CN104469792A - AP deployment optimization method based on multi-hypothesis analysis in fingerprint positioning - Google Patents
AP deployment optimization method based on multi-hypothesis analysis in fingerprint positioning Download PDFInfo
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- CN104469792A CN104469792A CN201410834937.9A CN201410834937A CN104469792A CN 104469792 A CN104469792 A CN 104469792A CN 201410834937 A CN201410834937 A CN 201410834937A CN 104469792 A CN104469792 A CN 104469792A
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
- H04W16/18—Network planning tools
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
- H04W16/22—Traffic simulation tools or models
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W64/00—Locating users or terminals or network equipment for network management purposes, e.g. mobility management
Abstract
The invention discloses an AP deployment optimization method based on multi-hypothesis analysis in fingerprint positioning, and relates to the technical field of fingerprint positioning. An optimal AP deployment scheme is determined by comparing resolution ratios of characteristic space so that the positioning precision can be improved. The method comprises the steps that the AP number needing to be arranged in the indoor environment is determined according to the cost and the coverage capacity, all reference points are designated; the position for arranging APs are numbered; all AP deployment schemes are set out, the first deployment scheme is input a classic indoor signal propagation model to obtain a fingerprint map under the deployment scheme, and statistics is carried out on all the reference points in the fingerprint map and the characteristic space resolution ratio D1 among the adjacent reference points; the second deployment scheme is input the classic indoor signal propagation model until all the characteristic space resolution ratios D1-DK are obtained; the maximum characteristic space resolution ratio Di is selected to obtain the optimal AP deployment scheme. According to the AP deployment optimization method, the resolving ability of the fingerprint map can be improved.
Description
Technical field
The present invention relates to a kind of AP disposition optimization method, relate to fingerprint location technology field.
Background technology
The enforcement of location fingerprint location generally can be divided into two stages: the first stage is training/off-line phase, groundwork gathers each signal characteristic parameter with reference to AP position of required locating area, such as signal strength, multipath phase angle component power etc., by corresponding for an one group of finger print information specific position forming position fingerprint database.Second stage is location/on-line stage, utilizes receiver to measure the parameter of Received signal strength, adopt matching algorithm determine with database in which organizes data match, thus draw the physical location of user.When adopting location fingerprint localization method, classical positioning experiment flow chart as indicated with 1, wherein, (RP
1, RP
2..., RP
n) represent the 1 to the n-th reference point, RSS
ij(i=1 ..., n; J=1 ..., T) and represent the RSS signal phasor of a jth AP (access point, Access Point) measured at i-th reference point place.(TP
1, TP
2..., TP
m) represent the 1 to the m reference point.In positioning stage, WLAN navigation system, based on Radio Map, is needing the real-time sampling being carried out spacing wave by the wlan client of locating, and utilizes the mobile computing environment of WLAN and data transmission environments to transmit and calculating sampling data.Computational process carries out search and the location of locus mainly through the search and matching algorithm of applying specific signal space, draws the position prediction result to sampled data, completes the location of locus.
In fingerprint location process, positioning precision is subject to the impact that AP disposes, when AP is in good distribution situation, the positioning precision of system can not good far above AP distribution situation time situation.In the past lay AP time first it is considered that wlan network covering problem, as long as signal can meet coverage requirement, just not going into seriously its impact on stationkeeping ability, the position that AP lays also being yield to doors structure as far as possible, being all in the convenient position Stochastic choice laid.
Summary of the invention
The object of this invention is to provide a kind of AP disposition optimization method based on many what-ifs in fingerprint location, determine optimum AP deployment scheme, to improve positioning precision by the resolution in contrast characteristic space.
The present invention solves the problems of the technologies described above the technical scheme taked to be:
Based on an AP disposition optimization method for many what-ifs in fingerprint location, the implementation procedure of described method is:
Step one: need the AP number M laid according to cost and covering power determination indoor environment;
Step 2: delimit all reference points;
Step 3: the position that may be used for laying AP is numbered 1 ~ N;
Step 4: enumerate out by all AP deployment schemes by the method for traversal, amounts to
the situation of kind is 1 ~ K to these deployment scheme labels;
Step 5: the first deployment scheme is inputted in classical indoor signal propagation model (the indoor signal propagation model that the communications field is conventional);
Step 6: obtain the fingerprint image (obtaining by conventional art means) under this deployment scheme, all reference points and the feature space resolution D around it between neighboring reference point in statistics fingerprint image
1, feature space is Euclidean distance or cosine similarity;
Step 7: the second deployment scheme is inputted again in classical indoor signal propagation model;
Step 8: repeat step 6 to step 7, until obtain all feature space resolution D
1~ D
k;
Step 9: select D maximum in feature space resolution
i;
Step 10: then namely i-th kind of AP deployment way be the AP deployment scheme of the optimum under this indoor environment.
