CN105682227A - IBeacon-based indoor positioning method - Google Patents

IBeacon-based indoor positioning method Download PDF

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Publication number
CN105682227A
CN105682227A CN201610205599.1A CN201610205599A CN105682227A CN 105682227 A CN105682227 A CN 105682227A CN 201610205599 A CN201610205599 A CN 201610205599A CN 105682227 A CN105682227 A CN 105682227A
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quantum
positioning
population
ibeacon
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何小锋
朱军
方程
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Shanghai Yafeng Information Technology Co Ltd
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Shanghai Yafeng Information Technology Co Ltd
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W64/00—Locating users or terminals or network equipment for network management purposes, e.g. mobility management
    • H04W64/003—Locating users or terminals or network equipment for network management purposes, e.g. mobility management locating network equipment
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/02—Services making use of location information
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/80—Services using short range communication, e.g. near-field communication [NFC], radio-frequency identification [RFID] or low energy communication
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

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  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Position Fixing By Use Of Radio Waves (AREA)

Abstract

The invention discloses an ibeacon-based indoor positioning method, which comprises the following positioning steps: S1) arranging positioning labels uniformly in a scene region, wherein the positioning labels adopt low-power-consumption bluetooth iBeacon, and the positioning labels are arranged every an interval of 8-15 m, and the positioning labels are arranged uniformly or are arranged uniformly along the walking route; S2) data acquisition: a hand-held acquisition terminal traverses all reference points in the same direction in the scene region, collects broadcast information sent by the positioning labels, enables the collected broadcast information to form a complete fingerprint database and stores the fingerprint database to a server; and S3) on-line positioning: a user enters a positioning region with a terminal device in hand, the mobile phone terminal receives iBeacon Bluetooth information, positioning matching is carried out on on-line measurement vector and the fingerprint database in the server, and positioning is realized through a quantum swarm intelligent optimization algorithm positioning matching model. The positioning is realized through the quantum swarm intelligent optimization algorithm and the mathematical positioning model obtained from evolution, and thus efficiency and precision of the positioning can be improved.

Description

A kind of indoor orientation method based on iBeacon
Technical field
The method that the present invention relates to the navigation of a kind of indoor positioning, is specially a kind of indoor orientation method based on iBeacon, judges to realize indoor positioning by the measurement of Bluetooth signal intensity.
Background technology
The application of current location technology is more and more extensive, it is main flow for outdoor positioning technology GPS, but for indoor positioning, GPS is by the interference of various factors, positioning precision is poor, GPS is not suitable for indoor positioning, and the mainstream technology of current indoor positioning includes: wireless network, ibeacon, RF identification, earth magnetism, ultrasound wave, Zigbee etc.
Wireless network positions: be mainly used in the application carried out data transmission between the various electronic equipments that short distance, low-power consumption, transfer rate are not high and periodic data, intermittent data and low reaction time data are transmitted, but its cost and power consumption are all higher.
RF identification positions: be mainly used in short distance precise positioning, but it is launched and the formal matter of reception is comparatively special, and do not have ability to communicate, therefore use and be limited in scope.
Ultrasonic locating: although its positioning precision is high, but it is relatively costly, is not suitable for scale and uses, and System Fault Tolerance and adaptability relatively low.
Ultra broadband positions: although its positioning precision, System Fault Tolerance and adaptability are all better, but its high cost.
Ground magnetic orientation: the accuracy of location is relatively low, it is impossible to accurately determine terminal location.
IBeacon is the mobile equipment OS(iOS7 of Apple's in JIUYUE, 2013 issue) the upper New function being equipped with, equipment equipped with low-power consumption bluetooth (BLE) communication function uses BLE technology to send oneself distinctive ID towards periphery, and the application software receiving this ID can take some actions according to this ID.
IBeacon location has relative to the advantage of other alignment systems: 1) power is little can accomplish that passive, common node can work some months to 2 years; 2) but iBeacon system can cloth must to compare close cost relatively low, the cost of iBeacon node is the general level RMB more than 20 one at present, realizes low relative to the physical layer of other location; 3) iBeacon is required of bluetooth at mobile platform, as long as bluetooth supported by mobile phone, all can realize location.
