CN109116299B - Fingerprint positioning method, terminal and computer readable storage medium - Google Patents
Fingerprint positioning method, terminal and computer readable storage medium Download PDFInfo
- Publication number
- CN109116299B CN109116299B CN201710486648.8A CN201710486648A CN109116299B CN 109116299 B CN109116299 B CN 109116299B CN 201710486648 A CN201710486648 A CN 201710486648A CN 109116299 B CN109116299 B CN 109116299B
- Authority
- CN
- China
- Prior art keywords
- fingerprint
- measurement report
- value
- report information
- positioning
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S5/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
- G01S5/02—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves
- G01S5/0252—Radio frequency fingerprinting
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W24/00—Supervisory, monitoring or testing arrangements
- H04W24/10—Scheduling measurement reports ; Arrangements for measurement reports
-
- 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
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D30/00—Reducing energy consumption in communication networks
- Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks
Landscapes
- Engineering & Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Collating Specific Patterns (AREA)
- Image Analysis (AREA)
Abstract
The invention discloses a fingerprint positioning method, a terminal and a computer readable storage medium, wherein the method comprises the following steps: acquiring measurement report information of a terminal to be positioned; extracting effective characteristic information values in measurement report information; the effective characteristic information value is used as input and is transmitted to a neural network model for fitting, and then positioning coordinate information is output; the establishment of the neural network model comprises the following steps: optimizing fingerprint data in a fingerprint database through a genetic algorithm; and training through the neural network positioning model to obtain the neural network model. The terminal includes a computer program stored on a memory and executable on a processor for implementing a fingerprint locating method of an embodiment of the invention. The computer readable storage medium stores a fingerprint positioning program which when executed by a processor implements a fingerprint positioning method of an embodiment of the present invention. The fingerprint positioning method and the fingerprint positioning system can solve the problems of fuzzy fingerprint database, insufficient positioning precision and the like in the existing fingerprint positioning algorithm.
Description
Technical Field
The present invention relates to the field of fingerprint positioning technologies, and in particular, to a fingerprint positioning method, a terminal, and a computer readable storage medium.
Background
In recent years, with the advent of the mobile internet and the rapid popularization of smart phone terminals, people's lifestyle and behavior habits are silently changing greatly. People inevitably contact the internet, and people routinely find restaurants, banks, takeouts, even friends, etc. in daily life through location-based services (Location Based Service, LBS). In addition, there are many life safety related scenes in which the need for location information is becoming more and more urgent, such as nursing of patients in hospitals, rescue of trapped people by rescue workers, safety monitoring of underground coal mines, and the like. The practical scenes provide wide market prospect and development space for LBS and application thereof.
With the rapid popularization of 3G/4G networks, the realization of real-time status of terminals using mobile communication networks has also gained increasing attention. The method comprises the steps of collecting measurement report (Measurement Report, MR) data reported by all terminal users of a mobile network, testing and analyzing received information intensity (Received Signal Strength, RSS) of all reference points in a positioning area, extracting signal characteristics of an RSS signal, storing the signal characteristics after abnormal values are removed and position coordinates of the corresponding reference points into a position fingerprint database, obtaining signal characteristics of a to-be-positioned point by the same method, and matching according to a certain matching algorithm and the position fingerprint database, so that an estimated position of the to-be-positioned point is obtained. However, the existing positioning algorithm has the defects of fuzzy position of a fingerprint database, low positioning precision and the like, so that the novel fingerprint positioning algorithm is particularly necessary.
Disclosure of Invention
In view of the above, the present invention aims to provide a fingerprint positioning method, a terminal, and a computer readable storage medium, so as to solve the problems of fuzzy fingerprint database position and low positioning accuracy.
The technical scheme adopted by the invention for solving the technical problems is as follows:
according to one aspect of the present invention, there is provided a fingerprint positioning method comprising:
acquiring measurement report information of a terminal to be positioned;
extracting effective characteristic information values in the measurement report information;
the effective characteristic information value is used as input and is transmitted to a neural network model for fitting, and then positioning coordinate information is output;
the establishment of the neural network model comprises the following steps:
collecting measurement report information in a fingerprint positioning area and generating a fingerprint database according to the measurement report information;
optimizing fingerprint data in the fingerprint database through a genetic algorithm;
and training and obtaining the neural network model through the neural network positioning model.
