CN111199162A - RFID reader fault self-adaptive positioning method - Google Patents

RFID reader fault self-adaptive positioning method Download PDF

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CN111199162A
CN111199162A CN202010028807.1A CN202010028807A CN111199162A CN 111199162 A CN111199162 A CN 111199162A CN 202010028807 A CN202010028807 A CN 202010028807A CN 111199162 A CN111199162 A CN 111199162A
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刘发贵
钟德祥
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South China University of Technology SCUT
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Abstract

The invention discloses a fault self-adaptive positioning method for an RFID reader. The method comprises the following steps: constructing a fault self-adaptive positioning system of the RFID reader; providing an online sequence fuzzy width learning system according to the RFID reader fault self-adaptive positioning system; an RFID reader fault self-adaption strategy is provided based on an online sequence fuzzy width learning system; and completing the RFID reader fault self-adaptive positioning based on the RFID reader fault self-adaptive strategy. By the aid of the RFID reader fault self-adaptive positioning method, data streams which continuously arrive in the environment can be processed, and subsequent data streams can be processed when partial readers are in fault, so that the aim of long-time stable positioning is achieved.

Description

RFID reader fault self-adaptive positioning method
Technical Field
The invention relates to the field of RFID, machine learning and positioning algorithms, in particular to a fault self-adaptive positioning method for an RFID reader.
Background
With the development of the internet of things technology, the demand of people for the application of the internet of things is rapidly increased, and among the technologies, the wireless positioning technology shows great liveliness in both military and civil aspects, and the wireless positioning technology and the wireless positioning service-based wireless positioning service play an increasingly greater role in the life of people. Among outdoor positioning technologies, the global positioning system is the most famous and representative positioning technology, and is widely used in military and civil applications. The demand of people on indoor positioning application is getting larger and larger, and the indoor long-time positioning demand has great potential.
Since the RFID has the characteristics of non-line-of-sight, non-contact, and capability of quickly identifying an object, it has certain advantages in indoor positioning. The basic RFID system consists of a label, an antenna and a reader, and the basic working principle is as follows: the RFID reader generates a magnetic field through an antenna, the tag enters the magnetic field, receives a radio frequency signal sent by the reader, and sends product information (a passive tag or a passive tag) stored in a chip or actively sends a radio frequency signal (an active tag or an active tag) of a certain frequency by means of energy obtained by electromagnetic induction.
At present, RFID positioning methods are mainly divided into two types, namely distance-based positioning and scene-based positioning. There are many distance-based RFID positioning methods, such as received signal strength indication, angle of arrival, time of arrival, and time difference of arrival, but these methods have high sensitivity to the environment and low accuracy (Ni L M, Zhang D, Souryal M r. RFID-based analysis and tracking technologies [ J ]. IEEE Wireless Communications,2011,18(2): 45-51.). Meanwhile, another type of RFID positioning method based on scene analysis is favored by researchers due to its advantages of high environmental adaptability, relatively low cost, etc. The LANDMARC is a classic RFID positioning algorithm (Ni LM, Liu Y, Lau Y C, et al LANDMARC: inductor location sensing active RFID [ C ]// Proceedings of the First IEEE International Conference on Perdynamic computing and Communications,2003 (PerCom 2003). IEEE,2003:407 + 415). The algorithm introduces the concept of a reference tag, and the partial tags with high signal strength indicating value similarity are listed as alternative reference tags, so as to obtain the position of a target tag through calculation. In the method based on scene analysis, some students use artificial neural networks and other methods to perform RFID positioning, and a passive RFID indoor positioning scheme (Kung H Y, Chaisits, wavelength N T M. optimization of an RFID positioning identification scheme [ J ]. International Journal of Communication Systems,2015,28(4): minus 644.) is provided, which combines the LANDMAC scheme and the BP neural network, and after LANDMAC positioning, the positioning result is further processed by using the BP neural network to obtain a more accurate positioning result. In addition, some researchers use fuzzy Neural Networks for RFID indoor localization, and use fuzzy Neural Networks to analyze The relation between The actual coordinates of The reference tags and The environmental errors, thereby adjusting The environmental parameters in The RFID localization system (Huang Y J, Chen C Y, Hong B W, et al. fuzzy Neural network based RFID index localization sensing technical [ C ]// The 2010International journal conference on Neural Networks (IJCNN). IEEE,2010: 1-5.).
However, in an actual RFID indoor positioning scenario, the positioning system needs to work for a long time, and therefore, a fault may occur. In order to solve the problem of RFID tag failure, some researchers have proposed a solution to handle independent permanent tag failure and regional permanent tag failure, but cannot handle RFID reader failure (Zhu W, Cao J, Xu Y, et. Fault-tolerant RFID reader localization based on passive RFID tags [ J ]. IEEETransactions on Parallel and Distributed Systems,2013,25(8): 2065) 2076.). When the RFID reader fails to be maintained in time, the existing positioning algorithm cannot maintain the precision of the original positioning system, so that the existing method needs to be improved, and a positioning method suitable for the RFID reader failure is provided to achieve the aim of long-time stable positioning.
