where the functions corresponding to the three optimization targets include:
the function of the base station deploying GDOP is
Wherein Q is an error covariance matrix;
the function of the positioning root mean square error is:
the function of the minimum number of base stations is as follows:
In some of these embodiments, the improved adaptive genetic algorithm performs a location update on the first population with a function of the GDOP as a fitness function;
and the Levis flight strategy algorithm updates the position of the second population by taking the function of the positioning root-mean-square error as a fitness function.
In some embodiments, the performing base station combination optimization to obtain an optimal base station deployment combination further includes:
calculating a GDOP and a root mean square error of a base station position after updating through an improved self-adaptive genetic algorithm and a Levis flight strategy algorithm, sequencing the GDOP and the root mean square error in an ascending order, and recording the minimum individual position and the minimum optimization times of the GDOP and the root mean square error;
and detecting whether the preset optimization times are reached, if not, re-optimizing the base station deployment combination until the preset optimization times are reached, recording a plurality of groups of optimized base station deployment position combinations, determining the optimal base station deployment position combination, and performing base station deployment and indoor positioning.
In a second aspect, an embodiment of the present application provides a UWB positioning based base station deployment optimization system, where the system includes:
the device comprises an initialization module, a processing module and a processing module, wherein the initialization module comprises a reading unit, an operating unit and a generating unit, wherein the reading unit is used for acquiring a deployable area of a base station in an indoor space and determining a space constraint of the deployable area, and the space constraint is an upper bound and a lower bound of the deployable area in a direction dimension;
the operation unit is configured to perform mesh division on the deployable area, where each mesh area includes M base stations;
the generating unit is used for randomly generating a group of base station position combinations in the deployable area, and marking the base station position combinations as initial base station position combinations;
the combined optimization module comprises a classification unit, an objective function unit and a position updating unit; the classification unit is used for classifying all base station populations in the deployable area into two populations;
the objective function unit comprises a function which takes the initial base station position combination as an initial value, and takes the minimum GDOP, the minimum positioning root mean square error and the minimum M of the base station individual as optimization objectives;
and the position updating unit is used for respectively updating the positions of the base stations in the two populations according to an improved adaptive genetic algorithm and a Levy flight strategy algorithm, so that the combination optimization of the base stations is carried out, and the optimal base station deployment combination is obtained.
In some of these embodiments, the system further comprises:
and the positioning module is used for solving the GDOP function, the positioning root mean square error function and the base station number function deployed by the base station.
In some of these embodiments, the system further comprises:
the storage module is used for recording data in the initialization module, the combination optimization module and the positioning module;
and the positioning result real-time display module is used for displaying the positioning result obtained according to the combined optimization module in real time.
By adopting the scheme, the problem that the existing base station based on UWB positioning is unreasonable in deployment can be solved, and the rationality of the base station based on UWB positioning is improved.
Drawings
Fig. 1 is a flowchart of a UWB positioning based base station deployment optimization method provided in this embodiment.
Fig. 2a is a plan view of the integer regular spatial structure of the present embodiment.
Fig. 2b is a plan view of the irregular space structure of the profile shape of the present embodiment.
Fig. 3 is a flow chart of the improved adaptive genetic algorithm provided by the present embodiment.
Fig. 4 is a block diagram of a UWB positioning based base station deployment optimization system provided in the present embodiment.
Detailed Description
For a clearer understanding of the objects, aspects and advantages of the present application, reference is made to the following description and accompanying drawings. However, it will be apparent to one of ordinary skill in the art that the present application may be practiced without these specific details. In some instances, well known methods, procedures, systems, components, and/or circuits have been described at a higher level without undue detail in order to avoid obscuring aspects of the application with unnecessary detail. It will be apparent to those of ordinary skill in the art that various changes can be made to the embodiments disclosed herein, and that the general principles defined herein may be applied to other embodiments and applications without departing from the principles and scope of the present application. Thus, the present application is not limited to the embodiments shown, but is to be accorded the widest scope consistent with the scope of the present application as claimed.
