CN105243348B - Reader optimal deployment method based on passive ultrahigh frequency RFID positioning system - Google Patents
Reader optimal deployment method based on passive ultrahigh frequency RFID positioning system Download PDFInfo
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Abstract
The invention belongs to the technical field of radio frequency communication, and relates to a reader optimal deployment method based on a passive ultrahigh frequency RFID positioning system, which comprises the following steps: analyzing the relation between the emission energy level of the reader and the radiation radius, constructing a target adaptive value function based on positioning time consumption according to the LANDMAC algorithm principle, taking the position of each reader in the system as an optimization variable, taking the target adaptive value function minimization as an optimization target, constructing an optimization particle model by adopting a typical particle swarm algorithm, and introducing a simulated annealing algorithm to improve the local searching capability and the global searching capability of the optimization particle model so as to determine the optimal deployment mode of each reader. The method is characterized in that a time-consuming particle optimizing model suitable for passive ultrahigh frequency RFID positioning is constructed by integrating the LANDMARC algorithm principle and the identification mechanism of the passive RFID, and the positioning efficiency of the system can be effectively improved by the obtained reader deployment mode.
Description
Technical field
The invention belongs to technology for radio frequency field, it is related to a kind of reader based on passive ultra-high frequency RFID alignment system
Optimization deployment method.
Background technology
In recent years, its noncontact of passive ultra-high frequency RFID system addresses, non line of sight, high-precision and inexpensive advantage quilt
It is widely used in indoor positioning.As the primary solutions based on RFID indoor positionings, LANDMARC systems introduce position
Known reference label carries out the field intensity information Euclidean distance of auxiliary positioning, comparison reference label and positioning label, finds neighbour
Reference label and empirically weight equation realize the location estimation of positioning label.Compared to other location algorithms, LANDMARC
The features such as algorithm has low cost, high accuracy.
Location efficiency and positioning precision are to evaluate the important indicator of alignment system performance.Due to the positioning of most of passive RFID
The position of reference label is fixed in system, and current research focus is concentrated mainly on the Optimization deployment of reader.At present, it is typical
Passive ultra-high frequency RFID reader can only reduce the minimum energy that power emission energy level obtains each label in the way of successively decreasing step by step
Level, when reader and nearer reference label position, the switching of non-essential energy level can cause should not positioning take, reduction positioning
Efficiency.Therefore, realize the Optimization deployment of reader to lift location efficiency, still with great practical significance.
To sum up, the present invention is based on Friss power attenuation models, sets up the pass of reader power emission level and radiation radius
System, by analyzing the mode of operation of reader, is built and adapts to value function based on the time-consuming target of positioning, made with each reading device position
For optimizing variable, using target adaptive value function minimization as optimization aim, optimizing particle is built using Typical particle group's algorithm
Model, introducing simulated annealing improves the local search ability and ability of searching optimum of optimizing particle model, so that it is determined that respectively
The optimal deployment way of reader.According to the above, the present invention proposes a kind of based on passive ultra-high frequency RFID alignment system
Reader Optimization deployment method.
The content of the invention
The problem of present invention need to be solved is to propose a kind of reader Optimization deployment method based on passive RFID alignment system.
Based on this method, the Optimization deployment of reader can be realized, effectively lifting system location efficiency.
