Gui-fu YANG, Xiao-yu XU, Wei-shuo LIU*, Cheng-lin PU, Lu YAO, Jing-bo ZHANG, Zhen-bang LIU
The Reverse Position Fingerprint Recognition Algorithm Abstract: In order to meet the special demand in the field of forensic science that indoor positioning without prior authorization of the equipment, a new algorithm reverse position fingerprint recognition algorithm is proposed in this paper. It converts the positioning mode from active positioning to passive positioning. So when it is used in the indoor positioning, we can fast and accurately positioning the target without authorization. By doing measurement experiment in real environment, the validity of the algorithm is verified. Besides, both the time for positioning and the measuring accuracy conform to the needs of forensic science. Keywords: indoor positioning; reverse position fingerprint recognition algorithm;
1 Introduction Indoor positioning technology as a new technology gets more and more attention because of the increasingly demand in medical industry, disaster relief and other fields. So there are a variety of ways to do distance measurement such as GPS [1], Ultrasonic wave [2], Infrared RSSI signal strength [3], Wi-Fi wireless network [4]. With the development of positioning technology, various positioning systems emerging, one of the more famous are A-GPS, Wave Lan Wi-Fi positioning system [5], Active Bats positioning system [6]. These system mainly use the Triangulation algorithm [7] or the Location fingerprint recognition algorithm [8]. However, both of the two algorithms are implemented in localization need to get the authorization of target in advance. To meet the demand of indoor positioning with Wi-Fi wireless network in the field of forensic science, the reverse position fingerprint recognition algorithm is proposed in this paper. On the basis of the fingerprint recognition algorithm the new algorithm converts the positioning mode from active positioning to passive positioning.
*Corresponding author: Wei-shuo LIU, School of Computer Science and Information Technology Northeast Normal University, Key Laboratory of Intelligent Information Processing of Jinlin Universities Changchun, Jilin, China, E-mail:
[email protected] Gui-fu YANG, Cheng-lin PU, Jing-bo ZHANG, School of Computer Science and Information Technology Northeast Normal University, Key Laboratory of Intelligent Information Processing of Jinlin Universities, Changchun, Jilin, China Xiao-yu XU, Material Evidence Identification Center of Jilin Provincial Public Security Department, Jilin Provincial Public Security Bureau, Changchun, Jilin, China Lu YAO, Changchun Railway Vehicles Co.,Ltd., CRRC, Changchun, Jilin, China Zhen-bang LIU, Changchun Institute of Applied Chemistry Chinese Academy of Sciences Northeast Chinese Academy of Sciences, Changchun, Jilin, China
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2 Materials and Methods 2.1 The Relationship of RSSI and Distance In the transmission process of the wireless signal, the signal transmitting power PR and the receiving power PT satisfies the following formula, as in (1).
PR = PT / r n . (1) Where r is the distance between the signal transmitting end and the receiving end, and n is the propagation factor. When use dBm as the unit of RSSI, this formula can be converted to the following form, as in (2)
PR ( dBm ) = A − 10 ⋅ n lg r .
(2)
Where A is the received signal value at a distance of 1 m. It can be found that in the case of the invariant of the propagation factor n, RSSI shows a linear attenuation state with the increase of the receiving distance. When the distance is near, the signal attenuation is more obvious, and when the distance is larger than a certain value, the attenuation of RSSI gradually becomes more and more slowly. There is a definite relationship between the RSSI and the signal transmission distance. So the relationship can be used for indoor and outdoor ranging and positioning in the real environment [9].
2.2 The reverse position fingerprint recognition algorithm The reverse position fingerprint recognition algorithm is divided into two stages: offline calibration and positioning. 1) Off-line calibration The main work of this stage is to collect all the information that positioning stage needs of the target area, and use the collected information to build a database of the relevant information corresponding to the physical coordinates. The procedure employed by Off-line calibration consists of the following five steps. a) Divide the target area (wireless network coverage) in the form of a grid as the reference point of the database. All nodes of the grid are the target position need to collect the relevant information. So the size of the grid has a great impact on the database. If the size of grid is too small, it not only increases the workload of data acquisition because of a doubling of measuring the amount of data, but makes the database increases a lot without obviously advance of accuracy. On the other hand, if the size of grid is too large, it causes the loss of accuracy of measurement [12]. b) Select at least three sets of data acquisition equipment as the observers, and respectively fix them to any position in the target area. The observers (usually are laptops) need to let their wireless network card in promiscuous mode.
