CN105243348A - Reader optimal deployment method based on passive ultrahigh frequency RFID positioning system - Google Patents

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

A kind of reader Optimization deployment method based on passive ultra-high frequency RFID positioning system

Technical field

The invention belongs to technology for radio frequency field, relate to a kind of reader Optimization deployment method based on passive ultra-high frequency RFID positioning system.

Background technology

In recent years, the advantage of its noncontact of passive ultra-high frequency RFID system addresses, non line of sight, high precision and low cost is widely used in indoor positioning.As the primary solutions based on RFID indoor positioning, the reference label that LANDMARC system introduces position known carries out auxiliary positioning, the relatively field intensity information Euclidean distance of reference label and positioning label, finds neighbour's reference label and empirically weight equation realizes the location estimation of positioning label.Compared to other location algorithms, LANDMARC algorithm has the feature such as low cost, high precision.

Location efficiency and positioning precision evaluate the important indicator of positioning system performance.Because in most of passive RFID positioning system, the position of reference label is fixing, current research focus mainly concentrates on the Optimization deployment of reader.At present, Representative passive ultrahigh frequency RFID reader can only reduce in the mode of successively decreasing step by step the minimum energy level that power emission energy level obtains each label, when reader and reference label position nearer time, non-essential energy level switching can cause location not consuming time, reduces location efficiency.Therefore, realize the Optimization deployment of reader thus promote location efficiency, still there is great practical significance.

To sum up, the present invention is based on Friss power attenuation model, set up the relation of reader power emission level and radiation radius, by analyzing the mode of operation of reader, build based on location target adaptive value function consuming time, using each reading device position as optimizing variable, using target adaptive value function minimization as optimization aim, Typical particle group algorithm is adopted to build optimizing particle model, introduce local search ability and ability of searching optimum that simulated annealing improves optimizing particle model, thus determine the optimum deployment way of each reader.According to foregoing, the present invention proposes a kind of reader Optimization deployment method based on passive ultra-high frequency RFID positioning system.

Summary of the invention

The problem that the present invention need solve proposes a kind of reader Optimization deployment method based on passive ultra-high frequency RFID positioning system.Based on the method, the Optimization deployment of reader can be realized, effectively elevator system location efficiency.

1, based on a reader Optimization deployment method for passive ultra-high frequency RFID positioning system, comprise the following steps:

Step 1: location efficiency is as one of important performance indexes in positioning system, usually consuming time as evaluation criterion to locate.In passive ultra-high frequency RFID positioning system, reader obtains the collection of letters field intensity of each label with the working method of decreasing power emission level step by step, the position of reader will directly determine the energy level switching times read needed for whole label, thus the location of influential system is consuming time.In order to the energy level switching times needed for reader reading tag each in certainty annuity, need in conjunction with Friss power attenuation model, the emissive power P of setting reader _tcorresponding greatest irradiation radius R, reference radius R ₀, signal wavelength lambda, path loss coefficient ε, passive label activate threshold value P _r, reader antenna gain G _r, label antenna gain G _t, adjacent power emission level power step size I _p, Gaussian distribution neighbourhood noise X _σ, with the relation of the emission level and radiation radius of setting up reader $R\≤e^{P_t^{\max }-P_r^{th}-(G^{\max }-j)*I_p-20\lg (4\mathrm{\π}/\mathrm{\λ})+10(\mathrm{\ϵ}-2)\lg \left(R_0\right)+G_r+G_t+X_{\mathrm{\σ}}}-e^{10\mathrm{\ϵ}};$

Step 2: the principle of work of successively decreasing step by step according to reader emission level in LANDMARC algorithm and the exemplary parallel mode of operation of system reader, the single obtaining system locates T consuming time _l=max _{u ∈ [1, U]}t _u+ Ct _c, wherein t _crepresent that the location of single label is consuming time, T _urepresent that u reader obtains the comprehensively consuming time of the energy level information of C positioning label, U represents the quantity of system reader, and l is the sequence number of positioning service;

Step 3: the average location according to the service of system multiple bearing is consuming time establishing target adaptive value function F (Ω), wherein superior vector Ω represents each reading device position;

