CN102156850A - UHF (Ultra High Frequency) RFID (Radio Frequency Identification) gateway blind spot testing system and probabilistic forecasting method - Google Patents
UHF (Ultra High Frequency) RFID (Radio Frequency Identification) gateway blind spot testing system and probabilistic forecasting method Download PDFInfo
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Abstract
The invention relates to a UHF (Ultra High Frequency) RFID (Radio Frequency Identification) gateway blind spot testing system and a probabilistic forecasting method. The UHF RFID gateway blind spot testing system comprises an up-conversion board card, a down-conversion board card, an intermediate frequency field programmable gate array (FPGARIO) board card capable of rearranging an input port and an output port, a PLC (Programmable Logic Controller), a motor, a microcomputer, a transmission band, a test antenna, a transmitting antenna and a radio-frequency cable. The invention also comprises the probabilistic forecasting method based on the UHF (Ultra High Frequency) RFID (Radio Frequency Identification) gateway blind spot testing system. The invention has high automation degree, low complexity and high accuracy, and is close to the application scenario of a real gateway, achieves better measurement accuracy during the slow change or fast change of field intensity distribution, and can estimate the probability and the rate of coverage of blind spots during actual gateway application according to lognormal model parameter tables obtained under various typical environments.
Description
Technical Field
The invention relates to an automatic testing system and a probability prediction method for blind spot distribution of ultrahigh frequency radio frequency identification portal (portal) application.
Background
The entrance is an important component of the ultrahigh frequency radio frequency identification technology applied to the aspects of the supply chain, the storage and the like of the Internet of things, and the application has high requirements on the reading rate and the reliability of the label. Due to the influence of radio wave propagation multipath effect in the actual environment, a blind spot area of label identification often appears in the normal identification range of the reader, and the reliability of application is reduced. Therefore, blind spot measurements and prediction of probability of occurrence are the basis for deploying rfid portal applications.
The existing channel test method mainly comprises a small-scale multipath measurement method and a large-scale measurement method in the field of mobile radio, wherein the small-scale multipath measurement method comprises a direct radio frequency pulse system, spread spectrum sliding correlator channel detection, frequency domain channel detection and the like, and the large-scale measurement method comprises vehicle-mounted power metering instrument test and the like. For the application of the radio frequency identification entrance in the field of indoor or internet of things, a power meter or a spectrum analyzer is usually placed on a mobile robot for field intensity measurement, or a mode of manual measurement only by using the power meter or the spectrum analyzer is usually adopted. These methods are superior and inferior in terms of workload, accuracy, input resources, and application scene authenticity of measurement: the entrance application adopts a passive label reverse modulation communication mechanism, the working frequency band, the communication speed and the communication distance are greatly different, and a small-scale propagation channel measurement method in the field of mobile radio is too complex and is not suitable; the manual measurement mode of a power meter or a spectrum analyzer is adopted, so that the ideal effect on the aspects of measurement workload and precision is difficult to achieve; although the precision and workload of the mode using the special robot and the power metering equipment meet the requirements, the mode has more input resources and is difficult to be widely adopted.
The existing methods for predicting the blind spot distribution and the occurrence probability include a ray tracing method, a finite element method for electromagnetic field numerical calculation, a statistical method and the like. Although the two methods have high prediction accuracy, a spatial three-dimensional model of an application scene needs to be established, the workload is large, and although the calculation amount of the statistical method is small, the estimation accuracy cannot achieve the ideal effect. In summary, these methods are deficient in certain respects.
For the radio frequency identification entrance application in the field of internet of things, the blind spot distribution and coverage rate are influenced by the surrounding environment of the application occasion and the position of the label attached with the marker and other factors, so that a vivid entrance application blind spot test system is established, all influencing factors of the label reading rate are fully understood, and a new product capable of resisting the blind spot is researched and developed. The blind spot probability prediction method also enables that when the radio frequency identification entrance application is actually deployed, the lognormal model parameters can be determined according to the type table lookup of the surrounding environment, the precision of the blind spot probability of each point in the space at the entrance can be estimated, and therefore expected reading reliability can be achieved. Therefore, it is very important to design a blind spot measurement system and a prediction method which are automated, have low complexity, have high precision and are close to a real entrance application scene.
