CN107801147A - One kind is based on the adaptive indoor orientation method of the improved multizone of RSSI rangings - Google Patents
One kind is based on the adaptive indoor orientation method of the improved multizone of RSSI rangings Download PDFInfo
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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
- H04W64/006—Locating users or terminals or network equipment for network management purposes, e.g. mobility management with additional information processing, e.g. for direction or speed determination
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- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S11/00—Systems for determining distance or velocity not using reflection or reradiation
- G01S11/02—Systems for determining distance or velocity not using reflection or reradiation using radio waves
- G01S11/06—Systems for determining distance or velocity not using reflection or reradiation using radio waves using intensity measurements
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Abstract
The invention discloses one kind to be based on the adaptive indoor orientation method of the improved multizone of RSSI rangings, target area is divided into more sub-regions environment according to doors structure feature first, build the Shadowing extended models based on ambient parameter storehouse, the hardware system structure that design matches with Shadowing extended models, then the renewal in ambient parameter storehouse is realized by Shadowing extended models and with hardware system structure, the RSSI value received at reference distance is filtered using Kalman filtering algorithm in the process and timing is updated to ambient parameter storehouse, finally, Maximum-likelihood estimation carries out location estimation, the present invention solves the problems, such as that indoor orientation method present in prior art depends on external environment condition unduly and causes indoor positioning precision difference.
Description
Technical field
The invention belongs to indoor positioning technologies field, and in particular to one kind is adaptive based on the improved multizone of RSSI rangings
Answer indoor orientation method.
Background technology
Indoor positioning refers to realize that position positions in environment indoors, mainly using wireless telecommunications, architecture, inertial navigation
The multiple technologies such as positioning are integrated to form a set of indoor location locating system, so as to realize personnel, object etc. indoors in space
Monitoring position.Purpose is weaker when being to solve the problems, such as satellite-signal arrival ground, can not penetrate building.Indoor positioning with
The growing of development of Mobile Internet technology, in some specific occasions (such as on shopping plaza, museum, museum, airport etc.)
Practicality and necessity increasingly significant, it has a extensive future.Indoor positioning realizes that major technique includes at present：
Zigbee, Wi-Fi, bluetooth, RFID etc..Wherein, there is the higher problem of input cost in RFID technique, and Wi-Fi is present necessarily
Radiation problem.Based on this, the minimum bluetooth equipment of low cost, low energy consumption, harm to the human body is by indoor positioning research person
Favor.But because indoor positioning precision is too dependent on external environment condition, accurate positioning is caused to spend low problem so far not yet
Effectively solved.Thus, inquire into the indoor orientation method with adaptive environment, high accurancy and precision turns into urgent need to resolve
Realistic problem.
Found by review of literature：Indoor positioning often by auxiliary hardware devices be mainly wireless signal transmitter
(such as：Wi-Fi, bluetooth etc.), and the mode of propagation of these equipment wireless signals is direct projection, diffraction and scattering (reflection), Er Qiexin
The signal intensity (RSSI) received at any point in number overlay area a not still stochastic variable, and be one
The vector of various propagation paths, and changing with the dynamic change of communication environments, severe jamming indoor positioning algorithms it is accurate
Degree.Fundamentally, had its source in caused by this indoor positioning error problem：First, different buildings, its indoor cloth
Put, material structure, building yardstick etc. it is different, cause the path loss of signal different；Second, the immanent structure of building
Reflection, diffraction, refraction and the scattering of signal can be caused, be overlapped mutually to form a kind of multipath phenomenon, cause the width of reception signal
Degree, phase and arrival time produce error, and cause the loss of signal, seriously affect the precision of indoor positioning.
Further, since RSSI value is highly prone to the interference of extraneous environmental noise, easily in a kind of fluctuation status.Now with away from
Exemplified by 100 RSSI values from continuous acquisition at transmitting terminal 1m, caused RSSI value is as shown in Figure 3.Due to the positioning of the present invention
Method realized based on RSSI rangings, therefore, RSSI it is accurate, be stably to influence follow-up ranging or even setting accuracy
A key factor.
