CN116801380A - UWB indoor positioning method based on improved full centroid-Taylor - Google Patents

UWB indoor positioning method based on improved full centroid-Taylor Download PDF

Info

Publication number
CN116801380A
CN116801380A CN202310290792.XA CN202310290792A CN116801380A CN 116801380 A CN116801380 A CN 116801380A CN 202310290792 A CN202310290792 A CN 202310290792A CN 116801380 A CN116801380 A CN 116801380A
Authority
CN
China
Prior art keywords
centroid
base stations
taylor
algorithm
value
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.)
Granted
Application number
CN202310290792.XA
Other languages
Chinese (zh)
Other versions
CN116801380B (en
Inventor
杨秀建
皇甫尚昆
余明江
张生斌
袁志豪
颜绍祥
冯楚琦
吴相稷
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Kunming University of Science and Technology
Original Assignee
Kunming University of Science and Technology
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Kunming University of Science and Technology filed Critical Kunming University of Science and Technology
Priority to CN202310290792.XA priority Critical patent/CN116801380B/en
Publication of CN116801380A publication Critical patent/CN116801380A/en
Application granted granted Critical
Publication of CN116801380B publication Critical patent/CN116801380B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04WWIRELESS COMMUNICATION NETWORKS
    • H04W64/00Locating users or terminals or network equipment for network management purposes, e.g. mobility management
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01CMEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/20Instruments for performing navigational calculations
    • G01C21/206Instruments for performing navigational calculations specially adapted for indoor navigation
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04WWIRELESS COMMUNICATION NETWORKS
    • H04W4/00Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/30Services specially adapted for particular environments, situations or purposes
    • H04W4/33Services specially adapted for particular environments, situations or purposes for indoor environments, e.g. buildings

Landscapes

  • Engineering & Computer Science (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Automation & Control Theory (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Position Fixing By Use Of Radio Waves (AREA)

Abstract

The invention relates to an improved full centroid-Taylor-based UWB indoor positioning method, which comprises the following steps: setting up positioning areas with 4 or more base stations to obtain the distance between the base stations and the mobile tags; dividing all base stations into 3 groups, and calculating the number of all casesAnd displaying; for display ofThe base stations of the group respectively use a full centroid algorithm to obtainA group coordinate; to be used forThe sum of the distances from the group coordinates to the nodes to be positioned is used as an objective function, the value range is constrained and then is imported into a simulated annealing algorithm to find an optimal value; and the optimal value is used as an initial value of a Taylor algorithm, and the final coordinates of the node to be positioned are obtained after the Taylor algorithm is solved. The invention uses a full centroid algorithm aiming at the grouped base stations, searches the optimal value of the built objective function through a simulated annealing algorithm, adapts to the positioning of UWB in a non-line-of-sight environment, and effectively improves the robustness and the positioning precision.

