CN103997717A - Real-time indoor positioning system and method - Google Patents
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Abstract
The invention relates to a real-time indoor positioning system and method. The method comprises the steps that (1) in an interval time period, a card reader continuously collects RSSI data of a tag; (2) the RSSI data are stored in a buffer to form an RSSI data sequence S; (3) whether the RSSI data sequence S converges or not is judged, if yes, the step (6) is executed, and if not, the step (4) is executed; (4) an RSSI[i] value is predicted, and the covariance is predicted; (5) the RSSI[i] value is updated, the covariance is updated, and the step (3) is executed; (6) the RSSI data sequence in the buffer is updated; (7) normalization neutral-position weighting processing is carried out on the updated RSSI data sequence to obtain corresponding signal data RSSI[end]; (8) a logarithm and distance path loss model is used for carrying out RSSI[end]distance estimation; (9) the centroid is calculated by using the estimated distance as input of a centroid locating algorithm, wherein the centroid is the coordinate of a target node. The real-time indoor positioning system and method are high in positioning accuracy and stability.
Description
Technical field
The present invention relates to indoor positioning technical field, particularly a kind of indoor locating system and method in real time.
Background technology
Along with the development of radio frequency identification (RFID) technology, the cost of RFID equipment is more and more cheap, and performance is more and more higher.RFID technology has been moved towards large-scale application, is widely adopted in conference management, attendance management, asset tracking, severe punishment supervision over prisoners and mental patient's management etc.And the uniqueness of utilizing rfid system signal strength signal intensity and label to be applied to the indoor location service compute of target object be a challenge field that has bright prospects, accurately location can provide very important information service for decision support.
The calculating of outdoor location service adopts GPS or Big Dipper localization method conventionally, but is not suitable for indoor environment, becomes complex because complicated indoor environment is propagated signal.Indoor positioning based on RFID can make full use of the existing network facilities, realizes location tasks.With respect to traditional position location techniques, it does not need extra hardware to realize angular surveying and time synchronized.RFID navigation system is subject to the impact of the factors such as node shape, placement direction, object block, reflection, diffusion, antenna gain, make to transmit data and not only mix the less and a fairly large number of random error of amplitude, but also produce the range error (appreciable error causing because of factors such as signal transmission obstacles, gross errors), these two kinds of errors make the navigation system based on signal strength signal intensity may produce insufferable position error.
There is at present the cross-section study of many received signal strength (RSSI) indoor positioning based on RFID.SpotON utilizes at least three card reader to carry out three limit telemetrys based on RSSI label is positioned to research, and its error is in 3m left and right.The introducing reference label of LANDMARC novelty is auxiliary as location, is a kind of nearest-neighbors indoor positioning algorithm of classics, and its position error is greatly about 2m.LPM method adopt reference label synchronize with card reader locate method, the error of location is also at 3m.The many labels of the employings such as Bekkali form a kind of mode of probability mapping, in conjunction with Kalman filtering, target are positioned, and its error is the scope to 5m at 0.5m, and jumping characteristic is very large.Apostolia etc. have proposed a kind of location mechanism based on reasoning.The people such as Festa have proposed a kind of material and have followed the tracks of and the method for indoor positioning, its error precision at 1m between 1.9m.The researchers such as Zou propose weight path loss and have extraordinary positioning performance.
Summary of the invention
The object of the present invention is to provide a kind of indoor locating system and method in real time, this system and method positioning precision is high, and stability is strong.
For achieving the above object, technical scheme of the present invention is: a kind of indoor locating system in real time, comprising:
Hardware layer, comprise localized excitation device Exciter, card reader Reader and be located at the label tag on moving target object, described localized excitation device Exciter is arranged at each area entry, as the mark in mobile tag tag discrepancy region, localized excitation device Exciter sends regional location ID mark to label tag in coverage; In each region, be laid with some described card reader Reader, described card reader Reader is used for receiving the regional location ID of label tag transmission and ID own, and is forwarded to communication layers;
Communication layers, for obtaining after the data that hardware layer sends, resolves and obtains the data that can be understood according to relevant device communications protocol;
Data acquisition layer, for cleaning and preliminary treatment the data after resolving;
Position calculation layer, for carrying out label tag position calculation according to cleaning and pretreated data;
Application layer, for showing user by the result of calculation of position calculation layer.