Summary of the invention
In order to solve the deficiencies in the prior art, the invention provides a kind of indoor orientation method based on ibeacon, by adopting quantum group intelligent optimization algorithm, locating speed is fast, and positioning precision is high.
A kind of indoor orientation method based on ibeacon, it is achieved the step of indoor positioning includes:
S1: being uniformly arranged location label in scene areas, location label adopts low-power consumption bluetooth iBeacon.
Described location label arranges that density is that interval 8-15 rice arranges one, and arrangement is for being evenly arranged or being evenly arranged along track route, it is preferable that adopts triangular network to interlock and is evenly arranged, positions the highly preferred in the region of 2.5-3 rice of label.
S2: data acquisition, hand-held acquisition terminal travels through all reference points at same direction in scene areas, gathers the broadcast message that location label sends, and the broadcast message collected is built a complete fingerprint base, this fingerprint base is stored in server.
Described broadcast message includes positional information, base station information and signal intensity, as comprised the information such as UUID, major, minor, rssi.
Described handheld terminal is the mobile phone or the panel computer that are loaded with data acquisition test end software, and software copyright applied at present by data acquisition test end software.
S3: tuning on-line, user's hand-held terminal device enters region, location, and mobile phone terminal receives iBeacon Bluetooth information, and on-line measurement vector positions with the fingerprint base in server and mates, and adopts the position matching model realization location of quantum group intelligent optimization algorithm.
The radio waves propagation model that fingerprinting is more traditional can describe the relation of RSS and locus more accurately, and without the prior information of AP particular location; The mathematics location model adopting quantum group intelligent optimization algorithm and evolution and come positions, it is possible to improve the efficiency of location and the precision of location.
Accompanying drawing explanation
Below in conjunction with drawings and Examples, the present invention is further described below
Fig. 1 is positioning step schematic diagram of the present invention;
The outdoor scene floor map of this assignment test of Fig. 2;
Fig. 3 is the average localization error variation diagram with iterations of three kinds of different quantum group intelligent optimization algorithms;
In Fig. 3: bar shaped post represents quantum artificial bee colony algorithm, quantum artificial fish-swarm algorithm, quantum ant colony algorithm from left to right successively;
Fig. 4 is above-mentioned quantum artificial bee colony algorithm position error cumulative distribution table under a certain equivalent environment;
Fig. 5 is above-mentioned quantum artificial fish-swarm algorithm position error cumulative distribution table under a certain equivalent environment;
Fig. 6 is above-mentioned quantum ant colony algorithm position error cumulative distribution table under a certain equivalent environment;
Fig. 7 is above-mentioned quantum artificial bee colony algorithm locating effect schematic diagram in outdoor scene.
Detailed description of the invention
A kind of indoor orientation method based on ibeacon, it is achieved the step of indoor positioning includes:
S1: being uniformly arranged location label in scene areas, location label adopts low-power consumption bluetooth iBeacon.
Described location label arranges that density is that interval 8-15 rice arranges one, and arrangement is for being evenly arranged or being evenly arranged along track route, it is preferable that adopts triangular network to interlock and is evenly arranged, positions the highly preferred in the region of 2.5-3 rice of label.
S2: data acquisition, hand-held acquisition terminal travels through all reference points at same direction in scene areas, gathers the broadcast message that location label sends, and the broadcast message collected is built a complete fingerprint base, this fingerprint base is stored in server.
Described broadcast message includes positional information, base station information and signal intensity, as comprised the information such as UUID, major, minor, rssi.
Described handheld terminal is the mobile phone or the panel computer that are loaded with data acquisition test end software, and software copyright applied at present by data acquisition test end software.
S3: tuning on-line, user's hand-held terminal device enters region, location, and mobile phone terminal receives iBeacon Bluetooth information, and on-line measurement vector positions with the fingerprint base in server and mates, and adopts the position matching model realization location of quantum group intelligent optimization algorithm.
The radio waves propagation model that fingerprinting is more traditional can describe the relation of RSS and locus more accurately, and without the prior information of AP particular location.