Description

UWB indoor positioning method based on improved full centroid-Taylor
Technical Field
The invention belongs to the technical field of positioning and tracking, and particularly relates to an improved full centroid-Taylor-based UWB indoor positioning method.
Background
In recent years, as mobile robots are applied in a wider range of scenes, such as a greeting robot in a market, a delivery robot in a restaurant, a delivery robot in a logistics warehouse, a navigation robot in a library, etc., they perform self-tasks in the scenes, and apart from effectively controlling them, it is necessary to ensure that they can know exactly where they are, i.e. need to be positioned exactly, all the time. In the indoor positioning field, there are numerous indoor positioning technologies such as radio frequency (radio frequency identification, RFID), wiFi, zigBee and Ultra Wideband (UWB), and these positioning technologies may be applied under different indoor positioning scenarios, where the indoor positioning technology using UWB as a typical one has raised a high research surge in the indoor positioning field in its convenient base station building manner and high absolute positioning accuracy.
The UWB positioning technology is a wireless carrier communication technology, which utilizes nanosecond non-sine wave narrow wave pulse to transmit data, so that the occupied frequency spectrum range is very wide, and theoretically, the time resolution can be very high due to the high-frequency transmission signal, thereby realizing the centimeter-level precision of indoor positioning. The UWB is mainly technically characterized by high transmission rate, large space capacity, low cost, low power consumption and the like, the transmitter of the UWB is a pulse small-sized excitation antenna, up-conversion required by a traditional transceiver is not required, and therefore a functional amplifier and a mixer are not required, the structure is simpler to realize, however, the UWB can make positioning accuracy greatly reduced due to multipath effect in NLOS environment, the research on UWB positioning algorithm still has the defect at present, the positioning accuracy is higher under the condition that errors obey ideal Gaussian distribution by the Fang algorithm, chan algorithm and Taylor series expansion method, but the error distribution under NLOS has various forms, and the actual positioning accuracy is reduced; the existing full centroid algorithm is insensitive to distance measurement errors, but is easily interfered by points with larger errors under the condition that the NLOS environment of UWB is more remarkable, and the positioning accuracy is not high.
Disclosure of Invention
In order to solve the technical problems, the invention provides the UWB indoor positioning method based on the improved full centroid-Taylor, overcomes the disadvantage that the positioning accuracy is reduced due to the multipath effect in the NLOS environment of UWB in the traditional full centroid algorithm, increases the step length of full centroid calculation, sets an objective function to find an optimal value through a simulated annealing algorithm, and effectively improves the robustness and the positioning accuracy of UWB in the positioning process.
In order to achieve the technical purpose, the invention is realized by the following technical scheme:
UWB indoor positioning method based on improved full centroid-Taylor comprises the following steps:
s1: setting up a positioning area of more than 4 base stations in an LOS or NLOS environment of UWB, and acquiring the distance between the base stations and the mobile tag by adopting a TDOA ranging method;
s2: dividing all base stations into 3 groups, excluding the situation that 3 base stations are on a straight line, ensuring that 3 base stations of each group can enclose a triangle, and calculating the number of all casesm is more than or equal to 3 and is displayed;
s3: for the display in S2The base stations with m more than or equal to 3 groups respectively use a full centroid algorithm to obtain +.>m is more than or equal to 3 groups of coordinates;
s4: in S3m is greater than or equal to 3 groups of coordinates, namely the sum of the distances from the coordinates to the node to be positioned (pseudo centroid) is taken as an objective function, the value range of the coordinates of the node to be positioned (pseudo centroid) is constrained, and then the constraint is introduced into a simulated annealing algorithm to find the minimum value of the objective function, namely the self-variable value of the objective function at the minimum value is the optimal value of the coordinates of the node to be positioned (pseudo centroid);
s5: and (3) taking the optimal value of the independent variable obtained from the simulated annealing algorithm in the step (S4) as an initial value of a Taylor algorithm, and obtaining the coordinates of the final node to be positioned after solving the Taylor algorithm.
Preferably, the coordinate resolving process of the final node to be located is as follows:
s5.1: the constructed UWB positioning area has more than 4 base stations, which are BS respectively 1 、BS 2 、BS 3 、BS 4 And the distance from each base station to the node to be positioned is d 1 、d 2 、d 3 、d 4
S5.2: after obtaining the distance values from each base station to the node to be positioned in S5.1, grouping all base stations including more than 4 base stations, wherein 3 base stations are in a group, and determining the number of final base station combinations through the conditions that the sum of any two sides of the triangle is larger than the third side and the difference between any two sides is smaller than the third sidem≥3;
S5.3: and (3) applying a full centroid algorithm to the base station combination subjected to screening in the step (S5.2), wherein the calculation process is as follows:
