CN101466070A - Wireless indoor orientation method based on automatic learning of wireless signal acceptance strength distribution - Google Patents
Wireless indoor orientation method based on automatic learning of wireless signal acceptance strength distribution Download PDFInfo
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- CN101466070A CN101466070A CNA2009100286737A CN200910028673A CN101466070A CN 101466070 A CN101466070 A CN 101466070A CN A2009100286737 A CNA2009100286737 A CN A2009100286737A CN 200910028673 A CN200910028673 A CN 200910028673A CN 101466070 A CN101466070 A CN 101466070A
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Abstract
The invention discloses a wireless indoor positioning method capable of automatically learning the distribution of the signal receiving intensity. The theory adopted by the method is that: a wireless signal propagation model and a parameter are taken as an initial distribution curve of the field intensity; in the operating process of a system, a mobile equipment continuously learns a distribution function of the field intensity by the received signal receiving intensity and an EM (expectation maximum) algorithm, and the exact distribution curve of the field intensity is obtained. After the new distribution curve of the field intensity is obtained, the position of the mobile equipment is estimated through adopting an MMSE (minimum mean square error) statistical estimation algorithm. The provided method is characterized in that: the tedious calibrating process of the wireless positioning system is avoided; when the system is changed, the recalibration can be automatically carried out; and the installation and the maintenance costs of the positioning system are greatly reduced. The positioning method is suitable for various indoor positioning systems using wireless signals, such as a wireless local area network, a wireless sensor network, a Bluetooth or mobile phone network, and the like.
Description
Technical field
The present invention relates to the wireless indoor field of locating technology, utilize the wireless signal receiving intensity to carry out equipment location, the wireless indoor location method that particularly a kind of learning signal receiving intensity automatically distributes.
Background technology
The indoor positioning technology is a kind of technology of obtaining personnel's article position information in indoor environment.The indoor positioning technology has a wide range of applications at numerous areas, as museum guide, market shopping guide, article personnel tracking, intelligent building, robot, general fit calculation and logistics or the like.Because building has very strong shielding action to wireless signal, satellite-based navigation system such as GPS etc. indoor can't operate as normal.Present indoor positioning technology mainly is meant the technology of utilizing indoor wireless signal to position, and these wireless signals comprise signal of wireless lan signal, city cordless telephone signal, wireless sensor network and other exclusive frequency ranges or the like.As a rule, the propagation time and the received signal intensity of the radiofrequency signal of sending by measuring position known transmitter reference point (abbreviation reference point) can be calculated the distance of transmitting terminal to receiving terminal, then by the method for triangle location, the position that just can calculate object.But all there is bigger defective in these two kinds of methods, and at first, because indoor distance is very short, very high to the computational accuracy requirement in propagation time, common wireless system all can't reach at present, must use the equipment of particular design, so cost is very high.Secondly, because indoor environment comprises all multi-obstacle avoidances such as body of wall, door, furniture, propagation of wireless signal is very irregular, can't represent with simple model, therefore is all to have bigger error with the propagation time or the distance of received signal intensity calculating gained.The technology of the indoor locating system of main flow employing at present is that the distribution of wireless signal receiving intensity is calibrated in advance, that is to say, before system uses, measure the receiving intensity of wireless signal in advance in each place of building by the technical staff, obtain the relation curve of signal receiving strength and position, and record in the database, when system uses, with device observes to be positioned to wireless signal strength and database compare, find out in the database record, thereby obtain the position at equipment place near acknowledge(ment) signal intensity.The precision of this method is often than higher, but owing to before system's operation, need calibrate in advance everywhere at building, therefore expend a large amount of time and installation cost, simultaneously, when system changes, when situations such as the position as reference point changes, indoor layout changes took place, radio-frequency (RF) energy distributes to be needed to recalibrate, and therefore the maintenance cost of this method is also very high.
