CN108834058B - Indoor positioning signal source optimized deployment method based on genetic and firework combined algorithm - Google Patents
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Abstract
The invention relates to the field of indoor positioning, in particular to an indoor positioning signal source optimized deployment method based on a genetic and firework combined algorithm. The invention provides the optimized deployment of the indoor positioning signal source, which is more suitable for the optimized deployment of the indoor positioning signal source, and has the advantages of more reasonable deployment and lower cost. The deployment method is a simulation method, and a signal source deployment scheme which can not only meet low cost but also enable positioning errors to be low is realized by simulating information such as buildings, attributes of the buildings, a signal source model and the like and combining a genetic algorithm and a firework algorithm.
Description
Technical Field
The invention relates to the field of indoor positioning, in particular to a solution method for multi-factor optimization of comprehensive signal source positioning error and signal source cost.
Background
Currently, positioning technologies are divided into indoor positioning and outdoor definition. The outdoor positioning is common, and a Global Positioning System (GPS) and a domestic positioning system Beidou which are frequently contacted by people belong to outdoor positioning technologies. The outdoor positioning mainly achieves the purpose of accurate positioning by a receiver receiving radio signals transmitted by satellites for correlation calculation. The outdoor positioning technology plays a significant role in the fields of navigation, measurement, aerospace and the like, and becomes an indispensable leading-edge technology in the daily life and the technological development of human beings. Since the outdoor positioning technology performs positioning through satellites, the requirement on time accuracy is very strict, but in the process of radio signal propagation, the outdoor positioning technology is affected by factors such as an air ionosphere and obstacles, and thus errors are generated. If positioning operation is required in a large building, the radio transmitted by the satellite may cause measurement deviation due to obstacles, and thus the positioning effect is affected.
The indoor positioning technology mainly comprises an LED positioning technology, a WI-FI positioning technology, a Bluetooth indoor positioning technology and the like. For the indoor positioning technology in the current industry, no matter which technology is adopted, a base station needs to be deployed in advance to transmit and receive signals. Traditional base station deployment is too much dependent on experience, so that cost reduction is not maximized, and base station deployment in the mode is not always standard in deployment, and cost waste is caused if the deployment is too dense; if the deployment is sparse, the positioning effect is affected. Therefore, how to achieve a balance between the cost and the positioning effect to achieve optimal deployment cost and signal effect weighting is an urgent problem to be solved.
An indoor positioning signal source optimization deployment scheme aims to find a signal source combination with a low positioning error under multi-factor constraint.
In the existing indoor positioning signal source optimized deployment scheme, the more traditional scheme is realized based on position coordinates, such as uniform coverage, maximum and minimum coverage, Clary-Lao and the like. However, these schemes completely depend on the position coordinates of the signal source point, and cannot actually consider the factors such as the influence of different environments of the signal source point on the signal, so that using such an optimized deployment scheme is easily restricted by the factors such as the environment and quality of the signal source, and in addition, the optimized deployment of the signal source is likely to be limited by considering the conditions such as the cost, and all the conventional optimized deployment schemes based on the position coordinates have obvious shortages.
When optimized deployment is performed, if low signal source cost (i.e., the number is as small as possible) and small positioning error are simultaneously required, an optimization algorithm is required to solve the problems. Genetic algorithms have been used in the field of indoor positioning signal source optimization deployment so far, but not only may be in local optimization, but also if the indoor signal sources are combined too many, a large number of invalid solutions may be generated, thereby affecting the efficiency of solution. Meanwhile, the particle swarm search is not wide enough, and is easy to fall into local optimum. Although the artificial immunity can solve the problem of global search to a certain extent, the optimal solution is not easy to find due to insufficient search depth.
Disclosure of Invention
Aiming at the technical problems existing in the field of indoor positioning signal source optimization deployment at present, the invention aims to provide a method capable of solving the problems of signal source positioning error and signal source cost to find an optimal solution.
The invention provides the optimized deployment of the indoor positioning signal source, which is more suitable for the optimized deployment of the indoor positioning signal source, and has the advantages of more reasonable deployment and lower cost. The deployment method is a simulation method, and a signal source deployment scheme which can not only meet low cost but also enable positioning errors to be low is realized by simulating information such as buildings, attributes of the buildings, a signal source model and the like and combining a genetic algorithm and a firework algorithm.