in (x) 1 ,y 1 )、(x 2 ,y 2 ) And (x) 3 ,y 3 ) Coordinates of three base station nodes not in a straight line, (X) 1 ,Y 1 ) The coordinates of the node to be located obtained for the first base station combination, and wherein
D in 1 、d 2 And d 3 Ranging values from base stations 1, 2 and 3 to the node to be located, respectively; and define
Writing (1) as
Qθ=b (4)
Solving (4) by least square method
θ LS =(Q T Q) -1 Q T b (5)
Solving for the resulting theta LS For a solution obtained by combining base stations 1, 2 and 3, the solutions obtained by combining 1, 2 and 4 and 1, 3 and 4 are calculated in the same way to obtainA group coordinate; the same applies to the calculation method when the number of base stations is greater than 4.
Preferably, the objective function set in S4 is
Where ζ is the coordinate value of the node to be located (pseudo centroid)While being an argument of the objective function
Represents the distance from the full centroid coordinate value solved in the ith combination in S3 to the pseudo centroid, where (X i ,Y i ) The full centroid coordinate values solved for the ith combination.
Preferably, the pseudo centroid coordinate valueThe value range of (2) is initially set asCompensating the value range of the pseudo centroid coordinate value, expanding the original range, searching global optimum in a larger value range, and introducing an error threshold eta α And correcting the value range of the pseudo centroid coordinates.
Preferably, the range of the pseudo centroid coordinate value is
Preferably, η is an error compensation value of UWB and the value range is (0.1, 0.3).
Preferably, α is a control factor, and the value range is (0, 1).
Preferably, the value introduced into the Taylor series expansion algorithm in S5 is the optimal value obtained from the simulated annealing algorithm in S4Then the optimal value is overlapped by Taylor algorithmAnd obtaining coordinate values (X, Y) of the final node to be positioned after the generation calculation.
The beneficial effects of the invention are as follows:
the invention improves the full centroid algorithm, the traditional full centroid algorithm is to calculate all base stations participating in positioning to obtain the coordinates of the node to be positioned, the improved full centroid algorithm firstly divides all base stations into 3 groups, carries out the full centroid algorithm on the base stations of each group, and then sets an objective function and corrects the value range of independent variables, so that the estimated coordinate value obtained after the simulated annealing algorithm has higher precision and better robustness, and provides a more reliable initial value for the Taylor algorithm. The invention can be suitable for UWB to carry out positioning task under LOS and NLOS environment, and can provide higher positioning precision for positioning carrier.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed for the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of the improved full centroid-Taylor based UWB indoor two-dimensional positioning method of the present invention;
FIG. 2 is a block diagram of an implementation of the UWB indoor two-dimensional positioning method based on the improved full centroid-Taylor of the present invention;
FIG. 3 is a graph of root mean square error (RootMean Square Error, RMSE) versus three algorithms in an LOS environment;
FIG. 4 is a graphical representation of the RMSE alignment of three algorithms in an NLOS environment;
FIG. 5 is a trace motion diagram of a NLOS-based simulation environment for dynamically calculating a motion trace using the method, chan-Talyor algorithm and WLS-Taylor algorithm, respectively;
fig. 6 is a graph of euclidean distance error comparison of motion trajectories dynamically calculated by using the method, the Chan-Talyor algorithm and the WLS-Taylor algorithm, respectively, in an NLOS-based simulation environment.
Detailed Description
The following description of the technical solutions in the embodiments of the present invention will be clear and complete, and it is obvious that the described embodiments are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1
UWB indoor two-dimensional positioning method based on improved full centroid-Taylor comprises the following steps:
s1: setting up a positioning area of more than 4 base stations in an LOS or NLOS environment of UWB, and acquiring the distance between the base stations and the mobile tag by adopting a TDOA ranging method;
s2: dividing all base stations into 3 groups, excluding the situation that 3 base stations are on a straight line, ensuring that 3 base stations of each group can enclose a triangle, and calculating the number of all casesm is more than or equal to 3 and is displayed;
s3: for the display in S2The base stations with m more than or equal to 3 groups respectively use a full centroid algorithm to obtain +.>m is more than or equal to 3 groups of coordinates;
s4: in S3m is greater than or equal to 3 groups of coordinates, namely the distance from the coordinates to the node to be positioned is taken as an objective function, the value range of the coordinates of the node to be positioned (pseudo centroid) is constrained, and then the constraint range is imported into a simulated annealing algorithm to find the minimum value of the objective function, namely the value of an independent variable of the objective function at the minimum value is the optimal value of the coordinates of the node to be positioned (pseudo centroid);
s5: taking the optimal value obtained from the simulated annealing algorithm in the step S4 as an initial value of a Taylor algorithm, and obtaining the coordinates of the final node to be positioned after solving the Taylor algorithm;
preferably, the coordinate resolving process of the final node to be located is as follows:
s5.1: the constructed UWB positioning area has more than 4 base stations, which are BS respectively 1 、BS 2 、BS 3 、BS 4 And the distance from each base station to the node to be positioned is d 1 、d 2 、d 3 、d 4