The content of the invention
It is an object of the invention to provide one kind to be based on the adaptive indoor orientation method of the improved multizone of RSSI rangings, solves
Indoor orientation method present in prior art of having determined depends on the problem of external environment condition causes indoor positioning precision difference unduly.
The technical solution adopted in the present invention is that one kind is based on the adaptive indoor positioning of the improved multizone of RSSI rangings
Method, specifically implement according to following steps：
Step 1：Target area is divided into more sub-regions environment according to doors structure feature, structure is joined based on environment
The Shadowing extended models in number storehouse；
Step 2：Design and build the hardware system structure that the Shadowing extended models obtained with step 1 match；
Step 3：The Shadowing extended models built by step 1 and the hardware system structure obtained with step 2, it is fixed
When ambient parameter storehouse is updated, in the process using Kalman filtering algorithm to the parameter reference distance in model at
The RSSI value received is filtered；
Step 4：Maximum-likelihood estimation carries out location estimation.
The features of the present invention also resides in,
Step 1 is specifically implemented according to following steps：
Step (1.1), positioning overlay area split according to target area structure, form n sub-regions, will be fixed
Position overlay area represents with A, then A={ a1, a2, a3..., an}；
Step (1.2), set every sub-regions aiUnder same environment, wherein i=1,2 ..., n, then
According to Shadowing models, each aiThe path loss index n in other corresponding regionsiIt is expressed as：
ni={ ni1, ni2..., nin}
Similarly, RSSI value is at reference distanceThen path loss refers to
Number N and reference distance d0Received signal power Pr (D corresponding to point0) represent as follows respectively：
Wherein, nijFor any one path loss index in N, signal projector AP affiliated areas a is representediWith tested point p
Region ajBetween path loss index；Pr(d0ij) it is Pr (D0) in RSSI value at any one reference distance, represent AP
Affiliated area aiWith tested point p region ajIn reference distance d0Received signal power value corresponding to place, and nij=nji, Pr
(d0ij)=Pr (d0ji), constructing environment parameter library K=(Pr (D0), N),
So Shadowing extended models are：
Shadowing model expressions are：
Pr (d)=Pr (d0)-10nlg(d/d0)+Xδ
Wherein, Pr (d) represents the RSSI value received of receiving terminal；Pr(d0) it is reference distance d0Received corresponding to place
RSSI value；N is path loss index, n and environmental correclation；D represents the distance between receiving terminal and transmitting terminal；d0For reference
Distance；XδRepresent Gaussian random variable, XδAverage value is 0, and main reflection is when the timing of distance one, the change of received signal power.
Ambient parameter storehouse K is updated by following steps：
Step a, RSSI at reference distance is obtained in real time and carries out Kalman filtering filtering；
Step b, path loss index n corresponding to each AP is calculated by Shadowing extended models, and to same area
Domain or each path loss index n equalizations processing for meeting region；
Step c, ambient parameter storehouse K is updated.
Step 2 design and build with Shadowing extended models be adapted indoor positioning hardware system model meet with
Lower condition：
LAN environment is built in area to be targeted, and in the regional arrangement AP and anchor node divided, Mei Gequ
1 anchor node and 4 AP equipment are at least arranged in domain, and wherein AP equipment must have 1 AP to serve as reference point AP, meanwhile, the network
Conditions warrant anchor node device can be uploaded onto the server all AP information scanned in time by internet, in case ring
Border parameter library renewal, real-time ambient parameter storehouse is obtained when point to be determined gets periphery AP information, and from internet end,
The position at point to be determined is completed to determine,
By indoor positioning hardware system model and then obtain：If the hardware device in a region is represented with H, and is existed and closed
System：H={ APij,Sij}；APijRepresent any one separate unit AP, SijThen represent any one anchor node S, and layout area and
, it is known that wherein i represents equipment affiliated area, j represents device numbering, i, j ∈ (1,2,3 ..., n) for position.
