CN211831165U - Indoor positioning system capable of improving positioning efficiency - Google Patents
Indoor positioning system capable of improving positioning efficiency Download PDFInfo
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- CN211831165U CN211831165U CN202020330810.4U CN202020330810U CN211831165U CN 211831165 U CN211831165 U CN 211831165U CN 202020330810 U CN202020330810 U CN 202020330810U CN 211831165 U CN211831165 U CN 211831165U
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Abstract
The utility model discloses an improve indoor positioning system of positioning efficiency, including fixed setting at indoor location anchor node and sensor module to and fixed setting is indoor and be used for realizing the synchronous ware of time synchronization between each location anchor node, each location anchor node is respectively through corresponding sensor module is connected with mobile client. The utility model discloses its reasonable in design has improved positioning efficiency and positioning accuracy, can the person of facilitating the use find indoor position (like shop or storefront) fast, has reduced the scope of location and has reduced the time of location.
Description
Technical Field
The utility model relates to an indoor location technical field especially relates to an improve indoor positioning system of positioning efficiency.
Background
With the rapid development of the mobile internet, people have more and more extensive requirements on location services, whether indoors or outdoors. Especially, for passengers, customers and other persons entering the field, even if the field introduction or the sign indication exists, it is difficult and inefficient to find the corresponding goods or services in a spacious field.
For example, the current indoor positioning technology mainly adopts hardware-based equipment, signal transmission model-based positioning and signal strength value-based positioning, wherein the signal strength value-based positioning is mainly realized by data matching of signal strength values, which is a typical indoor fingerprint positioning technology. The common fingerprint positioning technology based on the WiFi network is mainly divided into two parts: an offline phase and an online phase. In the first part off-line stage, firstly, interval traversal sampling is carried out on a target area, and then RSSI values of a plurality of nearby APs received by each positioning point are used as position fingerprint information of the positioning point and are stored; in the second part of online stage, firstly, the position fingerprint formed by RSSI of lattice AP received by the terminal at an unknown position is matched with the fingerprint information of the known position in the database by using a matching algorithm, so that the position with the highest matching degree is the current position of the terminal, and the positioning function is mainly realized.
In a word, in the prior art, the fingerprint positioning technology based on the WiFi network has not only low positioning accuracy but also low efficiency in an indoor positioning system, so that the use requirements of people cannot be met.
SUMMERY OF THE UTILITY MODEL
To the defect among the prior art, the utility model provides an improve indoor positioning system of positioning efficiency has realized higher positioning efficiency and higher positioning accuracy to can satisfy people's user demand more.
In order to realize the purpose, the utility model adopts the technical scheme that:
an indoor positioning system for improving positioning efficiency comprises positioning anchor nodes and sensor modules which are fixedly arranged indoors, and synchronizers which are fixedly arranged indoors and used for realizing time synchronization among the positioning anchor nodes, wherein the positioning anchor nodes are respectively connected with a mobile client through the corresponding sensor modules.
The mobile client is provided with a processor, the processor is provided with a database, a Kalman filter and a processing unit, and the processing unit is connected with the database and the Kalman filter respectively.
The mobile client is a smart phone or a tablet computer.
The positioning anchor node and the sensor module are fixedly arranged in each indoor commodity or shop. The positioning anchor node is a high-precision positioning signal measuring device, can reliably detect low-power ultra-wideband pulses, and the pulse signals are periodically sent out by a mobile tag, and can detect and distinguish signal differences of a straight line path and a broken line path. The positioning anchor node is provided with a high-quality antenna array. The hardware composition of the positioning anchor node is provided with a Wi-Fi module and an LED indicator lamp besides a low-power CPU with a wireless communication function and a UWB wireless receiver. The Wi-Fi module is used for data transmission between the synchronizer and each positioning anchor node and for packaging and uploading data of each positioning anchor node to the upper computer for processing. The positioning anchor node comprises a first CPU, a UWB wireless receiver, a first Wi-Fi module, a first LED indicator lamp and a first precise clock; the UWB wireless receiver, the first Wi-Fi module, the first LED indicating lamp and the first accurate clock are all connected with the first CPU.