Disclosure of Invention
The invention provides a self-adaptive fault positioning method for an RFID reader. The basic idea of the invention is to provide an online sequential fuzzy width learning system, which is improved to have online sequential learning capability, provide a pseudo-inverse update formula of an update matrix of a model and an update formula of a parameter matrix, and can process data streams which continuously arrive in an environment; and meanwhile, a fault self-adaption strategy of the RFID reader is provided, when part of the readers have faults, a conversion matrix is provided to process the coefficients and membership function centers randomly generated in the initialized fuzzy subsystem, new coefficient and membership function centers are generated, and the subsequent data streams are processed.
The purpose of the invention is realized by at least one of the following technical solutions.
An RFID reader fault self-adaptive positioning method comprises the following steps:
s1, constructing a fault self-adaptive positioning system of the RFID reader;
s2, providing an online sequence fuzzy width learning system according to the RFID reader fault self-adaptive positioning system;
s3, providing a fault self-adaptive strategy of the RFID reader based on the online sequence fuzzy width learning system;
s4, completing RFID reader fault adaptive positioning based on the RFID reader fault adaptive strategy.
Further, in step S1, the RFID reader fault adaptive positioning system includes M RFID readers, Q RFID reference tags, and a target tag to be positioned; the reference tags with known coordinate positions are deployed in a plane where the target tags to be positioned are located in an equilateral triangle mode, and the readers are deployed on four edges of the plane.
Further, in step S2, the online sequential fuzzy width learning system includes an input layer, a fuzzy subsystem, an enhancement layer and an output layer, wherein the fuzzy subsystem is a TS fuzzy model, and in the ith fuzzy subsystem, the weight is set
Figure BDA0002363493030000021
Figure BDA0002363493030000031
Wherein xsMFor input data, s1, 2, N is the number of input data, M is the input data dimension, KiIn order to be able to blur the number of rules,
Figure BDA0002363493030000032
for randomly generated coefficients, the membership function is a Gaussian function
Figure BDA0002363493030000033
Wherein
Figure BDA0002363493030000034
Respectively, the center and width of the membership function, and the weighted output of the fuzzy rule is
Figure BDA0002363493030000035
Wherein
Figure BDA0002363493030000036
The enhancement layer is a nonlinear transformation layer;
the number of the fuzzy subsystems is n, the number of the enhanced node groups is m, wherein the fuzzy rule number of the ith fuzzy subsystem is KiThe number of the j-th group of enhanced nodes is LjOf 1 at
Figure BDA0002363493030000037
The signal strength of the RFID reader reading the reference label at the s time is
Figure BDA0002363493030000038
Figure BDA0002363493030000039
Generation of N0The bar input data is
Figure BDA00023634930300000310
The output data is
Figure BDA00023634930300000311
Wherein
Figure BDA00023634930300000312
Is the Nth0An array of signal strengths of the strip input data,
Figure BDA00023634930300000313
is the Nth0Coordinates of the input data are obtained, and C is a characteristic dimension of the output data;
will N0The bar input data is used as the input of a fuzzy subsystem in the online sequential fuzzy width learning system, the fuzzy subsystem intermediate output and the fuzzy subsystem output are obtained through calculation, the fuzzy subsystem intermediate output is used as the input of an enhancement node, the enhancement layer output is further obtained through calculation, the fuzzy subsystem output and the enhancement layer output are connected with the output layer, and the weight to be calculated is obtained through pseudo-inverse.
Further, in step S3, the RFID reader fault adaptive policy is as follows:
when some RFID readers are in fault, the number of the RFID readers is changed into M' and 0<M' is less than or equal to M, then subsequently generated NaThe bar input data is
Figure BDA00023634930300000314
RSSIs＝(RSSIs1,RSSIs2,...,RSSIsM′),s＝1,2,...,NaThe output data is
Figure BDA00023634930300000315
Wherein
Figure BDA00023634930300000316
Is the NthaSignal Strength array, RSSI, of the strip input datasM′For the signal strength of the mth reader reading the reference tag at the s-th time,
Figure BDA00023634930300000317
is the NthaCoordinates of the bar input data;
after partial RFID readers are in fault, the characteristic dimension of input data is changed from M to M', and a conversion matrix T needs to be introduced into an initialization fuzzy subsystem
Figure BDA00023634930300000318
Coefficients generated randomly by a function
Figure BDA00023634930300000319
And center of membership function
Figure BDA00023634930300000320
Is processed to generate
Figure BDA00023634930300000321
And
Figure BDA00023634930300000322
Figure BDA00023634930300000323
Figure BDA00023634930300000324
wherein
Figure BDA00023634930300000325
The rule of the transformation matrix T is as follows:
(1) each row in the matrix T has at most one element with the value of 1, and the values of the other elements are 0;
(2) each column in the matrix T has only one element with the value of 1, and the values of the other elements are all 0;
(3) if all the elements of the ith row in the matrix T are 0, indicating that the ith reader in the original RFID reader fault self-adaptive positioning system has a fault;
since the transform matrix T is a sparse logic matrix, an array of size 1 × M' is created to store the row number i, i ═ 1,2ij＝1。
Further, step S4 specifically includes the following steps:
s4.1, acquiring the signal intensity and the position data set of the RFID reference tag in real time;
s4.2, initializing the online sequential fuzzy width learning system in an offline stage;
and S4.3, updating the initial online sequence fuzzy width learning system obtained in the offline stage in the online stage and completing positioning.
