CN108924748B - Wireless indoor positioning method based on cellular automaton - Google Patents
Wireless indoor positioning method based on cellular automaton Download PDFInfo
- Publication number
- CN108924748B CN108924748B CN201810842883.9A CN201810842883A CN108924748B CN 108924748 B CN108924748 B CN 108924748B CN 201810842883 A CN201810842883 A CN 201810842883A CN 108924748 B CN108924748 B CN 108924748B
- Authority
- CN
- China
- Prior art keywords
- positioning
- cell
- space
- cellular
- rss
- 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
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/02—Services making use of location information
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/30—Services specially adapted for particular environments, situations or purposes
- H04W4/33—Services specially adapted for particular environments, situations or purposes for indoor environments, e.g. buildings
-
- 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 relates to a wireless indoor positioning method based on cellular automata, which specifically comprises the following steps: step 1, performing grid division on an indoor positioning space, and mapping the indoor positioning space to a cellular space; step 2, setting related parameters such as cell neighbor types, cell state sets, state values and the like; step 3, setting initial conditions such as the evolution initial position of the cellular automaton and the like; step 4, storing the measured values in a memory of the positioning terminal in a queue mode; step 5, weighted summation is carried out on the positioning measurement values of all cells in the cell neighborhood; step 6, performing differential calculation on the result obtained in the step 5 on a time dimension to obtain the variable quantity of the weighted sum of the positioning measurement values in the cell neighborhood; step 7, selecting a discrete threshold according to the positioning requirement, and carrying out logic judgment on the result of the discrete variation obtained in the step 6; and 8, iteratively operating the step 4 to the step 7 according to the motion condition of the positioning target to obtain the position of the moving target.
Description
Technical Field
The invention relates to the technical field of positioning methods, in particular to a wireless indoor positioning method based on a cellular automaton.
Background
More than 80% of the activities of human beings in the indoor environment in modern society, with the rapid development of wireless communication technology and the popularization of intelligent mobile terminals such as smart phones and wearable devices, indoor location services almost become the daily requirements of everyone, indoor positioning becomes an important component of the development of the current information industry, and gradually deepens into the aspects of national economy and social development, and the method has important significance in the aspects of emergency rescue, public safety, military, medical treatment and the like. The indoor positioning market prospect is wide, and only China needs scales of more than one billion yuan. Research for developing high-availability and high-precision indoor positioning key technologies is brought into the compendium of the national long-term scientific and technological development planning. Therefore, the positioning method becomes the focus of attention and research, wherein the indoor positioning method is particularly important as the core of the positioning system, and the application effect and popularization of indoor positioning are greatly influenced.
The implementation principle of the prior art is introduced as follows:
(1) the geometric positioning method comprises the steps of acquiring wireless positioning measurement values, calculating the distance according to a wireless signal space propagation rule, and calculating the coordinates of the unknown nodes by using geometric relations. Common geometric relationships include the triangle rule and the hyperbola rule.
(2) The fingerprint positioning method usually adopts RSS as a wireless positioning measurement value, the main idea of the positioning principle is derived from a pattern recognition theory, and the position calculation is finally realized by utilizing the one-to-one correspondence relationship between the positioning measurement value and a position coordinate and matching the wireless positioning measurement value acquired on line with the positioning measurement value of a known position in a prior knowledge base. The application process of the fingerprint positioning method is generally divided into two stages, namely a fingerprint data offline sampling stage and a fingerprint database online matching positioning stage.
For example, chinese patent application No. 201710961571.5 discloses an indoor positioning system and method, the system includes a positioning device having an image acquisition unit and a data processing unit, and a mobile robot, when the mobile robot moves on the floor of the room, a transmitter of the mobile robot emits a light beam toward the ceiling of the room to form a first light spot and a second light spot with different sizes and/or shapes; an image acquisition unit which is obliquely arranged on an indoor wall acquires a contour image of a ceiling and images of a first light spot and a second light spot; the data processing unit determines the pose of the mobile robot on the indoor floor according to the contour image of the ceiling and the images of the first light spot and the second light spot, so that the influence of shielding of light beams emitted by the emitter from the ceiling to the light path of the image acquisition unit by objects such as indoor furniture, electrical appliances and the like is greatly reduced when the pose of the mobile robot on the indoor floor is monitored, and the accuracy of positioning of the mobile robot is improved.
