CN108168563B - WiFi-based large-scale shopping mall indoor positioning and navigation method - Google Patents
WiFi-based large-scale shopping mall indoor positioning and navigation method Download PDFInfo
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- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
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- G01C21/206—Instruments for performing navigational calculations specially adapted for indoor navigation
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Abstract
The invention discloses a WiFi-based indoor positioning and navigation method for superstores, which comprises the steps of firstly scanning AP signals of WiFi labels at the periphery through a mobile terminal with an AP signal scanner to obtain the MAC address of the scanned AP signals; sampling and measuring the strength of the scanned AP signal to obtain measurement data; calculating the positions of all the scanned Wifi labels according to the MAC address of the AP signal scanned in the step 1 and the measurement data in the step 2, and then positioning by using a positioning algorithm, wherein the specific process comprises the following steps: estimating the position of a Wifi label of the next position to be reached by the mobile terminal; predicting the position of a Wifi label of the next position to be reached by the mobile terminal; and then positioning is carried out according to the estimated position and the predicted position. According to the method, the positions of the nodes are dynamically predicted, the RPs without similar RSS vectors at the labels are filtered from the map to search the nearest neighbors, the time and the calculation complexity of the KNN algorithm are reduced, and the positioning accuracy is greatly improved.
Description
Technical Field
The invention relates to the technical field of indoor positioning navigation, in particular to a WiFi-based shopping mall indoor positioning navigation method and system.
Background
With the development of the internet of things and smart cities, a large amount of information services need to be supported by position information, namely, WiFi-based large-scale shopping mall indoor positioning and navigation services. Similarly, in a large supermarket or a shopping mall, due to the fact that the field is large and the commodities are various, functions of positioning navigation, commodity searching and the like are needed to improve customer experience.
At present, documents [ Zhang Yongzhu ] indoor pedestrian positioning and tracking technology research [ D ] Qingdao [ China ocean university, 2014 ] and [ Xuwei ] indoor positioning technology research and realization [ D ] Wuhan [ China Master university, 2014 ] based on Android mobile phones and the like realize indoor positioning navigation by utilizing RSSI signals, personal navigation position guess (PDR) and particle filtering of WIFI. Since wifi tags in the environment are far from a certain location, wifi tags in certain locations may not be heard, and the RSS vector at each location may not include signals received by all AP receivers. Therefore, there may be a similar RSS vector in the vicinity position reference point rp (reference point). In the K Nearest Neighbor (KNN) algorithm, all RPs in the map only consider finding nearest neighbors and do not consider this phenomenon; on the other hand, neighbors found in the KNN algorithm may be scattered beyond the measured environment because the AP signal attenuation of each wifi tag is not only distance dependent but also affected by many indoor environmental factors, which results in the statistical minimum signal distance between the found RSS marker position vector and the individual RPs not being equal to the minimum physical distance of the actual marker position and the recorded RP position.
The study is prone to cumulative errors due to the absence of a reasonable mathematical model and effective error correction for PDR.
And because the traditional GPS positioning navigation is difficult to play the role of indoor positioning due to poor indoor signals, the indoor positioning navigation technology needs to be solved urgently.
Disclosure of Invention
Aiming at the problems of poor signal, low positioning precision, large indoor propagation signal fluctuation, unstable positioning result and the like commonly existing in the current indoor positioning, and aiming at improving the unstable factors, the invention aims to provide the WiFi-based indoor positioning and navigation method for the large-scale shopping mall.
The technical scheme adopted by the invention is as follows:
in order to solve the problems, the invention adopts the following technical scheme:
a WiFi-based shopping mall indoor positioning and navigation method comprises the following steps:
step 1, a mobile terminal with an AP signal scanner scans AP signals of wifi tags around to obtain MAC addresses of the AP signals which can be scanned;
step 3, calculating the positions (x) of all the scanned Wifi tags according to the MAC addresses of the AP signals scanned in the step 1 and the measurement data in the step 2i,yi) Wherein i represents a label of any scanned Wifi label, and positioning is performed by using a positioning algorithm, and the specific process is as follows:
adopting a K-weighted neighbor algorithm to utilize the positions (x) of all scanned Wifi labelsi,yi) Estimating the position of a Wifi label of the next position to be reached by the mobile terminal;
predicting the position of a Wifi label of the next position to be reached by the mobile terminal according to the position of the Wifi label of the current position of the mobile terminal and the position of the Wifi label of the destination to be reached by the mobile terminal;
if the position of the Wifi label of the next position to be reached by the mobile terminal is predicted, taking the arithmetic mean value of the position of the Wifi label of the next position to be reached by the mobile terminal and the predicted position of the Wifi label of the next position to be reached by the mobile terminal as the position of the mobile terminal in the moving process;
and if the position of the Wifi label at the next position to be reached by the mobile terminal is not predicted, taking the position of the Wifi label at the next position to be reached by the mobile terminal as the position of the mobile terminal in the moving process.