The present invention also provides a kind of indoor orientation method in real time, comprises the following steps:
Step S1: in section interval time
tin, card reader continuous acquisition is located at the RSSI data of the label tag on moving target object;
Step S2: the RSSI data that collect are stored in to core buffer, form RSSI data sequence S={
rSSI1,
rSSI2 ...,
rSSIn};
Step S3: judge that whether described RSSI data sequence S restrains, and is to go to step S6, otherwise goes to step S4;
Step S4: prediction
rSSI i value, prediction covariance, goes to step S5;
Step S5: upgrade
rSSI i value, upgrades covariance, returns to step S3;
Step S6: upgrade the RSSI data sequence in core buffer;
Step S7: the RSSI data sequence after upgrading is normalized to meta weighting processing, obtains corresponding signal data
rSSI end ;
Step S8: with logarithm-carry out apart from path loss model
rSSI end distance estimations;
Step S9: the distance of estimation is calculated to barycenter as the input of barycenter location algorithm, and barycenter is the coordinate of destination node.
Further, in step S4, adopt following formula (1) prediction
rSSI i value:
（1）
In formula,
x(
k|
k-1) be utilize a upper moment (
k-1) current time that calculates of result of prediction (
k-1)
rSSI i value,
x(
k-1|
k-1) be a upper moment (
k-1) optimum prediction value,
fwith
bit is system parameters;
u(
k) be the controlled quentity controlled variable of present status.
Further, in step S4, adopt following formula (2) prediction covariance:
（2）
In formula
p(
k|
k-1) be
x(
k|
k-1) corresponding covariance,
p(
k-1|
k-1) be
x(
k-1|
k-1) corresponding covariance,
f t transposed matrix,
qit is system noise.
Further, in step 5, adopt following formula (3) to upgrade
rSSI i value:
（3）
In formula
z(
k) be
kthe measured value in moment,
hit is system parameters.
Further, in step S5, adopt following formula (4), (5) to upgrade covariance:
（4）
（5）
In formula (4)
rit is the noise of measurement data; Each like this
p(
k|
k) and
k g (
k) all need the value of previous moment to upgrade, the estimation of recurrence is gone down, until sequence convergence.
Further, in step S7, as follows the RSSI data sequence after upgrading is normalized to meta weighting processing:
rSSIin data sequence, find median
m rSSI , then with this median
m rSSI for basic calculation each
rSSI i the weights of value:
（6）
（7）
（8）
Wherein,
d i for variance;
By each signal in sequence
rSSI i value is with corresponding
w i multiply each other, and add up, formula is as follows:
（9）
Like this, obtain
rSSI end just as between two nodes
rSSIvalue output.
Further, in step S8, with logarithm-carry out apart from path loss model
rSSI end the method of distance estimations is as follows:
Free space radio electric transmission path loss model:
（10）
In formula (10)
dfor the distance apart from information source, unit is m;
ffor frequency, unit is MHz;
kfor the path attenuation factor;
Logarithm-apart from path loss model:
（11）
In formula (11),
loss(
d) represent to have passed through distance
dafter path loss, unit is dBm;
loss(
d 0) represent to have passed through distance
d 0path loss after (conventionally getting 1m), unit is dBm;
x σ for average be zero and variance be
σ 2gaussian random distribution function,
nfor radio propagation path loss coefficient;
Selection logarithm-apart from path loss model;
By formula (11) the following formula of can deriving:
（12)
In formula (12), A represents to work as distance
d 0for 1m time, node receives
rSSI,
rSSI end the signal processing costs obtaining for formula (9).
Further, in step S9, the computational methods of barycenter are as follows:
If the coordinate of destination node be (
x,
y), the coordinate of three anchor nodes and receiving
rSSI end correspond to:
(
a 1,
b 1)，
RSSI 1；(
a 2,
b 2)，
RSSI 2；(
a 3,
b 3)，
RSSI 3；
Then, list following formula:
（13）
Meanwhile, order
i=1,2,3 (14)
（15）
To be converted to above:
（16）
Order:
，
，
Have:
（17）
Obtain least square solution:
（18）
Finally
in (
x,
y) be exactly the coordinate that system is predicted.
