CN110888109B - RFID label positioning method based on generalized multidimensional scale - Google Patents

RFID label positioning method based on generalized multidimensional scale Download PDF

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CN110888109B
CN110888109B CN201911159101.2A CN201911159101A CN110888109B CN 110888109 B CN110888109 B CN 110888109B CN 201911159101 A CN201911159101 A CN 201911159101A CN 110888109 B CN110888109 B CN 110888109B
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map
distance
matrix
distance matrix
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马永涛
田成龙
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Tianjin 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
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06K—GRAPHICAL DATA READING; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K7/00—Methods or arrangements for sensing record carriers, e.g. for reading patterns
    • G06K7/10—Methods or arrangements for sensing record carriers, e.g. for reading patterns by electromagnetic radiation, e.g. optical sensing; by corpuscular radiation
    • G06K7/10009—Methods or arrangements for sensing record carriers, e.g. for reading patterns by electromagnetic radiation, e.g. optical sensing; by corpuscular radiation sensing by radiation using wavelengths larger than 0.1 mm, e.g. radio-waves or microwaves
    • G06K7/10019—Methods or arrangements for sensing record carriers, e.g. for reading patterns by electromagnetic radiation, e.g. optical sensing; by corpuscular radiation sensing by radiation using wavelengths larger than 0.1 mm, e.g. radio-waves or microwaves resolving collision on the communication channels between simultaneously or concurrently interrogated record carriers.
    • G06K7/10079—Methods or arrangements for sensing record carriers, e.g. for reading patterns by electromagnetic radiation, e.g. optical sensing; by corpuscular radiation sensing by radiation using wavelengths larger than 0.1 mm, e.g. radio-waves or microwaves resolving collision on the communication channels between simultaneously or concurrently interrogated record carriers. the collision being resolved in the spatial domain, e.g. temporary shields for blindfolding the interrogator in specific directions
    • G06K7/10089—Methods or arrangements for sensing record carriers, e.g. for reading patterns by electromagnetic radiation, e.g. optical sensing; by corpuscular radiation sensing by radiation using wavelengths larger than 0.1 mm, e.g. radio-waves or microwaves resolving collision on the communication channels between simultaneously or concurrently interrogated record carriers. the collision being resolved in the spatial domain, e.g. temporary shields for blindfolding the interrogator in specific directions the interrogation device using at least one directional antenna or directional interrogation field to resolve the collision
    • G06K7/10099—Methods or arrangements for sensing record carriers, e.g. for reading patterns by electromagnetic radiation, e.g. optical sensing; by corpuscular radiation sensing by radiation using wavelengths larger than 0.1 mm, e.g. radio-waves or microwaves resolving collision on the communication channels between simultaneously or concurrently interrogated record carriers. the collision being resolved in the spatial domain, e.g. temporary shields for blindfolding the interrogator in specific directions the interrogation device using at least one directional antenna or directional interrogation field to resolve the collision the directional field being used for pinpointing the location of the record carrier, e.g. for finding or locating an RFID tag amongst a plurality of RFID tags, each RFID tag being associated with an object, e.g. for physically locating the RFID tagged object in a warehouse

Abstract

The invention relates to a large-scale RFID label positioning method based on generalized multidimensional scale, which comprises a data preprocessing stage, a map fragment generating stage and a map fragment assembling stage. Wherein, the data preprocessing stage comprises the following steps: constructing a distance matrix according to the obtained distance information; and constructing an indication matrix according to the distance matrix. The map fragment generation phase comprises the following steps: processing the indication matrix I by a Bron-Kerbosch algorithm to obtain a series of subnetworks with labels capable of communicating, and calling the subnetworks as a maximum clique; merging the read number set into the maximum clique to obtain an augmented maximum clique; slicing the distance matrix by using the augmented maximum cluster to obtain a distance matrix of the label cluster determined by the augmented maximum cluster; and obtaining the corresponding map fragments by using the distance matrix obtained by processing.