Further, in step S4.1, the signal strength of the RFID reference tag is obtained in real time by controlling the read-write state of the RFID reader, and the corresponding position information of the reference tag in the system is obtained by the RFID reader fault adaptive positioning system.
Further, step S4.2 comprises the steps of:
s4.2.1, preprocessing the data obtained in the step S4.1;
s4.2.2 at [0,1 ]]In-range, randomly generating fuzzy subsystems according to uniform distribution
Figure BDA0002363493030000041
Coefficient of function
Figure BDA0002363493030000042
S4.2.3, calculating the intermediate output of all input samples in the fuzzy subsystem in the ith fuzzy subsystem
Figure BDA0002363493030000043
And an output
Figure BDA0002363493030000044
Figure BDA0002363493030000045
S4.2.4 according to the formula
Figure BDA0002363493030000046
Computing the intermediate outputs of all n fuzzy subsystems
Figure BDA0002363493030000047
As input to the enhancement node;
s4.2.5 according to the formula
Figure BDA0002363493030000048
And formula
Figure BDA0002363493030000049
Computing output of jth group of enhanced nodes
Figure BDA00023634930300000410
And total output of the enhancement layer
Figure BDA00023634930300000411
Wherein
Figure BDA00023634930300000412
Heel
Figure BDA00023634930300000413
As intermediate output of the fuzzy subsystem
Figure BDA00023634930300000414
The weight and bias of the connection with the enhanced node group are set at 0,1]Randomly generating within the range, wherein m is the group number of the enhanced nodes;
s4.2.6 according to the formula
Figure BDA00023634930300000415
Calculating the total output of all n fuzzy subsystems
Figure BDA00023634930300000416
Wherein
Figure BDA00023634930300000417
Ω＝(Ω1,...,Ωn)，Δ＝((δ1)T,...,(δn)T)T，
Figure BDA00023634930300000418
And
Figure BDA00023634930300000419
in order to input the data, the data is,
Figure BDA00023634930300000420
is the weighted output of the fuzzy rule and,
Figure BDA00023634930300000421
for the parameters introduced, the initial ones can be used
Figure BDA00023634930300000422
Is converted into
Figure BDA00023634930300000423
When the solution is carried out through the pseudo-inverse in the subsequent steps, the weight number of the solution is determined by
Figure BDA00023634930300000424
Become into
Figure BDA00023634930300000425
S4.2.7 according to the formula
Figure BDA00023634930300000426
Computing the matrix A0And a parameter matrix W0Wherein W iseFor the connection weights of the enhancement layer to the output layer,
Figure BDA00023634930300000427
Figure BDA00023634930300000428
is A0The calculation formula of (A) is
Figure BDA00023634930300000429
Further, step S4.2.1 includes the steps of:
s4.2.1.1, the first
Figure BDA0002363493030000051
Repeatedly reading the signal intensity of the same label by each reader for N times, and recording the signal intensity read at the kth time as
Figure BDA0002363493030000052
S4.2.1.2, calculating the variance delta of RSSI value2：
Figure BDA0002363493030000053
Wherein
Figure BDA0002363493030000054
S4.2.1.3 for the kth signal strength
Figure BDA0002363493030000055
If it is not
Figure BDA0002363493030000056
Then removing the RSSI set with the size of N', and calculating the average value of the set
Figure BDA0002363493030000057
As the average signal intensity:
Figure BDA0002363493030000058
s4.2.1.4, obtaining N0Bar input data
Figure BDA0002363493030000059
Figure BDA00023634930300000510
Step S4.2.3 includes the steps of:
s4.2.3.1, input data X using k-means clustering0Clustering is carried out to obtain KiA cluster center;
s4.2.3.2, use K obtained in step S4.2.3.1iCenter of individual clustering center to Gaussian membership function
Figure BDA00023634930300000511
Initialising, width of membership function
Figure BDA00023634930300000512
S4.2.3.3 according to the formula
Figure BDA00023634930300000513
And formula
Figure BDA00023634930300000514
Computing an intermediate output of an s-th input sample in a fuzzy subsystem
Figure BDA00023634930300000515
And an output
Figure BDA00023634930300000516
S4.2.3.4 according to the formula
Figure BDA00023634930300000517
And formula
Figure BDA00023634930300000518
Computing intermediate outputs of all input samples in a fuzzy subsystem
Figure BDA00023634930300000519
And an output
Figure BDA00023634930300000520
Further, in step S4.3, the online stage includes an online sequential learning stage and an online working stage, the online sequential learning stage is used for completing updating of the initial online sequential fuzzy width learning system, the online working stage is used for completing positioning, and the online sequential learning stage and the online working stage can run in parallel;
in the online sequential learning stage, the number of readers is changed from M to M' and 0<M' is less than or equal to M, and newly added data are
Figure BDA00023634930300000521
The method specifically comprises the following steps:
s4.3.1.1, preprocessing the data using step S4.2.1;
s4.3.1.2, generating a transformation matrix T according to the rule of the transformation matrix T;
s4.3.1.3 according to the formula
Figure BDA00023634930300000522
And formula
Figure BDA00023634930300000523
Calculating transformed coefficients
Figure BDA00023634930300000524
And center of membership function
Figure BDA0002363493030000061
S4.3.1.4 according to the formula
Figure BDA0002363493030000062
Calculating the operation output matrix A of the newly added dataaWherein
Figure BDA0002363493030000063
Figure BDA0002363493030000064
Figure BDA0002363493030000065
And
Figure BDA0002363493030000066
in order to add new input data to the data,
Figure BDA0002363493030000067
intermediate output of all newly added input samples in the fuzzy subsystem;
s4.3.1.5 according to the formula
Figure BDA0002363493030000068
And
Figure BDA0002363493030000069
computing an update matrixxA and the pseudo-inversexA+Wherein
Figure BDA00023634930300000610
S4.3.1.6 according to the formula
Figure BDA00023634930300000611
Computing an updated parameter matrixxW。
Further, the online working phase specifically includes the following steps:
s4.3.2.1, sending the RSSI information of the target label to an online sequence fuzzy width learning system after being updated in an online sequence learning stage;
s4.3.2.2, the position of the target label is estimated by using the RSSI information as the input of the online sequential fuzzy width learning system after the updating of the online sequential learning stage.