The invention has the beneficial effects as follows a kind of real-time indoor locating system and the method for having proposed, this system and method carries out covariance prediction by the RSSI sequence to receiving, the level and smooth random error of iteration; Utilize meta weight mechanism, suppressed the impact of appreciable error, utilize apart from path loss model, obtain fading curve, and calculate estimated distance; Utilize barycenter method for solving to obtain destination node location, possess goodish positioning precision and stability, there is very strong practicality and wide application prospect.
Brief description of the drawings
Fig. 1 is the system configuration schematic diagram of the embodiment of the present invention.
Fig. 2 is the method realization flow figure of the embodiment of the present invention.
Fig. 3 is the experimental configuration topological diagram of the embodiment of the present invention.
Fig. 4 is the Comparison of experiment results schematic diagram of the embodiment of the present invention.
Embodiment
The real-time indoor locating system of the present invention, as shown in Figure 1, comprising:
Hardware layer, comprise localized excitation device Exciter, card reader Reader and be located at the label tag on moving target object, described localized excitation device Exciter is arranged at each area entry, as the mark in mobile tag tag discrepancy region, localized excitation device Exciter uses low frequency induction technology to send regional location ID mark to label tag in coverage; In each region, be laid with some described card reader Reader, described card reader Reader is used for receiving the regional location ID of label tag transmission and ID own, and is forwarded to communication layers;
Communication layers, for obtaining after the data that hardware layer sends, resolves and obtains the data that can be understood according to relevant device communications protocol;
Data acquisition layer, for cleaning and preliminary treatment the data after resolving;
Position calculation layer, for carrying out label tag position calculation according to cleaning and pretreated data;
Application layer, for showing user with graphical interfaces by the result of calculation of position calculation layer.
The present invention also provides the real-time indoor orientation method adapting with said system, as shown in Figure 2, comprises the following steps:
Step S1: in section interval time
tin, card reader continuous acquisition is located at the RSSI data of the label tag on moving target object.
Step S2: the RSSI data that collect are stored in to core buffer, form RSSI data sequence S={
rSSI1,
rSSI2 ...,
rSSIn}.
Step S3: judge that whether described RSSI data sequence S restrains, and is to go to step S6, otherwise goes to step S4.
Step S4: prediction
rSSI i value, prediction covariance, goes to step S5.
In step S4, adopt following formula (1) prediction
rSSI i value:
（1）
In formula,
x(
k|
k-1) be utilize a upper moment (
k-1) current time that calculates of result of prediction (
k-1)
rSSI i value,
x(
k-1|
k-1) be a upper moment (
k-1) optimum prediction value,
fwith
bsystem parameters, for Multi-model System,
fwith
bfor matrix;
u(
k) be the controlled quentity controlled variable of present status.
Adopt following formula (2) prediction covariance:
（2）
In formula
p(
k|
k-1) be
x(
k|
k-1) corresponding covariance,
p(
k-1|
k-1) be
x(
k-1|
k-1) corresponding covariance,
f t transposed matrix,
qit is system noise.
Step S5: upgrade
rSSI i value, upgrades covariance, returns to step S3.
In step 5, adopt following formula (3) to upgrade
rSSI i value:
（3）
In formula
z(
k) be
kthe measured value in moment,
hsystem parameters, for many measuring systems,
hfor matrix; Known in order to realize recurrence by upper surface analysis, each
k g it is all real-time update.
Adopt following formula (4), (5) to upgrade covariance:
（4）
（5）
In formula (4)
rit is the noise of measurement data; Each like this
p(
k|
k) and
k g (
k) all need the value of previous moment to upgrade, the estimation of recurrence is gone down, until sequence convergence.
Step S6: upgrade the RSSI data sequence in core buffer.
Step S7: the RSSI data sequence after upgrading is normalized to meta weighting processing, obtains corresponding signal data
rSSI end .
