CN109239656B - Radio frequency map establishing method in position fingerprint positioning - Google Patents

Radio frequency map establishing method in position fingerprint positioning Download PDF

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CN109239656B
CN109239656B CN201811221042.2A CN201811221042A CN109239656B CN 109239656 B CN109239656 B CN 109239656B CN 201811221042 A CN201811221042 A CN 201811221042A CN 109239656 B CN109239656 B CN 109239656B
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sourcing
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rss data
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CN109239656A (en
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孙永亮
柏君航
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Nanjing Tech University
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S5/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
    • G01S5/02—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves
    • G01S5/0252—Radio frequency fingerprinting
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B17/00—Monitoring; Testing
    • H04B17/30—Monitoring; Testing of propagation channels
    • H04B17/309—Measuring or estimating channel quality parameters
    • H04B17/318—Received signal strength

Abstract

The invention discloses a radio frequency map building method in position fingerprint positioning. On the basis of a traditional position fingerprint positioning system, K crowd intelligent points are additionally selected, and an crowd intelligent participant is used for collecting RSS data and position coordinates at the crowd intelligent points and uploading the data and the position coordinates to a positioning server; the positioning server divides the target area into K Voronoi cells by utilizing a Voronoi diagram according to the positions of the K crowd-sourcing points, optimizes propagation model parameters by utilizing crowd-sourcing data in each Voronoi cell, and estimates RSS data of interpolation points in the Voronoi cells by utilizing the optimized propagation model parameters. Meanwhile, the estimated interpolation point RSS data is calibrated by using crowd sensing RSS data in the same cell, and the establishment of the radio frequency map is completed. Compared with the prior art, the invention can establish the radio frequency map by interpolation only by using RSS data and position coordinates acquired from a few crowd intelligent points, thereby not only effectively reducing the workload of establishing the radio frequency map, but also improving the performance of a positioning system.