Position matching described in described S3 step, it specifically comprises the following steps that
In t, online RSS measures vectorWith fingerprint base is positioned at reference point RPjPlace's RSS vectorBetween distance definition be:
(1)
Select drjLess n (n ∈ [1, N]) individual point is as a reference point, carries out location estimation by the method averaged, it may be assumed that
(2)
Wherein, ωjRepresent the weight of jth reference point, ωj=1/(ε+drj), ε represents the degree of association coefficient of actual location point and reference point, andFor reference point RPjTwo-dimensional coordinate, j ∈ 1,2 ..., N}; When ε (ε > 1) is infinitely close to 1 and is not 1, drjClose to zero, namely can be regarded as anchor point
RPrWith reference point RPjInfinite approach; Then the mathematical model of position matching problem is expressed as:
Described quantum group intelligent optimization algorithm, adopts quantum ant colony algorithm, and concrete steps include:
Step 1: initializing quantum population, Population Size is n (n=N), and quantum bit number is m (m=L), population p (t)=(p1 t, p2 t, p3 t..., pn t), wherein, pj t(j=1,2,3 ..., it is n) individual for the jth of the t time iteration in population,, all of α when startingi, βi(i=1,2,3 ..., m) value is 1/ √ 2, i.e. Pj 1=Pj t t=1=1/ √ 2, primary iteration number of times t=0;
Step 2: parameter initialization, sets the value of each parameter alpha, β, ρ, the value of stochastic generation Q, and the number population quantity of Formica fusca is consistent, maximum iteration time tmax, current iteration number of times t=0;
Step 3: calculate, select drjLess n (n ∈ [1, N]) individual point is as a reference point;
Step 4: Formica fusca k (k=1,2,3 ..., n) randomly choose a reference point RPj;
Step 5: every Formica fusca constructs an array solution independently: by the probability calculation selected probability of each reference point, Formica fusca k is according to select probability Pi kAnd update array.
(6)
In formula, τjInformation heuristic factor, represents the amount of jth reference point pheromone, ηiT () represents the value of reference point, i.e. ηj=1/dij, α and β represents the weight (α >=0, β >=0) that pheromone amount and reference point are worth respectively; μjFor the quantum information intensity of AP in reference point, its expression formula is: μj=1/|αj|2, in formula | αj|2Represent that the quantum state of jth quantum bit collapses to | the probability of 0 >, γ (γ >=0) is quantum bit heuristic greedy method, represents the relative importance of the quantum state probability amplitude of AP in reference point;
Pheromone update equation: τi(t+1)=(1-ρ)τi(t)+△τi(k), △ τiK () represents the quantity of the Formica fusca k pheromone stayed in i-th reference point, its expression formula is:
(7)
Q is a constant, and J (k) represents the set of the reference point selected by the Formica fusca that in a generation, total value is maximum, and ρ is the volatility (0≤ρ < 1) of pheromone;
Step 6: if m the Formica fusca respective solution of all construction completes, then go to step 7, otherwise go to step 4;
Step 7: the optimal solution that record current iteration produces;
Step 8: application Quantum rotating gate rule carries out global information element renewal;
Step 9: if meeting termination condition, if i.e. cycle-index t > tmax, then loop ends, export optimal solution, otherwise t=t+1, go to step 4.
Described quantum group intelligent optimization algorithm, adopts quantum artificial bee colony matching algorithm, and concrete steps include:
Step 1: initializing quantum population, Population Size is n=(n=N), and quantum bit number is m (m=L), population p (t)=(p1 t, p2 t, p3 t..., pn t), wherein, pj t(j=1,2,3 ..., n) individual for the jth of the t time iteration in population, all of α when startingi, βi(i=1,2,3 ..., m) value is 1/ √ 2, i.e. pj 1=pj t t=1=1/ √ 2; Primary iteration number of times t=0;
Step 2: parameter initialization, sets the value of each parameter alpha, β, ρ, the value of stochastic generation Q, and initializing all Apiss is search bee, and its quantity is, maximum iteration time tmax;
Step 3: calculate, select drjLess n (n ∈ [1, N]) individual point is as a reference point;
Step 4: search bee k (k=1,2 ..., n) randomly choose a reference point RPj;
Step 5: the size according to the income degree of article, search bee converts to and leads honeybee and follow honeybee,
Step 6: following honeybee one character string dimension of random setting in remaining reference point, then by the probability calculation selected probability of each article, Apis k constantly updates array according to select probability, until having updated all remaining reference points;
In formula, ρiT () is the reference point heuristic information when t iteration, its renewal equation is:
Wherein, Q is for leading constant, and J (k) represents the set of the reference point selected by the Apis that in a generation, total value is maximum; ηiT () represents the value of reference point, i.e. ηj=1/dij; α and β represents the weight (α >=0, β >=0) that heuristic information and reference point are worth respectively;
Step 7: the optimal solution that record current iteration produces;
Step 8: application Quantum rotating gate rule carries out information updating;
Step 9: if meeting termination condition, if i.e. cycle-index t > tmax, then loop ends, export optimal solution, otherwise t=t+1, go to step 3.
