CN107290714B - Positioning method based on multi-identification fingerprint positioning - Google Patents
Positioning method based on multi-identification fingerprint positioning Download PDFInfo
- Publication number
- CN107290714B CN107290714B CN201710538630.8A CN201710538630A CN107290714B CN 107290714 B CN107290714 B CN 107290714B CN 201710538630 A CN201710538630 A CN 201710538630A CN 107290714 B CN107290714 B CN 107290714B
- Authority
- CN
- China
- Prior art keywords
- training
- tuple
- tuples
- priority queue
- 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
-
- 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
- H04W64/006—Locating users or terminals or network equipment for network management purposes, e.g. mobility management with additional information processing, e.g. for direction or speed determination
-
- 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
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Position Fixing By Use Of Radio Waves (AREA)
- Mobile Radio Communication Systems (AREA)
Abstract
The invention discloses a positioning method based on multi-identification fingerprint positioning, which comprises the steps of firstly drawing a wifi deployment map, and marking the position of a wireless AP on the map; secondly, during off-line sampling, the map is coordinated, each coordinate point is marked as a training tuple, the training tuples are divided by utilizing the RSSI information of the wireless AP collected by the mobile terminal at each coordinate point in the WiFi network, and an off-line database is established after filtering treatment, wherein the off-line database comprises the training tuples and labels and coordinates corresponding to the training tuples; and finally, calculating the Manhattan distance between the training tuples and the test tuples by traversing the training tuple set in the off-line database during on-line positioning, updating the priority queue according to the Manhattan distance, counting the occurrence frequency of each training tuple in the priority queue, and taking the coordinate point corresponding to the training tuple with the largest occurrence frequency as the coordinate point of the position of the mobile terminal to be positioned.
Description
Technical Field
The invention belongs to the technical field of positioning and tracking, and particularly relates to a positioning method based on multi-identification fingerprint positioning.
Background
At present, positioning service becomes an essential service for daily life, and along with the massive construction of public facilities such as market stations and the like, the demand of people for indoor positioning is continuously improved, and indoor positioning is gradually becoming a hotspot of research of people. However, the indoor multipath condition is complex, the traditional bluetooth positioning method has large error and is difficult to distinguish users, and special equipment is needed to measure the coordinates of the users, thereby increasing the complexity of the system. The positioning method based on the wireless local area network RSSI signal measurement is gradually becoming a hot spot for indoor positioning due to its low cost, large coverage and simple system layout.
The main methods of indoor positioning technology are divided into two categories: deterministic and probabilistic approaches. The classical algorithms in the deterministic method include a Nearest Neighbor (NN), a K Neighbor (KNN) and a Weighted K Neighbor (WKNN), and these algorithms only consider the signal feature mean in the design and have a low utilization rate on the original data, so that the positioning error is large.
The existing algorithm does not well process the pre-measured data, and no matter the average value is obtained or the average value is obtained by weighting, a certain data loss exists in the middle, and the algorithm error is indirectly increased.
Disclosure of Invention
In order to solve the problems in the prior art, the invention provides a positioning method based on multi-identification fingerprint positioning, which can reduce algorithm errors and increase algorithm accuracy.
In order to achieve the above purpose, the technical scheme adopted by the invention is as follows: the method comprises the following steps:
1) drawing a wifi deployment map, and marking the position of the wireless AP on the map;
2) offline sampling: the map is coordinated, each coordinate point is marked as a training tuple, the training tuples are divided by utilizing the RSSI information of the wireless AP collected by the mobile terminal at each coordinate point in the WiFi network and sent to a positioning server, the positioning server sorts the training tuple set and then stores the training tuple set in a search tree, meanwhile, the labels and the coordinates corresponding to the training tuples are stored to obtain an offline database, and the offline database is filtered;
3) and (3) online positioning: the method comprises the steps that a mobile terminal to be positioned collects signal strength RSSI information of a wireless AP at a random point of a WiFi network to serve as a test tuple, a nearest neighbor training tuple is selected from training tuples in an offline database to construct a priority queue, the Manhattan distances between the test tuple and the nearest neighbor training tuple are respectively calculated, the Manhattan distances between the training tuples and the test tuple are calculated by traversing a training tuple set in the offline database, the priority queue is updated according to the Manhattan distances, the occurrence frequency of each training tuple in the priority queue is counted, and a coordinate point corresponding to the training tuple with the largest occurrence frequency serves as a coordinate point of the position where the mobile terminal to be positioned is located.