S5.2: after obtaining the distance values from each base station to the node to be positioned in S5.1, grouping all base stations including more than 4 base stations, wherein 3 base stations are in a group, and determining the number of final base station combinations through the conditions that the sum of any two sides of the triangle is larger than the third side and the difference between any two sides is smaller than the third sidem≥3;
S5.3: and (3) applying a full centroid algorithm to the base station combination subjected to screening in the step (S5.2), wherein the calculation process is as follows:
in (x) 1 ,y 1 )、(x 2 ,y 2 ) And (x) 3 ,y 3 ) Coordinates of three base station nodes not in a straight line, (X) 1 ,Y 1 ) The coordinates of the node to be located obtained for the first base station combination, and wherein
D in 1 、d 2 And d 3 Ranging values from base stations 1, 2 and 3 to the node to be located, respectively; and define
Writing (1) as
Qθ=b (4)
Solving (4) by least square method
θ LS =(Q T Q) -1 Q T b (5)
Solving for the resulting theta LS For a solution obtained by combining base stations 1, 2 and 3, the solutions obtained by combining 1, 2 and 4 and 1, 3 and 4 are calculated in the same way to obtainA group coordinate; the same applies to the calculation method when the number of base stations is greater than 4.
Preferably, the objective function set in S4 is
Where ζ is the coordinate value of the node to be located (pseudo centroid)While being an argument of the objective function
Represents the distance from the full centroid coordinate value solved in the ith combination in S3 to the pseudo centroid, where (X i ,Y i ) The full centroid coordinate values solved for the ith combination.
Preferably, the pseudo centroid coordinate valueThe value range of (2) is initially set asCompensating the value range of the pseudo centroid coordinate valueExpanding the original range, searching global optimum in a larger value range, and introducing an error threshold eta α And correcting the value range of the pseudo centroid coordinates.
Preferably, the range of the pseudo centroid coordinate value is
Preferably, η is an error compensation value of UWB and the value range is (0.1, 0.3).
Preferably, α is a control factor, and the value range is (0, 1).
Preferably, the value introduced into the Taylor series expansion algorithm in S5 is the optimal value obtained from the simulated annealing algorithm in S4And then carrying out iterative computation on the optimal value through a Taylor algorithm to obtain the coordinate values (X, Y) of the final node to be positioned.
As shown in FIG. 2, the random points are valued as shown by scattered points in the graph when the simulated annealing algorithm is performed by adopting the method, and the valued range of the centroid coordinate value is obtained according to the range expansion range of all the random points, so that a more reliable initial value is obtained and provided for the Taylor algorithm.
Performing static point positioning analysis to establish a 6m×6m positioning area, wherein coordinates of 4 base stations are BS respectively 1 (0,0),BS 2 (6,0),BS 3 (6, 6) and BS 4 (0, 6) setting coordinates of a node to be positioned as MS (4, 5), and calculating the coordinates of the node to be positioned by using the method, a Chan-Taylor method (initial value is provided for the Taylor algorithm by using a Chan algorithm) and a WLS-Taylor method (initial value is provided for the Taylor algorithm by using a weighted least square WLS algorithm) respectively based on UWB measurement noise conforming to zero-mean Gaussian distribution to obtain a root mean square error (RootMeanSquareError, RMSE) comparison chart of three algorithms in the LOS environment as shown in fig. 3; UWB measurement noise based on Gaussian distribution conforming to positive mean value is treated by the method, the Chan-Taylor method and the WLS-Taylor method respectivelyThe coordinates of the bit nodes are calculated to obtain RMSE comparison graphs of three algorithms in the NLOS environment as shown in fig. 4, the accuracy of the method is obviously higher when the node to be positioned is statically positioned by UWB after comparison, the error level of smaller noise interference in the LOS environment is smaller than or equal to 10cm, the error level of smaller noise interference in the NLOS environment is smaller than or equal to 30cm, and the error level is within the normal UWB measurement error range.
Dynamic point positioning analysis is carried out, a positioning area of 6m multiplied by 6m is established, and coordinates of 4 base stations are BS respectively 1 (0,0),BS 2 (6,0),BS 3 (6, 6) and BS 4 (0, 6), a section of motion track is set, the starting point is A (2,1.007), the end point is B (3.895,4.753), the motion track is dynamically calculated by using the method, the Chan-Talyor algorithm and the WLS-Taylor algorithm based on the simulation environment of the NLOS, so that a track motion diagram as shown in fig. 5 and an Euclidean distance error comparison diagram as shown in fig. 6 are obtained, and similarly, the positioning accuracy of the method is obviously higher than that of the other two algorithms, and the average error of the track points of the method is 14.1cm under the NLOS environment through calculation, so that the positioning accuracy of UWB under the NLOS environment is better improved.
In the description of the present specification, the descriptions of the terms "one embodiment," "example," "specific example," and the like, mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The preferred embodiments of the invention disclosed above are intended only to assist in the explanation of the invention. The preferred embodiments are not exhaustive or to limit the invention to the precise form disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best understand and utilize the invention. The invention is limited only by the claims and the full scope and equivalents thereof.