Step 3 is specially：
The indoor positioning hardware system model obtained with reference to step 2, the setting signal transmitter at the surrounding of overlay area
AP, heart position sets anchor node in the zone, and is placed in "On" state, then anchor node is circulated in constant duration t and swept
Neighbouring AP information is retouched, while these information are sent to server by LAN, server is according to the anchor node received
ID, signal projector AP MAC judge respective affiliated area and position coordinates, meanwhile, according to Shadowing extended model meters
Calculate and update N and received signal power Pr (D0)。
Calculated according to Shadowing extended models and update N and received signal power Pr (D0) be specially：
Assuming that there is m signal projector AP in a certain subregion or recombination region, then zone routing loss index n is counted
Calculation process is as follows：
In above formula, i represents i-th of region signal projector AP, according to path loss index n calculating process, it is necessary to
Anchor node obtains surrounding AP UUID, RSSI information in real time, carry out region recognition and parameter calculates, and real-time update is current
Ambient parameter storehouse K, so as to reach to the adaptive of environment, realize the purpose that indoor positioning precision improves；
Assuming that Shadowing extended models are discrete control system, and the system can be retouched with the linear random differential equation
State, then the process equation of Kalman filtering and observational equation are as follows：
xk=Axk-1+Buk-1+qk-1
yk=Hxk+rk
In above formula, xkIt is k moment RSSI value to be optimized, uk-1It is controlled quentity controlled variable of the k-1 moment to system, A and B are systems
Parameter, ykIt is k moment rssi measurement values, H is measurement parameter, rkAnd qk-1Noise is represented respectively, and both averages are 0, because
This, both covariances are expressed as：
In above formula, QkAnd RkThe covariance matrix of system noise and measurement noise is represented respectively,
Therefore, Kalman filter is expressed as following two processes, i.e.,：
(1) time updates：
(2) state updates：
In above formula,Represent the k moment system mode values of prediction；Represent the new error of process noise Q predictions；KkTable
Show kalman gain；Represent k moment system optimal state values.
Step 4 is specially：
Assuming that target location is p (x0,y0), then the positional representation where point p receives n AP information is AP (xi,
yi), i ∈ 1,2 ..., n, then the distance between target and each AP di, i ∈ 1,2 ..., n, calculating process is as follows：
In above formula, mathematic interpolation is done with other respectively using last, obtains following equation：
Make x=(x0,y0)T, then above formula be expressed as with matrix form Ax=b：
Matrix is solved by least square method, obtained：
X=(ATA)-1ATb
By x=(x0,y0)T=(ATA)-1ATB can determine point to be determined p (x0,y0) location coordinate information, according to mesh
Area coordinate figure is marked to p (x0,y0) point coordinates realizes location estimation.
The invention has the advantages that a kind of be based on the adaptive indoor orientation method of the improved multizone of RSSI rangings, first
Target area is divided into more sub-regions environment according to doors structure feature, builds the Shadowing based on ambient parameter storehouse
Extended model, and design the hardware system structure to match with extended model, so using Kalman filtering algorithm to reference to away from
Filtered from the RSSI value that place receives and timing is updated to ambient parameter storehouse, finally entered using Maximum-likelihood estimation
Row target positions, so as to the precision realized the adaptive of environment He improve indoor positioning.
Brief description of the drawings
Fig. 1 is a kind of system hardware based on the adaptive indoor orientation method of the improved multizone of RSSI rangings of the present invention
Organization Chart；
Fig. 2 is that one kind of the invention is based on the adaptive indoor orientation method schematic flow sheet of the improved multizone of RSSI rangings；
Fig. 3 is of the invention a kind of based on unit length in the adaptive indoor orientation method of the improved multizone of RSSI rangings
Locate RSSI value curve map；
Fig. 4 is that one kind of the invention is based on the adaptive indoor orientation method experiment scene figure of the improved multizone of RSSI rangings；
Fig. 5 is of the invention a kind of based on range error in the adaptive indoor orientation method of the improved multizone of RSSI rangings
Comparing result figure；
Fig. 6 is that one kind of the invention is based on the adaptive indoor orientation method position error pair of the improved multizone of RSSI rangings
Compare result figure.
Embodiment
The present invention is described in detail with reference to the accompanying drawings and detailed description.