In one possible design, the valid characteristic information value includes at least one of a TA value, an RSRP value.
In one possible design, the capturing measurement report information within the fingerprint location area and generating a fingerprint database therefrom includes:
performing network rasterization on the fingerprint positioning area to obtain a plurality of grids;
acquiring all service cells contained in the measurement report information in each grid;
calculating an RSRP value and a TA value of the serving cell in the grid;
and storing the RSRP value and the TA value of all the service cells corresponding to the grid in the grid into the fingerprint database as fingerprint data.
In one possible design, the calculating the RSRP value and the TA value of the serving cell on the grid includes:
judging whether the number of the measurement report information corresponding to the service cell is one;
if yes, the RSRP value and the TA value in the measurement report information are used as the RSRP value and the TA value of the serving cell in the grid;
if not, acquiring all the measurement report information corresponding to the service cell;
calculating the Manhattan distance from the measurement report information to the central point of the grid according to the AGPS value in the measurement report information;
and selecting the first 2 or 3 pieces of measurement report information with the minimum Manhattan distance, and calculating the RSRP value of the serving cell in the grid through a WKNN algorithm.
In one possible design, the calculating the RSRP value and the TA value of the serving cell on the grid includes: and averaging the TA values of all the measurement report information corresponding to the service cell to obtain the TA value of the service cell in the grid.
In one possible design, the optimizing fingerprint data in the fingerprint database by a genetic algorithm includes:
determining a target optimization function;
and obtaining optimal fingerprint data through initialization, cross operation, mutation operation and selection operation.
In one possible design, after the obtaining the measurement report information of the terminal to be located, the method further includes:
and judging whether the measurement report information meets a preset rule, if so, updating the measurement report information to the fingerprint database.
In one possible design, the predetermined rule includes that the time since the last update of the fingerprint database reaches a predetermined threshold.
In one possible design, training the neural network model through the neural network positioning model includes:
constructing a neural network positioning model and initializing network parameters;
forward calculation is carried out through initialized learning parameters and input data, so that a predicted value is obtained;
comparing the predicted value with a preset expected value, and judging whether the difference value of the predicted value and the expected value is smaller than a preset threshold value;
if not, the network parameters are adjusted to reduce the cross entropy of the two, and iteration is carried out for a plurality of times until the entropy value is stable.
According to another aspect of the present invention, there is provided a terminal including: the fingerprint positioning method comprises the steps of a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the computer program is executed by the processor.
According to another aspect of the present invention, there is provided a computer readable storage medium having stored thereon a fingerprint positioning program which when executed by a processor implements the steps of the fingerprint positioning method provided by the embodiment of the present invention.
The fingerprint positioning method, the terminal and the computer readable storage medium of the embodiment of the invention utilize the self-adaptive optimization and population iteration characteristics of the genetic algorithm so as to optimize the accuracy of the fingerprint database; by establishing the BP neural network model, the learning of the nonlinear relation between the fingerprint signal space and the physical position space can be realized, so that the positioning accuracy is optimized.