Summary of the invention
The objective of the invention is to propose a kind of can be in system's running, automatically study wireless signal strength distribution curve, and can self adaptation because system environments changes the variation of the wireless signal strength distribution curve that the isoparametric variation in position of position as reference point, reference point transmitting power, indoor equipment causes.
The subject matter that the present invention solves is that indoor locating system needs artificial problem of regularly the wireless signal strength distribution curve being calibrated.Adopt indoor orientation method of the present invention, signal intensity profile need not to measure, but system's study acquisition automatically in running.
In order to achieve the above object, the present invention adopts following scheme:
Step 1: sampled in indoor position, obtain a series of sampled points.
Step 2: adopt theoretical wireless signal propagation model and parameter as initial field strength distribution curve, and the value of signal strength signal intensity on sampled point that each reference point is sent calculated.
Step 3: system brings into operation in the process, and mobile device constantly is sent to the received signal strength information of receiving in the location-server.
Step 4: after location-server is collected abundant received signal strength information,, obtain accurate field strength distribution curve with regard to the field intensity value on the sampled point being learnt with these signal strength informations and greatest hope algorithm.
Step 5: location-server utilizes up-to-date field strength distribution curve, adopts Minimum Mean Square Error statistical estimate algorithm that the position of mobile device is estimated.
Step 6: whenever spend one period long period or when system environments changes, rerun step 4.
In the above-mentioned steps 1 indoor location being sampled, can be uniform sampling, also can be any sampling.
Setting up initial field intensity distribution curve in the above-mentioned steps 2, can be the linear-logarithmic model, shown in following formula:
Wherein, P
R, dBRepresent received signal intensity, d
0Represent reference distance, be generally 1 meter, P (d
0) be the intensity of signal on reference distance, η is the energy consumption parameter, under the indoor environment, this parameter is generally 1.5 ~ 6.W is that average is 0 Gaussian-distributed variable.
Initial field intensity distribution curve also can be many walls distribution curve, shown in following formula:
Wherein, λ
iIt is the loss that signal is subjected to when passing the i wall.Other parameters are consistent with the parameter of linear-logarithmic model.
In step 2, the value of parameter need not to measure, and get standard value and get final product, for example, d
0Get 1 meter, P (d
0) get-20dBm, η gets 2, λ
iGet 10dB, the variance of W is got 4dB.
Mobile device is sent to location-server with the signal strength signal intensity that receives in the above-mentioned steps 3, as Fig. 4, is specially:
1. equipment moves arbitrarily indoor.
2. mobile device carries out one-shot measurement every 1 second to the intensity of all reference point received signals.
3. to not receiving the reference point of signal, received signal intensity is made as the minimum of equipment and accepts sensitivity.
4. send the signal strength signal intensity that receives to location-server at regular intervals.
4 pairs of signal intensity profile curves of above-mentioned steps are learnt estimation in the value of sampled point, thereby obtain signal intensity profile curve accurately, and concrete steps are:
1. the received signal intensity vector o that each mobile device is sent
i, to calculate under existing signal intensity profile curve condition, its position is at each reference point x
kProbability distribution p (x
k| o
i).
3. the max log likelihood equation of signal calculated intensity distributions
Value, if convergence is then calculated and stopped, the signal intensity profile curve can if do not restrain, then get back to 1 by the value representation on the reference point accurately) continue.
The positional information that above-mentioned steps 5 is calculated under current field strength distribution condition, concrete computing formula is
Above-mentioned steps 6 is used at system's running the signal intensity profile function being upgraded, thereby adapts to automatically because system environments changes the variation of the signal intensity profile function that device parameter change etc. causes.