The sensor module includes an acceleration sensor.
The acceleration sensor is LIS3 DH.
The sensor module is provided with a wireless transmission unit, and the wireless transmission unit is connected with the mobile client through WIFI. The WiFi signal intensity is adopted, so that the processing of later-period data is facilitated, the data loss is reduced, the destination can be accurately reached through the shortest route, and the time efficiency is improved. The positioning efficiency and precision are improved.
The synchronizer comprises a wireless communication module, a second CPU, a second LED indicator lamp, a second accurate clock and a standby UWB wireless receiver; the wireless communication module, the second class LED indicator lamp, the second accurate clock and the standby UWB wireless receiver are all connected with the second CPU. The synchronizers must be time synchronized when using the TDOA algorithm for location. The precondition for achieving time synchronization is that the synchronizer must perform timing communication with the positioning anchor node at intervals. The crystal oscillators on the synchronizer and the positioning anchor node cannot drift too much along with the temperature, so that the positioning anchor node and the synchronizer clock are ensured to keep strict synchronization.
The utility model has the advantages that: compared with the prior art, the utility model discloses a fix and set up indoor location anchor node and sensor module to and fix and set up indoor and be used for realizing the synchronizer of time synchronization between each location anchor node, each location anchor node is connected with mobile client through corresponding sensor module respectively, and its reasonable in design is favorable to realizing that indoor data gathers the aftertreatment, thereby has improved location efficiency and positioning accuracy, can make things convenient for the user to find indoor position (such as shop or storefront) fast, has reduced the scope of location and has reduced the time of location; be provided with the treater among the mobile client, be provided with database, kalman filter and processing unit on the treater, processing unit is connected with respectively the database with kalman filter can adopt wiFi signal intensity data to carry out preliminary treatment and fingerprint data clustering partitioning through database, kalman filter and the processing unit on the treater, then utilizes the variogram curve in the SVR fitting out the Kriging interpolation with the position fingerprint information of sampling point, carries out quick fingerprint matching, has improved efficiency, also is favorable to preventing the result dispersion, has improved positioning efficiency and positioning accuracy.
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FIG. 1 is a schematic diagram of the system framework of the present invention;
fig. 2 is an indoor use state diagram of the present invention.
Detailed Description
In the description of the present invention, it should also be noted that, unless otherwise explicitly specified or limited, the terms "disposed," "mounted," "connected," and "connected" are to be construed broadly, e.g., as meaning either a fixed connection, a removable connection, or an integral connection; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
In order to make the objects, technical solutions and advantages of the present invention more clear, the present invention is further described in detail through the accompanying drawings and embodiments. It should be understood, however, that the description herein of specific embodiments is only intended to illustrate the invention and not to limit the scope of the invention. Moreover, in the following description, descriptions of well-known structures and techniques are omitted so as to not unnecessarily obscure the concepts of the present invention.
Referring to fig. 1, an indoor positioning system for improving positioning efficiency includes positioning anchor nodes 1 and sensor modules 2 fixedly arranged indoors, and synchronizers 3 fixedly arranged indoors and used for achieving time synchronization between the positioning anchor nodes 2, wherein each positioning anchor node 1 is connected with a mobile client 4 through a corresponding sensor module 2, and the positioning anchor nodes 1 and the sensor modules 2 are fixedly arranged indoors 5 in each commodity or shop. The mobile client 4 is provided with a processor 41, the processor 41 is provided with a database 411, a kalman filter 412 and a processing unit 413, and the processing unit 413 is connected with the database 411 and the kalman filter 412 respectively. The user is connected with the sensor module 2 through a smart phone or a tablet computer of the mobile client 4. The mobile client 4 is connected with the sensor module 2 through the wireless transmission unit 22 (adopting a WIFI unit), which is beneficial for the processor 41 to perform preprocessing and fingerprint data clustering and partitioning by adopting WIFI signal intensity data, and then fit the position fingerprint information of the sampling point to a variation function curve in Kriging interpolation by utilizing SVR, thereby preventing result dispersion and improving positioning efficiency and precision. The sensor module 2 is provided with a wireless transmission unit 22, and the wireless transmission unit 22 is connected with the mobile client 4 through WIFI.