Unless defined otherwise, technical or scientific terms used herein shall have the same general meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in this application, the terms "a," "an," "the," and the like do not denote a limitation of quantity, but rather are used in the singular or the plural. The terms "comprises," "comprising," "has," "having," and any variations thereof, as referred to in this application, are intended to cover non-exclusive inclusions; for example, a process, method, and system, article, or apparatus that comprises a list of steps or modules (elements) is not limited to the listed steps or modules, but may include other steps or modules (elements) not listed or inherent to such process, method, article, or apparatus.
Reference to "a plurality" in this application means two or more. In general, the character "/" indicates a relationship in which the objects associated before and after are an "or". The terms "first," "second," "third," and the like in this application are used for distinguishing between similar items and not necessarily for describing a particular sequential or chronological order.
The embodiments of the present application will be described in further detail with reference to the drawings attached hereto.
UWB is a carrier-free spread spectrum technique using impulse pulses with a very low duty cycle as information carrier, belonging to the carrier-free communication technique, UWB uses not a carrier but a short sequence of energy pulses, and spreads the pulses into a frequency range by orthogonal frequency division modulation or direct sequencing, by directly modulating impulse pulses with very steep rise and fall times.
Fig. 1 is a flowchart of a UWB positioning based base station deployment optimization method provided in this embodiment, and as shown in fig. 1, the flowchart includes the following steps:
step S101, acquiring a deployable area of a base station in an indoor space, determining space constraint of the deployable area, performing grid division on the deployable area, and randomly generating a group of base station position combinations in each grid, and marking as initial base station position combinations, wherein the space constraint is an upper bound and a lower bound of the deployable area in a direction dimension, and each grid area comprises M base stations.
Step S102, dividing all base station populations in the deployable area into two populations, taking the initial base station position combination as an initial value, and taking the minimum GDOP, the minimum positioning root mean square error and the minimum M number of the base stations as optimization targets, and respectively updating the positions of the base stations in the two populations by adopting an improved self-adaptive genetic algorithm and a Levier flight strategy algorithm, thereby carrying out base station combination optimization and obtaining the optimal base station deployment combination.
According to the UWB-positioning-based base station deployment optimization method provided by the embodiment, after an indoor space in which base station deployment work needs to be performed is determined, firstly, a deployable area of a base station in the indoor space is obtained, and according to space constraints of the deployable area in direction dimensions, the deployable area is subjected to space constraint; however, since the indoor space may be relatively large, it is used in warehousing, chemical plants and logistics warehousesThe method comprises the steps of dividing the deployable area into grids, wherein at most N base stations can be deployed in the base station combination of each grid, estimating the range interval of the number of the base stations in advance according to the area of each grid and by combining experience, and randomly generating the number M of the base stations in the base station deployment combination of each grid within the range of the number of the base stations, wherein the number M of the base stations in the base station deployment combination of each grid is generated
(ii) a And finally, dividing all base station populations in the deployable area into two populations, taking the initial base station position combination as an initial value, taking the minimum GDOP, the minimum positioning root mean square error and the minimum number M of the base stations as optimization targets, respectively updating the positions of the base stations in the two populations by adopting an improved adaptive genetic algorithm and a Levin flight strategy algorithm, and selecting the optimal base station deployment combination from a plurality of groups of base station deployment combinations.
The spatial constraint of the deployable region in the directional dimension described above can be expressed as:
In this embodiment, the UWB base station deployment optimization problem is converted into a multi-objective base station deployment optimization problem, where the multi-objective includes: the base station deployment GDOP is minimum, the positioning root mean square error is minimum and the number M of the base stations is minimum, the priority is GDOP, the root mean square error and the number M of the base stations from high to low, the spatial constraint conditions of the deployable areas are combined, and the variable of the multi-objective optimization problem is
Wherein
Represents the first
The number of the radicals is equal to that of the base,
is the actual coordinate value of the coordinate value,
is a positioning estimate. Wherein the content of the first and second substances,
function of base station deployment GDOPNumber is
Wherein Q is an error covariance matrix.
Positioning the root mean square error as a function of
；
Wherein, N represents the maximum number of base stations that can be deployed in the base station combination of each grid, and the minimum number M of base stations in three-dimensional positioning is 4.