The defects of the prior art are as follows: the geometric positioning method only considers the relationship between the wireless positioning measurement value and the distance, but does not consider the fluctuation of wireless signals caused by the influence of environmental factors and equipment aging, so that the positioning error cannot be effectively inhibited. The fingerprint positioning method only considers the uniqueness of a plurality of positioning base stations to a certain coordinate point, but neglects the spatial relevance of the plurality of positioning base stations relative to the same coordinate; only the matching relation between the positioning target and the sampling point is considered, and the strong time-varying property of a wireless signal caused by obstacles such as people, metal and the like is ignored, so that the positioning accuracy is low.
At present, cellular automata is not adopted in the indoor positioning method at home.
Disclosure of Invention
Aiming at the defects and blanks in the prior art, the invention provides a wireless indoor positioning method based on cellular automata.
The technical scheme of the invention is as follows: a wireless indoor positioning method based on cellular automata specifically comprises the following steps:
step 1, setting the size of a grid according to the requirement of positioning precision, and mapping an indoor positioning space after grid division to a cellular space so that the cellular space and the positioning space have the same shape and size, wherein one grid in the positioning space corresponds to one cellular in the cellular space;
step 2, a single cell usually has a plurality of cell states, each state is represented by a numerical value, the numerical value is a state value of the cell, the combination of the state values determines a cell state set, and the type of a cell neighbor and the setting of the cell state set are directly related to the design of an indoor positioning evolution rule;
step 3, giving initial state values of all cells in the cell space according to the building structure in the positioning space and the actual distribution condition of the obstacles, representing the obstacles by giving different assignments to the cells, wherein the obstacles are divided into fixed impassable obstacles, fixed traversable obstacles, movable impassable obstacles and movable traversable obstacles, and initial cells of the evolution of the cellular automaton need to be appointed, namely positioning initial points;
step 4, a positioning base station is arranged in the positioning space, is connected with a positioning terminal through a wireless network, takes the wireless signal intensity or signal propagation time obtained in the wireless communication process as a positioning measurement value, and stores the positioning measurement value in a memory of the positioning terminal in a queue manner;
step 5, setting a weight according to the spatial relationship between a central cell and a neighbor cell in a cell neighborhood, wherein the weight is inversely proportional to the distance between the central cell and the neighbor cell, and adding a spatial correlation constraint in a positioning method by utilizing the weighted summation of positioning measurement values;
the weight of the neighbor cell i is
The sum of the intra-cell neighborhood localization measurements is denoted as Σ RSSmObtained from equation (1):
∑RSSm＝∑(RSSm,i×wi)+RSSm,c,i∈(1,...,8)……(1)
wherein the content of the first and second substances,
si is the distance from the ith neighbor cell to a certain positioning base station;
s is the distance from the central cell to the positioning base station;
RSSm,irepresenting the signal strength variation of the neighbor cell i relative to the positioning base station m;
wirepresenting the weight of the neighbor cell i relative to the positioning base station m;
RSSm,crepresents the amount of change in signal strength of the center cell with respect to the base station m;
step 6, carrying out differential calculation on adjacent time measurement values to obtain the variation of the weighted sum obtained in the step 5 between adjacent times, adding time association constraint in the positioning method while removing common-mode noise through difference to reduce positioning error, forming space-time association constraint together with the step 5, and effectively removing positioning measurement value fluctuation caused by poor equipment consistency, equipment aging, temperature and humidity and other environmental factor changes;
the weighted sum of the positioning measurements obtained at time t in step 5 is
the weighted sum of the positioning measurements taken at time t +1 is
The output differential value is the difference of the positioning measurement values of the corresponding position cells relative to the same positioning equipment, and the expression (2) is as follows:
step 7, discretizing the data obtained in the step 6 by setting a discrete threshold GRss, wherein the discrete threshold corresponds to the change of the distance relation between the central cell and the positioning base station, a positioning result is obtained by logical judgment of the change of the distance relation of a plurality of base stations, and the positioning by the logical judgment has the characteristics of small calculation amount and parallel calculation of a plurality of positioning targets, so that the method is suitable for application of a large-scale positioning scene;
acquiring the signal intensity variation delta RSSS of the central cell and the neighbor cells relative to the 4 positioning base stations from t to t +1m(m ∈ A, B, C, D), then discretizing by using a discrete threshold value to obtain (omega)A,ωB,ωC,ωD) The moving direction of the central cell is judged according to different combination relations, so that the cell positioning is realized;
and 8, carrying out cellular automata evolution once every time the positioning target moves, namely, carrying out iteration execution on the steps 4-7, wherein the output result of each iteration is the position coordinate of the moved positioning target.