Step 4: using target adaptive value function minimization as optimization aim, set up optimizing model F (Ω)=arg (min (T)), particle swarm optimization algorithm is adopted to carry out optimizing to optimizing model, evenly to lay the original state that mode is disposed as reader, and generate primary population, define initial optimization speed and the optimizing radius of each reader, introduce maximum evolutionary rate V simultaneously _maximprove effect of optimization, in objective function, introduce penalty function Q prevent the position of reader in optimizing process from exceeding the scope of optimizing radius;

Step 5: the position and the optimal speed that upgrade each reader according to particle cluster algorithm, for τ generation breeding, records the mean value F (Pbest of the population local extremum of current reproductive status ^τ) _avgwith global extremum F (Gbest ^τ), wherein Pbest ^τ, Gbest ^τrepresent local extremum and the particle state corresponding to global extremum of τ generation breeding respectively, i.e. the deployed position of a group system reader; Step 6: be local search ability and the ability of searching optimum of further equilibrium particle colony optimization algorithm, introduces simulated annealing and dynamically adjusts particle rapidity renewal weight, calculate the annealing temperature ξ of the τ time reproductive status _τ=[F (Pbest ^τ) _avg/ F (Gbest ^τ)]-1;

Step 7: according to annealing temperature ξ _τ, current reproductive status global extremum F (Gbest ^τ) and last generation reproductive status global extremum F (Gbest ^τ-1), calculate the annealing probability p of the τ time iteration, if F is (Gbest ^τ-1)≤F (Gbest ^τ), make p=1, if F is (Gbest ^τ-1) > F (Gbest ^τ), order

$p=e^{\[F\left({\mathrm{Gbest}}^{\mathrm{\τ}}\right)-F\left({\mathrm{Gbest}}^{\mathrm{\τ}-1}\right)\]/{\mathrm{\ξ}}_{\mathrm{\τ}}};$

Step 8: according to the annealing probability of current reproductive status, regulates the renewal weights omega of particle rapidity, if p>=β, makes ω=α ₁+ 0.5 β, if p < is β, makes ω=α ₂+ 0.5 β, wherein α ₁and α ₂preset parameter, and meet 0 < α ₂< α ₁< 1, β be value between 0 and 1 parameter preset;

Step 9: according to weight coefficient selected by step 8, performs step 5 and carries out next generation breeding optimizing real-time update particle state and speed, and then execution step 6 to step 8 to carry out the adjustment of weight coefficient according to population real-time status successively.When iterations reach default maximal value or global extremum meet default consuming time require time stop optimizing;

Step 10: using particle state corresponding to population global extremum as the optimum deployment way of reader, complete actual deployment, terminates search process.

Accompanying drawing illustrates:

Fig. 1 is the structure process flow diagram that the present invention is based on location target adaptive value function consuming time;

Fig. 2 is the process flow diagram that the present invention introduces the improve PSO algorithm of simulated annealing;

Fig. 3 is the optimum deployed position of the reader under the environment concentrated at positioning label;

Fig. 4 is the optimum deployed position of the reader under the environment of positioning label dispersion;

Fig. 5 is reader under the environment that positioning label the is concentrated location comparison diagram consuming time when laying respectively at initial position and optimal location;

Fig. 6 is the location consuming time comparison diagram of the reader under the environment of positioning label dispersion when laying respectively at initial position and optimal location;

Embodiment:

Purport of the present invention proposes a kind of reader Optimization deployment method based on passive ultra-high frequency RFID positioning system, and the reader deployment way that the method obtains can the location efficiency of elevator system effectively.

Below in conjunction with accompanying drawing 1, accompanying drawing 2, accompanying drawing 3, accompanying drawing 4, accompanying drawing 5, embodiment of the present invention is described further in detail for accompanying drawing 6.

One, the power emission energy level of reader and the foundation of radiation radius mapping relations

Based on Friss power attenuation model, the emissive power P of reader can be obtained _tcorresponding greatest irradiation radius R

$R={(G_r{G}_t{\mathrm{\&lambda;}}^2{R}_0^{(\mathrm{\&epsiv;}-2)}{P}_t/(16{\mathrm{\&pi;}}^2{R}^{\mathrm{\&epsiv;}}{P}_r^{th}\left)\right)}^{1/\mathrm{\&epsiv;}}---\left(1\right)$

Wherein R ₀represent reference radius, λ represents signal wavelength, and ε represents path loss coefficient, P _rrepresent that passive label activates threshold value, G _rrepresent reader antenna gain, G _trepresent label antenna gain.