Description

Radio frequency map establishing method in position fingerprint positioning
Technical Field
The invention belongs to the technical field of radio indoor positioning, and particularly relates to a radio frequency map establishing method in position fingerprint positioning.
Background
With the development of mobile computing, location-based services become more diverse, flexible, and intelligent, and can provide accurate location information for item tracking, social networks, and the like. Because outdoor positioning methods are limited in their application in indoor environments, a variety of indoor positioning methods have been developed. Among the existing indoor positioning methods, a location fingerprint positioning method based on a Wireless Local Area Network (WLAN) is favored because of the advantages of wide arrangement of access points, popularization of terminal devices, better positioning performance in a non-line-of-sight environment, and the like.
The traditional position fingerprint positioning method can be divided into two stages: an offline phase and an online phase. In the off-line phase, the practitioner first selects a number of reference points with known location coordinates in the indoor target area. Then, RSS samples are collected by the professional at the reference points and a radio frequency map is composed with the RSS samples and corresponding location coordinates of all the reference points. And in the online positioning process, after the terminal equipment of the target user measures the RSS sample, calculating the coordinate of the target user by utilizing a radio frequency map and a position fingerprint positioning algorithm. The traditional position fingerprint positioning method needs to establish a radio frequency map by professional staff in an off-line stage, the establishment process of the radio frequency map is time-consuming and labor-consuming, and the radio frequency map needs to be updated regularly according to indoor wireless environment changes, so that the application and popularization of the position fingerprint positioning method are limited to a certain extent.
Since crowd sensing has huge application potential and practical value, researchers at home and abroad begin relevant research for establishing radio frequency maps by using crowd sensing after the crowd sensing is provided. Compared with the traditional method for acquiring data by using professionals, the radio frequency map establishment method based on crowd sensing acquires RSS data and position coordinates together by using a large number of common users, namely crowd participants, so as to complete establishment of the radio frequency map. However, building radio frequency maps, typically with crowd sensing, still requires crowd participants to gather large amounts of RSS data and location coordinates.
Disclosure of Invention
In order to solve the technical problems in the background art, the present invention aims to provide a method for establishing a radio frequency map in location fingerprint positioning, which solves the problem that the radio frequency map in the existing location fingerprint positioning is time-consuming and labor-consuming to establish.
In order to achieve the technical purpose, the technical scheme of the invention is as follows:
a radio frequency map building method in position fingerprint positioning comprises the following steps:
(1) on the basis of the existing position fingerprint positioning system, K specific positions are selected as crowd sourcing points, and then two-dimensional code labels containing position information of the crowd sourcing points are pasted at the crowd sourcing points so that crowd sourcing participants collect RSS data and position coordinates at the crowd sourcing points;
(2) the method comprises the following steps that terminal equipment of crowd participants measure RSS data from J access points at crowd points and scan two-dimensional code position labels to obtain position coordinates, and the RSS data measured at the crowd points and the corresponding position coordinates are uploaded to a positioning server;
(3) after receiving the data of all the crowd sourcing points, the positioning server divides the target area into K sub-areas by using a Voronoi diagram according to the positions of the crowd sourcing points, wherein each sub-area is a Voronoi cell;
(4) selecting a plurality of interpolation points with known positions in each Voronoi cell, optimizing propagation model parameters between the crowd wisdom points and the J access points by using data of the crowd wisdom points in the same Voronoi cell, and estimating RSS data of the J access points at the interpolation points by using the optimized propagation model parameters;
(5) and calibrating the RSS data at the estimated interpolation point by using the RSS data of the crowd points in the same cell, and establishing a new radio frequency map in an interpolation mode after the RSS data estimation and calibration of the interpolation points of all K Voronoi cells are completed.
Further, in step (3), first, the set of all crowd-sourced intelligent point positions in the target area is made ξ ═ c(1),c(2),…,c(K)Therein of
Figure BDA0001834771570000031
Let in the target area
Figure BDA0001834771570000032
Last first interpolation point
Figure BDA0001834771570000033
Wherein
Figure BDA0001834771570000034
The horizontal and vertical coordinates of the ith interpolation point; if it reaches the k-th crowd node c(k)Is less than or equal to the distance to other crowd nodes c(i)I ≠ k, then the ith interpolation point is located in the kth Voronoi cell VkWithin, the kth Voronoi cell VkExpressed as:
Figure BDA0001834771570000035
wherein d is(k,l)Representing the Euclidean distance between the kth crowd-sourcing point and the l-th interpolation point; d(i,l)Representing the Euclidean distance between the ith crowd-sourcing point and the ith interpolated point, respectively calculated by:
Figure BDA0001834771570000036
Figure BDA0001834771570000037
further, in step (5), an average of the RSS data from the j-th access point at the k-th crowd-sourcing point is calculated
Figure BDA0001834771570000038
Figure BDA0001834771570000039
In the above formula, rssi,j,kThe ith sample value of the RSS data from the jth access point at the kth crowd-sourcing point is shown, and I represents the number of RSS samples from the jth access point at the kth crowd-sourcing point;
if it is
Figure BDA00018347715700000310
At 0, the RSS data of all jth aps in the kth Voronoi cell estimated by the optimized propagation model needs to be calibrated and set to a value of 0.
Adopt the beneficial effect that above-mentioned technical scheme brought:
the method combines the Voronoi diagram and the crowd sensing and is applied to the establishment of the radio frequency map in the position fingerprint positioning, the radio frequency map can be established by an interpolation method only by the RSS data and the position coordinates which are collected at a few crowd sensing points, the establishment cost of the radio frequency map can be effectively reduced, and the positioning performance is improved.
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FIG. 1 is a flow chart of the present invention;
FIG. 2 is a plan view of an experimental environment in the examples;
FIG. 3 is a diagram showing the results of the experimental environment space partitioning by Voronoi diagrams according to the position of a crowd wisdom point in the embodiment;
FIG. 4 is a diagram illustrating the distribution of crowd sourcing point locations, reference point locations, and interpolation point locations in an embodiment;
fig. 5 is a comparison graph of error accumulation probability after a radio frequency map established by a KNN algorithm by using a conventional method and a radio frequency map established by using a conventional method are fused with the radio frequency map established by interpolation of the invention.
Detailed Description
The technical scheme of the invention is explained in detail in the following with the accompanying drawings.
The invention designs a radio frequency map building method in position fingerprint positioning, which comprises the following specific steps as shown in figure 1.
Step 1: firstly, on the basis of a traditional position fingerprint positioning system, K specific positions are selected as crowd sourcing points, and then two-dimensional code labels containing position information of the crowd sourcing points are pasted at the crowd sourcing points, so that crowd sourcing participants can conveniently acquire RSS data and position coordinates at the crowd sourcing points.