Drawings
FIG. 1 is a flowchart of a fingerprint positioning method according to an embodiment of the present invention;
FIG. 2 is a flowchart illustrating a fingerprint database generating step according to an embodiment of the present invention;
FIG. 3 is a diagram illustrating generation of a fingerprint database based on the WKNN algorithm in accordance with an embodiment of the present invention;
FIG. 4 is a flowchart of a fingerprint database optimization algorithm based on a genetic algorithm according to an embodiment of the present invention;
FIG. 5 is a diagram of a training process of a neural network positioning model according to an embodiment of the present invention;
FIG. 6 is a hierarchical view of a neural network positioning model according to an embodiment of the present invention;
the achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
In order to make the technical problems, technical schemes and beneficial effects to be solved more clear and obvious, the invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
As shown in fig. 1, a fingerprint positioning method provided in a first embodiment of the present invention includes:
s101, acquiring measurement report information of a terminal to be positioned;
s102, extracting effective characteristic information values in the measurement report information;
in general, the valid characteristic information refers to characteristic information capable of distinguishing a plurality of measurement report information, and the valid characteristic information value includes at least one of a TA value, an RSRP (Reference Signal Receiving Power, reference signal received power) value; of course, the method can also comprise effective characteristic information values besides TA values and RSRP values, such as neighbor information and the like, which is not limited by the invention; if the RSRP report value is smaller than or equal to the cell of the RSRP minimum receiving power threshold (-140 dBm), the RSRP value is no longer used as the effective characteristic information value; because the reported value of the RSRP which is smaller than the receiving power threshold is expressed by 0, the reported value cannot be used as an accurate characterization value of the MR;
s103, taking the effective characteristic information value as input, transmitting the effective characteristic information value to a neural network model for fitting, and then outputting positioning coordinate information;
after the effective input information is obtained, the output coordinate position information can be obtained through fitting of a positioning network model;
the establishment of the neural network model comprises the following steps:
s111, collecting measurement report information in a fingerprint positioning area and generating a fingerprint database according to the measurement report information;
s112, optimizing fingerprint data in the fingerprint database through a genetic algorithm;
s113, training through a neural network positioning model to obtain the neural network model.
On the basis of the corresponding embodiment of fig. 1, as shown in fig. 2, the collecting measurement report information in the fingerprint positioning area and generating a fingerprint database according to the measurement report information includes:
s201, performing network rasterization on the fingerprint positioning area to obtain a plurality of grids;
in the implementation, the SIZE grid_size of the GRID point of the fingerprint database can be determined first, too large selection of the GRID point can cause poor positioning accuracy, too small selection can cause too large fingerprint database, and the method is not helpful for improving positioning accuracy. From the results obtained by simulation using actual outfield data, the GRID point SIZE is 10m×10m (i.e., grid_size is 10 m) in this embodiment;
s202, obtaining all the service cells contained in the measurement report information in each grid;
s203, calculating an RSRP value and a TA value of the serving cell in the grid;
more specifically, it may be first determined whether the number of measurement report information corresponding to the serving cell is one;
if yes, the RSRP value and the TA value in the measurement report information are used as the RSRP value and the TA value of the serving cell in the grid;
if not, acquiring all the measurement report information corresponding to the service cell; calculating the Manhattan distance from the measurement report information to the central point of the grid according to the AGPS value in the measurement report information; selecting the first 2 or 3 pieces of measurement report information with the minimum Manhattan distance, and calculating the RSRP value of the serving cell in the grid through a WKNN algorithm;
as shown in fig. 3, that is, for each grid 301, a list of serving cells included in all MRs falling within the grid 301 is acquired, and Manhattan distances from points corresponding to all measurement report information to the center point 303 of the grid are calculated from the first serving cell of the list. The Manhattan distance and the RSRP report value of the point 302 corresponding to the first 3 measurement report information with the shortest distance are taken, and the RSRP value Rc of the cell ID in the grid is calculated by adopting WKNN (weight K neighbor algorithm) with k=3,
where k=3, d i For the Manhattan distance of the first 3 MR points, R i For the RSRP value corresponding to the first 3 MR points with the shortest distance, R C Is the RSRP value of the center point 303 of the grid. If the number of records of MR in the grid is less than 3 for a certain cell ID, then the process is as follows: only 1 record, the reported RSRP value is the RSRP value of the grid point, and 2 records are used to calculate the RSRP value (k=2 in the formula) as well.
Meanwhile, the TA values of all the measurement report information corresponding to the serving cell can be averaged to be used as the TA value of the serving cell in the grid;
s204, storing the RSRP value and TA value of all the service cells corresponding to the grids in the grids into the fingerprint database as fingerprint data.
In the fingerprint database construction process, if MR data of all time periods are built into the database, the process may take a relatively long time, and positioning cannot be performed during the time period. Therefore, on the basis of any one of the above embodiments, after the obtaining the measurement report information of the terminal to be located, the method further includes:
and judging whether the measurement report information meets a preset rule, if so, updating the measurement report information to the fingerprint database. The preset rule comprises that the time from the last update of the fingerprint database reaches a preset threshold value; the preset threshold is, for example, 12h or 24h. In the implementation, a window can be set, a fingerprint database with relatively poor precision is generated according to the data in the current window, and when the MR data of the next window arrives, the fingerprint database can be continuously updated, so that a better fingerprint database is obtained. Here we set the time window to 24h.