The step 2) of filtering the off-line database comprises the following steps:
2.1) every coordinate point (X) in the mapi,Yi) The RSSI value at (d) is noted as vector:
wherein i is the ith point, j is the jth data measured at that point, snThe vector in the training tuple has the specific meaning that the measured RSSI value of the nth AP point at the coordinate point is obtained;
2.2) average vector of each training tuple is obtained:
wherein m is the number of vectors in each training tuple,
is the vector average;
2.3) calculating the Euclidean distance between the vector and the average vector in each training tuple:
2.4) deleting the vector with the Euclidean distance from the average vector to be more than 30 units in each training tuple, and finishing the filtering of the off-line database.
The step 3) specifically comprises the following steps:
3.1) the mobile terminal to be positioned collects the RSSI information of the wireless AP at a random point of the WiFi network and sends the RSSI information to a positioning server, and the positioning server takes the cached RSSI information as a test tuple;
3.2) constructing a priority queue for storing nearest neighbor training tuples, randomly selecting a plurality of training tuples from the training tuple set as nearest neighbor training tuples, respectively calculating the Manhattan distance between the test tuple and the nearest neighbor training tuples, and storing the label and the coordinate of the nearest neighbor training tuple and the Manhattan distance between the test tuple and the nearest neighbor training tuples into the priority queue;
3.3) traversing the training tuple set in the off-line database, calculating the Manhattan distance between the current training tuple and the test tuple, and comparing the obtained Manhattan distance D with the maximum Manhattan distance D in the priority queuemaxComparing, if D is larger than or equal to DmaxIf so, discarding the current training tuple and traversing the next training tuple; if D is less than DmaxIf so, deleting the training tuple corresponding to the maximum Manhattan distance in the priority queue, storing the current training tuple in the priority queue, and repeating the process until a complete training tuple set is traversed to obtain an updated priority queue;
and 3.4) counting the occurrence frequency of each training tuple in the priority queue, wherein the coordinate point corresponding to the training tuple with the maximum occurrence frequency is used as the coordinate point of the position of the mobile terminal to be positioned.
The RSSI value of the mobile terminal to be positioned in the step 3) at the random point is recorded as a vector: (b) is1,b2,b3,…,bn)，bnFor the vector in the test tuple, the computing formula of the Manhattan distance between the test tuple and the training tuple is as follows:
in the step 3), 1000 training tuples are stored in the priority queue as nearest neighbor training tuples.
And 3) sorting the nearest neighbor training tuples in the priority queue in the step 3) from small to large according to the Manhattan distance between the nearest neighbor training tuples and the testing tuples.
The mobile terminal collects the signal strength RSSI information of the wireless AP by adopting the API.
Compared with the prior art, the method comprises the steps of firstly drawing a wifi deployment map, and marking the position of the wireless AP on the map; secondly, during off-line sampling, the map is coordinated, each coordinate point is marked as a training tuple, the training tuples are divided by utilizing the RSSI information of the wireless AP collected by the mobile terminal at each coordinate point in the WiFi network, and an off-line database is established after filtering treatment, wherein the off-line database comprises the training tuples and labels and coordinates corresponding to the training tuples; and finally, calculating the Manhattan distance between the training tuples and the test tuples by traversing the training tuple set in the off-line database during on-line positioning, updating the priority queue according to the Manhattan distance, counting the occurrence frequency of each training tuple in the priority queue, and finishing positioning by taking the coordinate point corresponding to the training tuple with the largest occurrence frequency as the coordinate point of the position of the mobile terminal to be positioned. The invention identifies each coordinate point by a method of dividing the components and a multi-identification method and by using a plurality of groups of data, and abandons the method of solving the average value in the prior method. The method adopts vectors to identify one coordinate point, reduces errors and improves positioning accuracy.