Description

RFID label positioning method based on generalized multidimensional scale
Technical Field
The invention belongs to the technical field of RFID positioning, and aims to solve the problem of positioning tags with low complexity, high precision and high concurrency by obtaining the distance between tags by utilizing a backscatter-based inter-tag communication network.
Background
With the explosive development of integrated circuits and Internet of Things (IoT), more and more researchers have been invested in the field of radio-frequency identification tag (RFID) based indoor positioning, especially Ultra High Frequency (UHF) based RFID, in the last decades. Sensing location information of an object to which an RFID tag is attached is particularly important. In many internet of things systems, the existence of position information directly determines whether the system can exert all functions and provide satisfactory services for users. The development of the future 5G network puts higher requirements on the accuracy, concurrency and real-time performance of indoor positioning.
Existing indoor positioning technologies can be classified into ranging-based and non-ranging-based positioning methods according to positioning principles. Typical ranging techniques include Received Signal Strength (RSS), time of arrival (ToA), time difference of arrival (TDoA), angle of arrival (angle of arrival, AoA), phase of arrival (PoA), phase difference of arrival (PDoA), and organic combinations thereof. Positioning methods based on ranging often only can achieve single-target positioning, however, many RFID applications require multi-target concurrent positioning technology.
Non-ranging-based localization technologies mainly include fingerprinting (fingerprint), radio frequency holographic imaging (radio frequency tomography) and radio frequency tomography (RTI). A representative work of fingerprinting is LANDMARC, a location-aware prototype system that uses reference tags to locate objects in a room. A.Buffi et al put forward a Synthetic Aperture Radar (SAR) -based positioning method in an unmanned plane scene equipped with UHFRFID reader. In addition, l.yang et al propose an RFID-based location system, Tagoram. The system uses a differential enhanced hologram (DAH) to realize real-time tracking and high-precision positioning of the mobile RFID tag. The other RFID positioning method of the brand-new outcrop corner is RTI, can realize the accurate positioning of multiple targets under the background that a fingerprint library is not established, and has the application precondition that the number of labels to be positioned must be known in advance. Y.ma et al propose a novel RTI positioning technique, which can accurately position multiple targets in advance under the background of unknown target number, and make up for the technical gap in RTI. These non-ranging methods either can achieve single-target continuous positioning, small-scale high-concurrency positioning, or large-scale low-concurrency positioning, and cannot meet the large-scale high-concurrency positioning requirements. A novel positioning technology is urgently needed in the industry, and the multi-label concurrent positioning under a large-scale deployment scene is supported.
Disclosure of Invention
The invention relates to a RFID label positioning method based on generalized multidimensional scaling, which utilizes a backscatter-based communication network between labels to obtain distance estimation between the labels and executes a multidimensional scaling algorithm from a distributed angle. Aiming at the situation that the labels cannot be communicated in a large-scale label deployment scene, the invention firstly excavates the sub-networks which can be communicated among the labels in the network; then, using MDS (multidimensional scaling) algorithm for each sub-network to obtain a pool of map fragments; finally, the fragmented maps are assembled to form a complete map. The positioning effect of low complexity, high precision and high concurrency in a large-scale label deployment scene is achieved. The technical scheme of the invention is as follows:
a large-scale RFID label positioning method based on generalized multidimensional scale comprises a data preprocessing stage, a map fragment generating stage and a map fragment assembling stage. Wherein the content of the first and second substances,
the data preprocessing stage comprises the following steps:
1) constructing a distance matrix D according to the obtained distance information:
Figure BDA0002285576970000021
in the formula:
Figure BDA0002285576970000022
i, j is 1,2, …, M, is the distance between tags,
Figure BDA0002285576970000023
i 1,2, …, M, j 1,2, … N, tag-reader distance,
Figure BDA0002285576970000024
i, j is 1,2, … N, which is the inter-reader distance; m is the number of tags, and N is the number of readers; if communication between tags is not possible, provision is made for
Figure BDA0002285576970000025
2) Constructing an indication matrix I according to the distance matrix:
Figure BDA0002285576970000026
in the formula: p, q ═ 1,2, …, M; [*]pqRepresenting the element at the position of the p row and the q column of the matrix;
the map fragment generation phase comprises the following steps:
1) processing the indication matrix I by a Bron-Kerbosch algorithm to obtain a series of subnetworks with labels capable of communicating, and calling the subnetworks as a maximum clique;
2) merging the read number set into the maximum clique to obtain an augmented maximum clique;
3) slicing the distance matrix by using the augmented maximum cluster to obtain a distance matrix of the label cluster determined by the augmented maximum cluster;
4) and processing the obtained distance matrix by using a multidimensional scaling algorithm to obtain a corresponding map fragment. In the map fragment assembling stage, the method comprises the following steps:
1) selecting a suitable map fragment
Figure BDA0002285576970000027
i0Numbering the map fragments, initializing an intermediate map
Figure BDA0002285576970000028
Let k be 1 and the candidate set C be {1,2, …, n } \ i0N is the number of map tiles, "\" is the difference operator of the set;
2) if the candidate set C is empty, turning to step 5); otherwise, selecting the map fragment with the most common nodes with the intermediate map
Figure BDA0002285576970000031
ikNumbering the map fragments;
3) assembly
Figure BDA0002285576970000032