Compared with the prior art, the invention has the following advantages and technical effects:
the core of the method is that the signal intensity and the position of a reference label are firstly obtained in an off-line stage, a fuzzy subsystem and an enhancement layer of a fuzzy width system are initialized after data preprocessing is carried out, and finally an initial positioning model is obtained through training; in the online sequence learning stage, firstly, the new data of the RFID readers with the changed number are preprocessed, then, the coefficients and the membership centers are converted according to the conversion matrix, and finally, the initial positioning model obtained by the off-line stage training is updated according to the updating function; in the on-line working stage, firstly, the data of the positioning request sent by the user is preprocessed, then the preprocessed data is input into the updated model, and the positioning result is calculated and sent to the client. By the aid of the RFID reader fault self-adaptive positioning method, data streams which continuously arrive in the environment can be processed, and subsequent data streams can be processed when partial readers are in fault, so that the aim of long-time stable positioning is achieved.
Drawings
FIG. 1 is a schematic diagram of a deployment of an RFID reader fault adaptive location system in an embodiment of the present invention;
FIG. 2 is a schematic diagram of a model structure of a fuzzy width learning system according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of an expanded structure of a fuzzy subsystem of the fuzzy width learning system according to the embodiment of the present invention;
FIG. 4 is a schematic diagram of network update of an online sequential fuzzy width learning system according to an embodiment of the present invention;
FIG. 5 illustrates an exemplary RFID reader failure in an embodiment of the invention;
FIG. 6 is a schematic diagram of an RFID reader fault adaptation strategy in an embodiment of the present invention;
FIG. 7 is a flowchart of an RFID reader fault adaptive location method according to an embodiment of the present invention.
Detailed Description
In order to make the technical solutions and advantages of the present invention more apparent, the following detailed description of the embodiments of the present invention is provided with reference to the accompanying drawings and examples, but the embodiments and protection of the present invention are not limited thereto.
Example (b):
an RFID reader fault self-adaptive positioning method comprises the following steps:
s1, constructing a fault self-adaptive positioning system of the RFID reader;
as shown in fig. 1, the RFID reader fault adaptive positioning system includes M RFID readers, Q RFID reference tags, and a target tag to be positioned; the reference tags with known coordinate positions are deployed in a plane where the target tags to be positioned are located in an equilateral triangle mode, and the readers are deployed on four edges of the plane.
S2, providing an online sequence fuzzy width learning system according to the RFID reader fault self-adaptive positioning system;
the model structure of the fuzzy width learning system is shown in fig. 2, wherein the fuzzy subsystem development structure is shown in fig. 3, and an online sequential fuzzy width learning system is proposed by improving the fuzzy width learning system, as shown in fig. 4The learning system comprises an input layer, a fuzzy subsystem, an enhancement layer and an output layer, wherein the fuzzy subsystem is a TS fuzzy model, and in the ith fuzzy subsystem, the weight
Figure BDA0002363493030000071
Wherein xsMFor input data, s1, 2, N is the number of input data, M is the input data dimension, KiIn order to be able to blur the number of rules,
Figure BDA0002363493030000072
for randomly generated coefficients, the membership function is a Gaussian function
Figure BDA0002363493030000073
Wherein
Figure BDA0002363493030000074
Respectively, the center and width of the membership function, and the weighted output of the fuzzy rule is
Figure BDA0002363493030000075
Wherein
Figure BDA0002363493030000076
The enhancement layer is a nonlinear transformation layer;
the number of the fuzzy subsystems is n, the number of the enhanced node groups is m, wherein the fuzzy rule number of the ith fuzzy subsystem is KiThe number of the j-th group of enhanced nodes is LjOf 1 at
Figure BDA0002363493030000077
The signal strength of the RFID reader reading the reference label at the s time is
Figure BDA0002363493030000078
Figure BDA0002363493030000079
Generation of N0The bar input data is
Figure BDA00023634930300000710
The output data is
Figure BDA00023634930300000711
Wherein
Figure BDA00023634930300000712
Is the Nth0An array of signal strengths of the strip input data,
Figure BDA00023634930300000713
is the Nth0Coordinates of the input data are obtained, and C is a characteristic dimension of the output data;
will N0The bar input data is used as the input of a fuzzy subsystem in the online sequential fuzzy width learning system, the fuzzy subsystem intermediate output and the fuzzy subsystem output are obtained through calculation, the fuzzy subsystem intermediate output is used as the input of an enhancement node, the enhancement layer output is further obtained through calculation, the fuzzy subsystem output and the enhancement layer output are connected with the output layer, and the weight to be calculated is obtained through pseudo-inverse.