The indoor positioning method has the following beneficial effects:
(1) the weight is set according to the spatial relationship among the cell cells in the cell neighborhood, the positioning error is restrained by utilizing the weighted summation of the positioning measurement values and the spatial correlation between the central cell and the neighbor cell, and the positioning precision is effectively improved;
(2) in the time dimension, the difference calculation is to obtain the variable quantity of the obtained weighted sum between adjacent moments, and the common-mode error is removed through difference, and meanwhile, the positioning error is restrained by utilizing the time relevance in the evolution rule of the cellular automaton, so that the positioning precision is improved;
(3) and (3) carrying out discretization processing on the data obtained in the step (6) by setting a discrete threshold, wherein the discrete threshold corresponds to the change of the far-near relationship between the central cell and the positioning base station, the positioning result is obtained by logical judgment of the change of the far-near relationship of a plurality of base stations, and the positioning by the logical judgment has the characteristics of small calculation amount and parallel calculation of a plurality of positioning targets, and is suitable for application of large-scale positioning scenes.
Drawings
Fig. 1 is a flowchart illustrating a wireless indoor positioning method according to the present invention.
Fig. 2 is a flowchart of the wireless indoor positioning method according to embodiment 1 of the present invention.
Fig. 3 is a flowchart of the wireless indoor positioning method according to embodiment 2 of the present invention.
Detailed Description
The wireless indoor positioning method of the present invention is further described in detail below with reference to the specific embodiments and the drawings.
Example 1
As shown in fig. 1 and 2, embodiment 1 is a method for locating a person in a certain gym by using the indoor location method of the present invention, and specifically includes the following steps:
step 1, setting a cellular space as a rectangle according to the shape of a positioning space in a gymnasium, setting the size of a grid as l, m and 1m, projecting an indoor positioning space divided by the grid to the cellular space, wherein the cellular space and the positioning space have the same shape, the size of the cellular space is the same as that of the indoor gymnasium by adopting the rectangle, and one grid in the positioning space corresponds to one cellular in the cellular space;
step 2 setting cellular parameters
The cellular parameters are set as follows:
1) setting 5 cell moving states, namely, in-situ immobilization, upward movement, downward movement, leftward movement and rightward movement;
2) setting a cell state parameter (omega) by adopting Moore type cell neighbors (the cells take 8 adjacent cells as neighbors)A,ωB,ωC,ωD) The corresponding relationship with the cell state is shown in table 1;
TABLE 1 positioning logic truth table
Step 3, setting initial conditions of cellular automata
Carrying out initial assignment on the cells according to the positions of stand columns, personnel positioning initial positions and positioning base stations in an indoor gymnasium, setting the initial value of a blank cell to be 0, the initial value of an obstacle to be-1 and the assignment of the initial position to be 1;
step 4, storing the positioning measurement values acquired by the positioning equipment in a memory of the upper computer in a queue mode, and taking out one PMV (positioning measurement value) for one iteration process each time;
step 5, obtaining a weight value according to the spatial relationship between the central cell and the neighbor cells in the cell neighborhood, wherein the distance from the central cell to the base station m is represented as S, the corresponding weight value is set as 1, and the weight value of the neighbor cell i is represented as S
The sum of the intra-cell neighborhood localization measurements is denoted as Σ RSSmObtaining a weighted sum of the position measurements, e.g. formula(1) Shown in the figure:
∑RSSm＝∑(RSSm,i×wi)+RSSm,c,i∈(1,...,8)……(1)
step 6 is to add the weights of the positioning measured values at time t determined in step 5
Weighted sum with time t +1
Performing difference calculation, wherein the time interval of adjacent moments is set to be 1 second, the difference calculation in the time dimension means that the weighted sum of all cell positioning measurement values in the cell neighborhood between the adjacent moments is subjected to difference, that is, the variation of the weighted sum obtained in the step 5 of the adjacent moments is obtained, as shown in a formula (2):
the common-mode error is removed through difference, and meanwhile, time correlation constraint is added into the positioning method, so that the positioning error is reduced; step 5 and step 6 jointly form space-time association constraint, and positioning measurement value fluctuation caused by poor equipment consistency, equipment aging, temperature and humidity and other environmental factor changes can be effectively removed;
step 7 sets a discrete threshold GRss equal to 1, and discretizes the data obtained in step 6 to obtain (ω)A,ωB,ωC,ωD) The discrete threshold value corresponds to the change of the far-near relationship between the central cell and the positioning base station, and the positioning result is obtained through the logic judgment of the change of the far-near relationship of a plurality of base stations;
and 8, iteratively operating the step 4 to the step 7 according to the motion condition of the positioning target to obtain the position of the moving target.