Claims (8)

1. The UWB indoor positioning method based on the improved full centroid-Taylor is characterized by comprising the following steps of:
s1: setting up a positioning area of more than 4 base stations in an LOS or NLOS environment of UWB, and acquiring the distance between the base stations and the mobile tag by adopting a TDOA ranging method;
s2: dividing all base stations into 3 groups, excluding the situation that 3 base stations are on a straight line, ensuring that 3 base stations of each group can enclose a triangle, and calculating the number of all casesm is more than or equal to 3 and is displayed;
s3: for the display in S2The base stations with m more than or equal to 3 groups respectively use a full centroid algorithm to obtain +.>m is more than or equal to 3 groups of coordinates;
s4: in S3m is more than or equal to 3 groups of coordinates, namely the sum of the distances from the coordinates to the node to be positioned to the pseudo centroid is taken as an objective function, and the objective function is introduced into a simulated annealing algorithm after the value range of the coordinates of the node to be positioned is constrained, so as to find the minimum value of the objective function, namely the self-variable value at the minimum value of the objective function is the optimal value of the coordinates of the node to be positioned to the pseudo centroid;
s5: and (3) taking the optimal value obtained from the simulated annealing algorithm in the step (S4) as an initial value of a Taylor algorithm, and obtaining the coordinates of the final node to be positioned after solving the Taylor algorithm.
2. The improved full centroid-Taylor-based UWB indoor positioning method of claim 1, wherein the coordinate resolving process of the final node to be positioned is:
s5.1: the constructed UWB positioning area has more than 4 base stations, which are BS respectively 1 、BS 2 、BS 3 、BS 4 And the distance from each base station to the node to be positioned is d 1 、d 2 、d 3 、d 4
S5.2: after obtaining the distance values from each base station to the node to be positioned in S5.1, grouping all base stations including more than 4 base stations, wherein 3 base stations are in a group, and determining the number of final base station combinations through the conditions that the sum of any two sides of the triangle is larger than the third side and the difference between any two sides is smaller than the third sidem≥3;
S5.3: and (3) applying a full centroid algorithm to the base station combination subjected to screening in the step (S5.2), wherein the calculation process is as follows:
in (x) 1 ,y 1 )、(x 2 ,y 2 ) And (x) 3 ,y 3 ) Coordinates of three base station nodes not in a straight line, (X) 1 ,Y 1 ) The coordinates of the node to be located obtained for the first base station combination, and wherein
D in 1 、d 2 And d 3 Ranging values from base stations 1, 2 and 3 to the node to be located, respectively; and define
Writing (1) as
Qθ=b (4)
Solving (4) by least square method
θ LS =(Q T Q) -1 Q T b (5)
Solving for the resulting theta LS For a solution obtained by combining base stations 1, 2 and 3, the solutions obtained by combining 1, 2 and 4 and 1, 3 and 4 are calculated in the same way to obtainA group coordinate; the same applies to the calculation method when the number of base stations is greater than 4.
3. The method for UWB indoor positioning based on improved full centroid-Taylor according to claim 1, wherein the objective function set in S4 is
In which xi is the coordinate value of the pseudo centroid of the node to be positionedWhile being an argument of the objective function
Represents the distance from the full centroid coordinate value solved in the ith combination in S3 to the pseudo centroid, where (X i ,Y i ) The full centroid coordinate values solved for the ith combination.
4. A method of improved full centroid-Taylor based UWB indoor positioning as defined in claim 3 wherein said pseudo centroid coordinate valuesThe value range of (2) is initially set as +.>Compensating the value range of the pseudo centroid coordinate value, expanding the original range, searching global optimum in a larger value range, and introducing an error threshold eta α And correcting the value range of the pseudo centroid coordinates.
5. The UWB indoor positioning method based on the improved full centroid-Taylor of claim 4, wherein the range of the pseudo centroid coordinate values is
6. The improved full centroid-Taylor-based UWB indoor positioning method of claim 5, wherein η is an error compensation value of UWB and the range of values is (0.1, 0.3).
7. The method for positioning a UWB indoor location based on an improved full centroid-Taylor according to claim 5, wherein α is a control factor, and the value range is (0, 1).
8. The method for UWB indoor positioning based on improved full centroid-Taylor according to claim 1, wherein the value imported into the Taylor series expansion algorithm in S5 is an optimal value obtained from the simulated annealing algorithm in S4And then carrying out iterative computation on the optimal value through a Taylor algorithm to obtain the coordinate values (X, Y) of the final node to be positioned.
CN202310290792.XA 2023-03-23 2023-03-23 UWB indoor positioning method based on improved full centroid-Taylor Active CN116801380B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202310290792.XA CN116801380B (en) 2023-03-23 2023-03-23 UWB indoor positioning method based on improved full centroid-Taylor