S3, providing a fault self-adaptive strategy of the RFID reader based on the online sequence fuzzy width learning system;
as shown in FIG. 5, when some of the RFID readers fail, the number of the RFID readers becomes M's, 0<M' is less than or equal to M, then subsequently generated NaThe bar input data is
Figure BDA0002363493030000081
RSSIs＝(RSSIs1,RSSIs2,...,RSSIsM′),s＝1,2,...,NaThe output data is
Figure BDA0002363493030000082
Wherein
Figure BDA0002363493030000083
Is the NthaSignal Strength array, RSSI, of the strip input datasM′For the signal strength of the mth reader reading the reference tag at the s-th time,
Figure BDA0002363493030000084
is the NthaCoordinates of the bar input data;
after partial RFID readers are in fault, the characteristic dimension of input data is changed from M to M', and a conversion matrix T needs to be introduced into an initialization fuzzy subsystem
Figure BDA0002363493030000085
Coefficients generated randomly by a function
Figure BDA0002363493030000086
And center of membership function
Figure BDA0002363493030000087
Is processed to generate
Figure BDA0002363493030000088
And
Figure BDA0002363493030000089
Figure BDA00023634930300000810
Figure BDA00023634930300000811
wherein
Figure BDA00023634930300000812
The rule of the transformation matrix T is as follows:
(1) each row in the matrix T has at most one element with the value of 1, and the values of the other elements are 0;
(2) each column in the matrix T has only one element with the value of 1, and the values of the other elements are all 0;
(3) if all the elements of the ith row in the matrix T are 0, indicating that the ith reader in the original RFID reader fault self-adaptive positioning system has a fault;
since the transform matrix T is a sparse logic matrix, an array of size 1 × M' is created to store the row number i, i ═ 1,2ij＝1。
S4, completing RFID reader fault self-adaptive positioning based on the RFID reader fault self-adaptive strategy; as shown in fig. 7, the method specifically includes the following steps:
s4.1, acquiring the signal intensity and the position data set of the RFID reference tag in real time; the method comprises the following steps:
the signal strength of the RFID reference label is obtained in real time by controlling the read-write state of the RFID reader, and the corresponding position information of the reference label in the system is obtained by the RFID reader fault self-adaptive positioning system.
S4.2, initializing the online sequential fuzzy width learning system in an offline stage; the method comprises the following steps:
s4.2.1, preprocessing the data obtained in the step S4.1; the method comprises the following steps:
s4.2.1.1, the first
Figure BDA0002363493030000091
Repeatedly reading the signal intensity of the same label by each reader for N times, and recording the signal intensity read at the kth time as
Figure BDA0002363493030000092
S4.2.1.2, calculating the variance delta of RSSI value2：
Figure BDA0002363493030000093
Wherein
Figure BDA0002363493030000094
S4.2.1.3 for the kth signal strength
Figure BDA0002363493030000095
If it is not
Figure BDA0002363493030000096
Then removing is carried out to obtain the size N′Calculating the mean of the set
Figure BDA0002363493030000097
As the average signal intensity:
Figure BDA0002363493030000098
s4.2.1.4, obtaining N0Bar input data
Figure BDA0002363493030000099
Figure BDA00023634930300000910
S4.2.2 at [0,1 ]]In-range, randomly generating fuzzy subsystems according to uniform distribution
Figure BDA00023634930300000911
Coefficient of function
Figure BDA00023634930300000912
S4.2.3, calculating the intermediate output of all input samples in the fuzzy subsystem in the ith fuzzy subsystem
Figure BDA00023634930300000913
And an output
Figure BDA00023634930300000914
Figure BDA00023634930300000915
The method comprises the following steps:
s4.2.3.1, input data X using k-means clustering0Clustering is carried out to obtain KiA cluster center;
s4.2.3.2, use K obtained in step S4.2.3.1iIndividual cluster centerTo center of Gaussian membership function
Figure BDA00023634930300000916
Initialising, width of membership function
Figure BDA00023634930300000917
S4.2.3.3 according to the formula
Figure BDA00023634930300000918
And formula
Figure BDA00023634930300000919
Computing an intermediate output of an s-th input sample in a fuzzy subsystem
Figure BDA00023634930300000920
And an output
Figure BDA00023634930300000921
S4.2.3.4 according to the formula
Figure BDA00023634930300000922
And formula
Figure BDA00023634930300000923
Computing intermediate outputs of all input samples in a fuzzy subsystem
Figure BDA00023634930300000924
And an output
Figure BDA00023634930300000925
S4.2.4 according to the formula
Figure BDA00023634930300000926
Computing the intermediate outputs of all n fuzzy subsystems
Figure BDA00023634930300000927
To act as an enhanced nodeInputting;
s4.2.5 according to the formula