Furthermore, the off-line database is filtered according to the Euclidean distance between the vector of the training tuple and the average vector, useless vectors in the off-line database are removed, the calculated amount and the calculated error are reduced, and the accuracy of the method is further improved. And the off-line database is filtered through the Euclidean distance, so that the error requirement is met, and the time complexity is reduced.
Drawings
Fig. 1 is a schematic information diagram of a mobile terminal and a wireless AP;
FIG. 2 is a flow chart of the method of the present invention.
Detailed Description
The invention is further explained below with reference to specific embodiments and the drawing of the description.
Referring to fig. 2, the present invention includes an initial configuration phase, an offline sampling phase, and an online real-time positioning phase:
1) an initial configuration stage: drawing a wifi deployment map, and marking the position of the wireless AP on the map;
2) an off-line sampling stage: the map is coordinated, each coordinate point is marked as a training tuple, the training tuples are divided by utilizing the RSSI information of the wireless AP collected by the mobile terminal at each coordinate point in the WiFi network and sent to a positioning server, the positioning server sorts the training tuple set and then stores the training tuple set in a search tree, meanwhile, the labels and the coordinates corresponding to the training tuples are stored to obtain an offline database, and the offline database is filtered;
the filtration treatment comprises the following steps:
2.1) every coordinate point (X) in the mapi,Yi) The RSSI value at (d) is noted as vector:
wherein i is the ith point, j is the jth data measured at that point, snAre vectors within the training tuples;
2.2) average vector of each training tuple is obtained:
wherein m is the number of vectors in each training tuple,
is the vector average;
2.3) calculating the Euclidean distance between the vector and the average vector in each training tuple:
2.4) deleting the vector of which the Euclidean distance from the average vector in each training tuple is greater than 30 units, and finishing filtering the off-line database;
3) and (3) an online real-time positioning stage: the method comprises the steps that a mobile terminal to be positioned collects signal strength RSSI (received signal strength indicator) information of a wireless AP (access point) at a random point of a WiFi (wireless fidelity) network as a test tuple, a nearest neighbor training tuple is selected from training tuples in an offline database to construct a priority queue, the Manhattan distances between the test tuple and the nearest neighbor training tuple are respectively calculated, the Manhattan distances between the training tuples and the test tuple are calculated by traversing a training tuple set in the offline database, the priority queue is updated according to the Manhattan distances, the occurrence frequency of each training tuple in the priority queue is counted, and a coordinate point corresponding to the training tuple with the largest occurrence frequency is used as a coordinate point of the position of the mobile terminal to be positioned; the method specifically comprises the following steps:
3.1) the mobile terminal to be positioned collects the RSSI information of the wireless AP at the random point of the WiFi network and sends the RSSI information to the positioning server, the positioning server takes the cached RSSI information as a test tuple, and the RSSI value of the mobile terminal to be positioned at the random point is recorded as a vector: (b) is1,b2,b3,…,bn)，bnVectors within test tuples;
3.2) constructing a priority queue for storing nearest neighbor training tuples, randomly selecting 1000 training tuples from the training tuple set as the nearest neighbor training tuples, respectively calculating the Manhattan distance between the test tuples and the nearest neighbor training tuples, storing the labels and the coordinates of the nearest neighbor training tuples and the Manhattan distance between the test tuples and the nearest neighbor training tuples into the priority queue, sorting the nearest neighbor training tuples in the priority queue from small to large according to the Manhattan distance between the nearest neighbor training tuples and the test tuples, and having the calculation formula of the Manhattan distance between the test tuples and the training tuples as follows:
3.3) traversing the training tuple set in the off-line database, calculating the Manhattan distance between the current training tuple and the test tuple, and comparing the obtained Manhattan distance D with the maximum Manhattan distance D in the priority queuemaxComparing, if D is larger than or equal to DmaxIf so, discarding the current training tuple and traversing the next training tuple; if D is less than DmaxIf so, deleting the training tuple corresponding to the maximum Manhattan distance in the priority queue, storing the current training tuple in the priority queue, and repeating the process until a complete training tuple set is traversed to obtain an updated priority queue;
and 3.4) counting the occurrence frequency of each training tuple in the priority queue, wherein the coordinate point corresponding to the training tuple with the maximum occurrence frequency is used as the coordinate point of the position of the mobile terminal to be positioned.