And
Figure BDA0002285576970000033
first, a rigid transformation is determined by Procrustes analysis, so that the distribution of common nodes within the transformed map fragments matches as closely as possible the intermediate map
Figure BDA0002285576970000034
Distribution of internal common nodes; then, the intermediate map and the transformed map fragments are stitched, the average value of the coordinates of the common nodes is taken, the coordinates of the respective unique nodes are kept at the same time, and the updated intermediate map is obtained
Figure BDA0002285576970000035
4) Updating candidate set C ═ C \ ikK is k + 1; turning to step 2);
5) absolute intermediate map: determining a rigid transformation by Procrustes analysis, so that the distribution of the reader nodes in the transformed intermediate map is matched with the distribution of a real reader as much as possible; this transformation is then applied to the entire intermediate map, resulting in the estimated position coordinates of the tag.
The invention relates to a large-scale RFID label positioning based on generalized multidimensional scale, which utilizes obtained distance estimation information to construct a distance matrix and an indication matrix of a network, introduces a Bron-Kerbosch algorithm in a graph theory to obtain a potential completely-communicable sub-network in the network, expands the sub-network to ensure the feasibility of assembly, utilizes an MDS algorithm to obtain a relative map of each expanded network, and obtains the position information of a large-scale label through hierarchical assembly. Compared with the traditional RFID label positioning method, the method is based on the cooperation, dimension reduction and statistical thought, executes the multi-dimensional scale algorithm from a distributed angle, greatly expands the application scene of the multi-dimensional scale algorithm, and realizes the positioning effect with low complexity, high precision and high concurrency. Meanwhile, the operation mechanism of the hierarchical assembly enables the assembly error of the front stage to rise in a controllable and stable mode, and the rear stage assembly has strong anti-interference capability. In the face of map fragments with severe quality fluctuation, the input mismatch rate can be greatly reduced by multi-stage assembly, and a middle map with stable quality is output.
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FIG. 1 is a generalized multi-dimensional scaling technique based label localization scenario.
Fig. 2 is an algorithm flow chart.
Detailed Description
The following describes a large-scale RFID tag positioning method based on generalized multi-dimensional scale according to the present invention with reference to the accompanying drawings.
A label localization scenario based on generalized multi-dimensional scaling techniques is shown in fig. 1. Several tags are scattered randomly in a large-scale (10m × 10m) positioning scene, and isolated tags in the scene are removed.
The positioning method estimates the position of the positioning device according to the estimated distance information, the algorithm flow is shown in figure 2, and the steps are as follows:
1) constructing a distance matrix according to the obtained distance information:
Figure BDA0002285576970000041
in the formula:
Figure BDA0002285576970000042
i, j is 1,2, …, M, is the distance between tags,
Figure BDA0002285576970000043
i 1,2, …, M, j 1,2, … N, tag-reader distance,
Figure BDA0002285576970000044
i, j is 1,2, … N, which is the inter-reader distance; m is the number of tags, and N is the number of readers. If communication between tags is not possible, provision is made for
Figure BDA0002285576970000045
2) Constructing an indication matrix according to the distance matrix:
Figure BDA0002285576970000046
in the formula: p, q ═ 1,2, …, M; [*]pqRepresenting the element at the position of the p-th row and q-th column of the matrix.
In the map fragment generation phase, the method comprises the following steps:
3) the Bron-Kerbosch algorithm processes the indication matrix I to obtain a series of subnetworks where the tags can communicate. Set label number set as Lt1,2, …, M, the number set corresponding to the label in the subnet is
Figure BDA0002285576970000047
α ═ 1,2, …, Ω, where Ω is the number of subnets. And call SαIs a very large group of networks.
4) Let the number set of the reader be Lr(M, M +1, …, M + N), LrIncorporated into the maximal pellet to obtain the augmented maximal pellet
Figure BDA0002285576970000048
β＝1,2,…,Ω。
5) And slicing the distance matrix by using the augmented maximum cluster to obtain the distance matrix of the label cluster determined by the augmented maximum cluster.
6) And processing the obtained distance matrix by using a multidimensionalscaling algorithm to obtain a corresponding map fragment.
In the map fragment assembling stage, the method comprises the following steps:
7) selecting a suitable map fragment
Figure BDA0002285576970000049
i0Numbering the map fragments, initializing an intermediate map
Figure BDA00022855769700000410
Let k be 1 and the candidate set C be {1,2, …, n } \ i0And n is the number of map tiles.
8) If candidate set C is empty, go to step 11). Otherwise, selecting the map fragment with the most common nodes with the intermediate map
Figure BDA0002285576970000051
ikThe map tiles are numbered.
9) Assembly
Figure BDA0002285576970000052
And
Figure BDA0002285576970000053
first, a rigid transformation is determined by Procrustes analysis, so that the distribution of common nodes within the transformed map fragments matches as closely as possible the intermediate map
Figure BDA0002285576970000054
Distribution of internal common nodes; subsequently, the intermediate map and the transformed map fragments are stitched, and the common node coordinates are averaged while preservingThe coordinates of the respective unique nodes are obtained to obtain an updated intermediate map
Figure BDA0002285576970000055
10) Updating candidate set C ═ C \ ikAnd k is k + 1. And 8) turning.
11) The intermediate map is absolute. Determining a rigid transformation by Procrustes analysis, so that the distribution of the reader nodes in the transformed intermediate map is matched with the distribution of a real reader as much as possible; this transformation is then applied to the entire intermediate map, resulting in the estimated position coordinates of the tag.

