Abstract: This paper introduces an improved LANDMARC positioning system that can be applied to indoor item positioning. The algorithm classifies the RFID tags based on the original algorithm and introduces the concept of reference error to improve the system positioning accuracy. The improved algorithm combines RF code hardware equipment to build a positioning system. The experimental results show that the improved algorithm reduces the positioning time and improves the positioning accuracy of indoor items.
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At present, the technologies used for indoor positioning mainly include infrared positioning technology, WiFi positioning technology, ZigBee positioning technology, ultra-wideband positioning technology, and RFID positioning technology. Among them, RFID positioning technology has the advantages of non-line-of-sight propagation, large transmission range, fast reading and writing speed, high security, etc. Compared with other positioning technologies, it is more suitable for positioning and tracking indoor objects.
1 Introduction to RFID technology
RFID is a non-contact information transmission method that utilizes radio frequency signals through space coupling (inductance or electromagnetic coupling). It automatically recognizes target objects and acquires relevant data through radio frequency signals, and can work in various harsh environments and simultaneously recognize multiple Labels, quick and easy to operate.
2 LANDMARC system
In order to increase the accuracy of indoor system positioning without increasing the number of readers, the LANDMARC system introduces a fixed reference tag to aid in positioning. The system structure is shown in Figure 1, which includes 4 RF readers, 49 reference tags, and 9 tags to be located.
The LANDMARC positioning technology adopts the idea of â€‹â€‹being statistically referred to as "nearest neighbor", selects k reference symbols whose signal strength values â€‹â€‹are close to the tag to be located, and uses the weighting algorithm to calculate the coordinates of the tag to be located. The algorithm process is as follows.
Suppose there are n RF readers, m reference tags, and u to-be-tracked tags. The signal strength vector defining the tracking tag p is: Tp = (T1, T2, ..., Ti, ..., Tn)T. Where Ti represents the signal strength of the tracking tag perceived by the reader i, i âˆˆ (1, n). The signal strength vector defining the reference tag q is: Rq = (R1, R2, ..., Ri, ..., Rn). Where R1 represents the signal strength of the reference tag i. The Euclidean distance (D) of the tracking tag p and the reference tag q:
By comparing the values â€‹â€‹of the components in D, the k nearest neighbors of the tracking tag p are found, which is called the k-nearest neighbor algorithm. The other u-1 tracking tags use the same method to find the k nearest neighbors. The tracking label coordinates (x, y) can be calculated as follows:
3 Improved LANDMARC system
3.1 Improved LANDMARC system algorithm
Figure 2 shows the improved label layout of the algorithm. The improved algorithm introduces the concept of label layering, and divides the positioning area into several small sub-areas. Each sub-area consists of a primary reference tag (Graphic Reference Tag), a gray dot in Figure 2, and eight adjacent secondary reference tags (the white reference dot in Figure 2). In the improved algorithm, the hierarchical structure can be specifically adjusted according to the actual number of tracking tags.
The specific positioning process of the algorithm can be divided into the following four steps:
1 Determine the positioning sub-area where the tracking tag is located. Suppose there are p main reference labels. The distance vector D=(D1, D2,..., Dp) between the tracking label and the main reference label is calculated by equations (1) and (2), and the components in D are sorted. The area where the corresponding primary reference label is located is the sought positioning sub-area.
2 Each positioning sub-area can be further divided into four positioning areas, as shown in FIG.
Assume that the primary reference label determined in equation (1) is PTk, select eight adjacent secondary reference labels around PTk, calculate the Euclidean distance between the eight secondary reference labels and the tracking label, and select the nearest distance tracking label. Secondary reference label ST1. The secondary reference label closest to the tracking label is selected among the remaining 7 secondary reference labels, and the label must be one of the two adjacent labels of ST1. This determines 1 primary reference tag and 2 adjacent secondary reference tags, which determines the rectangular area in which the tracking tag is located. It can be seen from the above positioning step that this rectangular area is composed of one primary reference tag PTk and three adjacent secondary reference tags (ST1, ST2, ST3).
3 Calculate the position of the tracking tag using the k-nearest neighbor algorithm and the weighting algorithm. Here, when k=4 in the formula (3):
The improved algorithm divides the reference label into two layers, a primary reference label and a secondary reference label, and searches for the nearest neighbor of the tracking label layer by layer to achieve fast positioning.
4 In order to further reduce the positioning error of the improved algorithm and improve the positioning accuracy, the concept of correcting the error vector is introduced. Normally, the tracking tag and the nearest neighbor are no more than 1 m away, so it can be approximated that the positioning error vectors are the same. It can be used to correct the tracking label coordinates calculated by equation (5).
In order to calculate the correction error, it is first necessary to determine the reference tag closest to the tracking tag, called the Key Reference Tag (KT). The key reference tag is selected by comparing the Euclidean distance between the primary reference tag PTk and the three adjacent secondary reference tags (ST1, ST2, ST3) to the tracking tag. The calculated coordinates (p', q') of KT are calculated by using equations (2) to (4). It is known that the true coordinate of KT is (p, q), so the positioning error vector of KT can be obtained:
3.2 Improved LANDMARC System Hardware
The improved algorithm chose RF Code's M100 tag and M250 reader. The M100 tag is shown in Figure 4.
The M100 is an active tag with a typical transmission range of 90m and an operating frequency of 433.92 MHz. The tamper switch is installed in the M100 tag, so it is more suitable for item tracking. At low speeds, the tag battery life can be as long as 5 to 7 years.
The M250 reader is shown in Figure 5. The M250 reader directly provides RF signal strength values â€‹â€‹and can simultaneously monitor 1400 tags with a beacon rate of 10 s.
The M250 reader transmits data in a variety of interfaces, via a USB port, a wired Ethernet interface, or an 802.11b/g wireless network card integrated inside the reader. The reader supports encrypted connections (HTTPS and SSH) and supports Power over Ethernet (PoE) RF Code M250 reader product interface as shown in Figure 6.
In the experiment, the M250 reader Ethernet interface is connected to the router's LAN port, and the data is transmitted using the TCP/IP protocol. The router forwards the signal strength data of all tags to the background processing computer over the network. The computer-side programming implements the RFID positioning management platform, which can obtain tag data through the Internet, realize remote control and management of nodes, and perform positioning parameter setting, data processing, and positioning result display. The positioning platform is based on the .NET Framework 4.0 and is implemented in the C# programming language. The positioning management platform based on .NET Framework 4.0 is shown in Figure 7.
4 Experiments and discussion
Figure 8 shows the error comparison graph obtained by the two algorithms using 10 experiments. In the fifth experiment, the original algorithm error was 0.97 m, the improved algorithm error was 0.65 m, and the improved algorithm positioning error was reduced by 32.7%. In addition, in the other experiments, the improved algorithm also has an accuracy of more than 10%, and the positioning accuracy of the improved algorithm is higher than the original algorithm. The comparison of the two algorithm errors is shown in Figure 8.
The improved algorithm uses the reference error to correct the calculated coordinates of the tracking tag, which offsets the interference of some environmental factors on the positioning accuracy, and makes the improved algorithm better than the original algorithm.
Based on the discussion of RFID technology and LANDMARC algorithm, this paper proposes a hierarchical structure of reference labels, and introduces the concept of reference error to improve the original algorithm. It proves that the improved algorithm has faster positioning speed and higher positioning accuracy. Degree, can be widely used in the positioning and management of indoor items, with certain research and practical application value.
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