In step S7, as follows the RSSI data sequence after upgrading is normalized to meta weighting processing:
rSSIin data sequence, find median
m rSSI , then with this median
m rSSI for basic calculation each
rSSI i the weights of value:
（6）
（7）
（8）
Wherein,
d i for variance, clearly, if
rSSI i with
m rSSI differ larger, weight coefficient
w i less, and work as
rSSI i with
m rSSI while equating,
w i maximum;
By each signal in sequence
rSSI i value is with corresponding
w i multiply each other, and add up, formula is as follows:
（9）
Like this, obtain
rSSI end just as between two nodes
rSSIvalue output.
Adopt and there is certain advantage based on median method of weighting: on the one hand, during taking median as basic calculation weights, comprise appreciable error
rSSIvalue will be endowed very little weights, when cumulative, can be left in the basket, and has so just left out some appreciable error signaling points, and does not too simply reject appreciable error data; On the other hand, by the cumulative random error that can remove most.
Step S8: with logarithm-carry out apart from path loss model
rSSI end distance estimations.
In step S8, with logarithm-carry out apart from path loss model
rSSI end the method of distance estimations is as follows:
In communication process, there is the factors such as multipath, diffraction, barrier due to wireless signal, generally adopt free space radio electric transmission path loss model and logarithm-apart from path loss model;
Free space radio electric transmission path loss model:
（10）
In formula (10)
dfor the distance apart from information source, unit is m;
ffor frequency, unit is MHz;
kfor the path attenuation factor;
Logarithm-apart from path loss model:
（11）
In formula (11),
loss(
d) represent to have passed through distance
dafter path loss, unit is dBm;
loss(
d 0) represent to have passed through distance
d 0path loss after (conventionally getting 1m), unit is dBm;
x σ for average be zero and variance be
σ 2gaussian random distribution function,
nfor radio propagation path loss coefficient, get 2 ~ 3;
Consider the extreme complexity of actual environment and the dispersiveness of anchor node, radio propagation path loss and theoretic value are not pressed close to, and affect to a great extent final precision, thus selection logarithm-apart from path loss model;
By formula (11) the following formula of can deriving:
（12)
In formula (12), A represents to work as distance
d 0for 1m time, node receives
rSSI,
rSSI end the signal processing costs obtaining for formula (9).
Step S9: the distance of estimation is calculated to barycenter as the input of barycenter location algorithm, and barycenter is the coordinate of destination node.
In step S9, the computational methods of barycenter are as follows:
If the coordinate of destination node be (
x,
y), the coordinate of three anchor nodes and receiving
rSSI end correspond to:
(
a 1,
b 1)，
RSSI 1；(
a 2,
b 2)，
RSSI 2；(
a 3,
b 3)，
RSSI 3；
Then, list following formula:
（13）
Meanwhile, order
i=1,2,3 (14)
（15）
To be converted to above:
（16）
Order:
，
，
Have:
（17）
Obtain least square solution:
（18）
Finally
in (
x,
y) be exactly the coordinate that system is predicted.
Below in conjunction with drawings and the specific embodiments, the present invention is described in further detail.
in order to confirm that this algorithm has good accuracy and stability in complicated indoor environment, select pcs signal to disturb 506-A laboratory, software building many, that personnel walk about frequently, article ornaments are many as test site.
Testing hardware comprises multiple localized excitation devices (destination node), PC and 3 card reader (being anchor node).Localized excitation device is SYEXS1-LF1, and operating frequency is 125KHZ, and data-interface is RS485, and exciting distance is 0.5 ~ 10.0m, and operational environment is-20 ~ 65 degrees Celsius, 5 ~ 95%RH.PC is that processor is Inter (R) Core 2 Duo E7500 2.93GHZ, inside saves as Samsung DDR3, and the lenevo that speed is 1067MHZ opens a day M710E.Card reader is frequency 2.40 ~ 2.48GHZ, channel 316, and signal strength signal intensity 0 ~ 255, signal quality 0 ~ 255, traffic rate is the XT200 of 2400bps ~ 1152000bps.The environment in laboratory and the position of card reader (in figure below, black asterisk shows) are as shown in Figure 3.