1st, a kind of reader Optimization deployment method based on passive ultra-high frequency RFID alignment system, comprises the following steps：
Step 1：Location efficiency is generally evaluated as one of important performance indexes in alignment system using positioning to take to be used as
Standard.In passive ultra-high frequency RFID alignment system, reader obtains each with the working method of decreasing power emission level step by step
The collection of letters field strength of label, the position of reader is by the energy level switching times needed for directly determining the whole labels of reading, so as to influence
The positioning of system takes.For the energy level switching times needed for each reader reading label in determination system, Friss work(need to be combined
Rate loss model, sets the transmission power P of readertCorresponding greatest irradiation radius R, reference radius R0, signal wavelength lambda, path
Loss factor ε, passive label activation threshold value Pr, reader antenna gain Gr, label antenna gain Gt, adjacent power transmitting energy
The power step size I of levelp, Gaussian Profile ambient noise Xσ, to set up the emission level of reader and the relation of radiation radius
Step 2：The operation principle and system reader successively decreased step by step according to reader emission level in LANDMARC algorithms
Exemplary parallel mode of operation, obtain the time-consuming T of single positioning of systeml=maxU ∈ [1, U]Tu+C·tc, wherein tcRepresent single mark
The positioning of label takes, TuRepresent that the synthesis of the energy level information of C positioning label of u-th of reader acquisition takes, U represents that system is read
The quantity of device is read, l is the sequence number of positioning service；
Step 3：Average positioning according to the service of system multiple bearing takesBuild target adaptive value
Function F (Ω), wherein superior vector Ω represent each reading device position；
Step 4：Using target adaptive value function minimization as optimization aim, optimizing model F (Ω)=arg (min are set up
(T)), using particle swarm optimization algorithm to optimizing model carry out optimizing, using uniformly lay mode as reader dispose it is initial
State, and primary population is generated, the initial optimization speed and optimizing radius of each reader are defined, while introducing maximum evolve
Speed VmaxEffect of optimization is improved, penalty function Q is introduced into object function prevents the position of reader in optimization process from exceeding optimizing
The scope of radius；
Step 5：Position and the optimal speed of each reader are updated according to particle cluster algorithm, in τ in reproductive process, is remembered
Record the average value F (Pbest of the population local extremum of current reproductive statusτ)avgWith global extremum F (Gbestτ), wherein Pbestτ、
GbestτRepresent the local extremum and the corresponding particle state of global extremum of τ generation breedings respectively, i.e. system system reader
Deployed position；
Step 6：For the local search ability and ability of searching optimum of further equilibrium particle colony optimization algorithm, simulation is introduced
Annealing algorithm dynamically adjusts particle rapidity and updates weight, calculates the annealing temperature ξ of the τ times reproductive statusτ=[F (Pbestτ
)avg/F(Gbestτ)]-1；
Step 7：According to annealing temperature ξτ, current reproductive status global extremum F (Gbestτ) and prior-generation reproductive status
Global extremum F (Gbestτ-1), the annealing probability p of the τ times iteration is calculated, if F (Gbestτ-1)≤F(Gbestτ), p=1 is made, if
F(Gbestτ-1) ＞ F (Gbestτ), order
Step 8：According to the annealing probability of current reproductive status, the renewal weights omega of particle rapidity is adjusted, if p >=β, ω is made
=α1+ 0.5 β, if p ＜ β, make ω=α2+ 0.5 β, wherein α1And α2Preset parameter, and meet 0 ＜ α2＜ α1＜ 1, β are situated between for value
In 0 and 1 parameter preset；
Step 9：According to the weight coefficient selected by step 8, perform step 5 and carry out breeding optimizing real-time update grain of future generation
Sub- state and speed, then perform the adjustment that step 6 carries out weight coefficient to step 8 according to population real-time status successively.When repeatedly
Generation number reaches that default maximum or global extremum meet default take and stop optimizing when requiring；
Step 10：Using the corresponding particle state of population global extremum as the optimal deployment way of reader, complete real
Border is disposed, and terminates search process.
Brief description of the drawings：
Fig. 1 is the structure flow chart that target of the present invention based on the time-consuming model of positioning adapts to value function；
Fig. 2 is the flow chart of the improvement particle cluster algorithm present invention introduces simulated annealing；
Fig. 3 is the optimal deployed position of the reader under the environment in positioning tally set；
Fig. 4 is the optimal deployed position of the reader under the scattered environment of positioning label；
Fig. 5 be position tally set in environment under reader respectively be located at initial position and optimal location when positioning
Time-consuming comparison diagram；
Fig. 6 is the positioning when the reader under positioning the scattered environment of label is located at initial position and optimal location respectively
Time-consuming comparison diagram；
Embodiment：
The purport of the present invention is to propose a kind of reader Optimization deployment method based on passive ultra-high frequency RFID alignment system,
The reader deployment way that this method is obtained is capable of the location efficiency of effectively lifting system.
