CN106202670A - Based on the RFID reader smart antenna Pattern Synthesis algorithm improving population - Google Patents

Abstract

The invention belongs to technology for radio frequency field, relate to a kind of based on the RFID reader smart antenna Pattern Synthesis algorithm improving population, steps of the method are: the relation between analyzing radiation directional diagram and excitation amplitude, build smart antenna optimization object function, using the exciting current amplitude of array element as optimizing parameter, conventional particle group's algorithm is used to build optimizing particle model, introduce simulated annealing and improve local search ability and the ability of searching optimum of optimizing particle model, so that it is determined that the excitation amplitude size of each array element.The method can choose suitable array element excitation amplitude, optimizes antenna pattern, it is achieved zero falls into characteristic, effectively promotes the capacity of resisting disturbance of aerial array in UHF rfid system, has great practical significance to improving rfid system performance.

Description

Based on the RFID reader smart antenna Pattern Synthesis algorithm improving population

Technical field

The invention belongs to technology for radio frequency field, particularly to a kind of based on the RFID reader intelligence improving population Antenna measuring table algorithm.

Background technology

REID (radio frequency identification, RFID) is a kind of real by electromagnetic signal The wireless communication technology of existing data interaction.In recent years, rfid system is by its noncontact, non line of sight, high accuracy and low cost Advantage be widely used in the every field such as commercial production, intelligent transportation, asset management.Antenna realizes as in rfid system The key factor of RFDC, serves bridge beam action between reader and label.Common RFID reader is adopted Using single antenna radiated electromagnetic wave, its radiation scope is wider, and radiation direction diagram shape is fixed, and gain is relatively low, causes the anti-dry of system Disturb indifferent, locating effect is the best, collision rate is higher, significantly reduces the performance of system.

Smart antenna is defined as utilizing the combination of multiple bay to carry out signal processing, each by controlling in array antenna The parameter adjustment antenna patterns such as the excitation amplitude of antenna element, phase contrast so that systematic function is in different signal environments Reach optimum.It is applied to intelligent antenna technology in rfid system can effectively improve system at anti-interference, location, anticollision Etc. the performance of aspect.

Array pattern complex art is defined as by adjusting number of antennas in aerial array, array element distance, array element excitation And the satisfactory antenna pattern of gain of parameter such as the phase contrast between each array element.Along with communication environment is the most complicated, classical Dongle husband-Pattern Synthesis technology such as Chebyshev's complex art, Taylor's complex art can not meet communication needs, for The Pattern Synthesis problem of some Prescribed Properties, falls into characteristic to overcome interference etc. as produced zero on special angle, it is impossible to give Giving effective solution, therefore Application comparison difficulty in the Pattern Synthesis of smart antenna, is not suitable for RFID reader Wave beam forming.In recent years, intelligent optimization algorithm is increasingly becoming study hotspot, its essence be by simulate some natural phenomena or Some biological behavior and optimization method is proposed, it is possible to efficiently solve the optimization problem of complex nonlinear function.Intelligence Can optimized algorithm rise also for solve directional diagram synthtic price index provide brand-new direction, as particle swarm optimization algorithm (PSO), It is comprehensive that the intelligent optimization algorithms such as invasive weed optimized algorithm (IWO), ant group algorithm (ACO) have been applied successfully to array pattern In technology and obtain good effect.

Summary of the invention

The present invention proposes a kind of based on the RFID reader smart antenna Pattern Synthesis algorithm improving population.Based on this Algorithm, it is possible to the excitation amplitude optimizing each array element in each smart antenna obtains target emanation directional diagram, promotes the anti-of rfid system Jamming performance.

1, RFID reader smart antenna Pattern Synthesis algorithm based on improvement population, comprises the following steps:

Step 1: according to the target direction figure of RFID reader smart antenna, set up optimization object function；

Step 2: minimize as optimization aim using optimization object function, with each array element excitation amplitude for optimizing parameter, adopt Optimizing is carried out with particle swarm optimization algorithm.Generate primary population, initialize position and the optimal speed of each particle；

Step 3: each particle position is substituted into optimization object function, is calculated the target function value of current particle position, Determine the local extremum under current optimizing state and global extremum；

Step 4: each particle position and optimal speed are updated according to population more new formula, and according to object function Value, updates local extremum, global extremum and each particle position；

Step 5: for local search ability and the ability of searching optimum of further equilibrium particle colony optimization algorithm, introduce simulation Annealing algorithm dynamically adjusts inertia weight coefficient in particle rapidity more new formula, calculates the annealing temperature of current optimizing state；

Step 6: according to annealing temperature, the global extremum of current optimizing state and the global extremum of prior-generation optimizing state, Calculate the annealing probability under current optimizing state；

Step 7: according to the annealing probability of current optimizing state, adjust the inertia weight system in particle rapidity more new formula Number；

Step 8: judge whether current optimizing number of times reaches default maximum or whether global extremum meets requirement, if not having Have, then continue executing with step 3, otherwise perform step 9；

Step 9: using particle position corresponding to population global extremum as the optimal excitation amplitude of bay.