Set minimum and maximum discrete energy progression and be respectively 1 and G ^max, the maximum transmission power P of reader ^max, the power step size of adjacent power emission level is I _p, the emissive power of reader under a jth energy level can be obtained

$P_t^j=P_t^{\max }-(G^{max}-j)*I_p---\left(2\right)$

Introduce the neighbourhood noise X of Gaussian distributed _σ, set up power emission energy level and the radiation radius mapping relations of reader:

$R^J\&le;e^{P_t^{\max }-P_r^{th}(G^{max}-J)*I_p-20\lg (4\mathrm{\&pi;}/\mathrm{\&lambda;})+10(\mathrm{\&epsiv;}-2)\lg \left(R_0\right)+G_r+G_t+X_{\mathrm{\&sigma;}}}-e^{10\mathrm{\&epsiv;}}---\left(3\right)$

Two, the structure of target adaptive value function consuming time is on average located based on system

Initialization system reader is with exemplary parallel mode of operation, and the single for system locates T consuming time _lhave:

T _l＝max _u∈[1，U]T _u+C·t _c(4)

Wherein l represents the sequence number of positioning service, t _crepresent that the location of single label is consuming time, U represents system reader number, T _urepresent that u reader obtains the comprehensively consuming time of the energy level information of C positioning label, have

$T_u={\mathrm{\&Sigma;}}_{j=0}^{h_u}J(N_{Ru}^j,N_{Tu}^j,\mathrm{\&delta;})+h_u\&CenterDot;t_z---\left(5\right)$

Wherein, J represents the function consuming time adopting ALOHA algorithm, and δ is number of time slots, represent that u reader is operated in reference label number under a jth energy level and positioning label number respectively, t _zrepresent the switching time between adjacent energy levels, and when the work of u reader reduces h _uafter individual energy level, no longer include positioning label and read, now u reader stops emissive power signal, switches to park mode, terminates positioning action.

Setting location number of times L, the average location obtaining system is consuming time thus establishing target adaptive value function F (Ω), wherein Ω represents each reading position, and system when F (Ω) namely represents that each reader is placed in diverse location is on average located consuming time.

The attached structure process flow diagram that Figure 1 shows that based on location target adaptive value function consuming time.

Three, the improve PSO algorithm of simulated annealing is introduced

Using target adaptive value function minimization as optimization aim, set up optimizing model

F(Ω)＝arg(min(T))(6)

Particle cluster algorithm is adopted to carry out optimizing to optimizing model, evenly to lay original state that mode disposes as reader and to generate primary population

Z＝[Z ₁Z ₂...Z _W] ^T(7)

Wherein, represent particle in the initial position of each reader, W is total number of particles, defines the initial optimization speed of each reader to upgrade primary population according to particle cluster algorithm.

Limit to improve effect of optimization to optimal speed according to formula (8), setting largest optimization speed V _maxobey

V _max＝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 optimizing process from exceeding the scope of optimizing radius, introduce penalty function Q, its expression formula is

$Q=(K_2/K_1)\&CenterDot;{\mathrm{\&Sigma;}}_{u=1}^U{s}_u---\left(9\right)$

Wherein K ₁, K ₂for penalty factor, if the position of u reader exceeds the scope of optimizing radius, make s _u=1, otherwise make s _u=0, rebuild optimizing model, have

M＝T+Q(10)

F(Ω)＝arg(min(M))(11)

Position and the optimal speed of each reader is upgraded according to particle cluster algorithm, for the breeding of τ-1 generation, then the position of each reader under current reproductive status and optimal speed the position of each reader under reproductive status of future generation is updated to respectively according to formula (12), (13) and optimal speed

Wherein and Gbest ^τ-1represent the local extremum that last generation breeds and particle state corresponding to global extremum respectively.

In order to local search ability and the ability of searching optimum of further equilibrium particle group algorithm, introduce simulated annealing dynamically weight selection coefficient, first need the annealing temperature ξ calculating τ generation breeding _τ

ξ _τ＝[F(Pbest ^τ) _avg/F(Gbest ^τ)]-1(14)

Wherein f (Pbest ^τ) _avgrepresent the mean value of the local extremum of τ generation breeding, F (Gbest ^τ) represent the global extremum that τ generation breeds, Pbest ^τand Gbest ^τlocal extremum and the particle state corresponding to global extremum of τ generation breeding respectively.