The PC software that reads the signal message sending from localized excitation device from card reader is Xtive_XT200_V7 V0109.exe and MDNET_Tools_V0184.exe that it is adjusted to ginseng, and these two softwares are to be Xtive software kit by Xtive company.
the flow process of test is to transmit a signal to card reader from localized excitation device, utilizes the Xtive_XT200_V7 V0109 read signal information on PC, and key is exactly RSSI, therefore supposes below that localized excitation device only launches RSSI value to PC.10 RSSI values of localized excitation device transmission per second, 600 RSSI values of acceptance per minute.WMKF algorithm in this paper carries out preliminary treatment to these 600 RSSI values exactly, and distance estimations, finally draws positional information.
Coordinate system design is as follows: the upper left corner is initial point O (0,0), is y axle forward toward right, and in plane, another vertical direction is x axle forward, as Fig. 3.Three card reader coordinates are respectively (0.4,8,4), (6.6,0), (6.3,8.4), and correspondence sends to the RSSI value of PC to be designated as respectively RSSI_1, RSSI_2, RSSI_3.
obtain mean value model, Gauss model, Kalman filter model, meta weighted model, the pretreated RSSI value sequence of WMKF algorithm by many experiments, then utilize logarithm-carry out distance estimations apart from loss model, then utilize centroid algorithm to obtain final result.The distance of final result and actual result is as error, and the error of comparison object node placement in the time of diverse location as shown in Figure 4, wherein tested 4,7,8,9 appreciable error larger.
Obtain as drawn a conclusion by above experiment: 1) mean value model performance is the most ordinary.Mean value model accuracy is minimum and stability is very poor, especially in the time that appreciable error is larger, because this model can not effectively filter out appreciable error.2) Kalman filter model is little at the few time error of appreciable error, and precision is higher than meta weighted model, but stability is not as meta weighted model.In the time that appreciable error is more, smoothly appreciable error of Kalman filtering, remove the ability of appreciable error not as meta weighted model, because this system of the hypothesis of Kalman filtering meets Markov Hypothesis, the several RSSI values impacts in end when last positioning result tested person are larger, so the unsteadiness of demonstrating.3) WMKF algorithm is no matter be all better than other four models in accuracy or stability, and error is between 0.8m-1m in scope.
Be more than preferred embodiment of the present invention, all changes of doing according to technical solution of the present invention, when the function producing does not exceed the scope of technical solution of the present invention, all belong to protection scope of the present invention.
Claims (9)
1. a real-time indoor locating system, is characterized in that, comprising:
Hardware layer, comprise localized excitation device Exciter, card reader Reader and be located at the label tag on moving target object, described localized excitation device Exciter is arranged at each area entry, as the mark in mobile tag tag discrepancy region, localized excitation device Exciter sends regional location ID mark to label tag in coverage; In each region, be laid with some described card reader Reader, described card reader Reader is used for receiving the regional location ID of label tag transmission and ID own, and is forwarded to communication layers;
Communication layers, for obtaining after the data that hardware layer sends, resolves and obtains the data that can be understood according to relevant device communications protocol;
Data acquisition layer, for cleaning and preliminary treatment the data after resolving;
Position calculation layer, for carrying out label tag position calculation according to cleaning and pretreated data;
Application layer, for showing user by the result of calculation of position calculation layer.
2. a real-time indoor orientation method, is characterized in that, comprises the following steps:
Step S1: in section interval time
tin, card reader continuous acquisition is located at the RSSI data of the label tag on moving target object;
Step S2: the RSSI data that collect are stored in to core buffer, form RSSI data sequence S={
rSSI1,
rSSI2 ...,
rSSIn};
Step S3: judge that whether described RSSI data sequence S restrains, and is to go to step S6, otherwise goes to step S4;
Step S4: prediction
rSSI i value, prediction covariance, goes to step S5;
Step S5: upgrade
rSSI i value, upgrades covariance, returns to step S3;
Step S6: upgrade the RSSI data sequence in core buffer;
Step S7: the RSSI data sequence after upgrading is normalized to meta weighting processing, obtains corresponding signal data
rSSI end ;
Step S8: with logarithm-carry out apart from path loss model
rSSI end distance estimations;
Step S9: the distance of estimation is calculated to barycenter as the input of barycenter location algorithm, and barycenter is the coordinate of destination node.