Wherein the sensor module 2 comprises an acceleration sensor 21. The acceleration sensor 21 is LIS3 DH.
As shown in figure 2, in the using process of the utility model, firstly, the positioning anchor node 1 and the sensor module 2 are installed in each commodity or shop in the indoor 5 (large shopping mall), then when the indoor 5 of the user needs to be positioned, the user uses the mobile client 4 (smart phone or tablet computer), the mobile client 4 is communicated with the sensor module 2 through the positioning anchor node 1 which is closest to the indoor 5 at the current position, then the mobile client is passed through the processor in the mobile client, the database, the Kalman filter and the processing unit on the processor can adopt WiFi signal intensity data to carry out preprocessing and fingerprint data clustering partitioning through the database, the Kalman filter and the processing unit on the processor, then the position fingerprint information of the sampling point is fitted to form a variation function curve in Kriging interpolation by SVR to carry out rapid fingerprint matching, the display is carried out through the display screen on the mobile client 4, so that the efficiency is improved, the result dispersion is prevented, and the positioning efficiency and the positioning precision are improved.
The utility model discloses the location matching process.
(1) The target area (positioning area) is sampled and stored at intervals in an off-line stage through the mobile client, and n reference points can be set in the positioning area for the sampled data, and a certain length is taken as a sampling interval. For example: dividing the positioning area into small grids of 1m multiplied by 1m, taking the vertex of each grid as a reference point and numbering the reference points, wherein the position set of all the reference points is L ═ L1,l2,…,lnIn which L isi＝{xi,yiThe position coordinates of the ith reference point are obtained;
(2) the WiFi signal of the mobile client is turned on by an indoor user, so that the connection between the positioning anchor node of each commodity or shop in the room and the mobile client through the sensor module is facilitated, the mobile client can start the acquisition of signal intensity and export and store a data file, and the online characteristic data acquisition is realized;
(3) the known fingerprint matching mode positioning algorithm is established on the basis of test data and mainly comprises an off-line training stage and an on-line positioning stage, wherein the off-line training stage is used for establishing a one-to-one correspondence relationship between a radio frequency signal strength vector and a client position to form a fingerprint library. Fingerprint data collected by a processor of a mobile client is preprocessed, and because unstable factors such as multipath, scattering, obstacles, electromagnetic interference and the like often exist in the environment, signal data are unstable and have high volatility. Therefore, the Kalman filter and the processing unit on the processor need to carry out filtering optimization processing and then carry out positioning calculation. Preprocessing data by combining Kalman filtering and recursive filtering of a Kalman filter, and weakening data deviation caused by noise by using a minimum mean square error as an optimal estimation criterion and adopting a state space model of signals and noise;
(4) the processor of the mobile client clusters and blocks the fingerprint data, mainly aims to reduce the matching range of the fingerprint of an unknown node and improve the precision and the efficiency, clusters and blocks the fingerprint data by taking K reference points in a positioning area as the center, and divides the fingerprint data to be clustered and the clustering center distance according to the minimum principle, wherein the specific process comprises the following steps:
4.1 select K fingerprint data as initial clustering center in the positioning area, where R ═ R1,r2,…,rkThe K fingerprints can be randomly selected, but cannot be excessively dispersed and concentrated, so that the K fingerprints are uniformly selected in a positioning range;
4.2 according to the correlation between the signal intensity and the distance, calculating the Euclidean distance d between the fingerprint data to be clustered and the clustering centers, calculating the Euclidean distance to each clustering center at one time, and respectively allocating the Euclidean distances to the clusters closest to the Euclidean distance;
and 4.3, after all the fingerprints in the fingerprint database are distributed, updating the value of the clustering center, continuously repeating the steps of 4.1 and 4.2 until the clustering center is approximately fixed, terminating iteration and finishing primary classification.