In the above scenario, in order to achieve the purpose of signal source optimization deployment, the following technical scheme is adopted in the invention:
an indoor positioning signal source optimized deployment method based on a genetic and firework combined algorithm is characterized by comprising the following steps
Step 1, randomly generating a signal source combination by using a shuffling method;
step 2, respectively calculating the positioning errors of different signal source combinations
Step 3, performing a selection process on the combination meeting the requirement of the fitness function, and setting an explosion factor for the good variety by using a firework algorithm for use in the following steps;
step 4, supplementing signal source combination to the population according to the explosion factors of the fine varieties;
step 5, performing cross operation on the good varieties;
and 6, performing mutation operation on the excellent variety.
In the above indoor positioning signal source optimized deployment method based on the genetic and firework combination algorithm, the shuffling algorithm used in step 1 randomly initializes the signal source combination, and includes the following steps:
step 1.1, for each signal source combination in the initialized population, the signal sources are disordered by using a shuffling method once, and then a data structure for storing the signal source combinations is assigned. Until all signal source combinations in the population are initialized.
In the above indoor positioning signal source optimized deployment method based on the genetic and firework combination algorithm, the positioning error is calculated by comparing different signal sources in the signal source combination with data in the fingerprint database in step 2, which includes the following steps:
step 2.1, designing a signal source fingerprint library, wherein the content of the fingerprint library is that each single signal source receives signal intensity values from other signal sources;
and 2.2, calculating a signal source combination of the positioning error according to the requirement, and calculating the deviation degree of the position of the test point and the actual coordinate under the environment, namely the positioning error.
In the above indoor positioning signal source optimized deployment method based on the genetic and firework combination algorithm, in step 3, a signal source combination smaller than the value of the fitness function value is selected, and an explosion factor is set for the selected signal source combination of the good variety according to the superiority, including the following steps:
and 3.1, comparing the fitness function value of the signal source combination with a preset fitness function threshold value, reserving the signal source combination meeting the requirements, and eliminating the signal source combination which does not meet the requirements, wherein the signal source combination meeting the requirements is a good variety.
And 3.2, setting a fitness function value in the selection process, and using a dynamic adjustment strategy. When the signal source combination meeting the fitness function value is more than the set number, the preset fitness function value is reduced to 90% of the original value, and when the signal source combination meeting the fitness function value is less than the set number, the preset fitness function value is increased by 15%.
And 3.3, sequencing according to the adaptability values of the signal source combinations, and then sequentially setting explosion factors for the good varieties, wherein the better the adaptability value is, the smaller the explosion factor is.
In the above indoor positioning signal source optimized deployment method based on the genetic and firework combination algorithm, the supplements in step 4 are divided into two types, one is a signal source combination generated according to an explosion factor, and the other is a signal source combination generated randomly, and the method includes the following steps:
and 4.1, generating a new signal source combination by copying the combination to the signal source combination to which the explosion factor belongs according to the explosion factor according to a set proportion, and generating signal source points with the explosion factor different from the excellent variety in the newly generated combination (namely, generating the signal source points in a random generation mode according to the size of the explosion factor and ensuring that the signal source points do not appear in the original combination), so that the new signal source combination is generated according to the explosion factor.
And 4.2, randomly generating the vacant signal source combination in the population, wherein the random generation step is as follows: for vacant signal source combinations, the signal sources are shuffled once and then assigned to a data structure for storing the signal source combinations. Until the initialization of these vacant signal source combinations is completed.
In the above indoor positioning signal source optimized deployment method based on the genetic and firework combination algorithm, the step 5 of performing cross exchange operation on different good varieties includes the following steps:
step 5.1, randomly selecting two groups of signal source combinations in good varieties according to a preset cross proportion;
and 5.2, respectively selecting proper signal source points from the selected signal source combinations to carry out cross exchange.
And 5.3, repeating the steps 5.1 to 5.2 until the iteration number is up to the maximum iteration number (such as 80 times).
In the above indoor positioning signal source optimized deployment method based on the inheritance and firework combination algorithm, the variation operation is performed on the good variety in step 6, and the method comprises the following steps:
6.1, randomly selecting a group of signal source combinations in the fine varieties according to a preset variation proportion;
and 6.2, randomly changing n signal source points in the selected signal source combination.