Disclosure of Invention
In order to overcome the defects of the existing blind spot testing system and the existing prediction method and meet the testing and prediction requirements of the application of the ultrahigh frequency radio frequency identification entrance type in the field of the Internet of things on the identification of the blind spot distribution, the invention provides the ultrahigh frequency radio frequency identification entrance blind spot testing system and the prediction method which have the advantages of high automation degree, low complexity and high precision and are close to the real entrance application scene.
The basic idea of the invention is that based on a radio frequency test board card, test software and a lognormal fading model, the test, analysis and prediction of blind spot distribution are realized on a microcomputer, and meanwhile, a driving variable frequency motor is accurately controlled through an OPC communication interface and a Programmable Logic Controller (PLC), so that seamless integration with an inlet application simulation platform is completed, and the accurate positioning of a test position is realized.
The specific principle of the invention is as follows: a radio frequency board card is adopted, and the hardware structure of the superheterodyne transceiver with a software radio architecture and two-stage frequency conversion is realized by the aid of an analog up-converter, an analog down-converter and an intermediate frequency FPGA module.
Based on the principle, the ultrahigh frequency radio frequency identification inlet blind spot testing system comprises a radio frequency transmitting module, a radio frequency receiving module and an inlet application simulation module, wherein the radio frequency transmitting module comprises an up-conversion board card, an intermediate frequency field programmable gate array (FPGA RIO) board card capable of reconfiguring an input/output port and a transmitting antenna, the radio frequency receiving module comprises a down-conversion board card, an intermediate frequency field programmable gate array (FPGA RIO) board card and a testing antenna, and the inlet application simulation module comprises a Programmable Logic Controller (PLC), a motor and a transmission band; the intermediate frequency input interface of the up-conversion board card is connected with the intermediate frequency output interface of the intermediate frequency FPGA RIO board card; the down-conversion board intermediate frequency output interface is connected with the intermediate frequency input interface of the intermediate frequency FPGA RIO board; the intermediate frequency FPGA RIO board card is connected with the microcomputer through a PCI interface; the microcomputer establishes communication connection with the programmable logic controller through an OPC interface; the control end of the motor is connected with the output end of the programmable logic controller; the motor drives the conveyor belt to move; the transmitting antenna is connected with a radio frequency output interface of the up-conversion board card through a radio frequency cable I; the test antenna is attached to the marker and is connected with the radio frequency input interface of the down-conversion board card through a radio frequency cable II.
The transmitting antenna is preferably a reader circular polarization transmitting antenna.
The test antenna is preferably a dipole test antenna.
The testing, analyzing and predicting software, the PLC software and the OPC communication software form a software part of the ultrahigh frequency radio frequency identification entrance blind spot testing system.
The test software on the microcomputer firstly completes the initialization and configuration of each board card and an OPC port for communicating with the PLC. Then, test software running on the microcomputer calls a PLC control program through an OPC interface, drives a motor, drives a transmission belt to transmit the marker and the dipole test antenna at a speed of 0.1 m/s and passes through a reader circular polarization transmitting antenna radiation field at an ultrahigh frequency radio frequency identification entrance at a constant speed, and the dipole test antenna is attached to the surface of the marker (the same as the attachment position of the marker in actual application) to simulate the tag antenna. Meanwhile, calling a driving program of the intermediate frequency FPGA RIO board card, sending an intermediate frequency continuous carrier signal at the frequency of 10 times/second, transmitting the intermediate frequency continuous carrier signal to an up-conversion board card through an intermediate frequency signal IO port, up-converting the intermediate frequency signal to a signal with the same frequency as the carrier of the radio frequency identification system by the up-conversion board card, and sending the signal out through a reader circular polarization sending antenna; the dipole test antenna receives a radio-frequency signal, transmits the radio-frequency signal to a down-conversion board card through a radio-frequency cable to complete first-stage frequency mixing, the down-conversion board card converts the radio-frequency signal into an intermediate-frequency signal and transmits the intermediate-frequency signal to an intermediate-frequency FPGA RIO board card through an intermediate-frequency signal IO port to complete second-stage frequency mixing, and the intermediate-frequency FPGA RIO board card reduces the intermediate-frequency signal to a baseband signal and transmits a final waveform to a microcomputer; the analysis program on the microcomputer calculates the received signal power P by the baseband waveformr(i) And recording path loss PL (i), wherein i is an integer from 1 to N, the sequence number of each test point on the path passing through the ultrahigh frequency radio frequency identification entrance, and N is the number of the test points.