Figure BDA00023634930300000928
And formula
Figure BDA00023634930300000929
Computing output of jth group of enhanced nodes
Figure BDA00023634930300000930
And total output of the enhancement layer
Figure BDA00023634930300000931
Wherein
Figure BDA00023634930300000932
Heel
Figure BDA00023634930300000933
As intermediate output of the fuzzy subsystem
Figure BDA00023634930300000934
The weight and bias of the connection with the enhanced node group are set at 0,1]Randomly generating within the range, wherein m is the group number of the enhanced nodes;
s4.2.6 according to the formula
Figure BDA0002363493030000101
Calculating the total output of all n fuzzy subsystems
Figure BDA0002363493030000102
Wherein
Figure BDA0002363493030000103
Ω＝(Ω1,...,Ωn)，Δ＝((δ1)T,...,(δn)T)T，
Figure BDA0002363493030000104
And
Figure BDA0002363493030000105
in order to input the data, the data is,
Figure BDA0002363493030000106
is the weighted output of the fuzzy rule and,
Figure BDA0002363493030000107
for the parameters introduced, the initial ones can be used
Figure BDA0002363493030000108
Is converted into
Figure BDA0002363493030000109
When the solution is carried out through the pseudo-inverse in the subsequent steps, the weight number of the solution is determined by
Figure BDA00023634930300001010
Become into
Figure BDA00023634930300001011
S4.2.7 according to the formula
Figure BDA00023634930300001012
Computing the matrix A0And a parameter matrix W0Wherein W iseFor the connection weights of the enhancement layer to the output layer,
Figure BDA00023634930300001013
is A0The calculation formula of (A) is
Figure BDA00023634930300001014
S4.3, updating and positioning the initial online sequence fuzzy width learning system obtained in the offline stage in the online stage;
the online stage comprises an online sequential learning stage and an online working stage, the online sequential learning stage is used for completing updating of the initial online sequential fuzzy width learning system, the online working stage is used for completing positioning, and the online sequential learning stage and the online working stage can run in parallel;
in the online sequential learning stage, the number of readers is changed from M to M′，0<M′Less than or equal to M, newly added data is
Figure BDA00023634930300001015
The method specifically comprises the following steps:
s4.3.1.1, preprocessing the data using step S4.2.1;
s4.3.1.2, generating a transformation matrix T according to the rule of the transformation matrix T;
s4.3.1.3 according to the formula
Figure BDA00023634930300001016
And formula
Figure BDA00023634930300001017
Calculating transformed coefficients
Figure BDA00023634930300001018
And center of membership function
Figure BDA00023634930300001019
S4.3.1.4 according to the formula
Figure BDA00023634930300001020
Calculating the operation output matrix A of the newly added dataaWherein
Figure BDA00023634930300001021
Figure BDA00023634930300001022
Figure BDA00023634930300001023
And
Figure BDA00023634930300001024
in order to add new input data to the data,
Figure BDA00023634930300001025
intermediate output of all newly added input samples in the fuzzy subsystem;
s4.3.1.5 according to the formula
Figure BDA00023634930300001026
And
Figure BDA00023634930300001027
computing an update matrixxA and the pseudo-inversexA+Wherein
Figure BDA00023634930300001028
S4.3.1.6 according to the formula
Figure BDA00023634930300001029
Computing an updated parameter matrixxW。
The online working phase specifically comprises the following steps:
s4.3.2.1, sending the RSSI information of the target label to an online sequence fuzzy width learning system after being updated in an online sequence learning stage;
s4.3.2.2, the position of the target label is estimated by using the RSSI information as the input of the online sequential fuzzy width learning system after the updating of the online sequential learning stage.

Claims (10)

1. A self-adaptive fault positioning method for an RFID reader is characterized by comprising the following steps:
s1, constructing a fault self-adaptive positioning system of the RFID reader;
s2, providing an online sequence fuzzy width learning system according to the RFID reader fault self-adaptive positioning system;
s3, providing a fault self-adaptive strategy of the RFID reader based on the online sequence fuzzy width learning system;
s4, completing RFID reader fault adaptive positioning based on the RFID reader fault adaptive strategy.
2. The RFID reader fault self-adaptive positioning method according to claim 1, wherein in step S1, the RFID reader fault self-adaptive positioning system comprises M RFID readers, Q RFID reference tags and a target tag to be positioned; the reference tags with known coordinate positions are deployed in a plane where the target tags to be positioned are located in an equilateral triangle mode, and the readers are deployed on four edges of the plane.