In step 3, the selection principle of described position is the position that is easy to set up or the position close to power supply or mesh.
The invention has the beneficial effects as follows:
The inventive method realizes when the given AP number of user, finds the optimum AP layout scheme in specific environment.Improve the resolving power of fingerprint image itself, reduce in position fixing process and cause because noise exists the probability having chosen wrong reference point.
Accompanying drawing explanation
Fig. 1 is existing WLAN fingerprint location system flow schematic diagram; Fig. 2 is the emulation platform runnable interface figure utilizing the inventive method to develop, Fig. 3 is simulated program operation result (1 represents body of wall, and 2 represent AP); Fig. 4 is the graph of relation (AP arrangement optimizes comparison diagram) between signal noise and average localization error, and in figure, abscissa represents the standard deviation of signal noise, and ordinate is average localization error.
Embodiment
As shown in figures 1-4, the implementation procedure of the AP disposition optimization method based on many what-ifs in a kind of fingerprint location described in present embodiment is:
Step one: need the AP number M laid according to cost and covering power determination indoor environment;
Step 2: delimit all reference points;
Step 3: the position that may be used for laying AP is numbered 1 ~ N; The selection principle of described position is the position that is easy to set up or the position close to power supply or mesh.
Step 4: enumerate out by all AP deployment schemes by the method for traversal, amounts to
the situation of kind is 1 ~ K to these deployment scheme labels;
Step 5: the first deployment scheme is inputted in classical indoor signal propagation model (the indoor signal propagation model that the communications field is conventional);
Step 6: obtain the fingerprint image (obtaining by conventional art means) under this deployment scheme, all reference points and the feature space resolution D around it between neighboring reference point in statistics fingerprint image
1, feature space is Euclidean distance or cosine similarity;
Step 7: the second deployment scheme is inputted again in classical indoor signal propagation model;
Step 8: repeat step 6 to step 7, until obtain all feature space resolution D
1~ D
k;
Step 9: select D maximum in feature space resolution
i;
Step 10: then namely i-th kind of AP deployment way be the AP deployment scheme of the optimum under this indoor environment.
Realize AP disposition optimization with the emulation platform developed based on said method (AP deployment analysis software) to be below illustrated.The feature space adopted in this example is Euclidean distance, does not limit the position can laying AP, and in room, any position all can lay AP.
1, module architectures: the program that emulation platform comprises and function as shown in table 1.
Table 1 AP distributes emulation platform program and major function
First construct emulation experiment environment in program, and have invoked the signal propagation model used in positioning experiment, to gather fingerprint image.Simultaneously by constantly contrasting the AP distribution situation obtaining optimum fingerprint image.
2, algorithmic descriptions
When this program can be implemented in user's given AP number, find the function of the optimum AP layout scheme in specific environment.
The basic principle finding optimum AP layout scheme is: make the Euclidean distance between neighboring reference point maximum.Euclidean distance increase between neighboring reference point can reduce the similarity between different reference point, and the resolving power of fingerprint image itself is improved, thus causes because noise exists the probability having chosen wrong reference point in reduction position fixing process.
In algorithm realization process, for finding optimum AP arrangement, needing to place AP on different positions, and gathering fingerprint image.After the placement traversal that AP may be existed, find the maximum AP arrangement of Euclidean distance between fingerprint image reference point, and return to user.
The ergodic process of AP position is the emphasis of this program.First need often to move step-length once used when testing and determining that AP travels through in scene.Then experimental situation is divided into grid according to this step-length, and from left to right, order label from top to bottom.When program starts, all n AP is all sequentially arranged in n minimum grid of the upper left corner, room label, and gathers fingerprint image, calculates and adds up the Euclidean distance between neighboring reference point.Then start last, i.e. the n-th AP mobile lattice backward, and gather fingerprint image, calculate and add up the Euclidean distance between neighboring reference point.Next again these AP mono-lattice are moved, the process of repeating above said collection and record.Circulation like this is until this AP arrives at last grid, and namely after lower right corner place grid, by its previous AP mobile grid backward, this AP then following closely, turns back to the next grid of last AP, and surveying record.Then again constantly move last AP and the average Euclidean distance of acquisition and recording.So move in circles, when an AP arrives the farthest that it can arrive, then by an an AP mobile grid backward on it, and by all AP below sequentially as after the AP be moved, in beginning next round ergodic process.Period constantly gathers fingerprint image and adds up the Euclidean distance between neighboring reference point, until all AP total movements in a last n grid.All statistical conditions are contrasted, finds out the situation that between fingerprint image reference point, Euclidean distance is maximum, and the AP distribution situation corresponding to it is returned to user.