Below in conjunction with the accompanying drawings 1, accompanying drawing 2, accompanying drawing 3, accompanying drawing 4, accompanying drawing 5, accompanying drawing 6 are made further to embodiment of the present invention
It is described in detail.
First, the foundation of the power emission energy level of reader and radiation radius mapping relations
Based on Friss power attenuation models, the transmission power P of reader can be obtainedtCorresponding greatest irradiation radius R
Wherein R0Reference radius is represented, λ represents signal wavelength, and ε represents path loss coefficient, PrRepresent passive label activation
Threshold value, GrRepresent reader antenna gain, GtRepresent label antenna gain.
The discrete energy series for setting minimum and maximum is respectively 1 and Gmax, the maximum transmission power P of readermax, adjacent work(
The power step size of rate emission level is Ip, transmission power of the reader under j-th of energy level can be obtained
Introduce the ambient noise X of Gaussian distributedσ, set up power emission energy level and the radiation radius mapping pass of reader
System：
2nd, the structure that time-consuming target adapts to value function is averagely positioned based on system
Initialization system reader positions time-consuming T with exemplary parallel mode of operation for the single of systemlHave：
Tl=maxU ∈ [1, U]Tu+C·tc (4)
Wherein l represents the sequence number of positioning service, tcRepresent that the positioning of single label takes, U represents system reader
Number, TuRepresent that the synthesis of the energy level information of C positioning label of u-th of reader acquisition takes, have
Wherein, J represents the time-consuming function using ALOHA algorithms, and δ is number of time slots, Represent respectively u-th
Reader is operated in reference label number and positioning number of tags under j-th of energy level, tzWhen representing the switching between adjacent energy levels
Between, and when the work of u readers reduces huAfter individual energy level, read there is no positioning label, now u readers stop hair
Power signal is penetrated, park mode is switched to, terminates positioning action.
Setting positioning number of times L, the average positioning for obtaining system takesSo as to build target adaptive value
Function F (Ω), wherein Ω represent each reading position, and F (Ω) is to represent that system when each reader is placed in diverse location is averagely fixed
Position is time-consuming.
Accompanying drawing 1 show the structure flow chart that the target based on the time-consuming model of positioning adapts to value function.
3rd, the improvement particle cluster algorithm based on simulated annealing
Using target adaptive value function minimization as optimization aim, optimizing model is set up
F (Ω)=arg (min (T)) (6)
Optimizing is carried out to optimizing model using particle cluster algorithm, uniformly to lay the initial shape that mode is disposed as reader
State simultaneously generates primary population
Z=[Z1 Z2 … ZW]T (7)
Wherein,Represent particleIn each reader initial position, W is particle
Sum, defines the initial optimization speed of each reader to update primary population according to particle cluster algorithm.According to formula (8) to excellent
Change speed to be limited to improve effect of optimization, setting largest optimization speed VmaxObey
Vmax=η S (8)
Wherein η is restriction factor, and S is the optimizing radius of each reader.
In order to prevent the position of reader in optimization process from exceeding the scope of optimizing radius, penalty function Q, its expression formula are introduced
For
Wherein K1、K2For penalty factor, if the position of u-th of reader exceeds the scope of optimizing radius, s is madeu=1, otherwise make
su=0, optimizing model is rebuild, is had
M=T+Q (10)
F (Ω)=arg (min (M)) (11)
Position and the optimal speed of each reader are updated according to particle cluster algorithm, it is so that τ -1 generations breed as an example, then current numerous
Grow the position of each reader under stateAnd optimal speedIt is updated to respectively according to formula (12), (13) of future generation numerous
Grow the position of each reader under stateAnd optimal speed
WhereinAnd Gbestτ-1The local extremum and the corresponding particle of global extremum of prior-generation breeding are represented respectively
State.