Described quantum group intelligent optimization algorithm, adopts quantum artificial fish-swarm matching algorithm, specifically comprises the following steps that
Step 1: initializing quantum population, Population Size is n=(n=N), and quantum bit number is m (m=L), population p (t)=(p1 t, p2 t, p3 t..., pn t), wherein, pj t(j=1,2,3 ..., n) individual for the jth of the t time iteration in population, all of α when startingi, βi(i=1,2,3 ..., m) value is 1/ √ 2, i.e. pj 1=pj t t=1=1/ √ 2; Arranging artificial fish-swarm scale is n, initializes perceived distance Visual, Artificial Fish moving step length Step, crowding factor delta, maximum exploration number of times try_number, maximum iteration time T, primary iteration number of times t=0;
Step 2: calculate, select drjLess n (n ∈ [1, N]) individual point is as a reference point;
Step 3:n bar Artificial Fish randomly chooses m and treats that tote product generate initial position X0=(xij)m×n;
Step 4: Artificial Fish presses the probability calculation selected probability of each article, if meeting pi-1≤xkj≤pI,Then reference point RPiSelected by work fish k, and by RPiJoin taboo list tabukIn (s), and make ηi=0, again calculate the selected expected probability of each reference point according to select probability, Artificial Fish k is according to select probability pi kConstantly update array X, so circulate operation, terminate until all reference points update.
Step 5: calculate the value of each Artificial Fish current state of the initial shoal of fish, takes minima person and enters bulletin board, and corresponding Artificial Fish is assigned to bulletin board;
Step 6: each Artificial Fish carries out behavior of knocking into the back and behavior of bunching respectively, default way of act is foraging behavior, and after knock into the back behavior and behavior of bunching, minima, but without change, carries out random behavior;
Step 7: after each Artificial Fish action selection, checks the value of self and the value of bulletin board, if being better than bulletin board, then replaces bulletin board record with self;
Step 8: quantum artificial fish-swarm quantum information is updated by application Quantum rotating gate;
Step 9: judge whether to meet end condition, if meeting, then output bulletin board record, algorithm terminates; If being unsatisfactory for, then perform step 3;
Step 10: export currently most solution.
Above-mentioned three kinds of quantum group intelligent optimization algorithms are carried out emulation experiment:
The parameter of quantum ant colony algorithm is set to:, Formica fusca quantity is 8.
The parameter of quantum artificial bee colony algorithm is set to: α=1, β=2, Q=100, n=8.
The parameter of quantum artificial fish-swarm algorithm is set to AF_number=8, try_number=20, visual=2.5, step=0.4, δ=0.6, it is contemplated that the real-time of location, it is ensured that algorithm operation time is shorter, three kinds of algorithm iteration number of times are respectively less than 100 times.
As shown in Figure 2, open literary composition garden office building at Zhangjiang and carry out assignment test on the spot, 12 BeaconAP are had in experimental situation, the application and development of experiment is based on Android and iOS platform, testing used Samsung mobile phone GalaxyS6 and Fructus Mali pumilae iPhone6, the main test of experiment is static immobilization, the terminal unit received signal strength according to user present position, complete independently positions each time, and it is the integer between-110dBm to-15dBm that experiment gathers data reception signal intensity.
Fig. 2 orbicular spot is the track route signal of assignment test.
See Fig. 3, average localization error (the AverageRootMeanSquareError of three kinds of algorithms of different, ARMSE) with the change of iterations, three kinds of algorithms can obtain comparatively similar average localization error, and along with the increase of iterations, its average localization error also reduces therewith.