In described step 1, optimization object function is

In formula: MSLL_DRepresenting design minor level maximum, NLVL represents that on interference radiating way designed zero falls into level Value,Represent in interference angleOn the level value of antenna pattern, ω₁、ω₂Represent weight coefficient.

In described step 4, particle positionAnd optimal speedMore new formula be

In formula:For the renewal weight coefficient of particle rapidity, r₁, r₂For being positioned at the random number between [0,1], c₁, c₂Represent Studying factors,And Gbest^τ-1Represent the particle shape that the local extremum of prior-generation optimizing is corresponding with global extremum respectively State.

In described step 5, as a example by the τ time optimizing, annealing temperature T_τComputing formula is

T_τ=F (Pbest^τ)_avg/F(Gbest^τ) (4)

In formula: F (Pbest^τ)_avgRepresent the τ meansigma methods for the local extremum of optimizing, F (Gbest^τ) represent that τ is for optimizing Global extremum, Pbest^τAnd Gbest^τIt is τ respectively for the local extremum of the optimizing particle state corresponding with global extremum.

In described step 6, the computing formula of annealing probability P is

$P=\left\{\begin{array}{cc}1;& F\left({\mathrm{Gbest}}^{\mathrm{\τ}-1}\right)\≤F\left({\mathrm{Gbest}}^{\mathrm{\τ}}\right)\\ \exp (\[F({\mathrm{Gbest}}^{\mathrm{\τ}})-F\left({\mathrm{Gbest}}^{\mathrm{\τ}-1}\right)\]/T_{\mathrm{\τ}});& F\left({\mathrm{Gbest}}^{\mathrm{\τ}-1}\right)>F\left({\mathrm{Gbest}}^{\mathrm{\τ}}\right)\end{array}\right.---\left(5\right)$

In described step 7, the more new formula updating weight coefficient of particle rapidity is:

In formula: k₁And k₂For preset parameter, and meet 0 ＜ k₂＜ k₁＜ 1, β be value between 0 and 1 parameter preset.

Accompanying drawing illustrates:

For clearer explanation inventive embodiments or technical scheme of the prior art, below will be to embodiment or existing In technology description, the required accompanying drawing used is briefly described, and the accompanying drawing in describing below is only an enforcement of the present invention Example, for those of ordinary skill in the art, do not pay creation laborious on the premise of, it is also possible to obtain it with reference to the accompanying drawings His accompanying drawing.

Fig. 1 is the flow chart improving particle cluster algorithm that present invention introduces simulated annealing；

Fig. 2 is to improve particle cluster algorithm and conventional particle group's algorithm array antenna antenna pattern in the ideal case；

Fig. 3 is array antenna of dipoles phantom；

Fig. 4 is to improve array antenna of dipoles radiation direction in the case of particle cluster algorithm and conventional particle group's algorithm couples Figure；

Fig. 5 is Section of Microstrip Antenna Array phantom；

Fig. 6 is to improve Section of Microstrip Antenna Array radiation direction in the case of particle cluster algorithm and conventional particle group's algorithm couples Figure.

Detailed description of the invention:

The purport of the present invention is to propose a kind of RFID reader smart antenna Pattern Synthesis based on improvement population to calculate Method, this algorithm can optimize the excitation amplitude of each bay and obtain target emanation directional diagram, promote the anti-interference of aerial array Performance.

As it is shown in figure 1, specifically include following steps:

Step 1: build the optimization object function of smart antenna.As a example by linear array, for N unit line array, ignoring each sky Under coupling between linear array unit, antenna pattern function is represented by:

$F\left(\mathrm{\θ}\right)=\underset{l=1}{\overset{N}{\Σ}}{A}_t{e}^{ji(\frac{2\mathrm{\π}}{\mathrm{\λ}}dsin\mathrm{\θ}-{\mathrm{\Δ\φ}}_B)}---\left(1\right)$

In formula: A_iRepresenting the exciting current amplitude of i-th bay, λ represents that wavelength, d represent between each bay Away from, θ represents azimuth, Δ φ_BRepresent the phase contrast between each bay.

The maximum sidelobe level value relatively of definition is:

$MSLL=\underset{\mathrm{\θ}\∈p}{max}\left\{F\left(\mathrm{\θ}\right)\right\}---\left(2\right)$

In formula, max represents that max function, p represent the secondary lobe region of directional diagram.