The invention has the following advantages and positive effects: 1. the invention uses indoor positioning as background, and uses the combination of genetic algorithm and firework algorithm to solve the problems of low signal source positioning error and low signal source number cost. This is a solution to the optimization problem provided in the new application field. 2. The invention sets a dynamic adjustment strategy for the fitness function threshold in the selection process of the genetic algorithm. When the number of signal source combinations meeting the fitness function threshold is larger than a certain value, the preset fitness function threshold is properly reduced, and when the number of signal source combinations meeting the fitness function threshold is less, the preset fitness function threshold is properly increased. Since the traditional genetic algorithm is a fixed threshold value for setting the fitness function value, certain trouble is brought when the good varieties are too much or too few. When the excellent varieties are excessive, the signal source combination may fall into a local optimal state, and the global searching capability is insufficient; when the number of good varieties is too small, the influence on the selection and mutation process may not be enough. 3. In the invention, the firework algorithm explosion factor is added in the traditional genetic algorithm, and the condition that the genetic algorithm is easy to generate a large amount of invalid solutions in a large project is provided. In the project, the population supplement stage regularly supplements a new signal source combination through explosion factors, and invalid solutions are removed to a great extent. 4. The invention provides an analog simulation technology, which gets rid of the mode of deploying signal sources by depending on a large amount of experience in the traditional technology and provides a more reasonable and scientific signal source deployment scheme.
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FIG. 1 is a flowchart of an indoor positioning signal source deployment optimization method based on a genetic algorithm and a firework algorithm provided by the invention.
Fig. 2 is a schematic view of the parking lot in the present embodiment.
Detailed Description
The invention is further illustrated by the following specific examples in conjunction with the accompanying drawings:
in the experimental scenario, as shown in the following figure, 107 iBeacon signal sources are arranged in a matrix rule form, and the distance between a row and a column is about 4.5 meters.
1. Random generation of signal source combinations using shuffling algorithms
In the indoor positioning project, the number of signal sources is 107, and the signal sources are numbered by colleagues. The population refers to the number of signal source combinations, and the size of the population is set to be 50 in the invention.
When the population is initialized, for a single signal source combination, the number (n) of signal sources in the signal source combination is randomly generated, and then signal source points are generated according to the number of the signal sources. The shuffling method is used to disorder 107 signal source serial numbers, then the first n signal sources are selected, namely the random initialization work of one signal source combination is completed, and when the steps are circulated for 50 times, the initialization of the population is completed.
2. Calculating positioning error for different signal source combinations
Before error calculation, reference points are preset for the scene, the reference points collect the signal intensity of 107 signal source points, and the collected result is stored in a fingerprint library. The signal strength of 107 signal sources collected by a reference point is stored in a one-dimensional vector called a fingerprint, and the vectors generated by all the reference points constitute a fingerprint library. Subsequently, when the signal source combination is introduced into the error calculation function as a function parameter, the reference point receives the signal values of 107 signal sources in the parking lot (if a certain signal source is not in the combination, the signal value of the point is 0), the received row vector of 1 row and 107 columns is compared with each fingerprint in the fingerprint library, the euclidean distance between the received row vector and each fingerprint is calculated, 3 fingerprints with the minimum difference are found, the difference is w1, w2 and w3 respectively, and then the coordinates of the reference points corresponding to the 3 fingerprints are obtained, and are respectively: (x1, y1), (x2, y2), (x3, y3), the position coordinates are estimated by the following formula:
x＝((x(1)/w(1))+(x(2)/w(2))+(x(3)/w(3)))/((1/w(1))+(1/w(2))+(1/w(3)))；
y＝((y(1)/w(1))+(y(2)/w(2))+(y(3)/w(3)))/((1/w(1))+(1/w(2))+(1/w(3)))；
the formula can better reflect the inverse relation between the accurate position coordinate and the distance, and the larger the distance between the accurate position coordinate and the fingerprint is, the larger the positioning error is. And finally, comparing the calculated reference point coordinates with the actual coordinates, and calculating the distance between the reference point coordinates and the actual coordinates, wherein the distance is the size of the positioning error. This completes the calculation of a reference point error. And sequentially carrying out the calculation process on all indoor reference points, and averaging the obtained positioning errors after the calculation is finished to obtain the average positioning error of the signal source combination.