ARMSE represents physical location and the average root-mean-square estimated between position, is expressed as:
;
Wherein,, represent the actual two-dimensional coordinate being positioned at i-th test position in the t time test,Representing that the two-dimensional position coming from location simulation accordingly is estimated, N represents position measurement point altogether, and T represents in each test point assignment test number of times altogether, N=30, T=10 in experiment.
As shown in Fig. 4, Fig. 5, Fig. 6, under equivalent environment, inventor has added up the above-mentioned three kinds of algorithms error accumulation scattergram based on test data, it can be seen that, the deviation accumulation of three kinds of algorithms is all more close, by emulation experiment it can be seen that the locating effect of quantum group intelligent optimization algorithm entirety can be maintained at the precision of 2 meters.
As shown in Figure 7, adopt the locating effect checking that quantum artificial bee colony carries out under outdoor scene environment, the present invention carries out indoor positioning by quantum group intelligent optimization algorithm, the efficiency of location and the precision of location can be improved, the locating effect of quantum group intelligent optimization algorithm is verified by quantum artificial bee colony algorithm, quantum artificial fish-swarm algorithm, algorithm that quantum ant colony algorithm is three kinds different, indoor positioning is introduced, it is possible to be effectively improved the precision of location and the response efficiency of location by quantum group intelligent optimization algorithm quantum calculation and swarm intelligence algorithm blended.
Above example only in order to the present invention is described and and the unrestricted present invention, any to the amendment of the present invention, replacement, modification without deviating within the claim being encompassed by the present invention of scope of the present invention.

Claims (5)

1. the indoor orientation method based on iBeacon, it is achieved the step of indoor positioning includes:
S1: being uniformly arranged location label in scene areas, location label adopts low-power consumption bluetooth iBeacon;
Positioning label and arrange that density is that interval 8-15 rice arranges one, arrangement is for being evenly arranged or being evenly arranged along track route, it is preferable that adopts triangular network to interlock and is evenly arranged, positions the highly preferred in the region of 2.5-3 rice of label;
S2: data acquisition, hand-held acquisition terminal travels through all reference points at same direction in scene areas, gathers the broadcast message that location label sends, and the broadcast message collected is built a complete fingerprint base, this fingerprint base is stored in server;
Broadcast message includes positional information, base station information and signal intensity, as comprised the information such as UUID, major, minor, rssi;
Handheld terminal is the mobile phone or the panel computer that are loaded with data acquisition test end software, and software copyright applied at present by data acquisition test end software;
S3: tuning on-line, user's hand-held terminal device enters region, location, and mobile phone terminal receives iBeacon Bluetooth information, and on-line measurement vector positions with the fingerprint base in server and mates, and adopts the position matching model realization location of quantum group intelligent optimization algorithm.
2. a kind of indoor orientation method based on iBeacon according to claim 1, it is characterised in that the position matching described in described S3 step, it specifically comprises the following steps that
In t, online RSS measures vectorWith fingerprint base is positioned at reference point RPjPlace's RSS vectorBetween distance definition be:
(1)
Select drjLess n (n ∈ [1, N]) individual point is as a reference point, carries out location estimation by the method averaged, it may be assumed that
(2)
Wherein, ωjRepresent the weight of jth reference point, ωj=1/(ε+drj), ε represents the degree of association coefficient of actual location point and reference point, andFor reference point RPjTwo-dimensional coordinate, j ∈ 1,2 ..., N}; When ε (ε > 1) is infinitely close to 1 and is not 1, drjClose to zero, namely can be regarded as anchor point
RPrWith reference point RPjInfinite approach; Then the mathematical model of position matching problem is expressed as:
。
3. a kind of indoor orientation method based on iBeacon according to claim 1, it is characterised in that described quantum group intelligent optimization algorithm, adopts quantum ant colony algorithm, and concrete steps include:
