CN102156850B - probabilistic forecasting method of UHF (Ultra High Frequency) RFID (Radio Frequency Identification) gateway blind spot testing system - Google Patents
probabilistic forecasting method of UHF (Ultra High Frequency) RFID (Radio Frequency Identification) gateway blind spot testing system Download PDFInfo
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Abstract
The invention relates to a probabilistic forecasting method of a UHF (Ultra High Frequency) RFID (Radio Frequency Identification) gateway blind spot testing system. 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 present invention relates to the probability forecasting method of the blind spot distribution of a kind of super high frequency radio frequency identification entrance (portal) application.
Background technology
Entrance is the important component part that the super high frequency radio frequency recognition technology is applied to the aspects such as Internet of Things supply chain and storage, and such read rate and reliability of using label has very high requirement.Due to the impact that is subjected to radio wave propagation multipath effect in actual environment, tend to occur the blind spot region of tag recognition in the normal identification range of reader, reduce the reliability of using.Therefore, the blind spot measurement is to dispose the basis that the radio-frequency (RF) identification entrance is used with the prediction of probability of occurrence.
Existing channel test method mainly contains small scale method for multipath measurement and the large scale measuring method of mobile wireless electrical domain, the small scale method for multipath measurement comprises direct radio-frequency pulse system, spread spectrum sliding correlation detector Channel Detection and frequency domain channel detection etc., and the large scale measuring method comprises vehicle-mounted power measurement instrument test etc.And use for the radio-frequency (RF) identification entrance in indoor or Internet of Things field, normal adopt power meter or spectrum analyzer are placed in carry out field strength measurement on the mobile robot, or only carry out the mode of manual measurement with power meter or spectrum analyzer.These methods are respectively having quality aspect workload, precision, input resource and the application scenarios authenticity measured: such entrance is used and is adopted passive label back-modulation communication mechanism, the equal difference of working frequency range, traffic rate and communication distance is very large, the small scale propagation channel measuring method of mobile wireless electrical domain is too complicated, and inapplicable; Adopt the manual measurement mode of power meter or spectrum analyzer in the effect that all is difficult to aspect surveying work amount and precision reach desirable; Though use mode precision and the workload of special purpose robot and power measurement equipment to meet the demands, the input resource is many, is difficult to be widely adopted.
Existing prediction blind spot distributes and the method for probability of occurrence has the finite element method of ray tracking method, Numerical Calculation of Electromagnetic Fields and statistical method etc.Although two kinds of fronts method precision of prediction is high, need to set up the space three-dimensional model of application scenarios, workload is large, though and the statistical method calculated amount is little, estimated accuracy is difficult to reach ideal effect.In a word, these methods Shortcomings all in some aspects.
Radio-frequency (RF) identification entrance application for the Internet of Things field, its blind spot distributes and coverage rate is subjected to the application scenario surrounding environment influence, the many factors such as position that also are subjected to simultaneously label to attach marker affect, this makes sets up entrance application blind spot test macro true to nature, fully understand each influence factor of label read rate, and it is most important to research and develop the new product that can resist blind spot.When the blind spot probability forecasting method makes again actual deployment radio-frequency (RF) identification entrance use, can table look-up according to the type of surrounding environment and determine the lognormal model parameter, improve the precision of the blind spot probability of estimating space, porch each point, thereby realize the reading reliability of expectation.Therefore, design automation, low complex degree, precision height and very important near blind spot measuring system and the Forecasting Methodology of true entrance application scenarios.
Summary of the invention
The defects that exists in order to overcome existing blind spot test macro and Forecasting Methodology, satisfy the application of Internet of Things field super high frequency radio frequency identification entrance type to the test and prediction requirement of the distribution of identification blind spot, the invention provides a kind of automaticity high, complexity is low, precision is high, and identifies the probability forecasting method of entrance blind spot test macro near the super high frequency radio frequency of true entrance application scenarios.
Basic thought of the present invention is, based on radio frequency testing integrated circuit board, testing software and lognormality decline model, realize test, analysis and prediction that blind spot distributes on microcomputer, accurately control driving variable-frequency motor by OPC communication interface and programmable logic controller (PLC) (PLC) simultaneously, complete seamless integrated with entrance application simulation platform, the accurate location of realizing test position.