3, operation method and result example
As shown in Figure 2, user can according to the indoor AP number of prompting desired by " AP number " place input in the upper right corner for the runnable interface of AP distributed simulation platform.After clicking " click and start " button, emulation platform is started working.Because location algorithm during two below AP cannot reliably working, therefore require that AP number must not be less than 3 here, otherwise program has miscue.
After simulation run terminates, the coordinate of AP distribution in the middle of the bottom brace of " AP position ", can be demonstrated.Every a line represents a coordinate, and a left side is x coordinate, and the right side is y coordinate.Meanwhile, map and the AP installation position of scene will be drawn out in the coordinate system of lower left quarter.Wherein thick line represents body of wall, and round dot represents AP, as shown in Figure 3.
Invention effect:
As shown in Figure 4, this experiment is in comparison system (it is not optimised that AP disposition optimization crosses &), add the noise of same degree to received signal, and carry out positioning experiment, in order to verify deployment strategy improvement situation to position error situation under noise effect.In figure, transverse axis is the standard deviation of system noise, and the longitudinal axis is average localization error.Can find out that the AP deployment scheme after optimizing can under equal conditions for navigation system improves positioning precision (from Fig. 4, positioning precision improves 1m-3m).What adopt when carrying out AP disposition optimization here is exactly the above-mentioned optimization method based on many what-ifs.
Claims (2)
1. in fingerprint location based on an AP disposition optimization method for many what-ifs, it is characterized in that: the implementation procedure of described method is:
Step one: need the AP number M laid according to cost and covering power determination indoor environment;
Step 2: delimit all reference points;
Step 3: the position that may be used for laying AP is numbered 1 ~ N;
Step 4: enumerate out by all AP deployment schemes by the method for traversal, amounts to
the situation of kind is 1 ~ K to these deployment scheme labels;
Step 5: the first deployment scheme is inputted in classical indoor signal propagation model;
Step 6: obtain the fingerprint image under this deployment scheme, all reference points and the feature space resolution D around it between neighboring reference point in statistics fingerprint image
1, feature space is Euclidean distance or cosine similarity;
Step 7: the second deployment scheme is inputted again in classical indoor signal propagation model;
Step 8: repeat step 6 to step 7, until obtain all feature space resolution D
1~ D
k;
Step 9: select D maximum in feature space resolution
i;
Step 10: then namely i-th kind of AP deployment way be the AP deployment scheme of the optimum under this indoor environment.
2. in a kind of fingerprint location according to claim 1 based on the AP disposition optimization method of many what-ifs, it is characterized in that: in step 3, the selection principle of described position is the position that is easy to set up or the position close to power supply or mesh.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104968002A (en) * | 2015-05-21 | 2015-10-07 | 哈尔滨工业大学 | Indoor positioning AP selection method based on fuzzy clustering |
| CN106211179A (en) * | 2016-09-12 | 2016-12-07 | 东南大学 | The access point deployment of a kind of wireless cloud computing system and method for channel allocation |
| CN110430523A (en) * | 2019-06-10 | 2019-11-08 | 成都理工大学 | Indoor positioning access point three-dimensional Deployment Algorithm based on WiFi fingerprint |
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| CN103596267A (en) * | 2013-11-29 | 2014-02-19 | 哈尔滨工业大学 | Fingerprint map matching method based on Euclidean distances |
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| EP2650693A2 (en) * | 2012-04-13 | 2013-10-16 | Huawei Technologies Co., Ltd. | Positioning method, device and system |
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| CN103533650A (en) * | 2013-10-28 | 2014-01-22 | 哈尔滨工业大学 | Cosine-similarity-based indoor positioning method capable of improving positioning precision |
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104968002A (en) * | 2015-05-21 | 2015-10-07 | 哈尔滨工业大学 | Indoor positioning AP selection method based on fuzzy clustering |
| CN104968002B (en) * | 2015-05-21 | 2019-01-08 | 哈尔滨工业大学 | Indoor positioning AP selection method based on fuzzy clustering |
| CN106211179A (en) * | 2016-09-12 | 2016-12-07 | 东南大学 | The access point deployment of a kind of wireless cloud computing system and method for channel allocation |
| CN106211179B (en) * | 2016-09-12 | 2019-11-26 | 东南大学 | A kind of access point deployment and method for channel allocation of wireless cloud computing system |
| CN110430523A (en) * | 2019-06-10 | 2019-11-08 | 成都理工大学 | Indoor positioning access point three-dimensional Deployment Algorithm based on WiFi fingerprint |
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Application publication date: 20150325 |