One kind of the invention is based on the adaptive indoor orientation method of the improved multizone of RSSI rangings, flow chart such as Fig. 2 institutes
Show, specifically implement according to following steps：
Step 1：Target area is divided into more sub-regions environment according to doors structure feature, structure is joined based on environment
The Shadowing extended models in number storehouse, specifically implement according to following steps：
Step (1.1), positioning overlay area split according to target area structure, form n sub-regions, will be fixed
Position overlay area represents with A, then A={ a1, a2, a3..., an}；
Step (1.2), set every sub-regions aiUnder same environment, wherein i=1,2 ..., n, then according to
Shadowing models, each aiThe path loss index n in other corresponding regionsiIt is expressed as：
ni={ ni1, ni2..., nin}
Similarly, RSSI value is at reference distanceThen path loss refers to
Number N and reference distance d0Received signal power Pr (D corresponding to point0) represent as follows respectively：
In above formula, i=1,2,3 ..., n；J=1,2,3 ..., n；
Wherein, nijFor any one path loss index in N, signal projector AP affiliated areas a is representediWith tested point p
Region ajBetween path loss index；Pr(d0ij) it is Pr (D0) in RSSI value at any one reference distance, represent AP
Affiliated area aiWith tested point p region ajIn reference distance d0Received signal power value corresponding to place, and nij=nji, Pr
(d0ij)=Pr (d0ji), constructing environment parameter library K=(Pr (D0), N),
Then Shadowing extended models are：
Shadowing model expressions are：
Pr (d)=Pr (d0)-10nlg(d/d0)+Xδ
Wherein, Pr (d) represents the RSSI value received of receiving terminal；Pr(d0) it is reference distance d0Received corresponding to point
RSSI value；N is path loss index, n and environmental correclation；D represents the distance between receiving terminal and transmitting terminal；d0For reference
Distance, under normal circumstances d0Generally take 1m；XδRepresent Gaussian random variable, XδAverage value is 0, and main reflection is certain when distance
When, the change of received signal power；
Ambient parameter storehouse K is updated by following steps：
Step a, RSSI at reference distance is obtained in real time and carries out Kalman filtering filtering；
Step b, path loss index n corresponding to each AP is calculated by Shadowing extended models, and to same area
Domain or each path loss index n equalizations processing for meeting region；
Step c, ambient parameter storehouse K is updated；
Step 2：For Shadowing extended models, when local environment difference, then corresponding ambient parameter is not yet
Together, and this different environment and parameter easily cause path loss index N and are in an active state, it is difficult to which its value is entered
Row precisely determines, therefore, being based on Shadowing extended models, designs the Shadowing extended model phases obtained with step 1
The hardware system structure matched somebody with somebody, as shown in figure 1, LAN environment is built in area to be targeted, and in the regional cloth divided
AP and anchor node are put, 1 anchor node and 4 AP equipment are at least arranged in each region, and wherein AP equipment there must be 1 AP to serve as
Reference point AP, meanwhile, the network environment ensures that anchor node device can be timely by all AP information scanned by internet
Upload onto the server, in case ambient parameter storehouse updates, when point to be determined gets periphery AP information, and obtained from internet end
Real-time ambient parameter storehouse is taken, the position at point to be determined is completed and determines,
By indoor positioning hardware system model and then obtain：If the hardware device in a region is represented with H, and is existed and closed
System：H={ APij,Sij}；APijRepresent any one separate unit AP, SijThen represent any one anchor node S, and layout area and
, it is known that wherein i represents equipment affiliated area, j represents device numbering, i, j ∈ (1,2,3 ..., n) for position；
Step 3：The Shadowing extended models built by the step 1 and the hardware system obtained with the step 2
Unite structure, the renewal in ambient parameter storehouse is realized, in the process using Kalman filtering algorithm to receiving at reference distance
RSSI value is filtered, and is specially：
The indoor positioning hardware system model obtained with reference to step 2, the setting signal transmitter at the surrounding of overlay area
AP (away from wall at least 0.3m), heart position sets anchor node in the zone, and is placed in "On" state, then anchor node is waiting