3. The combination meeting the requirement of fitness function is selected, and simultaneously, the firework algorithm is utilized to set explosion factors for good varieties
The invention relates to a selection process of signal source combination. The above-mentioned positioning error calculation is first performed for different signal source combinations,
and then combining the signal source cost to form a fitness function value. The specific formula is as follows:
evaluation value is the signal source combined positioning error plus unit price
After the combined fitness function value of each signal source is calculated, a selection process is carried out. In the selection process, signal source combinations with the fitness value smaller than a preset threshold value are screened out, and signal source combinations with the fitness value larger than the value are eliminated.
The selected signal source combination is called as a good variety. For good breeds, they are set with a corresponding explosion factor at this stage. Before setting the explosion factors, the good varieties are sorted in the order of the evaluation values from small to large (the smaller the evaluation value is, the better the signal source combination is).
After the operation is finished, according to the signal source combinations which are selected and sorted from small to large according to the evaluation value, the explosion factors are sequentially set, wherein the explosion factors are i +3(i represents the ith signal source combination after sorting), and thus the setting work of the explosion factors is finished.
4. According to the explosion factor of the fine variety, the population is supplemented with signal source combination
The invention adds the explosion factor of the firework algorithm into the genetic algorithm, which is different from the pure genetic algorithm. The pure genetic algorithm population supplementation process is performed after the generation of cross variation is completed, and the population supplementation process of the combined algorithm is performed after the selection process. The reason for this is that if the selection process is completed and then the crossing and mutation process is performed, the good varieties may be degraded, and then population supplementation is performed, and new signal source combination generation is performed according to the previously set genetic factors, and the new signal source combination will not achieve the expected effect.
The population supplementing process is divided into two parts, one part is to generate a new signal source combination according to the explosion factor, and the other part is to randomly generate the signal source combination. The generation of new signal source combination according to explosion factors is to solve the problem that a large number of invalid solutions occur in the random generation process, and a part of random generation methods are still adopted to generate partial good solutions without losing the capability of global search in the population supplement stage.
And generating a new signal source combination according to the explosion factor. And copying the excellent signal source combination corresponding to the explosion factor, then selecting i +3 signal source points in the new combination according to the explosion factor, and changing the signal source points into signal source points which do not appear in the combination, thereby forming a new signal source combination. And repeating the operations until the signal source combination is generated according to the explosion factors.
A new combination of signal sources is randomly generated. For this portion, a combination of signal sources is generated, initialized as described above for the random population. The number of signal source points in the signal source combination is randomly generated, and then a new signal source combination is obtained by using a shuffling method according to the number of the signal source combinations.
5. Performing 'cross' operation on good varieties
In the present invention, the object to be processed in the crossover process is the good variety selected in the selection process, but the top 10% of the good variety is removed and is called "top-quality". Because these boutiques may be the final desired optimal solutions, boutiques are not considered when performing the crossover operation. Crossover operators, i.e. the probability of performing crossover operations. The crossover operation is specifically as follows:
when the probability requirement of the crossover operator is met, two groups of different signal source combinations are selected by using a random number algorithm and marked as a combination A and a combination B. A plurality of signal source points are selected from each signal source combination, such as a1, a2.. ang., B1, and B2.. ang., and the selected signal source points must satisfy the condition that the signal source points do not exist in another signal source combination, because if the signal source points in another signal source combination are crossed and interchanged, the situation that two identical signal source points exist in one combination occurs. And when the combination AB selects the corresponding signal source point, performing signal source point cross switching operation. Repeating the above steps 10 times to complete the cross operation between signal source combinations.
6. Performing mutation operation on the good variety
In the invention, the object of the process is still the excellent variety selected in the selection process, and the excellent variety is not considered. Mutation operator, i.e. the probability of a mutation operation occurring. The mutation operation is specifically as follows:
when the probability requirement of a mutation operator is met, a signal source combination A is randomly selected from the signal source combinations, then a plurality of signal source points are selected from the signal source combination A, and the signal source points are changed into signal source points which do not exist in the combination. Repeating the above operation 10 times to complete the variation operation of the signal source combination.
Experimental results show that the technical scheme can provide an effective solution for the field of indoor positioning signal source optimized deployment, and the scheme can effectively reduce the positioning error of the signal source on the basis of considering the signal source cost. Compared with the existing indoor positioning signal source deployment optimization scheme and the traditional signal source deployment scheme based on the position coordinates, the indoor positioning signal source deployment optimization scheme has better effectiveness and scientificity.