After the clustering process is finished, each position fingerprint is divided into an area closest to the clustering center, so that the matching range can be narrowed in the online matching stage, and the result dispersion is prevented;
(5) and (3) an interpolation stage: the method comprises the steps of interpolation point selection, variogram fitting and Kriging interpolation estimation. And fitting the preprocessed data based on the SVR variation function, and estimating the RSSI value of the AP at each interpolation point by using a Kriging interpolation method according to the fitted variation function curve. The method comprises the following steps:
5.1 calculating the distance h corresponding to all sample data by using the space variation functioniAnd the corresponding function of variation value Y (h)i) Composition data set [ h ]i,Y(hi)]. The spatial variation function is as follows:
Y(hi)＝1/2Nhi∑[R(xi+hi)-R(xi)]2
5.2 randomly extracting 80% of data from the data set to generate a training set, and taking the rest data as a test set;
5.3 training the training set by adopting SVR, and fitting a variation curve Y (h);
5.4, using the test set to evaluate the performance of the variation function curve Y (h), outputting the variation function curve if the performance is expected, and returning to 5.3 for re-fitting if the performance is not expected;
the 5.5 Kriging interpolation estimation method is to set the variation of interpolation point region satisfying the second order stationary in the region as R (x)0) And m regional variation quantities satisfying the second-order stationary sample in the neighborhood range are R (x)i) By the addition of a pair of known R (x)i) The weighted summation of the values can estimate the R (x) to be estimated for the interpolation point0)。
(6) The data after fitting interpolation is used for positioning and matching, so that the result dispersion can be prevented;
(7) the data of the processor of the mobile client is displayed on the display, so that more accurate positioning can be achieved, and the positioning can reach 10-40 cm.
It should be noted that, although the above embodiments have been described herein, the scope of the present invention is not limited thereby. Therefore, based on the innovative concept of the present invention, the changes and modifications of the embodiments described herein, or the equivalent structure or equivalent process changes made by the contents of the specification and the drawings of the present invention, directly or indirectly apply the above technical solutions to other related technical fields, all included in the protection scope of the present invention.
Claims (7)
1. The utility model provides an improve indoor positioning system of positioning efficiency which characterized in that: the system comprises positioning anchor nodes (1) and sensor modules (2) which are fixedly arranged indoors, and synchronizers (3) which are fixedly arranged indoors and used for realizing time synchronization among the positioning anchor nodes (1), wherein each positioning anchor node (1) is connected with a mobile client (4) through the corresponding sensor module (2).
2. An indoor positioning system for improving positioning efficiency as recited in claim 1, wherein: the mobile client (4) is provided with a processor (41), the processor (41) is provided with a database (411), a Kalman filter (412) and a processing unit (413), and the processing unit (413) is connected with the database (411) and the Kalman filter (412) respectively.
3. An indoor positioning system for improving positioning efficiency as claimed in claim 1 or 2, wherein: the mobile client (4) is a smart phone or a tablet computer.
4. An indoor positioning system for improving positioning efficiency as claimed in claim 1 or 2, wherein: the positioning anchor node (1) and the sensor module (2) are fixedly arranged in each indoor commodity or shop.
5. An indoor positioning system for improving positioning efficiency as claimed in claim 1 or 2, wherein: the sensor module (2) comprises an acceleration sensor (21).
6. An indoor positioning system for improving positioning efficiency as recited in claim 5, wherein: the acceleration sensor (21) is LIS3 DH.
7. An indoor positioning system for improving positioning efficiency as claimed in claim 1 or 2, wherein: the sensor module (2) is provided with a wireless transmission unit (22), and the wireless transmission unit (22) is connected with the mobile client (4) through WIFI.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117250583A (en) * | 2023-11-13 | 2023-12-19 | 汉朔科技股份有限公司 | Positioning method, system, computer equipment and storage medium of intelligent shopping cart |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117250583A (en) * | 2023-11-13 | 2023-12-19 | 汉朔科技股份有限公司 | Positioning method, system, computer equipment and storage medium of intelligent shopping cart |
| CN117250583B (en) * | 2023-11-13 | 2024-04-09 | 汉朔科技股份有限公司 | Positioning method, system, computer equipment and storage medium of intelligent shopping cart |
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