Concrete principle of the present invention is as follows: adopt the radio frequency integrated circuit board, comprise simulation upconverter, analog down converter, intermediate frequency FPGA module, realize the superhet transceiver hardware structure of software radio framework and two-stage frequency conversion.
Based on above-mentioned principle, described super high frequency radio frequency identification entrance blind spot test macro comprises radio frequency sending module, Receiver Module and entrance application simulation module, described radio frequency sending module comprises intermediate frequency field programmable gate array (FPGA RIO) integrated circuit board and the transmitting antenna of up-conversion integrated circuit board, reconfigurable input/output port, Receiver Module comprises down coversion integrated circuit board, intermediate frequency field programmable gate array (FPGA RIO) integrated circuit board and test antenna, and entrance application simulation module comprises programmable logic controller (PLC) (PLC), motor, transport tape; Up-conversion integrated circuit board intermediate frequency input interface is connected with intermediate frequency FPGA RIO integrated circuit board intermediate frequency output interface; Down coversion integrated circuit board intermediate frequency output interface is connected with intermediate frequency FPGA RIO integrated circuit board intermediate frequency input interface; Intermediate frequency FPGA RIO integrated circuit board is connected by pci interface with microcomputer; Microcomputer establishes a communications link by OPC interface and programmable logic controller (PLC); The control end of motor is connected with the output terminal of programmable logic controller (PLC); The driven by motor travelling belt moves; Transmitting antenna is connected with up-conversion integrated circuit board radio frequency output interface by radio-frequency cable I; Test antenna is attached on marker, is connected with down coversion integrated circuit board rf input interface by radio-frequency cable II.
Described transmitting antenna preferred reader circular polarisation transmitting antenna.
The preferred dipole test antenna of described test antenna.
Described microcomputer is provided with test, analysis and forecasting software, programmable logic controller (PLC) is provided with PLC software, the OPC interface of connected with computer and programmable logic controller (PLC) is provided with the OPC communication software, and test, analysis and forecasting software and PLC software and OPC communication software consist of the present invention's super high frequency radio frequency identification entrance blind spot testing system software part.
Testing software on microcomputer at first complete each integrated circuit board and with initialization and the configuration of the OPC port of plc communication.Then, the testing software that moves on microcomputer is by the OPC interface, call the PLC control program, drive motor, drive driving-belt and at the uniform velocity identify the reader circular polarisation transmitting antenna radiation field of porch by super high frequency radio frequency with speed transmission marker and the dipole test antenna of 0.1 meter per second, the dipole test antenna is attached at marker surface (with practical application time label sticking position identical), simulation label antenna.Simultaneously, call the driver of intermediate frequency FPGA RIO integrated circuit board, send the continuous carrier signal of intermediate frequency with the frequencies of 10 times/second, be sent to the up-conversion integrated circuit board by intermediate-freuqncy signal IO port, described up-conversion integrated circuit board with the intermediate-freuqncy signal up-conversion to the signal of radio-frequency recognition system carrier wave same frequency, then send by reader circular polarisation transmitting antenna; Dipole test antenna received RF signal, and be sent to the down coversion integrated circuit board by radio-frequency cable, complete first order mixing, described down coversion integrated circuit board is converted into intermediate-freuqncy signal with radiofrequency signal, and be sent to intermediate frequency FPGA RIO integrated circuit board by intermediate-freuqncy signal IO port, complete second level mixing, described intermediate frequency FPGA RIO integrated circuit board is downconverted to baseband signal with intermediate-freuqncy signal, and last waveform is sent to microcomputer; Routine analyzer on microcomputer calculates by this baseband waveform the signal power P that receives
r(i) and path loss PL (i), and record it, wherein, i is 1 to N integer, is the sequence number of every test point on the path of identifying entrance by super high frequency radio frequency, and N is number of test points.
Parser analysis N on a microcomputer test data P
r(i), use the linear regression analysis based on the MMSE criterion, obtain parameter n and the standard variance σ of lognormal model.
Lognormal model as shown in the formula:
Wherein, d
0Be the distance of the nearest test point of distance transmitting antenna, n is the model fading parameter, d
iFor test point i to transmitting antenna distance, X
σBe the normal random variable of σ for standard variance, PL (d
0) be d
0The path loss at place, calculated by following formula:
And P
r(d
0) calculated by the Friis formula:
（4）
Wherein, λ is the wavelength of carrier wave, P
tBe transmitted power, G
tBe transmitting antenna gain, G
rBe receiving antenna gain.Use the linear regression analysis of MMSE criterion, obtain parameter n and the standard variance σ of lognormal model.