In the method, the mobile terminal adopts the API to acquire the RSSI information of the signal strength of the wireless AP.
In the initial configuration stage, the following information is configured in advance: drawing a wifi deployment map, and marking the installation position of the wireless AP on the map;
the steps in the off-line sampling stage are as follows:
1. processing the off-line map, dividing points on the off-line map, dividing a coordinate point at intervals of one meter, and counting n points in total, wherein the training tuple mark of the ith point is i;
2. in an environment with a WiFi network, a mobile terminal of different models is held by hands to reach a specific position, the operation is stopped for 20-60s, so as to collect WiFi fingerprint characteristics of the position, and the collected fingerprints are as follows: the mobile terminal collects the information of the signal strength RSSI deployed at the periphery by installing a sampling APP and calling a system API and sends the information to the positioning server, and the positioning server stores the data to an offline database, wherein each coordinate point (X) isi,Yi) All are recorded as a training tuple, each training tuple is assigned with a label, and the label is recorded as AiEach then in (X)i,Yi) To the measured RSSI valuei is the ith point, j is the jth group of data measured at the point, and all belong to AiA training tuple. Each item of data is recorded as
The specific arrangement mode of the mobile terminal and the wireless AP is shown in figure 1;
3. actually measuring to obtain a parking lot route, drawing a route map in a coordinate system, and pre-sequencing and arranging stored training tuples in a search tree;
and in the filtering stage, useless information in the offline database is filtered through a filtering algorithm:
1. and solving an average vector of each training tuple, wherein the calculation formula is as follows:
2. deleting the vector which is different from the average vector by the Euclidean distance of 30 units in each training tuple, wherein the Euclidean distance is calculated according to the following formula:
3. storing the rest vectors into an offline database;
in the online real-time positioning stage, for any terminal to be positioned, the wireless deployment area is accessed, WiFi is connected, and positioning APP is installed, and the specific steps are as follows:
1. after the mobile terminal enters the wireless deployment area, positioning APP acquires the signal strength RSSI values of the surrounding wireless APs and sends the RSSI values to a positioning server;
2. the positioning server program takes the RSSI cached in 3-5S as the feature to be matched and identified, and records the RSSI as a vector S ═ b1,b2,b3,…,bn)；
3. Maintaining a priority queue with the size of 1000 training tuples and sorted from small to large according to the Manhattan distance, wherein the priority queue is used for storing nearest neighbor training tuples, and the Manhattan distance formula is as follows:
4. randomly selecting 1000 tuples from the training tuple set as initial nearest neighbor training tuples, respectively calculating the Manhattan distance from the test tuples to the 1000 training tuples, and storing the training tuple labels, coordinates and the Manhattan distance into a priority queue;
5. traversing the training tuple set, calculating the Manhattan distance between the current training tuple and the test tuple, and comparing the obtained distance D with the maximum distance D in the priority queuemaxComparing, if D is larger than or equal to DmaxThen the tuple is discarded,traversing the next tuple; if D is less than DmaxDeleting the tuple with the maximum distance in the priority queue, and storing the current training tuple in the priority queue;
6. and after traversing, counting the occurrence frequency of each training tuple in the priority queue, taking the training tuple with the maximum occurrence frequency as the category of the test tuple, and taking the coordinate point corresponding to the training tuple as the coordinate point of the position of the mobile terminal to be positioned.