Advantage of the present invention is, adopt the method for statistical learning, in system's running, automatically study wireless signal strength distribution curve, and can self adaptation because system environments changes the variation of the wireless signal strength distribution curve that the isoparametric variation in position of position as reference point, reference point transmitting power, indoor equipment causes.Avoid indoor locating system to need artificial problem of regularly the wireless signal strength distribution curve being calibrated, greatly reduced the installation and maintenance cost of wireless indoor navigation system, guaranteed positioning accuracy simultaneously.Below we provide specific description:
1. the present invention adopts the statistical learning method of greatest hope, need not in advance the wireless signal strength distribution curve to be measured, but at first adopt simple theoretical model, in system's running, the signal intensity profile curve is learnt to approach then, thereby under the situation of not sacrificing positioning accuracy, simplified the installation process and the cost of wireless indoor navigation system greatly.
The present invention simultaneously can self adaptation because the variation of the signal intensity profile curve that environmental change brings has reduced systematically maintenance cost.
3. the present invention adopts the lowest mean square difference method based on statistical theory to position, and take into full account noise factor, so positioning accuracy is higher.
4. the present invention can be applied to various wireless location systems, for example wireless local network positioning system, cordless telephone navigation system, wireless sensor network positioning system and GSM navigation system etc.
Description of drawings
Mobile device and location-server information interaction schematic diagram that Fig. 1 provides for the present invention.
Many walls of signal strength signal intensity distribution curve schematic diagram that Fig. 2 provides for the present invention.
The field strength distribution learning process schematic diagram that Fig. 3 provides for the present invention.
Embodiment
The present invention is described further below in conjunction with accompanying drawing and embodiment.
Equipment in the wireless indoor navigation system that the present invention provides mainly contains following 3 classes as shown in Figure 1:
The reference point of location aware: the location aware of this kind equipment, and send wireless signal to other nodes is generally the sensor node of location aware in the base station of access point, cordless telephone system of WLAN (wireless local area network) or the wireless sensor network.In an indoor locating system, usually require to have more than 4 reference point with guarantee three-dimensional localization accurately.
Mobile device: the intensity of the wireless signal that the reference point that receives is sent in position the unknown of this kind equipment, and move indoor, mobile device sends to and is used for position calculation in the location-server.
Location-server: be used to receive the data that mobile device sends, and carry out the calculating of profile distribution curve and the calculating of position of mobile equipment.
The wireless indoor location method based on automatic learning of wireless signal acceptance strength distribution that the present invention proposes is implemented according to following concrete steps:
Step 1: sampled in indoor position, obtain a series of sampled points.
The purpose of step 1 is to represent indoor location with a series of sampled points, thereby with received signal strength distribution curve discretization.Sampling can be a uniform sampling, also can be nonuniform sampling.Sampled point is many more, and the estimation of strength distribution curve to received signal is just accurate more.
Step 2: adopt theoretical wireless signal propagation model and parameter to calculate as the value of signal strength signal intensity on sampled point that initial field strength distribution curve sends each reference point.
Utilize theoretical wireless signal propagation model can obtain initial signal intensity profile curve.This wireless signal propagation model can be other various models such as log-linear model (as shown in Figure 2), many walls model or interpolation model, the parameter value of model adopts theoretical value to get final product, the present invention does not have concrete requirement to the value of initial signal propagation model and parameter thereof, but different signal propagation models will bring different model errors, the model error of complex model is less than the model error of naive model, when carrying out position estimation in the back, the big young pathbreaker of model error influences the accuracy of location.
Step 3: system brings into operation in the process, and mobile device constantly is sent to the received signal strength information of receiving in the location-server.
Mobile device moves in whole indoor environment, the signal strength signal intensity that each reference point that each second, measurement was once received is sent, to the reference point signal that does not receive, be recorded as the minimum sensitivity of equipment, be generally-90 ~-120dBm. is at set intervals, as 5 to 10 seconds, the data that receive will be sent in the location-server by TCP or UDP.Mobile device moves to each indoor position, and for example each room also can have a plurality of mobile devices to move in indoor environment simultaneously, accelerates the speed of information gathering.Different with the calibration in advance of the signal intensity profile curve of common localization method, the sort signal collection does not need the position of record acquisition point, therefore can finish fast automatically in system's running.