The target setting function:
Wherein,
It is the estimated value that i test point used the logarithm path loss model.Parameter n makes the quadratic sum of this difference minimum, after equation (5) the right differentiate, makes it equal zero, and solves an equation, and tries to achieve n, and σ is the root mean square RMS value of J (n).
Distribute by the blind spot of measuring under each quasi-representative entrance applied environment, obtain the representative value table of the parameter of lognormal model.During practical application, according to the surrounding environment type, by the predictor on microcomputer, search n and σ under the type environment.By the following formula transmitting antenna d place probable value for blind spot of finding range in advance.
Wherein γ represents to activate the minimal detectable power of label, P
r(d) be transmitted power P
tDifference with path loss.Q (*) is provided by following formula:
Wherein erf (*) is error function.
In formula (6), P
r(d) expression apart from the received power at transmitting antenna d place, is calculated by following formula:
Wherein, P
tBe transmitted power, d
0Be the distance of reference test point and transmitting antenna, PL (d
0) be d
0The path loss at place, n is the lognormal model fading parameter.
Based on above-mentioned principle, the probability forecasting method of the present invention's super high frequency radio frequency identification entrance blind spot test macro comprises the following steps:
1) test antenna is attached at marker surface (with practical application time label sticking position identical), and up-conversion integrated circuit board, down coversion integrated circuit board, intermediate frequency FPGA RIO integrated circuit board and super high frequency radio frequency identification entrance application simulation the integration environment are completed initialization and configuration operation;
2) start travelling belt, drive marker and test antenna, the radio frequency sending module sends the continuous carrier radiofrequency signal with the frequency of 10 times/second, and Receiver Module receives the continuous carrier signal simultaneously, calculates the performance number P at each position point i place
rAnd record it (i);
3) use least mean-square error to estimate the linear regression analysis of (MMSE) criterion, by formula (2) and (5), obtain parameter n and the standard variance σ of lognormal model, and change surrounding environment type, the canonical parameter table under the dissimilar environment that obtains being consisted of by parameter n and standard variance σ;
4) use the surrounding environment type according to the actual entry, look into the canonical parameter table, obtain the lognormal model parameter value, trying to achieve apart from transmitting antenna d place according to formula (2), (6) and (7) is the probable value of blind spot.
Use the present invention to have following beneficial effect:
1) automaticity is high.Only need fix test antenna, can begin test, test, analysis and prediction are all completed automatically by the operation microcomputer software, and can send the parameters such as carrier frequency and power according to the air interface parameters flexible of applying in radio frequency identification standard.
2) measuring accuracy is high.Variable-frequency motor transmits marker with the speed of 0.1 meter per second, and this blind spot test macro radio frequency sending module sends test massage with the frequency of 10 times/second, and measuring accuracy reaches 1 centimetre.
3) simulation application is true to nature.Test antenna uses and the similar dipole test antenna of label antenna, and it can be attached to marker each position, and transmitting antenna adopts the circular polarized antenna of typical gains value, all is equal to practical situations.
4) Forecasting Methodology is more accurate.Than other Statistical Prediction Model, this method can be set up canonical parameter value table according to entrance applied environment type, the accuracy of estimation blind spot probability when improving actual deployment.
in sum, automaticity of the present invention is high, low complex degree, precision is high and near true entrance application scenarios, when field strength distribution slowly changes and change fast, measuring accuracy is preferably all arranged, and the lognormal model parameter list that obtains in the time of can be according to all kinds of typical environment, blind spot probability and coverage rate when use the estimation actual entry, the measurement requirement of blind spot in the time of can satisfying Internet of Things field radio-frequency (RF) identification entrance and use, and the blind spot probability of any point in space, measurable porch, aspect the radio-frequency (RF) identification entrance application blind spot distribution tests and prediction in Internet of Things field, has significant theory and technology advantage, has very high using value.
Description of drawings
Fig. 1 is super high frequency radio frequency identification entrance blind spot test system structure schematic diagram of the present invention.
Embodiment
The invention will be further described below in conjunction with drawings and Examples.