If the fingerprint database data is updated and any one of the distances of the 3 measurement report information newly obtained by the serving cell of a certain grid is smaller than one of the 3 distance values in the fingerprint database, replacing the value in the fingerprint database with the new distance value and the new RSRP report value, and recalculating the RSRP value of the new grid by using the WKNN algorithm in step 203. And adding all TA values corresponding to a certain cell of the grid to the sum of the TA values in the database, and solving the average value to be used as the updated TA value.
If a certain grid has a new service cell report, storing the RSRP value of the grid corresponding to the new cell ID, the distance values of 3 MR with the shortest Manhattan distance and the RSRP report value into the corresponding position of the fingerprint database, solving the average value of all TAs corresponding to the new cell, and storing the average value into the corresponding position of the database.
In fact, the generated basic fingerprint database has the problem of fingerprint ambiguity, i.e. several different grid points have the same fingerprint data. The embodiment of the invention adopts a genetic algorithm to optimize the ambiguity of the fingerprint space. More specifically, on the basis of the corresponding embodiment of fig. 1, the optimizing fingerprint data in the fingerprint database by a genetic algorithm includes: determining a target optimization function; and obtaining optimal fingerprint data through initialization, cross operation, mutation operation and selection operation.
Determining a target optimization function of the fingerprint space:
f＝ωP(d＜d 0 )
wherein ω is a weight factor, P (d < d) 0 ) For the fingerprint fuzzy area smaller than the error distance d 0 Error probability of (2);
and obtaining the optimal fingerprint data through initialization, cross operation, mutation operation and selection operation. The specific algorithm flow is shown in fig. 4:
s401, determining a target optimization function of a fingerprint space;
s402, setting parameters, initializing population, and x= (x) 1 ，x 2 ，……，x N ) T ；
S403, calculating the fitness: i=1;
s404, judging whether n is larger than G; if yes, as shown in step S405, the process is ended; if not, go to step S406;
wherein G is the superposition times;
s406, judging whether i is larger than N; if yes, go to step S407, let n=n+1; then returns to step S403; if not, go to step 408;
wherein N is the population number;
s408, performing crossover operation and mutation operation to generate new offspring individual
S409, selecting and judging
Whether or not to establish; if yes, the individual is updated to let +.>
If not, the original individual is maintained as shown in step S411;
s412, let n=n+1, and return to step S406.
On the basis of the corresponding embodiment of fig. 1, as shown in fig. 5, the training to obtain the neural network model through the neural network positioning model includes:
s501, constructing a neural network positioning model, and initializing network parameters;
from the point of view of statistical learning, the method of locating by fingerprints can be regarded as a regression problem. The positioning problem can be regarded as a nonlinear regression problem because of the nonlinear relationship between the fingerprint signal space and the position space. The fingerprint positioning method based on machine learning is to convert the fingerprint positioning problem into the learning problem of the nonlinear relation between the off-line fingerprint database and the physical position, and the input signal space information is converted into the corresponding position space to be output by establishing a model, so that the positioning is realized. The fingerprint positioning model based on the BP neural network mainly comprises an input layer, a plurality of hidden layers and an output layer, and is a specific model layer. Referring to fig. 6, fig. 6 is a layer diagram of a neural network positioning model, in this embodiment, the neural network positioning model includes four layers, i.e. l=0, l=1, l=2, l=3, corresponding to the input layer n and the hidden layer n respectively 1 Hidden layer n 2 An output layer m; in this embodiment, the hidden layer is 2 layers, but the hidden layer may be 1 layer, 3 layers, 5 layers, etc., and the specific layer number may be set according to the requirement; in particular, when the measurement report information MR is input, n original features x are extracted therefrom 1 ，x 2 ……x n To input layer n; through the network weight W 1 ij Generating a first feature to hidden layer n 1 Then, the network weight W is used 2 ij Generating a second feature to the hidden layer n 2 Finally, through the network weight W 3 ij Generating a third feature to the output layer m; typically, the number of original features is less than the number of first features or second features; the number of the original features is not more than the number of the third features;
s502, performing forward calculation through initialized learning parameters and input data to obtain a predicted value;
s503, comparing the predicted value with a preset expected value, and judging whether the difference value of the predicted value and the expected value is smaller than a preset threshold value; if not, go to step S504; if yes, go to step S505;
s504, adjusting network parameters to reduce the cross entropy of the two, and iterating for a plurality of times until the entropy value is stable;
the network parameters mainly refer to network weights; in specific implementation, step S503 may be returned after the network weights are adjusted;
s505, finishing training and saving the model.