Analysis program on microcomputer for analyzing N test data Pr(i) The parameter n and the standard deviation sigma of the log-normal model are obtained by using linear regression analysis based on the MMSE criterion.
The log normal model is as follows:
wherein d is0Is the distance from the nearest test point of the transmitting antenna, n is the model fading parameter, diFor test point i to transmit antenna distance, XσIs a normal random variable with standard deviation of sigma, PL (d)0) Is d0The path loss of (d) is calculated by:（3）
and P isr(d0) Calculated from Friis's formula:
where λ is the wavelength of the carrier wave, PtTo transmit power, GtFor transmitting antenna gain, GrIs the receive antenna gain. The parameters n and the standard deviation sigma of the lognormal model are obtained by using linear regression analysis of MMSE criterion.
Setting an objective function:
（5）
wherein,
is an estimate of the use of a log path loss model for the ith test point. The parameter n minimizes the sum of the squares of the differences, and after derivation on the right side of equation (5), it is made equal to zero, and the equation is solved to obtain the root mean square RMS value of n, σ being j (n).
A typical value table of parameters of the log-normal model is obtained by measuring blind spot distribution under various typical entrance application environments. In practical application, n and sigma in the environment of the type are searched through a prediction program on a microcomputer according to the type of the surrounding environment. The probability value for a blind spot at distance d from the transmitting antenna is predicted by the following equation.
Where gamma denotes the minimum received power at which the tag can be activated, Pr(d) To transmit power PtDifference from path loss. Q (×) is given by:
where erf () is an error function.
In the formula (6), Pr(d) Representing the received power at distance d from the transmit antenna, is calculated by:
（1）
wherein, PtTo transmit power, d0For reference to the distance of the test point from the transmitting antenna, PL (d)0) Is d0Where n is a fading parameter of the log-normal model.
Based on the principle, the probability prediction method of the ultrahigh frequency radio frequency identification entrance blind spot test system comprises the following steps:
1) attaching the test antenna to the surface of the marker (the same as the label attaching position in actual application), and applying a simulation integration environment to an up-conversion board card, a down-conversion board card, an intermediate frequency FPGA RIO board card and an ultrahigh frequency radio frequency identification entrance to complete initialization and configuration operation;
2) starting the conveyor belt to drive the marker and the test antenna, sending a continuous carrier radio frequency signal by the radio frequency sending module at the frequency of 10 times/second, receiving the continuous carrier signal by the radio frequency receiving module, and calculating the power value P at each position point ir(i) And recording it;
3) obtaining a parameter n and a standard deviation sigma of a lognormal model by using linear regression analysis of a minimum mean square error estimation (MMSE) criterion according to the formulas (2) and (5), and changing the type of the surrounding environment to obtain a typical parameter table composed of the parameter n and the standard deviation sigma under different types of environments;
4) and (3) searching a typical parameter table according to the type of the surrounding environment applied by the actual entrance to obtain the parameter value of the log-normal model, and obtaining the probability value of a blind spot at the position d away from the transmitting antenna according to the formulas (2), (6) and (7).
The invention has the following beneficial effects:
1) the automation degree is high. The test can be started only by fixing the test antenna, the test, the analysis and the prediction are automatically completed by operating microcomputer software, and parameters such as transmission carrier frequency, power and the like can be flexibly adjusted according to air interface parameters of radio frequency identification application standards.