If null beam width is 2 α, then p={ θ | 0 ° of+α≤θ≤90 ° of-90 °≤θ≤0 °-α ∪ }, emulation needs set Put certain step-length and secondary lobe region is sampled obtaining the radiation gain of all angles, it is generally recognized that relatively low minor level Value can reduce interference to a certain extent.

It may be noted that when there is stronger interference on certain direction, in order to shield interference signal, need in interference signal side Being upwardly formed zero and fall into characteristic, therefore the requirement to minor level value is also not quite similar.Consider the lower pair of antenna pattern Lobe and zero falls into the index of characteristic, can set up smart antenna optimization object function as follows:

In formula, MSLL_DRepresenting design minor level maximum, NLVL represents that on interference radiating way designed zero falls into level Value,Represent in interference angleOn the level value of antenna pattern, ω₁、ω₂Represent weight coefficient.

Step 2: initialize position and the optimal speed of each particle in population, generate initial population

Z=[Z₁ Z₂ ... Z_W]^T (4)

In formula:Represent current particlePosition, the most each bay swash Encouraging amplitude, W is total number of particles, and U is the number of bay.

Step 3: each array element excitation amplitude is substituted into and calculates functional value in optimization object function formula (3), by each function It is worth as the local extremum under current optimizing state, using optimum functional value as the global extremum under current optimizing state.

Step 4: update speed and the position of each particle according to formula (5), (6), and calculate each particle under current optimizing state Optimization object function value, relatively more current optimization object function value and its history local extremum, if being better than history local extremum, Then replace history local extremum by current value, and compare with history global extremum, if being better than history global extremum, then with current Value replaces history global extremum, updates particle position simultaneously.Particle positionAnd optimal speedMore new formula be

In formula:For the renewal weight coefficient of particle rapidity, r₁, r₂For being positioned at the random number between [0,1], c₁, c₂Represent Studying factors,And Gbest^τ-1Represent the particle shape that the local extremum of prior-generation optimizing is corresponding with global extremum respectively State.

Step 5: for local search ability and the ability of searching optimum of further equilibrium particle group's algorithm, introduces simulation and moves back Fire algorithm is dynamically chosen in formula (5).As a example by τ is for optimizing, it is calculated annealing temperature T_τ

T_τ=F (Pbest^τ)_avg/F(Gbest^τ) (7)

In formula: F (Pbest^τ)_avgRepresent the meansigma methods of the local extremum of τ generation breeding, F (Gbest^τ) represent τ generation breeding Global extremum, Pbest^τAnd Gbest^τIt is τ respectively for the local extremum of the optimizing particle state corresponding with global extremum.

Step 6: according to annealing temperature T_τ, the global extremum F (Gbest of current optimizing state^τ) and prior-generation optimizing state under Global extremum F (Gbest^τ-1), it is calculated annealing probability P

$P=\left\{\begin{array}{cc}1,;& F\left({\mathrm{Gbest}}^{\mathrm{\τ}-1}\right)\≤F\left({\mathrm{Gbest}}^{\mathrm{\τ}}\right)\\ \exp (\[F({\mathrm{Gbest}}^{\mathrm{\τ}})-F\left({\mathrm{Gbest}}^{\mathrm{\τ}-1}\right)\]/T_{\mathrm{\τ}}),;& F\left({\mathrm{Gbest}}^{\mathrm{\τ}-1}\right)>F\left({\mathrm{Gbest}}^{\mathrm{\τ}}\right)\end{array}\right.---\left(8\right)$

Step 7: according to the annealing probability P under current optimizing state, by formula (9) in formula (5)It is updated, has

In formula: β represents value parameter preset between 0 and 1, k₁、k₂Represent preset parameter and meet 0 ＜ k₂＜ k₁＜ 1。

Step 8: judge whether current optimizing number of times reaches default maximum or whether global extremum meets requirement, if not having Have, then continue executing with step 3, otherwise perform step 9.

Step 9: stop optimizing, using particle position corresponding to population global extremum as the excitation amplitude of optimal antenna.

Instance analysis explanation

Above-mentioned embodiment is illustrated by Fig. 2, Fig. 3, Fig. 4, Fig. 5, Fig. 6 below in conjunction with example.