Step 2: and the terminal equipment of the crowd sourcing participant measures RSS data from J access points at the crowd sourcing point, scans the two-dimensional code position tags to obtain position coordinates, and uploads the RSS data measured at the crowd sourcing point and the corresponding position coordinates to the positioning server.
And step 3: after receiving the data of all the crowd sourcing points, the positioning server divides the target area into K sub-areas by using a Voronoi diagram according to the positions of the crowd sourcing points, wherein each sub-area is a Voronoi cell.
The method utilizes the Voronoi diagram to divide the target area according to the position of each crowd-sourcing point. Firstly, the set of all the crowd intelligence point positions in the target area is xi ═ c(1),c(2),…,c(K)Therein of
Figure BDA0001834771570000051
In the target area
Figure BDA0001834771570000052
Last first interpolation point
Figure BDA0001834771570000053
If it reaches the k-th crowd node c(k)Is less than or equal to the distance to other crowd nodes c(i)I ≠ k, then the ith interpolation point is located in the kth Voronoi cell VkWithin. At this time, the kth Voronoi cell VkCan be expressed as:
Figure BDA0001834771570000054
wherein d is(k,l)Representing the Euclidean distance between the kth crowd-sourcing point and the l-th interpolation point; d(i,l)Representing the euclidean distance between the ith crowd-sourcing point and the l-th interpolated point, may be calculated by:
Figure BDA0001834771570000055
Figure BDA0001834771570000056
and 4, step 4: in each Voronoi cell, selecting a certain number of interpolation points with known positions, optimizing propagation model parameters between the crowd sourcing points and the J access points by using data of the crowd sourcing points in the same Voronoi cell, and estimating RSS data of the J access points at the interpolation points by using the optimized propagation model parameters.
Optimizing a propagation model between an interpolation point and J access points of the same Voronoi cell by using data of a kth crowd sourcing point, and setting the propagation model between the jth access point and the kth crowd sourcing point as follows:
PTr (j)-PRe (j,k)＝20lg f+N(j,k)lgd(j,k)-X(j,k)
in the above formula, PTr (j)For the transmission power of the jth access point, obtained from the configuration of the access point, PRe (j,k)Measuring the received power from the jth access point for the user at the kth crowd node, obtained from the measured RSS data; f is the propagation frequency; d(j,k)Is the distance between the jth access point and the kth crowd sourcing point; n is a radical of(j,k)And X(j,k)The parameters of the model that need to be optimized, respectively;
(a) according to the propagation model, the jth nodeDistance d between entry point and kth crowd node(j,k)：
Figure BDA0001834771570000057
(b) According to the position coordinates of the jth access point
Figure BDA0001834771570000061
And the position coordinates of the kth crowd's wisdom point
Figure BDA0001834771570000062
Obtaining the real distance between the jth access point and the kth crowd sourcing point
Figure BDA0001834771570000063
Figure BDA0001834771570000064
(c) Optimizing parameter N according to(j,k)And X(j,k)：
Figure BDA0001834771570000065
In the above formula, the first and second carbon atoms are,
Figure BDA0001834771570000066
and
Figure BDA0001834771570000067
and is the optimized parameter value;
(d) and (4) repeating the steps (41) to (43) to obtain optimized parameter values of the propagation model of the kth crowd sourcing and the J access points, averaging the optimized parameter values, and taking the averaged parameter values as final parameters of the propagation model to obtain the optimized propagation model. Calculating the real distance between the jth access point and the ith interpolation point
Figure BDA0001834771570000068
And substituting the optimized propagation model to obtain the RSS data of the ith interpolation point.
And 5: the RSS data at the estimated interpolation point is calibrated with crowd-sourcing point RSS data within the same cell. After the RSS data estimation and calibration of the interpolation points of all K Voronoi cells are completed, a new radio frequency map is established in an interpolation mode.
RSS data from an access point, which may not be measured by the terminal device at a certain location in the room, is displayed as 0 on the terminal device due to blockage by indoor building structures, furniture, etc. However, when the RSS data at the interpolation point is estimated using the optimized propagation model, the value 0 is not calculated, and therefore, the estimated RSS data of the interpolation point needs to be calibrated. The present invention calibrates the estimated RSS data of the interpolation point using the RSS data collected at the crowd-sourcing point in the real environment. By calculating the mean of the RSS data from the j-th access point at the k-th crowd-sourcing point
Figure BDA0001834771570000069
To determine the RSS data that needs calibration:
Figure BDA00018347715700000610
where I represents the number of RSS samples from the jth access point at the kth crowd-sourcing point. If the RSS mean value
Figure BDA00018347715700000611
At 0, the RSS data of all jth aps in the kth Voronoi cell estimated by the optimized propagation model needs to be calibrated and set to a value of 0. Through such a calibration operation, the RSS data estimated by the present invention can be closer to the RSS data measured in a real environment.
Step 6: and during online positioning, combining the newly established radio frequency map with the radio frequency map of the traditional position fingerprint positioning, and calculating positioning coordinates by using a K Nearest Neighbors (KNN) algorithm.
The invention is analyzed below by way of an example. As shown in FIG. 2, the experimental environment was an indoor office environment, the area of the experimental area was 51.6m × 20.4m, and the height was 2.7 m. There are 7 access points of WLAN with TP-LINK TL-WR845N arranged in total in the floor, with a height of 2.2 meters. RSS samples were collected using a charm blue 2 cell phone at a sampling rate of 1 RSS sample per second. The charm blue 2 mobile phone is placed on a tripod with the height of 1.2 meters. A total of 92 reference points were selected in the hallway and room 620, with 120 RSS samples collected at each reference point. 10 crowd wisdom points are selected in the floor, stickers printed with two-dimensional code position information are attached to the ground, and 60 RSS samples in total for 1 minute are collected on each crowd wisdom point as crowd wisdom RSS data. A total of 128 locations are selected in the hallway and room 620 as interpolated point locations to build the radio frequency map. 90 locations were selected as test points from the corridor into the room 620 at 0.6m intervals, for a total of 5400 RSS samples collected as test data at all test points.
First, the experimental area is divided into 10 Voronoi cells by using a Voronoi diagram according to the positions of crowd sourcing points, and the result is shown in fig. 3. 128 interpolation points are selected, and the RSS data at these interpolation points is estimated and the estimated RSS data is calibrated. Crowd sourcing point locations, reference point locations and interpolation point locations are shown in fig. 4. The experimental results are shown in table 1 and fig. 5, on the basis of the traditional position fingerprint positioning, the radio frequency map established by the interpolation of the invention can greatly improve the positioning accuracy, and the average positioning error of the KNN algorithm is reduced from 4.29m to 3.33 m. The method provided by the invention does not need a time-consuming and labor-consuming data acquisition process, not only can effectively reduce the cost for establishing the radio frequency map, but also can improve the positioning accuracy, and has higher theoretical value and practical significance.
TABLE 1
Figure BDA0001834771570000081
The embodiments are only for illustrating the technical idea of the present invention, and the technical idea of the present invention is not limited thereto, and any modifications made on the basis of the technical scheme according to the technical idea of the present invention fall within the scope of the present invention.