Example 2
As shown in fig. 1 and 3, in embodiment 2, in order to locate passengers and workers in an airport terminal hall by using the indoor locating method of the present invention, the specific steps are as follows:
step 1, setting a cellular space as a rectangle according to the shape of a positioning space of a waiting hall, setting the size of a grid as l, m and 2m, projecting an indoor positioning space divided by the grid to the cellular space, wherein the cellular space and the positioning space have the same shape, the size of the cellular space is the same as that of the waiting hall by adopting the rectangle, and one grid in the positioning space corresponds to one cellular in the cellular space;
step 2 setting cellular parameters
The cellular parameters are set as follows:
1) setting 9 cell moving states, namely, in-situ immobilization, upward movement, downward movement, leftward movement, rightward upward movement, leftward upward movement, rightward downward movement and leftward downward movement;
2) setting a cell state parameter (omega) by adopting Moore type cell neighborsA,ωB,ωC,ωD) The correspondence relationship with the cell state is shown in table 2:
TABLE 2 positioning logic truth table
Step 3, setting initial conditions of cellular automata
Carrying out initial assignment on the cells according to the positions of stand columns, personnel positioning initial positions and positioning base stations in an indoor gymnasium, setting the initial value of a blank cell to be 0, the initial value of an obstacle to be-1 and the assignment of the initial position to be 1;
step 4, storing the positioning measurement values acquired by the positioning equipment in a memory of the upper computer in a queue mode, and taking out one PMV for one iteration process each time;
step 5, the weight is obtained according to the space relation between the central cell and the neighbor cells in the cell neighborhood, and the distance from the neighbor cells to the central cell is SiThe standard weight of the central cell is set as S, and the weight of the neighbor cell i is expressed as
The sum of the intra-cell neighborhood localization measurements is denoted as Σ RSSmA weighted sum of the positioning measurements is obtained, as shown in equation (1):
∑RSSm＝∑(RSSm,i×wi)+RSSm,c,i∈(1,...,8)……(1)
step 6 is to add the weights of the positioning measured values at time t determined in step 5
Weighted sum with time t +1
Performing difference calculation, wherein the time interval of adjacent moments is set to 10 seconds, the difference calculation in the time dimension refers to weighting and differencing all cell positioning measurement values in the cell neighborhood between the adjacent moments, that is, obtaining the variation of the weighted sum obtained in the step 5 of the adjacent moments, as shown in formula 2:
time correlation constraint is added in the positioning method while common mode errors are differentially removed, the positioning errors are reduced, space-time correlation constraint is formed by the step 5 and the step 6, and positioning measurement value fluctuation caused by poor equipment consistency, equipment aging, temperature and humidity and other environmental factor changes can be effectively removed;
step 7 sets the dispersion threshold GRss to 3.5, and discretizes the data obtained in step 6 to obtain (ω)A,ωB,ωC,ωD) The discrete threshold value corresponds to the change of the far-near relationship between the central cell and the positioning base station, and the positioning result is obtained through the logic judgment of the change of the far-near relationship of a plurality of base stations;
and 8, iteratively operating the step 4 to the step 7 according to the motion condition of the positioning target to obtain the position of the moving target.
The present invention is not limited to the above-described embodiments, and any variations, modifications, and alterations that may occur to one skilled in the art without departing from the spirit of the invention are intended to be within the scope of the invention.