With reference to accompanying drawing, super high frequency radio frequency identification entrance blind spot test macro comprises radio frequency sending module, Receiver Module and entrance application simulation module, described radio frequency sending module comprises intermediate frequency field programmable gate array (FPGA RIO) integrated circuit board 3 and the reader circular polarisation transmitting antenna 9 of up-conversion integrated circuit board 1, reconfigurable input/output port, Receiver Module comprises down coversion integrated circuit board 2, FPGA RIO integrated circuit board 3 and dipole test antenna 8, and entrance application simulation module comprises programmable logic controller (PLC) (PLC) 4, motor 5, transport tape 7; Up-conversion integrated circuit board 1 intermediate frequency input interface is connected with intermediate frequency FPGA RIO integrated circuit board 3 intermediate frequency output interfaces; Down coversion integrated circuit board 2 intermediate frequency output interfaces are connected with intermediate frequency FPGA RIO integrated circuit board 3 intermediate frequency input interfaces; Intermediate frequency FPGA RIO integrated circuit board 3 is connected by pci interface 12 with microcomputer 6; Microcomputer 6 establishes a communications link with PLC4 by OPC interface 13; The control end of motor 5 is connected with the output terminal of PLC4; Motor 5 drives travelling belt 7 and moves; Reader circular polarisation transmitting antenna 9 is connected with up-conversion integrated circuit board 1 radio frequency output interface by radio-frequency cable I10-1; Dipole test antenna 8 is attached on marker 11, is connected with down coversion integrated circuit board 2 rf input interfaces by radio-frequency cable II10-2.
Described microcomputer 6 is provided with test, analysis and forecasting software, programmable logic controller (PLC) 4 is provided with PLC software, connected with computer 6 is provided with the OPC communication software with the OPC interface 13 of programmable logic controller (PLC) 4, and test, analysis and forecasting software and PLC software and OPC communication software consist of the present invention's super high frequency radio frequency identification entrance blind spot testing system software part.
Use super high frequency radio frequency identification entrance blind spot test macro, the step of carrying out test that blind spot distributes and probabilistic forecasting is as follows:
(1) up-conversion integrated circuit board 1, down coversion integrated circuit board 2, intermediate frequency FPGA RIO integrated circuit board 3, PLC4 initialization and configuration operation are arranged and completed to the simulation test scene.Particular content comprises: reader circular polarisation transmitting antenna 9 is placed in super high frequency radio frequency identifies the upper end, porch, marker 11 is placed in transport tape 7 right side start positions, and dipole test antenna 8 is attached on sign object 11; Complete up-conversion integrated circuit board 1, down coversion integrated circuit board 2, intermediate frequency FPGA RIO integrated circuit board 3 and the work of PLC4 power-up initializing, and configure carrier frequency, the transmitted power of up-conversion integrated circuit board 1, configure carrier frequency, the reception reference power of down coversion integrated circuit board 2, configure intermediate-freuqncy signal frequency, the signalling channel parameter of intermediate frequency FPGA RIO integrated circuit board 3.
(2) the OPC interface 13 by microcomputer 6 and PLC4 calls drive motor 5 control programs, starts travelling belt 7, drives sign object 11 and dipole test antenna 8, at the uniform velocity passes through porch reader circular polarisation transmitting antenna 9 radiation fields with the speed of 0.1 meter per second.Microcomputer 6 is by calling the driving of intermediate frequency FPGA RIO integrated circuit board 3, make intermediate frequency FPGA RIO integrated circuit board 3 with the frequency of 10 times/second, send the continuous carrier radiofrequency signal, simultaneously, down coversion integrated circuit board 2 receives the continuous carrier signal, be passed to microcomputer 6 through intermediate frequency FPGA RIO integrated circuit board 3 storehouses, calculate the performance number P at each position point i place by microcomputer 6
rAnd record it (i).
(3) thing 11 to be identified arrives travelling belt 7 terminal points, and operation microcomputer 6 stops test procedure, and microcomputer 6 routine analyzers use the linear regression analysis of MMSE criterion, by formula (2) and (5), obtain parameter n and the σ of lognormal model.
(4) change surrounding environment type, repeating step 1)-3), obtain the canonical parameter table.As shown in routine table 1:
Lognormal model parameter list under the dissimilar environment of example table 1
| Entrance is used the surrounding environment type | n | σ |
| The spacious warehouse of factory | 2.1 | 11dB |
| The airtight warehouse of factory | 3.5 | 15dB |
| Office | 2.5 | 9dB |
(5) use the surrounding environment type according to the actual entry, look into the canonical parameter table, the lognormal model parameter value, trying to achieve apart from transmitting antenna d rice according to formula (2), (6) and (7) is the probable value of blind spot.