Claims (3)

1. A radio frequency map building method in position fingerprint positioning is characterized by comprising the following steps:
(1) selecting K specific positions as crowd sourcing points, and then pasting two-dimensional code labels containing the position information of the crowd sourcing points at the crowd sourcing points so that crowd sourcing participants collect RSS data and position coordinates at the crowd sourcing points;
(2) the method comprises the following steps that terminal equipment of crowd participants measure RSS data from J access points at crowd points and scan two-dimensional code position labels to obtain position coordinates, and the RSS data measured at the crowd points and the corresponding position coordinates are uploaded to a positioning server;
(3) after receiving the data of all the crowd sourcing points, the positioning server divides the target area into K sub-areas by using a Voronoi diagram according to the positions of the crowd sourcing points, wherein each sub-area is a Voronoi cell;
(4) selecting a plurality of interpolation points with known positions in each Voronoi cell, optimizing propagation model parameters between the crowd wisdom points and the J access points by using data of the crowd wisdom points in the same Voronoi cell, and estimating RSS data of the J access points at the interpolation points by using the optimized propagation model parameters;
(5) and calibrating the RSS data at the estimated interpolation points by using the RSS data of the crowd points in the same Voronoi cell, and establishing a new radio frequency map in an interpolation mode after the RSS data estimation and calibration of the interpolation points of all K Voronoi cells are completed.
2. The method as claimed in claim 1, wherein in step (3), the set of all crowd-sourcing point locations in the target area is first set to ξ ═ c(1),c(2),…,c(K)Therein of
Figure FDA0002831874780000011
Let in the target area
Figure FDA0002831874780000012
Last first interpolation point
Figure FDA0002831874780000013
Wherein
Figure FDA0002831874780000014
The horizontal and vertical coordinates of the ith interpolation point; if it reaches the k-th crowd node c(k)Is less than or equal to the distance to other crowd nodes c(i)I ≠ k, then the ith interpolation point is located in the kth Voronoi cell VkWithin, the kth Voronoi cell VkExpressed as:
Figure FDA0002831874780000015
wherein d is(k,l)Representing the Euclidean distance between the kth crowd-sourcing point and the l-th interpolation point; d(i,l)Representing the Euclidean distance between the ith crowd-sourcing point and the ith interpolated point, respectively calculated by:
Figure FDA0002831874780000021
Figure FDA0002831874780000022
3. the method of claim 1 or 2, wherein in step (5), the mean of RSS data from the j-th AP at the k-th crowd-sourcing point is calculated
Figure FDA0002831874780000023
Figure FDA0002831874780000024
In the above formula, rssi,j,kThe ith sample value of the RSS data from the jth access point at the kth crowd-sourcing point is shown, and I represents the number of RSS samples from the jth access point at the kth crowd-sourcing point;
if it is
Figure FDA0002831874780000025
At 0, the RSS data of all jth aps in the kth Voronoi cell estimated by the optimized propagation model needs to be calibrated and set to a value of 0.
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