Adopting a K-weighted neighbor algorithm to utilize the positions (x) of all scanned Wifi labelsi,yi) Estimating the position (x, y) of the Wifi tag of the next position to be reached by the mobile terminal is as formula (1):
according to the position (x) of the Wifi label of the current position of the mobile terminalpre1,ypre1) And the position (x) of the Wifi tag of the destination to which the mobile terminal is goingpre2,ypre2) Predicting the position (x) of the Wifi tag of the next position to be reached by the mobile terminalnext,ynext) Wherein the Wifi tag of the next location is located at the location (x)next,ynext) The calculation process is as follows (2):
in equation (2), Δ X and Δ Y represent possible displacement amounts of the mobile terminal in the X direction and the Y direction, respectively.
The displacement amount Δ X of the mobile terminal in the X direction and the displacement amount Δ Y of the mobile terminal in the X direction are calculated by equation (3):
in equation (3), m is a slope of a moving path of the mobile terminal, and D is an offset of the Wifi tag in the rectangular coordinate system.
The calculation process of the slope m of the mobile terminal moving path is as follows (4):
m＝(ypre2-ypre1)/(xpre2-xpre1) (4)；
the calculation process of the offset D of the Wifi tag in the rectangular coordinate system is as follows (5):
D＝Vt (5)；
in equation (5), V is a speed at which the mobile terminal passes a distance between the Wifi tag of the previous location and the Wifi tag of the current location within time t.
The current velocity V of the mobile terminal is calculated by equation (6):
V＝(Vhuman+Vpre)/2 (6)；
in the formula (6), VhumanHuman walking speed, VpreIs the speed at which the mobile terminal has passed the Wifi tags corresponding to the two previous locations.
Speed V when the mobile terminal has passed through Wifi tags corresponding to two previous positionspreThe calculation process of (2) is as follows:
calculating the offset D of the Wifi label in the rectangular coordinate system and the speed V when the mobile terminal passes through the Wifi labels corresponding to the two previous positionspreTime t is set to 1 s.
Compared with the prior art, the invention has the following beneficial effects:
in order to obtain better accuracy, the method performs sampling measurement on the strength of the scanned AP signal; the dynamic prediction improved positioning algorithm P-KNN (predicted KNN) based on the KNN is adopted, namely the P-KNN searches nearest neighbors by filtering out the RP without similar RSS vectors at the labels from a map so as to reduce the time and the calculation complexity of the KNN algorithm, and meanwhile, the positioning accuracy is increased, and the method is particularly suitable for indoor positioning navigation of supermarkets and shopping malls, and specifically: if the ith AP Received Signal Strength (RSSi) in the RSS vector at the marker is not null, then the AP signal value received from the wifi tag is also not null in the RSS vector set for the selected RP. By using a subset of the RPs as the Filter-RPet, which subset has a greater likelihood of being selected as nearest neighbors when building the KNN algorithm, then the location of the current marker is estimated by the RSS vector at the marker and the Filter-RPet. The positioning navigation method adopts the wireless signal intensity ranging and the pedestrian navigation position conjecture, and realizes accurate positioning by utilizing an improved KNN algorithm on the basis. The positioning and navigation method has the advantages of small positioning error, no accumulated error, stable signal, accurate positioning and navigation, high commodity searching speed, great improvement on commodity searching efficiency of customers, and suitability for popularization and use in large supermarkets and sales places.
Drawings
FIG. 1 is an indoor plan view of a mall;
FIG. 2 is a graph illustrating the variation of received signal strength with distance;
FIG. 3 is a diagram illustrating finding predicted neighbor nodes according to the present invention;
FIG. 4 is a comparison graph of the drift of the positioning results obtained by the positioning algorithm of the present invention and an unmodified positioning algorithm;
FIG. 5 is an overall flowchart of the positioning method of the present invention;
FIG. 6 is a functional implementation diagram of the positioning method of the present invention on the measurement interface;
fig. 7 is a diagram of an experimental scene of the positioning method in a shopping mall.
Detailed Description
The invention will be further described with reference to the accompanying drawings.