The invention also provides a terminal, comprising: the fingerprint positioning device comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the computer program realizes the steps of the fingerprint positioning method provided by any embodiment of the invention when the computer program is executed by the processor.
It should be noted that the terminal embodiment and the method embodiment belong to the same concept, the specific implementation process of the terminal embodiment and the method embodiment are detailed in the method embodiment, and technical features in the method embodiment are correspondingly applicable in the device embodiment, which is not repeated herein.
The present invention also provides a computer readable storage medium having stored thereon a fingerprint positioning program which when executed by a processor implements the steps provided by any of the embodiments of the present invention.
It should be noted that the foregoing computer readable storage medium embodiments and the method embodiments belong to the same concept, the specific implementation process of the foregoing computer readable storage medium embodiments is detailed in the method embodiments, and technical features in the method embodiments are correspondingly applicable in the device embodiments, which are not described herein again.
The fingerprint positioning method, the terminal and the computer readable storage medium provided by the embodiment of the invention introduce a genetic algorithm, optimize grid points with the same fingerprint for different grid points by utilizing the self-adaptive optimizing characteristic of the genetic algorithm, determine the optimal fingerprint and generate a new fingerprint database. And introducing a BP neural network, and learning hidden information features by modeling the nonlinear relation between the fingerprint signal space and the physical position space. And then training by using MR data with position information characteristics to obtain a stable and reliable learning model.
From the above description of the embodiments, it will be clear to those skilled in the art that the above-described embodiment method may be implemented by means of software plus a necessary general hardware platform, but of course may also be implemented by means of hardware, although in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (e.g. ROM/RAM, magnetic disk, optical disk) comprising instructions for causing a terminal device (which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to perform the method according to the embodiments of the present invention.
The preferred embodiments of the present invention have been described above with reference to the accompanying drawings, and thus do not limit the scope of the claims of the present invention. Those skilled in the art will appreciate that many modifications are possible in which the invention is practiced without departing from its scope or spirit, e.g., features of one embodiment can be used with another embodiment to yield yet a further embodiment. Any modification, equivalent replacement and improvement made within the technical idea of the present invention should be within the scope of the claims of the present invention.
Claims (8)
1. A fingerprint positioning method, comprising:
acquiring measurement report information of a terminal to be positioned;
extracting effective characteristic information values in the measurement report information;
the effective characteristic information value is used as input and is transmitted to a neural network model for fitting, and then positioning coordinate information is output;
the establishment of the neural network model comprises the following steps:
collecting measurement report information in a fingerprint positioning area and generating a fingerprint database according to the measurement report information;
optimizing fingerprint data in the fingerprint database through a genetic algorithm;
training and obtaining a neural network model through the neural network positioning model;
the collecting measurement report information in the fingerprint positioning area and generating a fingerprint database according to the measurement report information comprises the following steps:
performing network rasterization on the fingerprint positioning area to obtain a plurality of grids;
acquiring all service cells contained in the measurement report information in each grid;
calculating an RSRP value and a TA value of the serving cell in the grid;
storing RSRP values and TA values of all the service cells corresponding to the grids in the grids as fingerprint data into the fingerprint database;
the calculating the RSRP value and the TA value of the serving cell in the grid includes:
judging whether the number of the measurement report information corresponding to the service cell is one;
if not, acquiring all the measurement report information corresponding to the service cell;
calculating the Manhattan distance from the measurement report information to the central point of the grid according to the AGPS value in the measurement report information;
and selecting the first 2 or 3 pieces of measurement report information with the minimum Manhattan distance, and calculating the RSRP value of the serving cell in the grid through a WKNN algorithm.