2) The measurement precision is high. The variable frequency motor transmits the marker at the speed of 0.1 m/s, the radio frequency transmitting module of the blind spot testing system transmits a testing signal at the frequency of 10 times/s, and the measuring precision reaches 1 cm.
3) The simulation application is lifelike. The test antenna uses a dipole test antenna similar to the tag antenna, which can be attached to each position of the marker, and the transmitting antenna uses a circularly polarized antenna with a typical gain value, which are all the same as the practical application situation.
4) The prediction method is more accurate. Compared with other statistical prediction models, the method can establish a typical parameter value table according to the type of the entrance application environment, and improves the accuracy of estimating the blind spot probability during actual deployment.
In conclusion, the method has high automation degree, low complexity and high precision, is close to a real entrance application scene, has better measurement precision when field intensity distribution changes slowly and rapidly, can estimate the blind spot probability and coverage rate when the actual entrance is applied according to the lognormal model parameter table obtained in various typical environments, can meet the measurement requirement of the blind spot when the radio frequency identification entrance in the field of the internet of things is applied, can predict the blind spot probability of any point in the entrance space, has obvious theoretical and technical advantages in the aspects of the test and prediction of the blind spot distribution of the radio frequency identification entrance in the field of the internet of things, and has high application value.
Drawings
FIG. 1 is a schematic structural diagram of an ultrahigh frequency radio frequency identification entrance blind spot test system of the present invention.
Detailed Description
The invention is further illustrated by the following figures and examples.
Referring to the attached drawings, the ultrahigh frequency radio frequency identification inlet blind spot testing system comprises a radio frequency transmitting module, a radio frequency receiving module and an inlet application simulation module, wherein the radio frequency transmitting module comprises an up-conversion board card 1, an intermediate frequency field programmable gate array (FPGA RIO) board card 3 with reconfigurable input and output ports and a reader circularly polarized transmitting antenna 9, the radio frequency receiving module comprises a down-conversion board card 2, the FPGA RIO board card 3 and a dipole testing antenna 8, and the inlet application simulation module comprises a Programmable Logic Controller (PLC) 4, a motor 5 and a transmission band 7; an intermediate frequency input interface of the up-conversion board card 1 is connected with an intermediate frequency output interface of an intermediate frequency FPGA RIO board card 3; the intermediate frequency output interface of the down-conversion board card 2 is connected with the intermediate frequency input interface of the intermediate frequency FPGA RIO board card 3; the intermediate frequency FPGA RIO board card 3 is connected with the microcomputer 6 through a PCI interface 12; the microcomputer 6 establishes communication connection with the PLC4 through the OPC interface 13; the control end of the motor 5 is connected with the output end of the PLC 4; the motor 5 drives the conveyor belt 7 to move; the reader circularly polarized transmitting antenna 9 is connected with a radio frequency output interface of the up-conversion board card 1 through a radio frequency cable I10-1; the dipole test antenna 8 is attached to the marker 11 and connected with the radio frequency input interface of the down-conversion board card 2 through a radio frequency cable II 10-2.
The microcomputer 6 is provided with testing, analyzing and predicting software, the programmable logic controller 4 is provided with PLC software, the OPC interface 13 connecting the microcomputer 6 and the programmable logic controller 4 is provided with OPC communication software, and the testing, analyzing and predicting software, the PLC software and the OPC communication software form the software part of the ultrahigh frequency radio frequency identification entry blind spot testing system.
The blind spot distribution test and probability prediction are carried out by using the ultrahigh frequency radio frequency identification entrance blind spot test system of the invention by the following steps:
(1) and simulating test scene arrangement and finishing initialization and configuration operations of an up-conversion board card 1, a down-conversion board card 2, an intermediate frequency FPGA RIO board card 3 and a PLC 4. The specific content comprises the following steps: placing a reader circularly polarized transmitting antenna 9 at the upper end of an ultrahigh frequency radio frequency identification entrance, placing a marker 11 at the starting point position on the right side of a transmission band 7, and attaching a dipole test antenna 8 on the marker 11; the power-on initialization work of the up-conversion board card 1, the down-conversion board card 2, the intermediate frequency FPGA RIO board card 3 and the PLC4 is completed, the carrier frequency and the sending power of the up-conversion board card 1 are configured, the carrier frequency and the receiving reference power of the down-conversion board card 2 are configured, and the intermediate frequency signal frequency and the signal channel parameters of the intermediate frequency FPGA RIO board card 3 are configured.