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c) Place a mobile device in the area as a test target. When the it is connected with the Wi-Fi network covering the area, all the observers obtain and record the RSSI values of the it,then save these data (in array form) and the corresponding position information in database. d) Move the test target in the scope of the area, place it all the nodes position on the grid in proper order. Thus the observers can obtain all the RSSI values corresponding all the nodes of the area. A location fingerprint database is formed by the location of each array corresponding to a reference point. The location information and fingerprint information corresponding relationship shown in Table 1. Table 1. The corresponding relationship between Location information and fingerprint information Location Information
Fingerprint Information
(x1,y1)
(RSSI1, RSSI2, RSSI3, …, RSSIn)
(x2,y2)
(RSSI1, RSSI2, RSSI3, …, RSSIn)
(x3,y3)
(RSSI1, RSSI2, RSSI3, …, RSSIn)
……
……
(xn,yn)
(RSSI1, RSSI2, RSSI3, …, RSSIn)
Where (x1,y1) is the horizontal and vertical coordinates of the reference point, and the array(RSSI1, RSSI2, RSSI3, …, RSSIn) is the signal strength values obtained by the observers. e) According to the damping characteristics of signal optimize the database by using the linear interpolation method [10]. Firstly, determine the interval of the linear interpolation. Then, calculate the coefficient named α,by using the formula, as in (3) or (4).
α = ( x - x0 ) / ( x1 - x0 ) (3) α = ( y - y0 ) / ( y1 - y0 ) (4) Finally, calculate the RSSI at this point, using the formula as in (5).
RSSI = RSSI 0 + α ( RSSI1 - RSSI 0 ) (5) Using linear interpolation method to optimize the database, makes the number of reference points much more than before. The new information and the actual information is basically consistent so that it cannot influence positioning accuracy [11]. 2) Positioning The main work of this stage is to match the relevant information acquired in actually positioning process with the information in the database, and then achieve the positioning of the target device.
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According to the multipath characteristics of the Wi-Fi channel, when the target device is located in different position in the area, the characteristic parameters of the information acquired by the observers are single. If the characteristic parameters set is exactly the same or very similar to the parameter set in the database, the position of the target device can be mainly confirmed. The euclidean distance between the two sets of characteristic parameters reflect the similarity degree of them, when assume them as a number of orthogonal vectors in the same multidimensional space. The similarity coefficient can be calculated using the following formula, as in (6).
r=
n
∑ ( RSSI i =1
' i
− RSSI i )
2
(6)
Where r is the similarity coefficient, n is the number of observers, RSSI is the information characteristic parameters in the database, RSSI 'is the information characteristic parameters acquired actually. If the r close to or equal to 0, the similarity of the two sets is high, else it is low. In the indoor space, because the area is limited, the characteristic parameter values of the reference nodes are close to each other, even if the two points that are far from each other. In order to avoid this situation, using the method of calculating the multiple point deviation to optimize and improve the algorithm in the actual positioning process. Each of the reference points has a number of adjacent reference points around it. When calculate the similarity coefficient of collected information and a reference points information, the deviation values of the signal strength between the adjacent points and the reference point can be calculated as in (7).
d=
n
∑ ( RSSI i =1
' i
− RSSI i )
2
(7)
Where d is the deviation value, n is the number of observers, RSSI is the information characteristic parameters in the database, RSSI 'is the information characteristic parameters acquired actually.the value of d is smaller, the distance of between the points and the reference point is closer. After calculating the multiple point deviations, the method of calculating similarity coefficient can be optimized as as in (8). n
R= r + ∑ di (8) i =1
Where R is the optimized similarity coefficient, n is the number of neighboring nodes to calculate the deviation value. The smaller R is, the higher the similarity of the two sets is.
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By using this method, calculate and compare the R of the collected data with the data in the database. The position of target is given corresponding to the set of parameters with the highest similarity.
3 Experiment and result 3.1 Experiment In order to verify the validity of the algorithm in Wi-Fi indoor positioning, design the following experiment. Chose an indoor hall with an area of about 250 m2 as the location area to be measured and fix 3 observers in it. Divide this area use the grid with the ratio of 15*10. Then, chose 37 nodes as the reference points to collect relevant information and save the information to database. After the establishment and optimization of database, place a mobile device in the area and use the observers to locate it. Compare the result with the actual position coordinates and calculate the error distance.
3.2 Analysis results Record the error distance of all positions. Through the record, draw on the pseudo color error distance is as follows: Figure 1 shows the error of the direct test results, and Figure 2 shows the error of the linear interpolation.
Figure 1. Pseudo color map of direct test result
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Figure 2. Pseudo color map of linear interpolation
From the analysis of the data obtained from Figure 3,the error distance is mostly concentrated in 0~0.03 m, the average error distance is 0.0363m.
Figure 3. Pseudo color map of error distance distribution
In this experiment the environment is more ideal, because there is no influence caused by personnel flow and other factors. So the positioning accuracy may be lower in the actual environment. But the results are still sufficient to determine the feasibility of the algorithm.
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4 Conclusion Through the analysis of the basic theory and the actual experiment results verify the reverse position fingerprint identification algorithm is applied in the indoor Wi-Fi positioning. This algorithm meet the special demand in the field of forensic science. It cost very little time for positioning with high positioning accuracy. Acknowledgment: This paper is sponsored by Science and Technology Department of Jilin Province with the project No.20140204031SF and. No.20130206097SF. Here, I sincerely thank the support of the Jilin Provincial Department of science and technology, make our research projects to be implemented. In addition, thank you very much for the Public Security Bureau of Jilin Province. The staffs of the criminal investigation department gave us a great help, so that our work can be carried out smoothly.
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