Claims (2)

1. A large-scale RFID label positioning method based on generalized multidimensional scale comprises a data preprocessing stage, a map fragment generation stage and a map fragment assembly stage, wherein,
the data preprocessing stage comprises the following steps:
1) constructing a distance matrix D according to the obtained distance information:
Figure FDA0003303096050000011
in the formula:
Figure FDA0003303096050000012
m, is the distance between the labels,
Figure FDA0003303096050000013
is the tag-reader distance and,
Figure FDA0003303096050000014
is the distance between readers; m is the number of tags, and N is the number of readers; if communication between tags is not possible, provision is made for
Figure FDA0003303096050000015
2) Constructing an indication matrix I according to the distance matrix:
Figure FDA0003303096050000016
in the formula: p, q ═ 1,2, …, M; [*]pqRepresenting the element at the position of the p row and the q column of the matrix;
the map fragment generation phase comprises the following steps:
1) processing the indication matrix I by a Bron-Kerbosch algorithm to obtain a series of subnetworks with labels capable of communicating, and calling the subnetworks as a maximum clique;
2) merging the read number set into the maximum clique to obtain an augmented maximum clique;
3) slicing the distance matrix by using the augmented maximum cluster to obtain a distance matrix of the label cluster determined by the augmented maximum cluster;
4) and processing the obtained distance matrix by using a multidimensional scaling algorithm to obtain a corresponding map fragment.
2. The method of claim 1, wherein the map fragment assembling stage comprises the following steps:
1) selecting a suitable map fragment
Figure FDA0003303096050000017
i0Numbering the map fragments, initializing an intermediate map
Figure FDA0003303096050000018
Let k be 1 and the candidate set C be {1,2, …, n } \ i0N is the number of map tiles, "\" is the difference operator of the set;
2) if the candidate set C is empty, turning to step 5); otherwise, selecting the map fragment with the most common nodes with the intermediate map
Figure FDA0003303096050000019
ikNumbering the map fragments;
3) assembly
Figure FDA0003303096050000021
And
Figure FDA0003303096050000022
first, a rigid transformation is determined by Procrustes analysis, so that the distribution of common nodes within the transformed map fragments matches as closely as possible the intermediate map
Figure FDA0003303096050000023
Distribution of internal common nodes; then, the intermediate map and the transformed map fragments are stitched, the average value of the coordinates of the common nodes is taken, the coordinates of the respective unique nodes are kept at the same time, and the updated intermediate map is obtained
Figure FDA0003303096050000024
4) Updating candidate set C ═ C \ ikK is k + 1; turning to step 2);
5) absolute intermediate map: determining a rigid transformation by Procrustes analysis, so that the distribution of the reader nodes in the transformed intermediate map is matched with the distribution of a real reader as much as possible; this transformation is then applied to the entire intermediate map, resulting in estimated position coordinates of the tags.
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Patent Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101350635A (en) * 2008-09-05 2009-01-21 清华大学 Method for self-locating sensor network node within sparseness measuring set base on shortest path
EP2428817A1 (en) * 2010-09-13 2012-03-14 Ricoh Company, Ltd. Motion tracking techniques for RFID tags
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