(2) Calling a control program of a driving motor 5 through an OPC interface 13 of a microcomputer 6 and a PLC4, starting a conveyor belt 7, driving a marking object 11 and a dipole test antenna 8 to pass through an entrance circularly polarized reader at a constant speed of 0.1 m/sThe transmitting antenna 9 radiates the field. The microcomputer 6 calls the drive of the intermediate frequency FPGA RIO board 3 to enable the intermediate frequency FPGA RIO board 3 to send continuous carrier radio frequency signals at the frequency of 10 times/second, meanwhile, the down-conversion board 2 receives the continuous carrier signals, the continuous carrier signals are transmitted to the microcomputer 6 through the intermediate frequency FPGA RIO board 3 in a stacking mode, and the microcomputer 6 calculates the power value P at each position point ir(i) And recording it.
(3) When the marker 11 reaches the end of the conveyor belt 7, the microcomputer 6 is operated to stop the test program, and the microcomputer 6 analysis program obtains the parameters n and σ of the log-normal model from the expressions (2) and (5) using the linear regression analysis of the MMSE criterion.
(4) Changing the type of the surrounding environment, and repeating the steps 1) -3) to obtain a typical parameter table. As shown in example table 1:
(5) according to the type of the surrounding environment applied by the actual entrance, looking up a typical parameter table and the parameter value of a lognormal model, and obtaining the probability value of a blind spot at a position d meters away from a transmitting antenna according to the formulas (2), (6) and (7).
Claims (6)
1. The ultrahigh frequency radio frequency identification entry blind spot test system is characterized by comprising a radio frequency transmitting module, a radio frequency receiving module and an entry application simulation module, wherein the radio frequency transmitting module comprises an up-conversion board card, an intermediate frequency field programmable gate array board card capable of reconfiguring an input/output port and a transmitting antenna; the intermediate frequency input interface of the up-conversion board card is connected with the intermediate frequency output interface of the intermediate frequency FPGA RIO board card; the down-conversion board intermediate frequency output interface is connected with the intermediate frequency input interface of the intermediate frequency FPGA RIO board; the intermediate frequency FPGA RIO board card is connected with the microcomputer through a PCI interface; the microcomputer establishes communication connection with the programmable logic controller through an OPC interface; the control end of the motor is connected with the output end of the programmable logic controller; the motor drives the conveyor belt to move; the transmitting antenna is connected with a radio frequency output interface of the up-conversion board card through a radio frequency cable I; the test antenna is attached to the marker and is connected with the radio frequency input interface of the down-conversion board card through a radio frequency cable II.
2. The UHF RFID entry blind spot testing system of claim 1, wherein the transmitting antenna is a reader circularly polarized transmitting antenna.
3. The uhf rfid entry blind spot testing system of claim 1 or 2, wherein the test antenna is a dipole test antenna.
4. The UHF RFID entry blind spot testing system of claim 1 or 2, wherein the microcomputer is provided with testing, analyzing and predicting software, the PLC is provided with PLC software, an OPC interface connecting the microcomputer and the PLC is provided with OPC communication software, and the testing, analyzing and predicting software, the PLC and the OPC communication software form a software part of the UHF RFID entry blind spot testing system.