As shown in fig. 5, the WiFi-based indoor positioning navigation method for a large mall of the present invention is performed through the following steps:
step 1, as shown in fig. 3, requesting a mobile terminal with an AP signal scanner to scan AP signals of wifi tags around through a bottom-layer wireless network, acquiring an MAC address of the AP signal that can be scanned, and storing the MAC address of the AP signal that is scanned;
step 3, calculating the positions (x) of all the scanned Wifi tags according to the MAC addresses of the AP signals scanned in the step 1 and the measurement data in the step 2i,yi) Wherein i represents a label of any scanned Wifi label, and positioning is performed by using a positioning algorithm, and the specific process is as follows:
step 3.1, as shown in fig. 4, using the K-weighted nearest neighbor algorithm (WKNN) to utilize the positions (x) of all the scanned Wifi tagsi,yi) The calculation process of estimating the position (x, y), (x, y) of the Wifi tag at the next position to be reached by the mobile terminal is as follows:
in the WKNN algorithm, K is averaged using equation (1) to find nearest neighbors;
using the functionality that a WiFi tag can be configured to report the received signal strength from the AP within a predetermined time period, the location of the tag can be predicted from the previous characteristics by deriving a smooth location curve from the received signal sequence, step 3.2. Therefore, if the distance between the position at which the WKNN is estimated and the previously predicted tag position exceeds the maximum possible offset, max _ displacement, then the estimation of WKNN is unreliable and such unexpected sudden changes or scattering can be reduced by prediction. Using the historical information of the tag position, and according to the position (x) of the Wifi tag of the current position of the mobile terminalpre1,ypre1) And the position (x) of the Wifi tag of the destination to which the mobile terminal is goingpre2,ypre2) Predicting the position (x) of the Wifi tag of the next position to be reached by the mobile terminalnext,ynext) Wherein the Wifi tag of the next location is located at the location (x)next,ynext) The calculation process is as follows (2):
in the formula (2), Δ X and Δ Y represent possible displacement amounts of the mobile terminal in the X direction and the Y direction, respectively;
the displacement amount Δ X of the mobile terminal in the X direction and the displacement amount Δ Y of the mobile terminal in the X direction are calculated by equation (3):
in the formula (3), m is the slope of the moving path of the mobile terminal, and D is the offset of the Wifi tag in the rectangular coordinate system;
the calculation process of the slope m of the mobile terminal moving path is as follows (4):
m＝(ypre2-ypre1)/(xpre2-xpre1) (4)；
the calculation process of the offset D of the Wifi tag in the rectangular coordinate system is as follows (5):
D＝Vt (5)；
in the formula (5), V is a speed at which the mobile terminal passes a distance between the Wifi tag at the previous location and the Wifi tag at the current location within time t;
the current velocity V of the mobile terminal is calculated by equation (6):
V＝(Vhuman+Vpre)/2 (6)；
in the formula (6), VhumanHuman walking speed, VpreIs the speed at which the mobile terminal has passed the Wifi tags corresponding to the two previous locations.
Speed V when the mobile terminal has passed through Wifi tags corresponding to two previous positionspreThe calculation process of (2) is as follows:
when calculating the offset D of the Wifi tag in the rectangular coordinate system and the Wifi tags corresponding to the two previous positions passed by the mobile terminalVelocity VpreWhen the time t is set to 1s, the possible position of the scanned label is at the next position (x) due to the influence of noise in the indoor environmentnext,ynext) And previous position (x)pre2,ypre2) Somewhere in between, the next location must be modified as the predicted location. Thus, as shown in fig. 3, some predicted neighbor RPs are found in the intersection space of two circles centered at the previous and next tag positions, respectively, and at a radius of D.
Step 3.3, if the position (x, y) of the Wifi label at the next position to be reached by the mobile terminal is predicted, estimating the position (x, y) of the Wifi label at the next position to be reached by the mobile terminal and the position (x, y) of the Wifi label at the predicted next position to be reached by the mobile terminalnext,ynext) The arithmetic mean value of the mobile terminal is used as the position of the mobile terminal in the moving process;
if the next position (x) to be reached by the mobile terminal is not predictednext,ynext) And if the position of the Wifi tag is not calculated, the position (x, y) of the Wifi tag at the next position to be reached by the mobile terminal is used as the position of the mobile terminal in the moving process.
As shown in fig. 1, 6 and 7, the point with the aperture in the figure is the positioning result point, and the other points are the sample points acquired during the measurement.