The traditional WKNN positioning algorithm is that n APs are arranged in an off-line stage, a map is coordinated (assuming that m points are shared), signal values of a plurality of groups of n APs are measured at each coordinate point, then a weighted average value of the n APs at the point is obtained, the n groups of data are used as an n-dimensional vector, the point is uniquely identified (the coordinates correspond to the n-dimensional vector one by one), and the n-dimensional vector is stored in a database; and in an online stage, measuring signal values of the n APs at a certain coordinate point, and matching the signal values with an offline database to obtain the coordinate value of the point. The present invention differs from the prior art methods of weighted averaging in (X)i,Yi) All data measured at a point identifies that point. Calculating the average vector of each training tuple, taking the vector with the Euclidean distance difference within 30 units from the average vector in each training tuple, discarding the rest vectors, storing the vectors in an offline database, and measuring the Manhattan distance or other distances during filtering; the data measured by the mobile terminal at (X, Y) during the on-line positioning is vector S ═ b1,b2,b3,…,bn) Calculating S and
the geometric distances are sorted, the first 1000 groups of data are taken, the occurrence frequency of each training tuple is counted, the training tuple with the largest occurrence frequency is the training tuple of the point, and finally the coordinate of the point is obtained through the training tuple.
According to the invention, by means of a method for dividing the components and a multi-identification method, each coordinate point is identified by using multiple groups of data, and a method for solving an average value in the existing method is abandoned, so that the error is reduced, and the positioning accuracy is improved. In addition, the off-line database is filtered according to the Euclidean distance between the vector of the training tuple and the average vector, useless vectors in the off-line database are removed, the calculated amount and the calculated error are reduced, and the accuracy of the method is further improved. And the off-line database is filtered through the Euclidean distance, so that the error requirement is met, and the time complexity is reduced.
Claims (6)
1. A positioning method based on multi-identification fingerprint positioning is characterized by comprising the following steps:
1) drawing a wifi deployment map, and marking the position of the wireless AP on the map;
2) offline sampling: the method comprises the following steps of coordinating a map, recording each coordinate point as a training tuple, dividing the training tuples by utilizing the RSSI information of wireless APs collected by the mobile terminal at each coordinate point in a WiFi network, sending the training tuples to a positioning server, sequencing a training tuple set by the positioning server, storing the training tuple set in a search tree, simultaneously storing labels and coordinates corresponding to the training tuples to obtain an offline database, filtering the offline database, and filtering the offline database, wherein the filtering comprises the following steps:
2.1) every coordinate point (X) in the mapi,Yi) The RSSI value at (d) is noted as vector:
wherein i is the ith point, j is the jth data measured at that point, snAre vectors within the training tuples;
2.2) average vector of each training tuple is obtained:
wherein m is the number of vectors in each training tuple,
is the vector average;
2.3) calculating the Euclidean distance between the vector and the average vector in each training tuple:
2.4) deleting the vector of which the Euclidean distance from the average vector in each training tuple is greater than 30 units, and finishing filtering the off-line database;
3) and (3) online positioning: the method comprises the steps that a mobile terminal to be positioned collects signal strength RSSI information of a wireless AP at a random point of a WiFi network to serve as a test tuple, a nearest neighbor training tuple is selected from training tuples in an offline database to construct a priority queue, the Manhattan distances between the test tuple and the nearest neighbor training tuple are respectively calculated, the Manhattan distances between the training tuples and the test tuple are calculated by traversing a training tuple set in the offline database, the priority queue is updated according to the Manhattan distances, the occurrence frequency of each training tuple in the priority queue is counted, and a coordinate point corresponding to the training tuple with the largest occurrence frequency serves as a coordinate point of the position where the mobile terminal to be positioned is located.