Step 1: initializing quantum population, Population Size is n (n=N), and quantum bit number is m (m=L), population p (t)=(p1 t, p2 t, p3 t..., pn t), wherein, pj t(j=1,2,3 ..., it is n) individual for the jth of the t time iteration in population,The all of α when startingi, βi(i=1,2,3 ..., m) value is 1/ √ 2, i.e. Pj 1=Pj t t=1=1/ √ 2, primary iteration number of times t=0;
Step 2: parameter initialization, sets the value of each parameter alpha, β, ρ, the value of stochastic generation Q, and the number population quantity of Formica fusca is consistent, maximum iteration time tmax, current iteration number of times t=0;
Step 3: calculate, select drjLess n (n ∈ [1, N]) individual point is as a reference point;
Step 4: Formica fusca k (k=1,2,3 ..., n) randomly choose a reference point RPj;
Step 5: every Formica fusca constructs an array solution independently: by the probability calculation selected probability of each reference point, Formica fusca k is according to select probability Pi kAnd update array;
(6)
In formula, τjInformation heuristic factor, represents the amount of jth reference point pheromone, ηiT () represents the value of reference point, i.e. ηj=1/dij, α and β represents the weight (α >=0, β >=0) that pheromone amount and reference point are worth respectively; μjFor the quantum information intensity of AP in reference point, its expression formula is: μj=1/|αj|2, in formula | αj|2Represent that the quantum state of jth quantum bit collapses to | the probability of 0 >, γ (γ >=0) is quantum bit heuristic greedy method, represents the relative importance of the quantum state probability amplitude of AP in reference point;
Pheromone update equation: τi(t+1)=(1-ρ)τi(t)+△τi(k), △ τiK () represents the quantity of the Formica fusca k pheromone stayed in i-th reference point, its expression formula is:
(7)
Q is a constant, and J (k) represents the set of the reference point selected by the Formica fusca that in a generation, total value is maximum, and ρ is the volatility (0≤ρ < 1) of pheromone;
Step 6: if m the Formica fusca respective solution of all construction completes, then go to step 7, otherwise go to step 4;
Step 7: the optimal solution that record current iteration produces;
Step 8: application Quantum rotating gate rule carries out global information element renewal;
Step 9: if meeting termination condition, if i.e. cycle-index t > tmax, then loop ends, export optimal solution, otherwise t=t+1, go to step 4.
4. a kind of indoor orientation method based on iBeacon according to claim 1, it is characterised in that described quantum group intelligent optimization algorithm, adopts quantum artificial bee colony matching algorithm, and concrete steps include:
Step 1: initializing quantum population, Population Size is n=(n=N), and quantum bit number is m (m=L), population p (t)=(p1 t, p2 t, p3 t..., pn t), wherein, pj t(j=1,2,3 ..., n) individual for the jth of the t time iteration in population, all of α when startingi, βi(i=1,2,3 ..., m) it is 1/ √ 2, i.e. pj 1=pj t t=1=1/ √ 2; Primary iteration number of times t=0;
Step 2: parameter initialization, sets the value of each parameter alpha, β, ρ, the value of stochastic generation Q, and initializing all Apiss is search bee, and its quantity is, maximum iteration time tmax;
Step 3: calculate, select drjLess n (n ∈ [1, N]) individual point is as a reference point;
Step 4: search bee k (k=1,2 ..., n) randomly choose a reference point RPj;
Step 5: the size according to the income degree of article, search bee converts to and leads honeybee and follow honeybee,
Step 6: following honeybee one character string dimension of random setting in remaining reference point, then by the probability calculation selected probability of each article, Apis k constantly updates array according to select probability, until having updated all remaining reference points;
In formula, ρiT () is the reference point heuristic information when t iteration, its renewal equation is:
Wherein, Q is for leading constant, and J (k) represents the set of the reference point selected by the Apis that in a generation, total value is maximum; ηiT () represents the value of reference point, i.e. ηj=1/dij; α and β represents the weight (α >=0, β >=0) that heuristic information and reference point are worth respectively;
Step 7: the optimal solution that record current iteration produces;
Step 8: application Quantum rotating gate rule carries out information updating;
Step 9: if meeting termination condition, if i.e. cycle-index t > tmax, then loop ends, export optimal solution, otherwise t=t+1, go to step 3.