For the local search ability and ability of searching optimum of further equilibrium particle group's algorithm, simulated annealing is introduced
Dynamically weight selection coefficient, needs to calculate the annealing temperature ξ for obtaining τ generation breedings firstτ
ξτ=[F (Pbestτ)avg/F(Gbestτ)]-1 (14)
WhereinF(Pbestτ)avgRepresent the part of τ generation breedings
The average value of extreme value, F (Gbestτ) represent global extremums of the τ for breeding, PbestτAnd GbestτIt is τ generation breedings respectively
Local extremum and the corresponding particle state of global extremum.
According to annealing temperature ξτ, current reproductive status global extremum F (Gbestτ) and prior-generation reproductive status under the overall situation
Extreme value F (Gbestτ-1), calculating obtains annealing probability p
According to the annealing probability p under current reproductive status, weight coefficient is chosen by formula (19), had
Wherein β represents value parameter preset between 0 and 1, α1、α2Represent preset parameter and meet 0 ＜ α2＜ α1＜ 1.
When iterations reach default maximum or global extremum meet it is default it is time-consuming require when stop optimizing, with grain
Global extremum corresponding particle state in subgroup completes actual deployment as the optimal deployment way of reader, terminates searching process.
Accompanying drawing 2 show the flow chart for improving particle cluster algorithm.
4th, instance analysis explanation
Above-mentioned embodiment is illustrated by Fig. 3, Fig. 4, Fig. 5, Fig. 6 with reference to example.
Introduce 4 readers in example to position 30 positioning labels, reference label is uniformly laid in 8 × 8 modes,
Reader maximum function energy level is respectively 4,8,16,32,64,128, using dynamic Frame Slotted Aloha anti-collision algorithm, tz=tc
=50ms, c1=c2=1.4962, η=0.3, α1=0.7, α2=0.3, W=40, ρ=300, U=4, Gt=1.2dBi, Gr=
8dBi, Pt=36dBm, K2/K1=200, S=3.5, Ip=0.25, Gmax=128, δ=64, L=50.4 points of accompanying drawing 3 and accompanying drawing
The optimal deployed position of reader under two kinds of environment is not reflected.Observation is visible, under the environment in positioning tally set, reader
Optimal location be located at simulating area border four apexes；Under the scattered environment of positioning label, the optimal position of reader
Setting in simulating area center.
As shown in Figure 5, under the environment in positioning tally set, system positioning when reader is located at optimal location takes
60.35s is reached, system when being located at initial position in contrast to reader positions time-consuming 119.02s, efficiency improves 49.3%.
As shown in Figure 6, under the scattered environment of positioning label, system positioning when reader is located at optimal location takes
91.37s is reached, system when being located at initial position in contrast to reader positions time-consuming 81.13s, efficiency is improved and reduced
12.6%.
Example shows, a kind of reader Optimization deployment based on passive ultra-high frequency RFID alignment system proposed by the invention
Method can realize the Optimization deployment of reader, effectively the location efficiency of lifting system, have to the performance for improving alignment system
There is great practical significance.