3. The RFID reader fault self-adaptive positioning method of claim 1, wherein in step S2, the online sequential fuzzy width learning system comprises an input layer, a fuzzy subsystem, an enhancement layer and an output layer, wherein the fuzzy subsystem is a TS fuzzy model, and in the ith fuzzy subsystem, the weight is used as a weight
Figure FDA0002363493020000011
Figure FDA0002363493020000012
Wherein xsMFor input data, s1, 2, N is the number of input data, M is the input data dimension, KiIn order to be able to blur the number of rules,
Figure FDA0002363493020000013
for randomly generated coefficients, the membership function is a Gaussian function
Figure FDA0002363493020000014
Figure FDA0002363493020000015
Wherein
Figure FDA0002363493020000016
Respectively, the center and width of the membership function, and the weighted output of the fuzzy rule is
Figure FDA0002363493020000017
Wherein
Figure FDA0002363493020000018
The enhancement layer is a nonlinear transformation layer;
the number of the fuzzy subsystems is n, the number of the enhanced node groups is m, wherein the fuzzy rule number of the ith fuzzy subsystem is KiThe number of the j-th group of enhanced nodes is LjOf 1 at
Figure FDA0002363493020000019
The signal strength of the RFID reader reading the reference label at the s time is
Figure FDA00023634930200000110
Figure FDA00023634930200000111
Generation of N0The bar input data is
Figure FDA00023634930200000112
RSSIs＝(RSSIs1,RSSIs2,...,RSSIsM),s＝1,2,...,N0The output data is
Figure FDA00023634930200000113
Wherein
Figure FDA00023634930200000114
Is the Nth0An array of signal strengths of the strip input data,
Figure FDA00023634930200000115
is the Nth0Coordinates of the input data are obtained, and C is a characteristic dimension of the output data;
will N0The bar input data is used as the input of a fuzzy subsystem in the online sequential fuzzy width learning system, and the intermediate output of the fuzzy subsystem is obtained through calculationAnd fuzzy subsystem output, wherein the middle output of the fuzzy subsystem is used as the input of the enhancement node, the enhancement layer output is obtained through further calculation, the fuzzy subsystem output and the enhancement layer output are connected with the output layer, and the weight to be calculated is obtained through pseudo-inverse.
4. The method according to claim 1, wherein in step S3, the adaptive strategy for RFID reader fault is as follows:
when some RFID readers are in fault, the number of the RFID readers is changed into M' and 0<M' is less than or equal to M, then subsequently generated NaThe bar input data is
Figure FDA0002363493020000021
RSSIs＝(RSSIs1,RSSIs2,...,RSSIsM′),s＝1,2,...,NaThe output data is
Figure FDA0002363493020000022
Wherein
Figure FDA0002363493020000023
Is the NthaSignal Strength array, RSSI, of the strip input datasM′For the signal strength of the mth reader reading the reference tag at the s-th time,
Figure FDA0002363493020000024
is the NthaCoordinates of the bar input data;
after partial RFID readers are in fault, the characteristic dimension of input data is changed from M to M', and a conversion matrix T needs to be introduced into an initialization fuzzy subsystem
Figure FDA0002363493020000025
Coefficients generated randomly by a function
Figure FDA0002363493020000026
And center of membership function
Figure FDA0002363493020000027
Is processed to generate
Figure FDA0002363493020000028
And
Figure FDA0002363493020000029
Figure FDA00023634930200000210
Figure FDA00023634930200000211
wherein
Figure FDA00023634930200000212
The rule of the transformation matrix T is as follows:
(1) each row in the matrix T has at most one element with the value of 1, and the values of the other elements are 0;
(2) each column in the matrix T has only one element with the value of 1, and the values of the other elements are all 0;
(3) if all the elements of the ith row in the matrix T are 0, indicating that the ith reader in the original RFID reader fault self-adaptive positioning system has a fault;
since the transform matrix T is a sparse logic matrix, an array of size 1 × M' is created to store the row number i, i ═ 1,2ij＝1。
5. The self-adaptive fault location method for the RFID reader according to claim 1, wherein the step S4 specifically comprises the following steps:
s4.1, acquiring the signal intensity and the position data set of the RFID reference tag in real time;
s4.2, initializing the online sequential fuzzy width learning system in an offline stage;
and S4.3, updating the initial online sequence fuzzy width learning system obtained in the offline stage in the online stage and completing positioning.
6. The self-adaptive fault location method for the RFID reader as claimed in claim 5, wherein in step S4.1, the signal strength of the RFID reference tag is obtained in real time by controlling the read-write state of the RFID reader, and the corresponding position information of the reference tag in the system is obtained by the self-adaptive fault location system for the RFID reader.