According to annealing temperature ξ _τ, current reproductive status global extremum F (Gbest ^τ) and last generation reproductive status under global extremum F (Gbest ^τ-1), calculate annealing probability p

$p=\left\{\begin{array}{cc}1,& ifF\left({\mathrm{Gbest}}^{\mathrm{\&tau;}-1}\right)\&le;F\left({\mathrm{Gbest}}^{\mathrm{\&tau;}}\right)\\ \exp (\&lsqb;F({\mathrm{Gbest}}^{\mathrm{\&tau;}})-F({\mathrm{Gbest}}^{\mathrm{\&tau;}-1})\&rsqb;/{\mathrm{\&xi;}}_{\mathrm{\&tau;}}),& ifF\left({\mathrm{Gbest}}^{\mathrm{\&tau;}-1}\right)>F\left({\mathrm{Gbest}}^{\mathrm{\&tau;}}\right)\end{array}\right.---\left(15\right)$

According to the annealing probability p under current reproductive status, by formula (19), weight coefficient is chosen, have

$\mathrm{\&omega;}=\left\{\begin{array}{cc}{\mathrm{\&alpha;}}_1+0.5\mathrm{\&beta;},& p\&GreaterEqual;\mathrm{\&beta;}\\ {\mathrm{\&alpha;}}_2+0.5\mathrm{\&beta;},& p<\mathrm{\&beta;}\end{array}\right.---\left(16\right)$

Wherein β represents value parameter preset between 0 and 1, α ₁, α ₂represent preset parameter and meet 0 < α ₂< α ₁< 1.

When iterations reach default maximal value or global extremum meet default consuming time require time stop optimizing, using particle state corresponding to population global extremum as the optimum deployment way of reader, complete actual deployment, terminate searching process.The attached process flow diagram that Figure 2 shows that improve PSO algorithm.

Four, instance analysis explanation

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

Introduce 4 readers in example to position 30 positioning labels, reference label is evenly laid in 8 × 8 modes, and reader maximum function energy level is respectively 4,8,16,32,64,128, adopts dynamic frame CDMA slotted ALOHA anti-collision algorithm, t _z=t _c=50ms, c ₁=c ₂=1.4962, η=0.3, α ₁=0.7, α ₂=0.3, W=40, ρ=300, U=4, G _t=1.2dBi, G _r=8dBi, P _t=36dBm, K2/K1=200, S=3.5, I _p=0.25, G ^max=128, δ=64, L=50.Accompanying drawing 3 and accompanying drawing 4 reflect the optimum deployed position of reader under two kinds of environment respectively.Observe visible, under the environment that positioning label is concentrated, the optimum deployed position of reader is positioned at four summits place on simulating area border; Under the environment of positioning label dispersion, the optimum deployed position of reader is positioned at simulating area center.

As shown in Figure 5, under the environment that positioning label is concentrated, system location when reader is positioned at optimum deployed position is consuming time reaches 60.35s, and the system in contrast to when reader is positioned at initial position locates 119.02s consuming time, and efficiency improves 49.3%.

As shown in Figure 6, under the environment of positioning label dispersion, system location when reader is positioned at optimum deployed position is consuming time reaches 91.37s, and the system in contrast to when reader is positioned at initial position locates 81.13s consuming time, and efficiency improves 12.6%.

Example shows, a kind of reader Optimization deployment method based on passive ultra-high frequency RFID positioning system proposed by the invention can realize the Optimization deployment of reader, the location efficiency of elevator system effectively, has great practical significance to the performance improving positioning system.

Claims (4)

1., based on a reader Optimization deployment method for passive ultra-high frequency RFID positioning system, comprise the following steps:

Step 1: location efficiency is as one of important performance indexes in positioning system, usually consuming time as evaluation criterion to locate, in passive ultra-high frequency RFID positioning system, reader obtains the collection of letters field intensity of each label with the working method of decreasing power emission level step by step, the position of reader will directly determine the energy level switching times read needed for whole label, thus the location of influential system is consuming time, in order to the energy level switching times needed for reader reading tag each in certainty annuity, need in conjunction with Friss power attenuation model, the emissive power P of setting reader _tcorresponding greatest irradiation radius R, reference radius R ₀, signal wavelength lambda, path loss coefficient ε, passive label activate threshold value P _r, reader antenna gain G _r, label antenna gain G _t, adjacent power emission level power step size I _p, Gaussian distribution neighbourhood noise X _σ, with the relation of the emission level and radiation radius of setting up reader