The function of the minimum number of base stations is
；
Wherein, in the step (A),
indicating that base station i is a valid base station,
indicating that base station i is an invalid base station,
representing the distance between two different base stations i and l,
the minimum distance allowed between two different base stations is shown, and the minimum number M of the base stations in the three-dimensional positioning is 4.
The GDOP geometric precision factor calculation mode is obtained by using a Taylor technology iterative algorithm under the TOA ranging mode. For determining three-dimensional coordinates of positioning points
By M (M)>4) The base stations are positioned, and the coordinates of the ith (i =1,2, …, M) base station are
，
Representing the ranging value from the anchor point to the ith base station, there is an expression
;
Solving the expression by adopting a Taylor series expansion algorithm, and providing a positioning initial value for the Taylor series expansion algorithm by adopting a Chan positioning algorithm
Neglecting the components with more than second order, the expression is arranged into a matrix form as follows:
wherein the content of the first and second substances,
in order to observe the matrix, the system,
，
and are and
respectively represent
The direction cosine of each UWB base station in the x, y, z direction. Error covariance matrix
。
In this embodiment, an improved adaptive genetic algorithm and a levy flight policy algorithm are introduced to optimize a base station deployment position combination, and first, all base station populations in a deployable area are sorted according to fitness and divided into two populations with different numbers, and a numerical value is randomly acquired from 50% to 100% and recorded as a sorting value of population division, wherein a base station with fitness sorting before the sorting value of population division is recorded as a first population, and the rest base stations are recorded as a second population, the first population updates the base station position by using the improved adaptive genetic algorithm, and the second population updates the base station position by using the levy flight policy algorithm. For example, the first 70% of the base stations in the fitness ranking are subjected to position updating according to an improved adaptive genetic algorithm, and the last 30% of the base stations are subjected to position updating by introducing a Levis flight strategy algorithm. And recalculating the GDOP value and the root mean square error of the base station deployment combination after position updating, and performing ascending sequencing and the individual position with the minimum probability to ensure that the combination of the optimal base station space distribution position in the position area is obtained under the indoor precision condition.
And after finishing the base station deployment position combination optimization each time, detecting whether the preset optimization times are reached, if not, re-performing the base station deployment combination optimization until the preset optimization times are reached to obtain a plurality of groups of optimized base station deployment position combinations, and selecting the base station deployment position combination with the minimum GDOP, the minimum root mean square error and the minimum base station number from the base station deployment position combinations as the optimal base station deployment position combination.
And the second group adopts a Levy flight strategy algorithm to update the combined position of the base station, the root mean square error is used as the fitness function judgment basis of the Levy flight strategy algorithm, some reference positioning points are randomly selected from the grid of the combined position of the base station before the positioning error is calculated, the actual positioning positions of the reference positioning points are stored in a data information base, then the combined position of the base station is updated through the Levy flight strategy algorithm, the positioning estimation value of the reference point is calculated through the updated position by adopting the positioning algorithm, and the positioning estimation value is compared with the actual coordinate value in the data information base to obtain the root mean square error value.
Fig. 3 is a flowchart of the improved adaptive genetic algorithm provided in this embodiment, and as shown in fig. 3, firstly, parameters and codes need to be set for individuals of a first population, and population initialization is performed, then, fitness of the population is calculated, whether a convergence criterion is satisfied is determined, if the convergence criterion is satisfied, an optimal base station deployment position combination is output, otherwise, selection, crossover and variation operations are performed, a new generation population is generated, and fitness of the new generation population is calculated, and the first population uses GDOP as a basis for determining a fitness function of the improved adaptive genetic algorithm. Wherein the content of the first and second substances,
the idea of the selection operation is to
A random number is generated and defined as a pointer
The population is individual in
Equidistant set of pointers
According to the random ergodic sampling selection at the population position, the individual selection probability is
，
Is a population of individuals
The probability of being selected is determined by the probability of being selected,
is a population of individuals
The fitness function value of (1).
In order to increase the diversity of the population, better population individuals are selected and crossed, and two parents of the crossed population are set as
And
after crossing, the offspring individuals are
The cross probability is set as the non-uniform cross for the self-adaptive change, the diversity of the population is increased, the global search capability of the population is improved, and the self-adaptive cross probability is as follows:
when the population fitness tends to be the same,
and the increase of the population fitness is that, when the population fitness is dispersed,
the number of the grooves is reduced, and the,
in order to maximize the probability of a cross-over,
is a minimum crossThe probability of the occurrence of the event,
the population average fitness is the average fitness of the population,
in order to obtain the degree of fitness of the parameters,
the maximum fitness of the population.