2. The positioning method based on multi-identification fingerprint positioning according to claim 1, wherein the step 3) specifically comprises the following steps:
3.1) the mobile terminal to be positioned collects the RSSI information of the wireless AP at a random point of the WiFi network and sends the RSSI information to a positioning server, and the positioning server takes the cached RSSI information as a test tuple;
3.2) constructing a priority queue for storing nearest neighbor training tuples, randomly selecting a plurality of training tuples from the training tuple set as nearest neighbor training tuples, respectively calculating the Manhattan distance between the test tuple and the nearest neighbor training tuples, and storing the label and the coordinate of the nearest neighbor training tuple and the Manhattan distance between the test tuple and the nearest neighbor training tuples into the priority queue;
3.3) traversing the training tuple set in the off-line database, calculating the Manhattan distance between the current training tuple and the test tuple, and comparing the obtained Manhattan distance D with the priority queueMaximum manhattan distance D inmaxComparing, if D is larger than or equal to DmaxIf so, discarding the current training tuple and traversing the next training tuple; if D is less than DmaxIf so, deleting the training tuple corresponding to the maximum Manhattan distance in the priority queue, storing the current training tuple in the priority queue, and repeating the process until a complete training tuple set is traversed to obtain an updated priority queue;
and 3.4) counting the occurrence frequency of each training tuple in the priority queue, wherein the coordinate point corresponding to the training tuple with the maximum occurrence frequency is used as the coordinate point of the position of the mobile terminal to be positioned.
3. The positioning method based on multi-identity fingerprint positioning according to claim 2, wherein the RSSI value of the mobile terminal to be positioned in step 3) at a random point is recorded as a vector: (b) is1,b2,b3,…,bn)，bnFor the vector in the test tuple, the computing formula of the Manhattan distance between the test tuple and the training tuple is as follows:
4. the method as claimed in claim 2, wherein 1000 training tuples are stored in the priority queue in step 3) as nearest neighbor training tuples.
5. The method according to claim 4, wherein the nearest neighbor training tuples in the priority queue in step 3) are sorted according to the Manhattan distance from the test tuple from small to large.
6. The method of claim 1, wherein the mobile terminal collects RSSI information of wireless APs using API.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710538630.8A CN107290714B (en) | 2017-07-04 | 2017-07-04 | Positioning method based on multi-identification fingerprint positioning |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710538630.8A CN107290714B (en) | 2017-07-04 | 2017-07-04 | Positioning method based on multi-identification fingerprint positioning |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN107290714A CN107290714A (en) | 2017-10-24 |
| CN107290714B true CN107290714B (en) | 2020-02-21 |
Family
ID=60100121
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710538630.8A Active CN107290714B (en) | 2017-07-04 | 2017-07-04 | Positioning method based on multi-identification fingerprint positioning |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN107290714B (en) |
Families Citing this family (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107886758A (en) * | 2017-10-30 | 2018-04-06 | 广州小鹏汽车科技有限公司 | A kind of intelligent car-searching method and system based on WIFI |
| CN108828643B (en) * | 2018-04-25 | 2022-04-29 | 长安大学 | Indoor and outdoor seamless positioning system and method based on grey prediction model |
| CN110267209B (en) * | 2019-06-28 | 2021-09-10 | 深圳数位传媒科技有限公司 | Indoor positioning method and device based on WiFi longitude and latitude |
| CN111246378B (en) * | 2020-01-10 | 2021-08-20 | 北京腾文科技有限公司 | Navigation method based on iBeacon Bluetooth positioning and related components |