7. The RFID reader fault self-adaptive positioning method according to claim 5, characterized in that the step S4.2 comprises the following steps:
s4.2.1, preprocessing the data obtained in the step S4.1;
s4.2.2 at [0,1 ]]In-range, randomly generating fuzzy subsystems according to uniform distribution
Figure FDA0002363493020000031
Coefficient of function
Figure FDA0002363493020000032
S4.2.3, calculating the intermediate output of all input samples in the fuzzy subsystem in the ith fuzzy subsystem
Figure FDA0002363493020000033
And an output
Figure FDA0002363493020000034
i＝1,2,...,n；
S4.2.4 according to the formula
Figure FDA0002363493020000035
Computing the intermediate outputs of all n fuzzy subsystems
Figure FDA0002363493020000036
As input to the enhancement node;
s4.2.5 according to the formula
Figure FDA0002363493020000037
And formula
Figure FDA0002363493020000038
Computing output of jth group of enhanced nodes
Figure FDA0002363493020000039
And total output of the enhancement layer
Figure FDA00023634930200000310
Wherein
Figure FDA00023634930200000311
Heel
Figure FDA00023634930200000312
As intermediate output of the fuzzy subsystem
Figure FDA00023634930200000313
The weight and bias of the connection with the enhanced node group are set at 0,1]Randomly generating within the range, wherein m is the group number of the enhanced nodes;
s4.2.6 according to the formula
Figure FDA00023634930200000314
Calculating the total output of all n fuzzy subsystems
Figure FDA00023634930200000315
Wherein
Figure FDA00023634930200000316
Ω＝(Ω1,...,Ωn)，Δ＝((δ1)T,...,(δn)T)T，
Figure FDA00023634930200000317
Figure FDA00023634930200000318
In order to input the data, the data is,
Figure FDA00023634930200000319
is the weighted output of the fuzzy rule and,
Figure FDA00023634930200000320
for the parameters introduced, the initial ones can be used
Figure FDA00023634930200000321
Is converted into
Figure FDA00023634930200000322
When the solution is carried out through the pseudo-inverse in the subsequent steps, the weight number of the solution is determined by
Figure FDA00023634930200000323
Become into
Figure FDA00023634930200000324
S4.2.7 according to the formula
Figure FDA00023634930200000325
Computing the matrix A0And a parameter matrix W0Wherein W iseFor the connection weights of the enhancement layer to the output layer,
Figure FDA00023634930200000326
Figure FDA00023634930200000327
is A0The calculation formula of (A) is
Figure FDA00023634930200000328
8. The method of claim 7, wherein the step S4.2.1 comprises the following steps:
s4.2.1.1, the first
Figure FDA00023634930200000329
Repeatedly reading the signal intensity of the same label by each reader for N times, and recording the signal intensity read at the kth time as
Figure FDA00023634930200000330
S4.2.1.2, calculating the variance delta of RSSI value2：
Figure FDA00023634930200000331
Wherein
Figure FDA0002363493020000041
S4.2.1.3 for the kth signal strength
Figure FDA0002363493020000042
If it is not
Figure FDA0002363493020000043
Then removing the RSSI set with the size of N', and calculating the average value of the set
Figure FDA0002363493020000044
As the average signal intensity:
Figure FDA0002363493020000045
s4.2.1.4, obtaining N0Bar input data
Figure FDA0002363493020000046
s＝1,2,...,N0；
Step S4.2.3 includes the steps of:
s4.2.3.1, input data X using k-means clustering0Clustering is carried out to obtain KiA cluster center;
s4.2.3.2, use K obtained in step S4.2.3.1iCenter of individual clustering center to Gaussian membership function
Figure FDA0002363493020000047
Initialising, width of membership function
Figure FDA0002363493020000048
S4.2.3.3 according to the formula
Figure FDA0002363493020000049
And formula
Figure FDA00023634930200000410
Computing an intermediate output of an s-th input sample in a fuzzy subsystem
Figure FDA00023634930200000411
And an output
Figure FDA00023634930200000412
s＝1,2,...,N0；
S4.2.3.4 according to the formula
Figure FDA00023634930200000413
And formula
Figure FDA00023634930200000414
Computing intermediate outputs of all input samples in a fuzzy subsystem
Figure FDA00023634930200000415
And an output
Figure FDA00023634930200000416
9. The RFID reader fault self-adaptive positioning method according to claim 5, characterized in that in step S4.3, the online stage comprises an online sequential learning stage and an online working stage, the online sequential learning stage is used for completing updating of an initial online sequential fuzzy width learning system, the online working stage is used for completing positioning, and the online sequential learning stage and the online working stage can run in parallel;
in the online sequential learning stage, the number of readers is changed from M to M' and 0<M' is less than or equal to M, and newly added data are
Figure FDA00023634930200000417
The method specifically comprises the following steps:
s4.3.1.1, preprocessing the data using step S4.2.1;
s4.3.1.2, generating a transformation matrix T according to the rule of the transformation matrix T;
s4.3.1.3 according to the formula
Figure FDA00023634930200000418
And formula
Figure FDA00023634930200000419
Calculating transformed coefficients
Figure FDA00023634930200000420
And center of membership function
Figure FDA00023634930200000421
S4.3.1.4 according to the formula
Figure FDA0002363493020000051
Calculating the operation output matrix A of the newly added dataaWherein
Figure FDA0002363493020000052
Figure FDA0002363493020000053
Figure FDA0002363493020000054
And
Figure FDA0002363493020000055
Figure FDA0002363493020000056
in order to add new input data to the data,
Figure FDA0002363493020000057
intermediate output of all newly added input samples in the fuzzy subsystem;
s4.3.1.5 according to the formula
Figure FDA0002363493020000058
And
Figure FDA0002363493020000059
computing an update matrixxA and the pseudo-inversexA+Wherein
Figure FDA00023634930200000510
S4.3.1.6 according to the formula
Figure FDA00023634930200000511
Computing an updated parameter matrixxW。
10. The method according to claim 9, wherein the online working phase specifically comprises the following steps:
s4.3.2.1, sending the RSSI information of the target label to an online sequence fuzzy width learning system after being updated in an online sequence learning stage;
s4.3.2.2, the position of the target label is estimated by using the RSSI information as the input of the online sequential fuzzy width learning system after the updating of the online sequential learning stage.
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