3. the real-time indoor orientation method of one according to claim 2, is characterized in that, in step S4, adopts following formula (1) prediction
rSSI i value:
（1）
In formula,
x(
k|
k-1) be utilize a upper moment (
k-1) current time that calculates of result of prediction (
k-1)
rSSI i value,
x(
k-1|
k-1) be a upper moment (
k-1) optimum prediction value,
fwith
bit is system parameters;
u(
k) be the controlled quentity controlled variable of present status.
4. the real-time indoor orientation method of one according to claim 3, is characterized in that, in step S4, adopts following formula (2) prediction covariance:
（2）
In formula
p(
k|
k-1) be
x(
k|
k-1) corresponding covariance,
p(
k-1|
k-1) be
x(
k-1|
k-1) corresponding covariance,
f t transposed matrix,
qit is system noise.
5. the real-time indoor orientation method of one according to claim 4, is characterized in that, in step 5, adopts following formula (3) to upgrade
rSSI i value:
（3）
In formula
z(
k) be
kthe measured value in moment,
hit is system parameters.
6. the real-time indoor orientation method of one according to claim 5, is characterized in that, in step S5, adopts following formula (4), (5) to upgrade covariance:
（4）
（5）
In formula (4)
rit is the noise of measurement data; Each like this
p(
k|
k) and
k g (
k) all need the value of previous moment to upgrade, the estimation of recurrence is gone down, until sequence convergence.
7. the real-time indoor orientation method of one according to claim 1, is characterized in that, in step S7, as follows the RSSI data sequence after upgrading is normalized to meta weighting processing:
rSSIin data sequence, find median
m rSSI , then with this median
m rSSI for basic calculation each
rSSI i the weights of value:
（6）
（7）
（8）
Wherein,
d i for variance;
By each signal in sequence
rSSI i value is with corresponding
w i multiply each other, and add up, formula is as follows:
（9）
Like this, obtain
rSSI end just as between two nodes
rSSIvalue output.
8. the real-time indoor orientation method of one according to claim 1, is characterized in that, in step S8, with logarithm-carry out apart from path loss model
rSSI end the method of distance estimations is as follows:
Free space radio electric transmission path loss model:
（10）
In formula (10)
dfor the distance apart from information source, unit is m;
ffor frequency, unit is MHz;
kfor the path attenuation factor;
Logarithm-apart from path loss model:
（11）
In formula (11),
loss(
d) represent to have passed through distance
dafter path loss, unit is dBm;
loss(
d 0) represent to have passed through distance
d 0path loss after (conventionally getting 1m), unit is dBm;
x σ for average be zero and variance be
σ 2gaussian random distribution function,
nfor radio propagation path loss coefficient;
Selection logarithm-apart from path loss model;
By formula (11) the following formula of can deriving:
（12)
In formula (12), A represents to work as distance
d 0for 1m time, node receives
rSSI,
rSSI end the signal processing costs obtaining for formula (9).
9. the real-time indoor orientation method of one according to claim 1, is characterized in that, in step S9, the computational methods of barycenter are as follows:
If the coordinate of destination node be (
x,
y), the coordinate of three anchor nodes and receiving
rSSI end value corresponds to:
(
a 1,
b 1)，
RSSI 1；(
a 2,
b 2)，
RSSI 2；(
a 3,
b 3)，
RSSI 3；
Then, list following formula:
（13）
Meanwhile, order
i=1,2,3 (14)
（15）
To be converted to above:
（16）
Order:
，
，
Have:
（17）
Obtain least square solution:
（18）
Finally
in (
x,
y) be exactly the coordinate that system is predicted.
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| CN106455051A (en) * | 2016-09-23 | 2017-02-22 | 上海图聚智能科技股份有限公司 | WiFi positioning precision increasing method using distance calibration device |
| CN107396280A (en) * | 2017-06-12 | 2017-11-24 | 东南大学 | A kind of indoor locating system and method based on RSSI |
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| CN107850656A (en) * | 2015-06-09 | 2018-03-27 | 赫尔环球有限公司 | The determination of model parameter for positioning purposes |
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