When the population performs variation operation, each generation of the population is subjected to a certain probability, individuals of the current population are selected as exploration individuals, and each natural individual is selected as an exploration individual
Generating
The number of the variant individuals is increased,
in order to explore the individuals, the individuals are searched,
to generate
The number of the variant individuals is increased,
to obey the (0,1) gaussian distribution random number,
the range of variation is the range of variation,
，
which indicates the range of the search,
in order to be the maximum number of iterations,
for the current number of iterations,
for linearly decreasing variables, in the beginning phase, the variables are
The value of (A) is larger, the individual variation range is large, the global search of the population is facilitated, and in the end stage, the variable is
The value of (A) is reduced, the individual variation range is small, and the local search of the population is facilitated.
Fig. 4 is a structural diagram of a UWB positioning based base station deployment optimization system provided in this embodiment, where the system includes an initialization module, a combination optimization module, a positioning module, a storage module, and a positioning result real-time display module.
The initialization module comprises a reading unit, an operation unit and a generation unit, wherein the reading unit is used for acquiring a deployable area of the base station in an indoor space and determining space constraints of the deployable area, and the space constraints are an upper bound and a lower bound of the deployable area in a direction dimension; the operation unit is used for carrying out grid division on the deployable area, and each grid area comprises M base stations; the generating unit is used for randomly generating a group of base station position combinations in the deployable area, and the base station position combinations are marked as initial base station position combinations.
The combined optimization module comprises a classification unit, an objective function unit and a position updating unit; the classification unit is used for classifying all base station populations in the deployable area into two populations; the target function unit comprises a function which takes the initial base station position combination as an initial value, and takes the minimum GDOP, the minimum positioning root mean square error and the minimum M of the base station individual as optimization targets; and the position updating unit is used for respectively updating the positions of the base stations in the two populations according to the improved adaptive genetic algorithm and the Levy flight strategy algorithm, so that the combination optimization of the base stations is carried out, and the optimal base station deployment combination is obtained.
And the positioning module is used for solving a GDOP function deployed by the base station, a positioning root mean square error function and a base station number function.
And the storage module is used for recording the data in the initialization module, the combination optimization module and the positioning module.
And the positioning result real-time display module is used for displaying the positioning result obtained according to the combined optimization module in real time.
It should be understood that, although the steps in the flowcharts of the figures are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and may be performed in other orders unless explicitly stated herein.
The foregoing is only a partial embodiment of the present application, and it should be noted that, for those skilled in the art, several modifications and decorations can be made without departing from the principle of the present application, and these modifications and decorations should also be regarded as the protection scope of the present application.
Claims (10)
1. A UWB positioning-based base station deployment optimization method is characterized in that the method comprises the following steps:
acquiring a deployable area of a base station in an indoor space, determining space constraint of the deployable area, performing grid division on the deployable area, and randomly generating a group of base station position combinations in each grid, and marking the base station position combinations as initial base station position combinations, wherein the space constraint is an upper bound and a lower bound of the deployable area in a direction dimension, and each grid area comprises M base stations;
dividing all base station populations in the deployable area into two populations, taking the initial base station position combination as an initial value, and taking the minimum GDOP, the minimum positioning root mean square error and the minimum number M of the base stations deployed as optimization targets, and respectively updating the positions of the base stations in the two populations by adopting an improved self-adaptive genetic algorithm and a Levy flight strategy algorithm, thereby carrying out base station combination optimization and obtaining the optimal base station deployment combination.
2. The method of claim 1, wherein acquiring the deployable area of the base station in the indoor space comprises:
and detecting whether the indoor space is a regular cuboid space structure, if not, taking two sides of the concave-convex angle side of the indoor space as extension lines, wherein the area where the extension lines intersect is a deployable area of the indoor space.