Step 4: after location-server is collected abundant received signal strength information,, obtain accurate field strength distribution curve with regard to the field intensity value on the sampled point being learnt with these signal strength informations and greatest hope algorithm.
Step 4 is cores of the present invention, and basic principle is the greatest hope algorithm in the Statistical Learning Theory, as shown in Figure 3, and in location-server, when mobile device has sent abundant received signal intensity vector o
iAfter, can calculate under existing signal intensity profile curve condition, its position is at each reference point x
kProbability distribution p (x
k| o
i).The signal intensity profile curve is at reference point x then
kValue be updated to
The max log likelihood equation of last signal calculated intensity distributions
Value, if convergence is then calculated and stopped, the signal intensity profile curve can if do not restrain, then be continued to calculate, till convergence by the value representation on the reference point accurately.
Step 5: location-server utilizes up-to-date field strength distribution curve, adopts Minimum Mean Square Error statistical estimate algorithm that the position of mobile device is estimated.
The computing formula of Minimum Mean Square Error statistical estimate algorithm is
P (x
k| o
i) be that received signal intensity is in the probability distribution of each reference point under new field strength distribution condition.
Step 6: whenever spend one period long period or when system environments changes, rerun step 4.
Through after a while, indoor environment may change, and for example, wireless signal transmission power may change, and indoor furniture is arranged and may be changed or the like.In this case, need relearn field strength distribution.As long as it is just passable therefore to rerun the algorithm of the present invention's proposition.
Above-mentioned concrete enforcement; the present invention has been done further explanation; institute is understood that; field strength distribution learning algorithm involved in the present invention; be applicable to various use wireless signals, comprise WLAN (wireless local area network) (802.11), wireless sensor network (802.15.4), bluetooth and mobile phone signal (GSM, CDMA) or the like; same method also is applicable to the study of various wireless signals in outdoor distribution, and these application all are included within protection scope of the present invention.
Claims (4)
1, a kind of wireless indoor location method based on automatic learning of wireless signal acceptance strength distribution, it is characterized in that can be in system's running, automatically learn the wireless signal strength distribution curve, and can self adaptation because system environments changes the variation of the wireless signal strength distribution curve that causes, concrete steps are:
Step 1: sampled in indoor position, obtain a series of sampled points.
Step 2: adopt theoretical wireless signal propagation model and parameter as initial field strength distribution curve, and the value of signal strength signal intensity on sampled point that each reference point is sent calculated.
Step 3: system brings into operation in the process, and mobile device constantly is sent to the received signal strength information of receiving in the location-server.
Step 4: after location-server is collected abundant received signal strength information,, obtain accurate field strength distribution curve with regard to the field intensity value on the sampled point being learnt with these signal strength informations and greatest hope algorithm.
Step 5: location-server utilizes up-to-date field strength distribution curve, adopts Minimum Mean Square Error statistical estimate algorithm that the position of mobile device is estimated.
Step 6: whenever spend one period long period or when system environments changes, rerun step 4.
2, a kind of wireless indoor location method according to claim 1 based on automatic learning of wireless signal acceptance strength distribution, it is characterized in that in step 2 wireless signal propagation model that uses can be other various models such as log-linear model, many walls model or interpolation model, the parameter value of model adopts theoretical value to get final product.
3, a kind of wireless indoor location method according to claim 1 based on automatic learning of wireless signal acceptance strength distribution, it is characterized in that in step 4, the signal intensity profile curve being learnt estimation in the value of sampled point, thereby obtain signal intensity profile curve accurately, be specially:
1) the received signal intensity vector o that each mobile device is sent
i, to calculate under existing signal intensity profile curve condition, its position is at each reference point x
kProbability distribution p (x
k| o
i).
4, a kind of wireless indoor location method based on automatic learning of wireless signal acceptance strength distribution according to claim 1 is characterized in that the method for Minimum Mean Square Error statistical estimate in step 5 is judged the position, and its computing formula is
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