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202310290792.XA CN116801380B (en) 2023-03-23 2023-03-23 UWB indoor positioning method based on improved full centroid-Taylor

Publications (2)

Publication Number Publication Date
CN116801380A true CN116801380A (en) 2023-09-22
CN116801380B CN116801380B (en) 2024-05-28

Family

ID=88037642

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202310290792.XA Active CN116801380B (en) 2023-03-23 2023-03-23 UWB indoor positioning method based on improved full centroid-Taylor

Country Status (1)

Country Link
CN (1) CN116801380B (en)

Citations (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20170083608A1 (en) * 2012-11-19 2017-03-23 The Penn State Research Foundation Accelerated discrete distribution clustering under wasserstein distance
CN107145791A (en) * 2017-04-07 2017-09-08 哈尔滨工业大学深圳研究生院 A kind of K means clustering methods and system with secret protection
US20180096230A1 (en) * 2016-09-30 2018-04-05 Cylance Inc. Centroid for Improving Machine Learning Classification and Info Retrieval
CN108168556A (en) * 2018-01-11 2018-06-15 中国矿业大学 Merge particle group optimizing and the driving support frame ultra wide band location method of Taylor series expansions
CN109186609A (en) * 2018-10-09 2019-01-11 南京航空航天大学 UWB localization method based on KF algorithm, Chan algorithm and Taylor algorithm
US20190124618A1 (en) * 2017-10-23 2019-04-25 Ubtech Robotics Corp Range finding base station selection method and apparatus
CN111896914A (en) * 2020-04-10 2020-11-06 中兴通讯股份有限公司 Cooperative positioning method, device, equipment and storage medium
CN111948602A (en) * 2020-08-17 2020-11-17 南京工程学院 Two-dimensional UWB indoor positioning method based on improved Taylor series
CN114915908A (en) * 2022-07-01 2022-08-16 江苏亨鑫科技有限公司 Indoor positioning method, device, equipment and storage medium
CN114979951A (en) * 2022-05-20 2022-08-30 电子科技大学长三角研究院(衢州) Three-dimensional positioning method for unknown interference under NLOS environment