2. The fingerprint positioning method according to claim 1, further comprising:
and if the number of the measurement report information corresponding to the service cell is judged to be one, taking the RSRP value and the TA value in the measurement report information as the RSRP value and the TA value of the service cell in the grid.
3. The fingerprint localization method of claim 1, wherein the optimizing fingerprint data in the fingerprint database by a genetic algorithm comprises:
determining a target optimization function;
and obtaining optimal fingerprint data through initialization, cross operation, mutation operation and selection operation.
4. The fingerprint positioning method according to claim 1, wherein after the obtaining the measurement report information of the terminal to be positioned, further comprising:
and judging whether the measurement report information meets a preset rule, if so, updating the measurement report information to the fingerprint database.
5. The fingerprint positioning method according to claim 4, wherein the predetermined rule comprises that a time since a last update of the fingerprint database reaches a predetermined threshold.
6. The fingerprint positioning method of claim 1, wherein the obtaining the neural network model through training of the neural network positioning model comprises: constructing a neural network positioning model and initializing network parameters;
forward calculation is carried out through initialized learning parameters and input data, so that a predicted value is obtained;
comparing the predicted value with a preset expected value, and judging whether the difference value of the predicted value and the expected value is smaller than a preset threshold value;
if not, the network parameters are adjusted to reduce the cross entropy of the two, and iteration is carried out for a plurality of times until the entropy value is stable.
7. A terminal, comprising: memory, a processor and a computer program stored on the memory and executable on the processor, which when executed by the processor, implements the steps of the fingerprint positioning method according to any of claims 1 to 6.
8. A computer readable storage medium, characterized in that the computer readable storage medium has stored thereon a fingerprint positioning program, which when executed by a processor, implements the steps of the fingerprint positioning method according to any of claims 1 to 6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710486648.8A CN109116299B (en) | 2017-06-23 | 2017-06-23 | Fingerprint positioning method, terminal and computer readable storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710486648.8A CN109116299B (en) | 2017-06-23 | 2017-06-23 | Fingerprint positioning method, terminal and computer readable storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109116299A CN109116299A (en) | 2019-01-01 |
| CN109116299B true CN109116299B (en) | 2023-06-16 |
Family
ID=64732200
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710486648.8A Active CN109116299B (en) | 2017-06-23 | 2017-06-23 | Fingerprint positioning method, terminal and computer readable storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109116299B (en) |
Families Citing this family (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111541986B (en) * | 2019-01-22 | 2022-09-09 | 博彦科技股份有限公司 | Positioning method, positioning device, storage medium and processor |
| CN110856255B (en) * | 2019-11-25 | 2021-01-19 | 北京眸星科技有限公司 | Anti-difference position fingerprint positioning method |
| CN111836358B (en) * | 2019-12-24 | 2021-09-14 | 北京嘀嘀无限科技发展有限公司 | Positioning method, electronic device, and computer-readable storage medium |
| CN114222238B (en) * | 2020-09-03 | 2024-04-26 | 中国电信股份有限公司 | Positioning method, apparatus and computer readable storage medium |
| CN112423333B (en) * | 2020-11-18 | 2022-07-12 | 上海大学 | Cellular network wireless positioning method based on position fingerprint matching |
| CN118383066A (en) * | 2021-12-03 | 2024-07-23 | Oppo广东移动通信有限公司 | Positioning method, model training method and device |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH04322437A (en) * | 1991-04-23 | 1992-11-12 | Fujitsu Ltd | Reflow soldering mehtod for electronic component |
| CN102264097A (en) * | 2011-08-26 | 2011-11-30 | 北京铭润创展科技有限公司 | Method for positioning GSM (global system for mobile communication) mobile communication network terminal |
| CN102932738A (en) * | 2012-10-31 | 2013-02-13 | 北京交通大学 | Improved positioning method of indoor fingerprint based on clustering neural network |
| CN106376081A (en) * | 2016-10-28 | 2017-02-01 | 江南大学 | Mixed similarity-based indoor fingerprint positioning method |