5. a kind of indoor orientation method based on iBeacon according to claim 1, it is characterised in that described quantum group intelligent optimization algorithm, adopts quantum artificial fish-swarm matching algorithm, specifically comprises the following steps that
Step 1: initializing quantum population, Population Size is n=(n=N), and quantum bit number is m (m=L), population p (t)=(p1 t, p2 t, p3 t..., pn t), wherein, pj t(j=1,2,3 ..., n) individual for the jth of the t time iteration in population, all of α when startingi, βi(i=1,2,3 ..., m) value is 1/ √ 2, i.e. pj 1=pj t t=1=1/ √ 2; Arranging artificial fish-swarm scale is n, initializes perceived distance Visual, Artificial Fish moving step length Step, crowding factor delta, maximum exploration number of times try_number, maximum iteration time T, primary iteration number of times t=0;
Step 2: calculate, select drjLess n (n ∈ [1, N]) individual point is as a reference point;
Step 3:n bar Artificial Fish randomly chooses m and treats that tote product generate initial position X0=(xij)m×n;
Step 4: Artificial Fish presses the probability calculation selected probability of each article, if meeting pi-1≤xkj≤pi, then reference point RPiSelected by work fish k, and by RPiJoin taboo list tabukIn (s), and make ηi=0, again calculate the selected expected probability of each reference point according to select probability, Artificial Fish k is according to select probability pi kConstantly update array X, so circulate operation, terminate until all reference points update;
Step 5: calculate the value of each Artificial Fish current state of the initial shoal of fish, takes minima person and enters bulletin board, and corresponding Artificial Fish is assigned to bulletin board;
Step 6: each Artificial Fish carries out behavior of knocking into the back and behavior of bunching respectively, default way of act is foraging behavior, and after knock into the back behavior and behavior of bunching, minima, but without change, carries out random behavior;
Step 7: after each Artificial Fish action selection, checks the value of self and the value of bulletin board, if being better than bulletin board, then replaces bulletin board record with self;
Step 8: quantum artificial fish-swarm quantum information is updated by application Quantum rotating gate;
Step 9: judge whether to meet end condition, if meeting, then output bulletin board record, algorithm terminates; If being unsatisfactory for, then perform step 3;
Step 10: export currently most solution.
CN201610205599.1A 2016-04-05 2016-04-05 IBeacon-based indoor positioning method Pending CN105682227A (en)

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CN105953802A (en) * 2016-07-22 2016-09-21 马宏宾 Indoor positioning system and method based on iBeacon
CN107064866A (en) * 2017-04-05 2017-08-18 河南师范大学 A kind of generation method in the dynamic fingerprint storehouse based on iBeacon indoor positionings
JP2019007863A (en) * 2017-06-26 2019-01-17 富士通株式会社 Position measurement method, position measuring program, and position measuring device
CN107680010A (en) * 2017-09-29 2018-02-09 桂林电子科技大学 A kind of scenic spot route recommendation method and its system based on visit behavior
CN107680010B (en) * 2017-09-29 2020-12-18 桂林电子科技大学 Scenic spot route recommendation method and system based on touring behavior
CN108120436A (en) * 2017-12-18 2018-06-05 北京工业大学 Real scene navigation method in a kind of iBeacon auxiliary earth magnetism room
CN109932687B (en) * 2017-12-18 2021-08-31 北京布科思科技有限公司 Bluetooth indoor positioning method based on probability model
CN109932687A (en) * 2017-12-18 2019-06-25 北京布科思科技有限公司 A kind of bluetooth indoor orientation method based on probabilistic model
CN109151716A (en) * 2018-09-06 2019-01-04 杭州电子科技大学 A kind of indoor orientation method of the preferred beaconing nodes based on iBeacon
CN109089313A (en) * 2018-09-12 2018-12-25 河南迈驰物联网有限公司 A kind of two fingers line joint positioning method and device
CN109089313B (en) * 2018-09-12 2020-10-09 河南迈驰物联网有限公司 Double-fingerprint joint positioning method and device
WO2020088644A1 (en) * 2018-11-01 2020-05-07 华为技术有限公司 Positioning method and device
CN109862546A (en) * 2019-01-21 2019-06-07 中天宽带技术有限公司 ONU Intelligent gateway system and its method of servicing based on low-power consumption bluetooth positioning
CN109862546B (en) * 2019-01-21 2022-03-01 中天宽带技术有限公司 ONU intelligent gateway system based on low-power-consumption Bluetooth positioning and service method thereof
CN110428029A (en) * 2019-07-23 2019-11-08 上海牛庄网络科技有限公司 Electronic location method, system and medical device positioning system
CN113518304A (en) * 2021-03-18 2021-10-19 深圳云里物里科技股份有限公司 Indoor positioning method and device
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