Family Cites Families (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2010197049A (en) * | 2009-02-20 | 2010-09-09 | Nippon Telegr & Teleph Corp <Ntt> | System for estimating position and device for measuring reference data |
| CN103200678B (en) * | 2013-04-09 | 2016-01-13 | 南京信息工程大学 | The Android device WiFi indoor orientation method of position-based algorithm for recognizing fingerprint |
| CN103297924B (en) * | 2013-05-24 | 2016-03-02 | 上海交通大学 | Based on novel location fingerprint method for designing and the indoor locating system of channel estimating |
| CN104185275B (en) * | 2014-09-10 | 2017-11-17 | 北京航空航天大学 | A kind of indoor orientation method based on WLAN |
| CN106879065A (en) * | 2015-12-11 | 2017-06-20 | 中南大学 | A kind of Wi-Fi indoor orientation methods based on improvement WKNN |
| CN105894177B (en) * | 2016-03-25 | 2020-12-15 | 国家电网公司 | Power equipment operation risk analysis and evaluation method based on decision tree algorithm |
| CN106507475B (en) * | 2016-11-14 | 2019-07-16 | 华南理工大学 | Room area WiFi localization method and system based on EKNN |
| CN106792554A (en) * | 2016-11-23 | 2017-05-31 | 长安大学 | A kind of localization method based on Dual Matching fingerprint location technology |
| CN106646338B (en) * | 2016-12-07 | 2019-07-16 | 华南理工大学 | A kind of quickly accurate indoor orientation method |
| CN106597370B (en) * | 2016-12-09 | 2019-11-19 | 厦门大学嘉庚学院 | A kind of indoor wireless positioning method based on RFID |
| CN106793075A (en) * | 2016-12-21 | 2017-05-31 | 武汉大学 | A kind of WiFi indoor orientation methods based on domain cluster |
| CN106851571B (en) * | 2017-01-20 | 2020-07-28 | 华南理工大学 | Decision tree-based rapid KNN indoor WiFi positioning method |
-
2017
- 2017-07-04 CN CN201710538630.8A patent/CN107290714B/en active Active
Also Published As
| Publication number | Publication date |
|---|---|
| CN107290714A (en) | 2017-10-24 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107290714B (en) | Positioning method based on multi-identification fingerprint positioning | |
| CN104185275B (en) | A kind of indoor orientation method based on WLAN | |
| CN107071743B (en) | Rapid KNN indoor WiFi positioning method based on random forest | |
| CN108536851B (en) | User identity recognition method based on moving track similarity comparison | |
| CN101639527B (en) | K nearest fuzzy clustering WLAN indoor locating method based on REE-P | |
| CN108650628B (en) | Indoor positioning method combining distance measurement and fingerprint based on Wi-Fi network | |
| CN106131959B (en) | A kind of dual-positioning method divided based on Wi-Fi signal space | |
| CN106851571B (en) | Decision tree-based rapid KNN indoor WiFi positioning method | |
| CN106646338A (en) | Rapidly accurate indoor location method | |
| CN110166930A (en) | A kind of indoor orientation method and system based on WiFi signal | |
| CN109672973B (en) | Indoor positioning fusion method based on strongest AP | |
| CN106707233A (en) | Multi-side positioning method and multi-side positioning device based on outlier detection | |
| CN110536257B (en) | Indoor positioning method based on depth adaptive network | |
| CN103068035A (en) | Wireless network location method, device and system | |
| CN104469932B (en) | A kind of location fingerprint localization method based on support vector machines | |
| CN110366244B (en) | WiFi fingerprint indoor positioning method | |
| CN108650626A (en) | A kind of fingerprinting localization algorithm based on Thiessen polygon | |
| CN103889051A (en) | Indoor WLAN fingerprint positioning method based on AP ID filtering and Kalman filtering | |
| CN109348416B (en) | Fingerprint indoor positioning method based on binary k-means | |
| CN106793085A (en) | Fingerprint positioning method based on normality assumption inspection | |
| CN110430578A (en) | The method for realizing cell Azimuth prediction based on mobile terminal data | |
| CN109511085A (en) | A kind of UWB fingerprint positioning method based on MeanShift and weighting k nearest neighbor algorithm | |
| CN111050282A (en) | Multi-time fuzzy inference weighted KNN positioning method | |
| CN109991591A (en) | Localization method, device, computer equipment and storage medium based on deep learning | |
| CN111586605A (en) | KNN indoor target positioning method based on adjacent weighted self-adaptive k value |
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 |