The above embodiments are provided only for illustrating the present invention and not for limiting the present invention, and those skilled in the art can make various changes or modifications without departing from the spirit and scope of the present invention, and therefore all equivalent technical solutions are within the scope of the present invention.
Claims (6)
1. An indoor positioning signal source optimized deployment method based on a genetic and firework combined algorithm is characterized by comprising the following steps
Step 1, randomly generating a signal source combination by using a shuffling method;
step 2, respectively calculating the positioning errors of different signal source combinations
Step 3, performing a selection process on the combination meeting the requirement of the fitness function, and setting an explosion factor for the good variety by using a firework algorithm for use in the following steps; the method comprises the following steps of selecting a signal source combination smaller than a fitness function value through the fitness function value, and setting an explosion factor for the selected signal source combination of the excellent variety according to the superiority, wherein the method comprises the following steps:
step 3.1, the fitness function value of the signal source combination is compared with a preset fitness function threshold value, the signal source combination meeting the requirements is reserved, the signal source combination not meeting the requirements is eliminated, and the signal source combination meeting the requirements is a good variety;
step 3.2, setting a fitness function value in the selection process, and using a dynamic adjustment strategy; when the signal source combination meeting the fitness function value is more than the set number, reducing the preset fitness function value to 90% of the original value, and when the signal source combination meeting the fitness function value is less than the set number, increasing the preset fitness function value by 15%;
3.3, sequencing according to the fitness value of the signal source combination, and then sequentially setting explosion factors for the good varieties, wherein the better the fitness value is, the smaller the explosion factor is;
step 4, supplementing signal source combination to the population according to the explosion factors of the fine varieties;
step 5, performing cross operation on the good varieties;
and 6, performing mutation operation on the excellent variety.
2. The indoor positioning signal source optimized deployment method based on the genetic and firework combination algorithm, as claimed in claim 1, wherein the shuffling algorithm used in step 1 randomly initializes the signal source combination, comprising the following steps:
step 1.1, for each signal source combination in the initialized population, disordering the signal sources by using a one-time shuffling method, and then storing and assigning a value to a data structure for storing the signal source combination; until all signal source combinations in the population are initialized.
3. The indoor positioning signal source optimized deployment method based on the inheritance and firework combination algorithm as claimed in claim 1, wherein the positioning error is calculated in the step 2 by comparing different signal source points in the signal source combination with data in a fingerprint database, comprising the following steps:
step 2.1, designing a signal source fingerprint library, wherein the content of the fingerprint library is that each single signal source receives signal intensity values from other signal sources;
and 2.2, calculating a signal source combination of the positioning error according to the requirement, and calculating the deviation degree of the position of the test point and the actual coordinate under the current environment, namely the positioning error.
4. The indoor positioning signal source optimized deployment method based on the genetic and firework combination algorithm as claimed in claim 1, wherein the supplements in step 4 are divided into two types, one type is a signal source combination generated according to an explosion factor, and the other type is a randomly generated signal source combination, and the method comprises the following steps:
step 4.1, according to the set proportion, according to the explosion factor, generating a new signal source combination by copying the combination to the signal source combination to which the explosion factor belongs, and generating signal source points with explosion factors different from the excellent variety in the newly generated combination, thereby forming a new signal source combination generated according to the explosion factor;
and 4.2, randomly generating the vacant signal source combination in the population, wherein the random generation step is as follows: for vacant signal source combinations, a one-time shuffling method is used for disordering the signal sources, and then values are stored and assigned to a data structure for storing the signal source combinations; until the initialization of these vacant signal source combinations is completed.
5. The indoor positioning signal source optimized deployment method based on the genetic and firework combination algorithm as claimed in claim 1, wherein the step 5 of performing cross interchange operation on different good varieties comprises the following steps:
step 5.1, randomly selecting two groups of signal source combinations in good varieties according to a preset cross proportion;
step 5.2, respectively selecting proper signal source points from the selected signal source combinations for cross exchange;
and 5.3, repeating the steps 5.1 to 5.2 until the iteration number reaches the maximum iteration number.
6. The indoor positioning signal source optimized deployment method based on the inheritance and firework combination algorithm as claimed in claim 1, wherein the performing of mutation operation on the good varieties in the step 6 comprises the following steps:
6.1, randomly selecting a group of signal source combinations in the fine varieties according to a preset variation proportion;
and 6.2, randomly changing n signal source points in the selected signal source combination.
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