Claims (2)
1. the probability forecasting method of super high frequency radio frequency identification entrance blind spot test macro, described super high frequency radio frequency identification entrance blind spot test macro, comprise radio frequency sending module, Receiver Module and entrance application simulation module, described radio frequency sending module comprises intermediate frequency field programmable gate array integrated circuit board and the transmitting antenna of up-conversion integrated circuit board, reconfigurable input/output port, Receiver Module comprises down coversion integrated circuit board, intermediate frequency field programmable gate array integrated circuit board and test antenna, and entrance application simulation module comprises programmable logic controller (PLC), motor, transport tape; Up-conversion integrated circuit board intermediate frequency input interface is connected with intermediate frequency FPGA RIO integrated circuit board intermediate frequency output interface; Down coversion integrated circuit board intermediate frequency output interface is connected with intermediate frequency FPGA RIO integrated circuit board intermediate frequency input interface; Intermediate frequency FPGA RIO integrated circuit board is connected by pci interface with microcomputer; Microcomputer establishes a communications link by OPC interface and programmable logic controller (PLC); The control end of motor is connected with the output terminal of programmable logic controller (PLC); The driven by motor travelling belt moves; Transmitting antenna is connected with up-conversion integrated circuit board radio frequency output interface by radio-frequency cable I; Test antenna is attached on marker, is connected with down coversion integrated circuit board rf input interface by radio-frequency cable II;
Described transmitting antenna is reader circular polarisation transmitting antenna;
Described test antenna is the dipole test antenna;
Described microcomputer is provided with test, analysis and forecasting software, programmable logic controller (PLC) is provided with PLC software, the OPC interface of connected with computer and programmable logic controller (PLC) is provided with the OPC communication software, and test, analysis and forecasting software and PLC software and OPC communication software consist of super high frequency radio frequency identification entrance blind spot testing system software part;
It is characterized in that, the probability forecasting method of described super high frequency radio frequency identification entrance blind spot test macro comprises the following steps:
(1) the dipole test antenna is attached to the marker surface, is placed in travelling belt right-hand member initial point position, and up-conversion integrated circuit board, down coversion integrated circuit board, FPGA RIO integrated circuit board and PLC are completed initialization and configuration operation;
(2) start travelling belt, driving sign object and dipole test antenna at the uniform velocity moves, while radio frequency sending module sends the continuous carrier radiofrequency signal with the frequency of 10 times/second, and Receiver Module reception continuous carrier signal, calculates the performance number P at each position point i place
rAnd record it (i);
(3) the test data P of the parser analysis N on microcomputer
r(i), use is based on the linear regression analysis of least mean-square error (MMSE) criterion, obtain parameter n and the standard variance σ of lognormal model, and change surrounding environment type, the canonical parameter table under the dissimilar environment that obtains being consisted of by parameter n and standard variance σ;
(4) use the surrounding environment type according to the actual entry, search canonical parameter table under dissimilar environment by the predictor on microcomputer, the lognormal model parameter value, trying to achieve apart from transmitting antenna d place is the probable value of blind spot.
2. the probability forecasting method of super high frequency radio frequency identification entrance blind spot test macro according to claim 1, it is characterized in that, in described step (4), search canonical parameter table under dissimilar environment by the predictor on microcomputer, the method of trying to achieve apart from transmitting antenna d place as the probable value of blind spot is that it obtains by following formula
Wherein, γ be for can activate the minimal detectable power of label, and Q (*) represents by error function, and σ is standard variance, P
r(d) expression apart from the received power at transmitting antenna d place, is calculated by following formula:
Wherein, P
tBe transmitted power, d
0Be the distance of reference test point and transmitting antenna, PL (d
0) be d
0The path loss at place, n is the lognormal model fading parameter;
Q (*) is provided by following formula:
Wherein erf (*) is error function, and σ is the standard variance of lognormal model;
Wherein, λ is the wavelength of carrier wave, P
tBe transmitted power, G
tBe transmitting antenna gain, G
rBe receiving antenna gain.
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| 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 |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| 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 |
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