Claims (4)
1. a kind of reader Optimization deployment method based on passive ultra-high frequency RFID alignment system, comprises the following steps：
Step 1：Location efficiency generally evaluates mark as one of important performance indexes in alignment system to position to take to be used as
Standard, in passive ultra-high frequency RFID alignment system, reader obtains each mark with the working method of decreasing power emission level step by step
The collection of letters field strength of label, the position of reader by the energy level switching times needed for directly determining to read whole labels so that influence be
The positioning of system takes, and for the energy level switching times needed for each reader reading label in determination system, need to combine Friss power
Loss model, sets the transmission power P of readertCorresponding greatest irradiation radius R, reference radius R0, signal wavelength lambda, path damage
Consume coefficient ε, passive label activation threshold value Pr, reader antenna gain Gr, label antenna gain Gt, adjacent power emission level
Power step size Ip, Gaussian Profile ambient noise Xσ, to set up the emission level of reader and the relation of radiation radius
Step 2：The operation principle and the allusion quotation of system reader successively decreased step by step according to reader emission level in LANDMARC algorithms
Type concurrent operating modes, obtain the time-consuming T of single positioning of systeml=maxU ∈ [1, U]Tu+C·tc, wherein tcRepresent single label
Positioning is time-consuming, TuRepresent that the synthesis of the energy level information of C positioning label of u-th of reader acquisition takes, U represents system reader
Quantity, l be positioning service sequence number；
Step 3：Average positioning according to the service of system multiple bearing takesBuild target and adapt to value function F
(Ω), wherein superior vector Ω represent each reading device position；
Step 4：Using target adaptive value function minimization as optimization aim, optimizing model F (Ω)=arg (min (T)) is set up,
Optimizing is carried out to optimizing model using particle cluster algorithm, uniformly to lay the original state that mode is disposed as reader, and it is raw
Into primary population, the initial optimization speed and optimizing radius of each reader are defined, while introducing maximum evolutionary rate VmaxCarry
High effect of optimization, penalty function Q is introduced into object function prevents the position of reader in optimization process from exceeding the model of optimizing radius
Enclose；
Step 5：Position and the optimal speed of each reader are updated according to particle cluster algorithm, in τ in reproductive process, record is worked as
Average value F (the Pbest of the population local extremum of preceding reproductive statusτ)avgWith global extremum F (Gbestτ), wherein Pbestτ、
GbestτRepresent the local extremum and the corresponding particle state of global extremum of τ generation breedings respectively, i.e. system system reader
Deployed position；
Step 6：For the local search ability and ability of searching optimum of further equilibrium particle group's algorithm, simulated annealing is introduced
Dynamically adjust particle rapidity and update weight, calculate the annealing temperature ξ of the τ times reproductive statusτ=[F (Pbestτ)avg/F(Gbestτ)]-1；
Step 7：According to annealing temperature ξτ, current reproductive status global extremum F (Gbestτ) and prior-generation reproductive status the overall situation
Extreme value F (Gbestτ-1), the annealing probability p of the τ times iteration is calculated, if F (Gbestτ-1)≤F(Gbestτ), p=1 is made, if F
(Gbestτ-1) ＞ F (Gbestτ), order
Step 8：According to the annealing probability of current reproductive status, the renewal weights omega of particle rapidity is adjusted, if p >=β, ω=α is made1+
0.5 β, if p ＜ β, make ω=α2+ 0.5 β, wherein α1And α2Preset parameter, and meet 0 ＜ α2＜ α1＜ 1, β are value between 0 and 1
Parameter preset；
Step 9：According to the weight coefficient selected by step 8, perform step 5 and carry out breeding optimizing real-time update particle shape of future generation
State and speed, then perform the adjustment that step 6 carries out weight coefficient to step 8 according to population real-time status, when iteration time successively
Number reaches that default maximum or global extremum meet default take and stop optimizing when requiring；
Step 10：Using the corresponding particle state of population global extremum as the optimal deployment way of reader, actual portion is completed
Administration, terminates search process.