Patent Citations (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20170083608A1 (en) * 2012-11-19 2017-03-23 The Penn State Research Foundation Accelerated discrete distribution clustering under wasserstein distance
US20180096230A1 (en) * 2016-09-30 2018-04-05 Cylance Inc. Centroid for Improving Machine Learning Classification and Info Retrieval
CN107145791A (en) * 2017-04-07 2017-09-08 哈尔滨工业大学深圳研究生院 A kind of K means clustering methods and system with secret protection
US20190124618A1 (en) * 2017-10-23 2019-04-25 Ubtech Robotics Corp Range finding base station selection method and apparatus
CN108168556A (en) * 2018-01-11 2018-06-15 中国矿业大学 Merge particle group optimizing and the driving support frame ultra wide band location method of Taylor series expansions
CN109186609A (en) * 2018-10-09 2019-01-11 南京航空航天大学 UWB localization method based on KF algorithm, Chan algorithm and Taylor algorithm
CN111896914A (en) * 2020-04-10 2020-11-06 中兴通讯股份有限公司 Cooperative positioning method, device, equipment and storage medium
CN111948602A (en) * 2020-08-17 2020-11-17 南京工程学院 Two-dimensional UWB indoor positioning method based on improved Taylor series
CN114979951A (en) * 2022-05-20 2022-08-30 电子科技大学长三角研究院(衢州) Three-dimensional positioning method for unknown interference under NLOS environment
CN114915908A (en) * 2022-07-01 2022-08-16 江苏亨鑫科技有限公司 Indoor positioning method, device, equipment and storage medium

Non-Patent Citations (4)

* Cited by examiner, † Cited by third party
Title
徐晓苏;刘兴华;杨博;王帅;: "基于二次解析的UWB室内定位高度方向优化方法", 中国惯性技术学报, no. 05, 15 October 2019 (2019-10-15) *
汤玮: "基于邻居信息的定位算法研究", 中国优秀硕士学位论文库》, 15 July 2013 (2013-07-15), pages 3 *
王磊等: "基于全质心-Taylor的UWB室内定位算法", 《传感器与微系统》, 4 July 2017 (2017-07-04), pages 1 - 4 *
高培;蒋学程;何栋炜;: "基于最小二乘和泰勒展开的超宽带无人运输车定位算法研究", 闽江学院学报, no. 02, 25 March 2019 (2019-03-25) *

Also Published As

Publication number Publication date
CN116801380B (en) 2024-05-28

Similar Documents

Publication Publication Date Title
Subedi et al. Improving indoor fingerprinting positioning with affinity propagation clustering and weighted centroid fingerprint
US9179331B2 (en) Wireless localization method and wireless localization apparatus using fingerprinting technique
US8478292B2 (en) Wireless localization method based on an efficient multilateration algorithm over a wireless sensor network and a recording medium in which a program for the method is recorded
CN104168650A (en) Indoor positioning method based on dynamic wireless access points
CN102395193B (en) Method for locating wireless sensor network (WSN)
CN102395192A (en) Method and device for locating wireless sensor terminal
CN107484123B (en) WiFi indoor positioning method based on integrated HWKNN
CN110636436A (en) Three-dimensional UWB indoor positioning method based on improved CHAN algorithm
Velimirovic et al. Fuzzy ring-overlapping range-free (FRORF) localization method for wireless sensor networks
CN104883737A (en) Hybrid location method for wireless sensor network
CN104254126A (en) CSS (chirp spread spectrum) distance measurement-based wireless sensor network distributed node positioning method
Wu et al. Improved localization algorithms based on reference selection of linear least squares in LOS and NLOS environments
CN105007624A (en) Indoor positioning method based on received signal strength
CN108737952A (en) Based on the improved polygon weighted mass center localization method of RSSI rangings
US10356744B2 (en) Node localization method and device
Morawska et al. Transfer learning-based UWB indoor localization using MHT-MDC and clusterization-based sparse fingerprinting
Mendrzik et al. Position-constrained stochastic inference for cooperative indoor localization
Pelka et al. Iterative approach for anchor configuration of positioning systems
CN116801380B (en) UWB indoor positioning method based on improved full centroid-Taylor
CN104581944A (en) WSN node locating method for self-adaptation precision control
US20210243560A1 (en) Distributed signal processing for radiofrequency indoor localization
CN104036136A (en) Close-range precise positioning method based on RSSI (Received Signal Strength Indication)
Xia et al. Uwb positioning system based on genetic algorithm
Cheng A robust indoor localization algorithm for wsn in los and nlos environment
Lee et al. Optimised solution for hybrid TDOA/AOA‐based geolocation using Nelder‐Mead simplex method

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