The invention is analyzed from a quantitative angle, only the comparison between the test coordinate and the actual coordinate position is needed, and the following groups of experiments are carried out, wherein the table 1 shows the comparison between the collected position and the actual coordinate:
TABLE 1
| Number of times/coordinate | Test coordinates (x, y) |
| 1 | 348.2769,1546.5085 |
| 2 | 348.2883,1546.5000 |
| 3 | 348.2439,1546.8516 |
| 4 | 348.7743,1544.9774 |
| 5 | 336.8340,1450.7119 |
| 6 | 343.7672,1522.8147 |
| 7 | 349.2508,1547.0031 |
| 8 | 349.2195,1547.1186 |
| 9 | 349.2417,1547.2193 |
| 10 | 349.0228,1547.8543 |
| Actual position | 350.1600 |
As can be seen from the test results of table 1: the positioning and navigation method has the advantages of small positioning error, no accumulated error, stable signal, accurate positioning and navigation, high commodity searching speed, great improvement of commodity searching efficiency of customers, and suitability for popularization and use in large supermarkets and sales places.
Claims (1)
1. A WiFi-based shopping mall indoor positioning and navigation method is characterized by comprising the following steps:
step 1, a mobile terminal with an AP signal scanner scans AP signals of wifi tags around to obtain MAC addresses of the AP signals which can be scanned;
step 2, sampling and measuring the strength of the scanned AP signal to obtain measurement data, and determining a Wifi label of the current position of the mobile terminal according to the measurement data;
step 3, calculating the positions (x) of all the scanned Wifi tags according to the MAC addresses of the AP signals scanned in the step 1 and the measurement data in the step 2i,yi) Wherein i represents a label of any scanned Wifi label, and positioning is performed by using a positioning algorithm, and the specific process is as follows:
adopting a K-weighted neighbor algorithm to utilize the positions (x) of all scanned Wifi labelsi,yi) Estimating the position of a Wifi label of the next position to be reached by the mobile terminal;
predicting the position of a Wifi label of the next position to be reached by the mobile terminal according to the position of the Wifi label of the current position of the mobile terminal and the position of the Wifi label of the destination to be reached by the mobile terminal;
if the position of the Wifi label of the next position to be reached by the mobile terminal is predicted, taking the arithmetic mean value of the position of the Wifi label of the next position to be reached by the mobile terminal and the predicted position of the Wifi label of the next position to be reached by the mobile terminal as the position of the mobile terminal in the moving process;
if the position of the Wifi label at the next position to be reached by the mobile terminal is not predicted, taking the position where the Wifi label at the next position to be reached by the mobile terminal is estimated as the position of the mobile terminal in the moving process;
adopting a K-weighted neighbor algorithm to utilize the positions (x) of all scanned Wifi labelsi,yi) Estimating the position (x, y) of the Wifi tag of the next position to be reached by the mobile terminal is as formula (1):
according to the position (x) of the Wifi label of the current position of the mobile terminalpre1,ypre1) And the position (x) of the Wifi tag of the destination to which the mobile terminal is goingpre2,ypre2) Predicting the position (x) of the Wifi tag of the next position to be reached by the mobile terminalnext,ynext) Wherein, the position (x) of the Wifi label of the next positionnext,ynext) The calculation process is as follows (2):
in the formula (2), Δ X and Δ Y represent possible displacement amounts of the mobile terminal in the X direction and the Y direction, respectively;
the displacement amount Δ X of the mobile terminal in the X direction and the displacement amount Δ Y of the mobile terminal in the Y direction are calculated by equation (3):
in the formula (3), m is the slope of the moving path of the mobile terminal, and D is the offset of the Wifi tag in the rectangular coordinate system;
the calculation process of the slope m of the mobile terminal moving path is as follows (4):
m＝(ypre2-ypre1)/(xpre2-xpre1) (4)；
the calculation process of the offset D of the Wifi tag in the rectangular coordinate system is as follows (5):
D＝Vt (5)；
in the formula (5), V is a speed at which the mobile terminal passes a distance between the Wifi tag at the previous location and the Wifi tag at the current location within time t;
the current velocity V of the mobile terminal is calculated by equation (6):
V＝(Vhuman+Vpre)/2 (6)；
in the formula (6), VhumanHuman walking speed, VpreThe speed is the speed when the mobile terminal passes through the Wifi tags corresponding to the two previous positions;
speed V when the mobile terminal has passed through Wifi tags corresponding to two previous positionspreThe calculation process of (2) is as follows:
calculating the offset D of the Wifi label in the rectangular coordinate system and the speed V when the mobile terminal passes through the Wifi labels corresponding to the two previous positionspreTime t is set to 1 s.
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