3. The method of claim 1, wherein the improved adaptive genetic algorithm comprises:
selecting random numbers of a first population, defining the random numbers as pointers P, and performing random traversal sampling selection on individuals of the first population in a pointer set [ P, P +1, …, P + L-1] at equal distances of L according to the positions of the first population;
obtaining a crossover probability of adaptively-varied non-uniform crossovers, wherein the crossover probability is
，
In order to maximize the probability of a cross-over,
in order to minimize the probability of a cross-over,
the population average fitness is the average fitness of the population,
in order to obtain the degree of fitness of the parameters,
the maximum fitness of the population;
selecting individuals of the first population as exploration individuals, wherein the expression of S variant individuals generated by each exploration individual is
，
Wherein, in the step (A),
in order to explore the individuals, the individuals are searched,
in order to generate the S variant individuals,
obeying a Gaussian normal distribution random number of (0,1), wherein R is a variation range, V is a search range,
and k is the current iteration number.
4. The method of claim 3, wherein the ranking of all base station populations of the deployable region according to fitness and dividing into two populations of unequal numbers comprises:
randomly acquiring a numerical value from 50% to 100%, and recording the numerical value as a ranking value of population division, wherein individuals with fitness ranking before the ranking value of the population division are recorded as a first population, and the rest base stations are recorded as a second population;
the first population updates the position of the base station by adopting an improved self-adaptive genetic algorithm;
and the second population updates the position of the base station by adopting a Levy flight strategy algorithm.
5. The method of claim 1, wherein the base station deployment GDOP minimization, positioning root mean square error minimization and base station number Mminimization have priorities of GDOP, root mean square error and base station number M from high to low, and wherein the functions corresponding to the three optimization objectives comprise:
the function of the base station deploying GDOP is
Wherein Q is an error covariance matrix;
the function of the positioning root mean square error is:
the function of the minimum number of base stations is as follows:
6. The method of claim 5, wherein the improved adaptive genetic algorithm performs a location update on the first population with a function of the GDOP as a fitness function;
and the Levis flight strategy algorithm updates the position of the second population by taking the function of the positioning root-mean-square error as a fitness function.
7. The method of claim 1, wherein the performing base station combination optimization to obtain the optimal base station deployment combination further comprises:
calculating a GDOP and a root mean square error of a base station position after updating through an improved self-adaptive genetic algorithm and a Levis flight strategy algorithm, sequencing the GDOP and the root mean square error in an ascending order, and recording the minimum individual position and the minimum optimization times of the GDOP and the root mean square error;
and detecting whether the preset optimization times are reached, if not, re-optimizing the base station deployment combination until the preset optimization times are reached, recording a plurality of groups of optimized base station deployment position combinations, determining the optimal base station deployment position combination, and performing base station deployment and indoor positioning.
8. A UWB positioning based base station deployment optimization system, the system comprising:
an initialization module including a reading unit, an operating unit, and a generating unit, wherein,
the reading unit is used for acquiring a deployable area of a base station in an indoor space and determining space constraints of the deployable area, wherein the space constraints are an upper bound and a lower bound of the deployable area in a direction dimension;
the operation unit is configured to perform mesh division on the deployable area, where each mesh area includes M base stations;
the generating unit is used for randomly generating a group of base station position combinations in the deployable area, and marking the base station position combinations as initial base station position combinations;
the combined optimization module comprises a classification unit, an objective function unit and a position updating unit; the classification unit is used for classifying all base station populations in the deployable area into two populations;
the objective function unit comprises a function which takes the initial base station position combination as an initial value, and takes the minimum GDOP, the minimum positioning root mean square error and the minimum M of the base station individual as optimization objectives;
and the position updating unit is used for respectively updating the positions of the base stations in the two populations according to an improved adaptive genetic algorithm and a Levy flight strategy algorithm, so that the combination optimization of the base stations is carried out, and the optimal base station deployment combination is obtained.
9. The system of claim 8, further comprising:
and the positioning module is used for solving the GDOP function, the positioning root mean square error function and the base station number function deployed by the base station.
10. The system of claim 8, further comprising:
the storage module is used for recording data in the initialization module, the combination optimization module and the positioning module;
and the positioning result real-time display module is used for displaying the positioning result obtained according to the combined optimization module in real time.
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