5. The method for predicting the probability of the UHF RFID entry blind spot test system of claim 4, comprising the steps of:
(1) attaching a dipole test antenna to the surface of the marker object, placing the dipole test antenna at the starting point position of the right end of the conveyor belt, and completing initialization and configuration operations on an up-conversion board card, a down-conversion board card, an FPGA RIO board card and a PLC;
(2) starting the conveyor belt to drive the identification objectAnd the dipole test antenna moves at a constant speed, meanwhile, the radio frequency transmitting module transmits continuous carrier radio frequency signals at the frequency of 10 times/second, the radio frequency receiving module receives the continuous carrier signals, and the power value P at each position point i is calculatedr(i) And recording it;
(3) analysis program on microcomputer for analyzing N test data Pr(i) Obtaining a parameter n and a standard deviation sigma of a lognormal model by using linear regression analysis based on a Minimum Mean Square Error (MMSE) criterion, and obtaining a typical parameter table under different types of environments formed by the parameter n and the standard deviation sigma by changing the type of the surrounding environment;
(4) according to the type of the surrounding environment applied by the actual entrance, the typical parameter table and the lognormal model parameter value under different types of environments are searched by a prediction program on a microcomputer, and the probability value of a blind spot at the position d away from the transmitting antenna is obtained.
6. The method of claim 5, wherein in the step (4), the typical parameter tables under different types of environments are searched by the prediction program on the microcomputer, and the probability value of the blind spot at the position d away from the transmitting antenna is obtained by the following formula
Where γ is the minimum received power at which the tag can be activated, Q (—) is represented by an error function, σ is the standard deviation, and P isr(d) Representing the received power at distance d from the transmit antenna, is calculated by:
wherein, PtTo transmit power, d0For reference to the distance of the test point from the transmitting antenna, PL (d)0) Is d0Path of (A)And n is a fading parameter of a log normal model.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104360199A (en) * | 2014-11-21 | 2015-02-18 | 国家电网公司 | Ultrahigh-frequency-band RFID testing system |
| CN105184206A (en) * | 2015-08-14 | 2015-12-23 | 河北豪美金融设备有限公司 | Electronic label detection apparatus |
| CN113315566A (en) * | 2019-05-23 | 2021-08-27 | 上海微小卫星工程中心 | Satellite ground comprehensive test system |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101299232A (en) * | 2008-06-16 | 2008-11-05 | 清华大学 | Transmitting circuit for super high frequency radio frequency recognition read-write machine to reduce power consumption |
| CN101441703A (en) * | 2008-10-08 | 2009-05-27 | 湖南大学 | Coding and decoding circuit of super high frequency radio frequency personal identification system |
| CN201259674Y (en) * | 2008-10-09 | 2009-06-17 | 上海聚星仪器有限公司 | RFID integrated test instrument |
| CN101806845A (en) * | 2010-03-11 | 2010-08-18 | 湖南大学 | Radio frequency identification system test board |
| CN101867093A (en) * | 2010-06-12 | 2010-10-20 | 湖南大学 | Ultrahigh frequency broadband quasi circular polarization micro-strip patch antenna |
-
2011
- 2011-04-22 CN CN 201110101598 patent/CN102156850B/en not_active Expired - Fee Related
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101299232A (en) * | 2008-06-16 | 2008-11-05 | 清华大学 | Transmitting circuit for super high frequency radio frequency recognition read-write machine to reduce power consumption |
| CN101441703A (en) * | 2008-10-08 | 2009-05-27 | 湖南大学 | Coding and decoding circuit of super high frequency radio frequency personal identification system |
| CN201259674Y (en) * | 2008-10-09 | 2009-06-17 | 上海聚星仪器有限公司 | RFID integrated test instrument |
| CN101806845A (en) * | 2010-03-11 | 2010-08-18 | 湖南大学 | Radio frequency identification system test board |
| CN101867093A (en) * | 2010-06-12 | 2010-10-20 | 湖南大学 | Ultrahigh frequency broadband quasi circular polarization micro-strip patch antenna |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104360199A (en) * | 2014-11-21 | 2015-02-18 | 国家电网公司 | Ultrahigh-frequency-band RFID testing system |
| CN105184206A (en) * | 2015-08-14 | 2015-12-23 | 河北豪美金融设备有限公司 | Electronic label detection apparatus |
| CN113315566A (en) * | 2019-05-23 | 2021-08-27 | 上海微小卫星工程中心 | Satellite ground comprehensive test system |
| CN113315566B (en) * | 2019-05-23 | 2023-03-24 | 上海微小卫星工程中心 | Satellite ground comprehensive test system |
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