Choosing array element number in example is 8 array elements, and array element distance is λ/2, each bay homophase, and maximum optimizing number of times is 80, the population of population is 50, the excitation amplitude range of each bay is (0,1), MSLL_D=-30dB, main lobe direction ± 40 ° are formed about zero and fall into characteristic and NLVL is-75dB, ω₁=2, ω₂=0.1, c₁=c₂=1.495, k₁=0.7, k₂=0.3, In conventional particle group's algorithm.Use conventional particle group's algorithm and improve particle cluster algorithm gained antenna pattern such as figure Shown in 2, for the inhibition of secondary lobe, use and improve the first minor level of the antenna pattern that particle cluster algorithm obtains Big value is-35dB, and the maximum ining contrast to the first minor level obtained by employing conventional particle group's algorithm reduces 5dB；Right Falling into characteristic in the zero of interference radiating way, employing improves the zero of the antenna pattern that particle cluster algorithm obtains and falls into level value about-78dB, Obtained by contrast employing conventional particle group's algorithm zero falls into level value and reduces about 11dB, resists dry for promoting aerial array The ability of disturbing has good effect.

Fig. 3, Fig. 5 are respectively in view of dipole antenna during coupling and the illustraton of model of micro-strip paster antenna, Fig. 4, Fig. 6 It is respectively as corresponding antenna pattern, it can be seen that use the antenna pattern improving particle cluster algorithm to the first minor level Value rejection ratio conventional particle group's algorithm have dropped about about 5dB, zero falls into level value and has also dropped than conventional particle group's algorithm simultaneously Low.

Example shows, carried algorithm exists the antenna pattern obtained by the case of coupling between in view of bay Still have effectiveness, and the most applicable for beam antenna and omnidirectional antenna, it is possible to effectively promote antenna in UHF rfid system The capacity of resisting disturbance of array, has great practical significance to improving rfid system performance.

It will be appreciated by those skilled in the art that accompanying drawing is the schematic diagram of an embodiment, the invention described above embodiment sequence number Just to describing, do not represent the quality of embodiment.

The foregoing is only presently preferred embodiments of the present invention, be not limiting as the present invention, all in the spirit and principles in the present invention Within, any modification, equivalent substitution and improvement etc. made, should be included within the scope of the present invention.

Claims (4)

1. a RFID reader smart antenna Pattern Synthesis algorithm based on improvement population, comprises the following steps:

Step 1: in intelligent antenna array, the number of antenna element, the spacing of each antenna element, exciting current amplitude, phase place are all The shape of directional diagram can be affected.In order to determine that in system, reader reads the accuracy of label, shielding interference signal, it is suitable to choose Array element number, the parameter such as array element distance, set up the optimization object function of smart antenna；

Step 2: minimize as optimization aim using optimization object function, with each array element excitation amplitude for optimizing parameter, use grain Subgroup optimized algorithm carries out optimizing.Generate primary population, initialize position and the optimal speed of each particle；

Step 3: each particle position is substituted into optimization object function, is calculated the target function value of current particle position, determines Local extremum under current optimizing state and global extremum；

Step 4: each particle position and optimal speed are updated according to population more new formula, and according to target function value, Update local extremum, global extremum and each particle position.Particle positionAnd optimal speedMore new formula be

In formula:For the renewal weight coefficient of particle rapidity, r₁, r₂For being positioned at the random number between [0,1], c₁, c₂Represent study The factor,And Gbest^τ-1Represent the particle state that the local extremum of prior-generation optimizing is corresponding with global extremum respectively；

Step 5: for local search ability and the ability of searching optimum of further equilibrium particle colony optimization algorithm, introduce simulation and move back Fire algorithm dynamically adjusts the renewal inertia weight coefficient of particle rapidity, calculates the annealing temperature of current optimizing state；

Step 6: according to annealing temperature, the global extremum of current optimizing state and the global extremum of prior-generation optimizing state, calculates Annealing probability under current optimizing state；

Step 7: according to the annealing probability of current optimizing state, in newer (1)

Step 8: judge whether current optimizing number of times reaches default maximum or whether global extremum meets requirement, if not having, Then continue executing with step 3, otherwise perform step 9；

Step 9: using particle position corresponding to population global extremum as the optimal excitation amplitude of bay.

A kind of RFID reader smart antenna Pattern Synthesis algorithm based on population the most according to claim 1, its It is characterised by: in step 5, annealing temperature T_τComputing formula is

T_τ=F (Pbest^τ)_avg/F(Gbest^τ) (3)

In formula: F (Pbest^τ)_avgRepresent the τ meansigma methods for the local extremum of optimizing, F (Gbest^τ) represent complete for optimizing of τ Office's extreme value, Pbest^τAnd Gbest^τIt is τ respectively for the local extremum of the optimizing particle state corresponding with global extremum.

A kind of RFID reader smart antenna Pattern Synthesis algorithm based on population the most according to claim 1, its Being characterised by: in step 6, the computing formula of annealing probability P is

。

A kind of RFID reader smart antenna Pattern Synthesis algorithm based on population the most according to claim 1, its It is characterised by: in step 7,More new formula be

In formula: k₁And k₂For preset parameter, and meet 0 ＜ k₂＜ k₁＜ 1, β be value between 0 and 1 parameter preset.