| CN106804046A (en) * | 2017-02-16 | 2017-06-06 | 广州杰赛科技股份有限公司 | Mobile location method and device based on measurement report |
| CN106879065A (en) * | 2015-12-11 | 2017-06-20 | 中南大学 | A kind of Wi-Fi indoor orientation methods based on improvement WKNN |
-
2017
- 2017-06-23 CN CN201710486648.8A patent/CN109116299B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH04322437A (en) * | 1991-04-23 | 1992-11-12 | Fujitsu Ltd | Reflow soldering mehtod for electronic component |
| CN102264097A (en) * | 2011-08-26 | 2011-11-30 | 北京铭润创展科技有限公司 | Method for positioning GSM (global system for mobile communication) mobile communication network terminal |
| CN102932738A (en) * | 2012-10-31 | 2013-02-13 | 北京交通大学 | Improved positioning method of indoor fingerprint based on clustering neural network |
| CN106879065A (en) * | 2015-12-11 | 2017-06-20 | 中南大学 | A kind of Wi-Fi indoor orientation methods based on improvement WKNN |
| CN106376081A (en) * | 2016-10-28 | 2017-02-01 | 江南大学 | Mixed similarity-based indoor fingerprint positioning method |
| CN106804046A (en) * | 2017-02-16 | 2017-06-06 | 广州杰赛科技股份有限公司 | Mobile location method and device based on measurement report |
Non-Patent Citations (1)
| Title |
|---|
| 城市移动无线传感器网络协同定位算法;赵宇 等;《电视技术》;20101117;第34卷(第11期);第82-85页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109116299A (en) | 2019-01-01 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109116299B (en) | Fingerprint positioning method, terminal and computer readable storage medium | |
| CN108829766B (en) | Interest point recommendation method, system, equipment and computer readable storage medium | |
| CN111461410B (en) | Air quality prediction method and device based on transfer learning | |
| CN112911626B (en) | Wireless network flow prediction method based on multi-graph convolution | |
| CN109726885A (en) | Electricity consumption anomaly assessment method, apparatus, equipment and computer storage medium | |
| CN109991591B (en) | Positioning method and device based on deep learning, computer equipment and storage medium | |
| CN111343650B (en) | Urban scale wireless service flow prediction method based on cross-domain data and loss resistance | |
| CN110784883B (en) | Base station construction evaluation method, device, equipment and storage medium | |
| CN112926999B (en) | Self-adaptive recommendation method and system for association supervision range of bulk commodity transaction platform | |
| CN107515949A (en) | User's space-time method for measuring similarity in interest point prediction and recommendation | |
| CN112905905A (en) | Interest point-area joint recommendation method in location social network | |
| CN112819324A (en) | Rescue goods and materials scheduling method and system based on Internet of things and readable storage medium | |
| CN117592819B (en) | Dynamic supervision method and device for land-sea complex oriented to ocean pasture | |
| CN111417132B (en) | Cell division method, device and equipment | |
| CN114828026A (en) | Base station planning method, device, equipment, storage medium and program product | |
| CN115510317B (en) | Cloud-edge collaborative interest point recommendation method and device | |
| CN112883292B (en) | User behavior recommendation model establishment and position recommendation method based on spatio-temporal information | |
| CN109493065A (en) | A kind of fraudulent trading detection method of Behavior-based control incremental update | |
| CN109635057B (en) | Electricity utilization business processing method and device, computer equipment and storage medium | |
| CN118171088B (en) | Edge computing method, device, equipment and readable storage medium | |
| CN117295093B (en) | Terminal business flow behavior modeling method based on data driving | |
| Mansouri et al. | Genetic algorithm optimization for quantized target tracking in wireless sensor networks | |
| JP7531605B2 (en) | Estimation of communication traffic demand | |
| CN113762485B (en) | Attention mechanism-based multi-dataset joint prediction method | |
| CN118586574B (en) | Failure risk prediction method and device for truck and unmanned aerial vehicle combined distribution path |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| TR01 | Transfer of patent right |
Effective date of registration: 20230919 Address after: Building 8, ZTE Changsha R&D and Production Base, No. 103, Wanglong Road, High tech Development Zone, Changsha, Hunan 410000 Patentee after: Changsha Zhongxing Software Co.,Ltd. Address before: 518000 Zhongnan communication tower, South China Road, Nanshan District high tech Industrial Park, Shenzhen, Guangdong Patentee before: ZTE Corp. |
|
| TR01 | Transfer of patent right |