Step 2: the principle of work of successively decreasing step by step according to reader emission level in LANDMARC algorithm and the exemplary parallel mode of operation of system reader, the single obtaining system locates T consuming time _l=max _{u ∈ [1, U]}t _u+ Ct _c, wherein t _crepresent that the location of single label is consuming time, T _urepresent that u reader obtains the comprehensively consuming time of the energy level information of C positioning label, U represents the quantity of system reader, and l is the sequence number of positioning service;

Step 3: the average location according to the service of system multiple bearing is consuming time establishing target adaptive value function F (Ω), wherein superior vector Ω represents each reading device position;

Step 4: using target adaptive value function minimization as optimization aim, set up optimizing model F (Ω)=arg (min (T)), particle cluster algorithm is adopted to carry out optimizing to optimizing model, evenly to lay the original state that mode is disposed as reader, and generate primary population, define initial optimization speed and the optimizing radius of each reader, introduce maximum evolutionary rate V simultaneously _maximprove effect of optimization, in objective function, introduce penalty function Q prevent the position of reader in optimizing process from exceeding the scope of optimizing radius;

Step 5: the position and the optimal speed that upgrade each reader according to particle cluster algorithm, for τ generation breeding, records the mean value F (Pbest of the population local extremum of current reproductive status ^τ) _avgwith global extremum F (Gbest ^τ), wherein Pbest ^τ, Gbest ^τrepresent local extremum and the particle state corresponding to global extremum of τ generation breeding respectively, i.e. the deployed position of a group system reader;

Step 6: be local search ability and the ability of searching optimum of further equilibrium particle group algorithm, introduces simulated annealing and dynamically adjusts particle rapidity renewal weight, calculate the annealing temperature ξ of the τ time reproductive status _τ=[F (Pbest ^τ) _avg/ F (Gbest ^τ)]-1;

Step 7: according to annealing temperature ξ _τ, current reproductive status global extremum F (Gbest ^τ) and last generation reproductive status global extremum F (Gbest ^τ-1), calculate the annealing probability p of the τ time iteration, if F is (Gbest ^τ-1)≤F (Gbest ^τ), make p=1, if F is (Gbest ^τ-1) > F (Gbest ^τ), order

Step 8: according to the annealing probability of current reproductive status, regulates the renewal weights omega of particle rapidity, if p>=β, makes ω=α ₁+ 0.5 β, if p < is β, makes ω=α ₂+ 0.5 β, wherein α ₁and α ₂preset parameter, and meet 0 < α ₂< α ₁< 1, β be value between 0 and 1 parameter preset;

Step 9: according to the weight coefficient selected by step 8, perform step 5 and carry out next generation's breeding optimizing real-time update particle state and speed, then perform step 6 to step 8 to carry out weight coefficient adjustment according to population real-time status successively, when iterations reach default maximal value or global extremum meet default consuming time require time stop optimizing;

Step 10: using particle state corresponding to population global extremum as the optimum deployment way of reader, complete actual deployment, terminates search process.

2. a kind of reader Optimization deployment method based on passive ultra-high frequency RFID positioning system according to claim 1, is characterized in that, in step 2, u reader obtains the T comprehensive consuming time of the energy level information of C positioning label _uexpression formula be

Wherein, J represents the function consuming time adopting ALOHA algorithm, and δ is number of time slots, represent that u reader is operated in reference label number under a jth energy level and positioning label number respectively, t _zrepresent the switching time between adjacent energy levels, and when u reader work reduces h _uafter individual energy level, no longer include positioning label and read, now u reader stops emissive power signal, switches to park mode, terminates positioning action.

3. a kind of reader Optimization deployment method based on passive ultra-high frequency RFID positioning system according to claim 1, is characterized in that, in step 4, and maximum evolutionary rate V _maxbe respectively with penalty function Q:

V _max＝η·S

Wherein η is restriction factor, and S is the optimizing radius of each reader, K ₁, K ₂for penalty factor, if the position of u reader exceeds the scope of optimizing radius, make s _u=1, otherwise make s _u=0.

4. a kind of reader Optimization deployment method based on passive ultra-high frequency RFID positioning system according to claim 1, is characterized in that, in step 5, and the mean value F (Pbest of the population local extremum of current reproductive status ^τ) _avgexpression formula be:

Wherein W is total number of particles.