Claims (3)
1. A wireless indoor positioning method based on cellular automata is characterized by comprising the following steps:
step 1, setting the size of a grid according to the requirement of positioning precision, and mapping an indoor positioning space after grid division to a cellular space so that the cellular space and the positioning space have the same shape and size, wherein one grid in the positioning space corresponds to one cellular in the cellular space;
step 2, a single cell has a plurality of cell states, each state is represented by a numerical value, the numerical value is a state value of the cell, and the combination of the state values determines a cell state set;
step 3, setting the initial value of the blank cell as 0, the initial value of the obstacle as-1 and the initial position assignment as 1; representing the barrier by giving different assignments to the cells, and specifying initial cells of the evolution of the cellular automaton, namely positioning the initial points;
step 4, a positioning base station is arranged in the positioning space and connected with a positioning terminal through a wireless network;
step 5, setting a weight according to the spatial relationship between a central cell and a neighbor cell in a cell neighborhood, wherein the weight is in inverse proportion to the distance between the central cell and the neighbor cell, and adding spatial association constraint in a positioning method by utilizing the weighted summation of positioning measurement values;
the weight of the neighbor cell i is
The weighted summation of the intra-cell neighborhood localization measurements is expressed as sigma RSSm：
∑RSSm＝∑(RSSm,i×wi)+RSSm,c,i∈(1,...,8)
Step 6, carrying out differential calculation on the measured values of the adjacent time, solving the variable quantity of the weighted sum obtained in the step 5 of the adjacent time, removing the common-mode noise through the difference, adding time association constraint in the positioning method, and forming space-time association constraint together with the step 5;
the output difference value is obtained by summing the measured values and calculating the difference value, i.e. sigma delta RSSmThe calculation formula is as follows:
in the above formula, t +1 represents time t +1, t represents time t,
step 7, discretizing the data obtained in the step 6 by setting a discrete threshold GRss, and obtaining a positioning result through logic judgment of the change of the near-far relationship of a plurality of base stations, wherein the discrete threshold corresponds to the change of the near-far relationship between the central cell and the positioning base station;
step 8, carrying out cellular automata evolution once every time the positioning target moves, namely carrying out iteration execution on the steps 4-7, wherein the output result of each iteration is the position coordinate of the moved positioning target;
wherein the content of the first and second substances,
si is the distance from the ith neighbor cell to the positioning base station m;
s is the distance from the central cell to the positioning base station m;
RSSm,irepresenting the signal strength variation of the neighbor cell i relative to the positioning base station m;
wirepresenting the weight of the neighbor cell i relative to the positioning base station m;
RSSm,cindicating the amount of change in signal strength of the center cell relative to base station m.
2. The wireless indoor positioning method according to claim 1, wherein in step 7, the signal strength variations Σ Δ RSS of the central cell and the neighboring cells from t to t +1 with respect to 4 positioning base stations are collectedm(m is belonged to A, B, C and D), discretizing by using a discrete threshold value to obtain a cell state parameter (omega)A,ωB,ωC,ωD) According to different combination relations, the movement of the central cell is judgedMoving the direction, thereby realizing the positioning of the cells.
3. The wireless indoor positioning method of claim 1, wherein the obstacle is one of a fixed non-traversable obstacle, a fixed traversable obstacle, a mobile non-traversable obstacle, and a mobile traversable obstacle.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810842883.9A CN108924748B (en) | 2018-07-27 | 2018-07-27 | Wireless indoor positioning method based on cellular automaton |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810842883.9A CN108924748B (en) | 2018-07-27 | 2018-07-27 | Wireless indoor positioning method based on cellular automaton |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN108924748A CN108924748A (en) | 2018-11-30 |
| CN108924748B true CN108924748B (en) | 2021-01-19 |
Family
ID=64417321