2. a kind of reader Optimization deployment method based on passive ultra-high frequency RFID alignment system according to claim 1,
Characterized in that, in step 2, the synthesis that u-th of reader obtains the energy level information of C positioning label takes TuExpression formula be
<mrow>
<msub>
<mi>T</mi>
<mi>u</mi>
</msub>
<mo>=</mo>
<msubsup>
<mi>&Sigma;</mi>
<mrow>
<mi>j</mi>
<mo>=</mo>
<mn>0</mn>
</mrow>
<msub>
<mi>h</mi>
<mi>u</mi>
</msub>
</msubsup>
<mi>J</mi>
<mrow>
<mo>(</mo>
<msubsup>
<mi>N</mi>
<mrow>
<mi>R</mi>
<mi>u</mi>
</mrow>
<mi>j</mi>
</msubsup>
<mo>,</mo>
<msubsup>
<mi>N</mi>
<mrow>
<mi>T</mi>
<mi>u</mi>
</mrow>
<mi>j</mi>
</msubsup>
<mo>,</mo>
<mi>&delta;</mi>
<mo>)</mo>
</mrow>
<mo>+</mo>
<msub>
<mi>h</mi>
<mi>u</mi>
</msub>
<mo>&CenterDot;</mo>
<msub>
<mi>t</mi>
<mi>z</mi>
</msub>
</mrow>
Wherein, J represents the time-consuming function using ALOHA algorithms, and δ is number of time slots,U-th of reading is represented respectively
Device is operated in reference label number and positioning number of tags under j-th of energy level, tzThe switching time between adjacent energy levels is represented, and
When u-th of reader work reduces huAfter individual energy level, read there is no positioning label, now u-th of reader stops hair
Power signal is penetrated, park mode is switched to, terminates positioning action.
3. a kind of reader Optimization deployment method based on passive ultra-high frequency RFID alignment system according to claim 1,
Characterized in that, in step 4, maximum evolutionary rate VmaxIt is respectively with penalty function Q：
Vmax=η S
<mrow>
<mi>Q</mi>
<mo>=</mo>
<mrow>
<mo>(</mo>
<msub>
<mi>K</mi>
<mn>2</mn>
</msub>
<mo>/</mo>
<msub>
<mi>K</mi>
<mn>1</mn>
</msub>
<mo>)</mo>
</mrow>
<mo>&CenterDot;</mo>
<msubsup>
<mi>&Sigma;</mi>
<mrow>
<mi>u</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>U</mi>
</msubsup>
<msub>
<mi>s</mi>
<mi>u</mi>
</msub>
</mrow>
Wherein η is restriction factor, and S is the optimizing radius of each reader, K1、K2For penalty factor, if the position of u-th of reader surpasses
Go out the scope of optimizing radius, make su=1, otherwise make su=0.
4. a kind of reader Optimization deployment method based on passive ultra-high frequency RFID alignment system according to claim 1,
Characterized in that, in step 5, the average value F (Pbest of the population local extremum of current reproductive statusτ)avgExpression formula be：
<mrow>
<mi>F</mi>
<msub>
<mrow>
<mo>(</mo>
<msup>
<mi>Pbest</mi>
<mi>&tau;</mi>
</msup>
<mo>)</mo>
</mrow>
<mrow>
<mi>a</mi>
<mi>v</mi>
<mi>g</mi>
</mrow>
</msub>
<mo>=</mo>
<msubsup>
<mi>&Sigma;</mi>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>W</mi>
</msubsup>
<mi>F</mi>
<mrow>
<mo>(</mo>
<msubsup>
<mi>Pbest</mi>
<mi>i</mi>
<mi>&tau;</mi>
</msubsup>
<mo>)</mo>
</mrow>
<mo>/</mo>
<mi>W</mi>
</mrow>
Wherein W is total number of particles.
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| CN106202670A (en) * | 2016-06-30 | 2016-12-07 | 天津工业大学 | Based on the RFID reader smart antenna Pattern Synthesis algorithm improving population |
| CN106211310A (en) * | 2016-07-01 | 2016-12-07 | 北京永安信通科技有限公司 | Indoor orientation method based on UWB technology |
| CN109033910B (en) * | 2018-07-04 | 2021-09-21 | 东莞市明日视界科技有限公司 | Reader for ultrahigh frequency passive RFID (radio frequency identification) in-road parking management system |
| CN110781700B (en) * | 2019-11-11 | 2021-05-14 | 湖南大学 | RFID multi-reader coordination method |
| CN110929535A (en) * | 2019-11-15 | 2020-03-27 | 武汉大学 | Vehicle-mounted warehousing asset management method and system based on UHF RFID path loss model |
| CN115460615A (en) * | 2022-09-06 | 2022-12-09 | 广西大学 | RFID antenna optimal deployment method based on brainstorming algorithm |
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