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810842883.9A Active CN108924748B (en) | 2018-07-27 | 2018-07-27 | Wireless indoor positioning method based on cellular automaton |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108924748B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109756970B (en) * | 2019-02-25 | 2020-12-22 | 广州市香港科大霍英东研究院 | Proximity relation wireless signal positioning method, device, equipment and storage medium |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104244287A (en) * | 2014-05-30 | 2014-12-24 | 电子科技大学 | Wireless sensor network modeling method based on nonvolatile two-dimensional cellular automata |
| CN105372628A (en) * | 2015-11-19 | 2016-03-02 | 上海雅丰信息科技有限公司 | Wi-Fi-based indoor positioning navigation method |
| CN106454747A (en) * | 2016-08-31 | 2017-02-22 | 重庆市志愿服务工作指导中心 | Wireless positioning method for mobile phone terminal |
| CN106604288A (en) * | 2016-12-01 | 2017-04-26 | 陕西师范大学 | Method and device for adaptively laying nodes in wireless sensor network according to needs |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102184512B (en) * | 2011-05-10 | 2012-11-28 | 天津大学 | Method for discovering abnormal events among city activities by using mobile phone data |
| US9224096B2 (en) * | 2012-01-08 | 2015-12-29 | Imagistar Llc | System and method for item self-assessment as being extant or displaced |
| CN105307266B (en) * | 2015-10-12 | 2018-08-03 | 中国人民解放军理工大学 | Sensor network compressed sensing accurate positioning method based on adaptive space lattice point |
-
2018
- 2018-07-27 CN CN201810842883.9A patent/CN108924748B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104244287A (en) * | 2014-05-30 | 2014-12-24 | 电子科技大学 | Wireless sensor network modeling method based on nonvolatile two-dimensional cellular automata |
| CN105372628A (en) * | 2015-11-19 | 2016-03-02 | 上海雅丰信息科技有限公司 | Wi-Fi-based indoor positioning navigation method |
| CN106454747A (en) * | 2016-08-31 | 2017-02-22 | 重庆市志愿服务工作指导中心 | Wireless positioning method for mobile phone terminal |
| CN106604288A (en) * | 2016-12-01 | 2017-04-26 | 陕西师范大学 | Method and device for adaptively laying nodes in wireless sensor network according to needs |
Non-Patent Citations (2)
| Title |
|---|
| A Multisensor Indoor Localization System for Biped Robots Operating in Industrial Environments;Angelos Amanatiadis;《IEEE Transactions on Industrial Electronics》;20160712;第63卷(第12期);全文 * |
| 无人机航迹规划算法研究;简康;《中国优秀硕士学位论文全文数据库 工程科技II辑》;20160315;全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN108924748A (en) | 2018-11-30 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN104703143B (en) | A kind of indoor orientation method based on WIFI signal intensity | |
| CN107371129B (en) | TDOA (time difference of arrival) positioning method based on indoor positioning of altitude-assisted correction | |
| CN110933632B (en) | Terminal indoor positioning method and system | |
| CN109379711B (en) | positioning method | |
| CN109151750A (en) | A kind of LTE indoor positioning floor method of discrimination based on Recognition with Recurrent Neural Network model | |
| CN112584311B (en) | Indoor three-dimensional space fingerprint positioning method based on WKNN fusion | |
| CN111901749A (en) | High-precision three-dimensional indoor positioning method based on multi-source fusion | |
| CN110650435B (en) | Machine learning fusion method based on UWB positioning and ZigBee positioning | |
| CN108924748B (en) | Wireless indoor positioning method based on cellular automaton | |
| Pan et al. | Map-aided and UWB-based anchor placement method in indoor localization | |
| CN108521631B (en) | Mobile AP (access point) identification method for indoor positioning | |
| Li et al. | An improved localization algorithm based on CHAN with high positioning accuracy in NLOS-WGN environment | |
| CN108574927B (en) | Mobile terminal positioning method and device | |
| CN106658538B (en) | Mobile phone base station signal coverage area simulation method based on Thiessen polygon | |
| CN105960011B (en) | Indoor objects localization method based on Sensor Network and bayes method | |
| CN109640253B (en) | Mobile robot positioning method | |
| CN108540926B (en) | Wireless signal fingerprint construction method and device | |
| CN114173281B (en) | TOA-based positioning system beacon node optimization layout method in indoor NLOS environment | |
| US20210325189A1 (en) | Localization using internet of things devices | |
| Diao et al. | Enhancing trilateration localization by adaptive selecting distances | |
| CN111929642B (en) | L in hybrid LOS/NLOS scenario P Norm positioning method | |
| KR100954455B1 (en) | Location recongnition method | |
| CN113822415B (en) | Method for predicting probability density of back propagation neural network of indoor position | |
| Yuen et al. | Wi-fi and bluetooth contact tracing without user intervention | |
| Wu et al. | Fingerprint location algorithm based on K‐means for spatial farthest access point in Wi‐Fi environment |
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 |