AP information in time interval t near scan round, while these information are sent to server, server by LAN
Anchor node ID, signal projector AP MAC according to receiving judge respective affiliated area and position coordinates, meanwhile, according to
Shadowing extended models calculate and renewal N and received signal power Pr (D0)；
Calculated according to Shadowing extended models and update N and received signal power Pr (D0) be specially：
Assuming that there is m signal projector AP in a certain subregion or recombination region, then zone routing loss index n is counted
Calculation process is as follows：
In above formula, i represents i-th of region signal projector AP, according to path loss index n calculating process, it is necessary to
Anchor node obtains the information such as surrounding AP UUID, RSSI, MAC in real time, carries out region recognition and parameter calculates, and real-time update
Current ambient parameter storehouse K, so as to reach to the adaptive of environment, realize the purpose that indoor positioning precision improves；
Because RSSI value is highly prone to the interference of extraneous environmental noise, easily in a kind of fluctuation status.This is to a certain degree
On show：RSSI value is both a parameter at reference distance, and an index for influenceing ambient parameter storehouse K, or is influenceed
Follow-up ranging or even a key factor of setting accuracy.And Kalman filtering is as a kind of optimal filter in Gaussian process
Ripple algorithm, on the premise of it is accurate enough in object model and system mode and parameter are not undergone mutation, have excellent well
Change performance, therefore, in ambient parameter storehouse K renewal process, by Kalman filtering algorithm, to the reference point AP got
RSSI values pre-processed, to reduce influence of the external environment to Shadowing extended models, so as to reach raising environment
The purpose of parameter library quality and indoor positioning precision；
The Shadowing extended models obtained by step 2, it is assumed that Shadowing extended models are discrete control system
System, moreover, the system can use linear random differential equation, then the process equation of Kalman filtering and observational equation be such as
Shown in lower：
xk=Axk-1+Buk-1+qk-1
yk=Hxk+rk
In above formula, xkIt is k moment RSSI value to be optimized, uk-1It is controlled quentity controlled variable of the k-1 moment to system, A and B are systems
Parameter, ykIt is k moment rssi measurement values, H is measurement parameter, is represented in more measuring systems with matrix, qk-1And rkRepresent respectively
Noise, according to Probability Statistics Theory, qk-1And rkWhite Gaussian noise is typically considered, it is 0 this weight that both, which have average,
Characteristic is wanted, therefore, both covariances are expressed as：
In above formula, QkAnd RkThe covariance matrix of system noise and measurement noise is represented respectively.
For meeting upper two conditions, Kalman filter has optimal message handler, therefore, in previous step
In discrete control system, Kalman filter is expressed as following two processes, i.e.,：
(1) time updates：
(2) state updates：
In above formula,Represent the k moment system mode values of prediction；Represent the new error of process noise Q predictions；KkTable
Show kalman gain；Represent k moment system optimal state values；
Step 4：Maximum-likelihood estimation carries out location estimation, is specially：
The RSSI value obtained at reference distance is filtered by step 3 Kalman filtering algorithm and calculated corresponding
Path loss index and renewal ambient parameter storehouse, it is assumed that target location is p (x0,y0), then n are received in point p, wherein, n>
Positional representation where=4, AP information is AP (xi,yi), i ∈ 1,2 ..., n, with reference to Shadowing extended models, calculate
The distance between target and each AP di, i ∈ 1,2 ..., n, specific calculating process is as follows：
In above formula, mathematic interpolation is done with other respectively using last, obtains equation as follows：
Make x=(x0,y0)T, then above formula be expressed as with matrix form Ax=b：
Then matrix is solved by least square method, obtained：
X=(ATA)-1ATb
Thus, target positioning is carried out using Maximum-likelihood estimation.
In order to verify proposition based on the adaptive indoor orientation method of the improved multizone of RSSI rangings, choose and layout
Experiment scene as shown in Figure 4.Meanwhile according to environment space feature, experiment scene is divided into A, B and C3 regions (is respectively
6.65m × 3.6m, 8m × 1.75m and 6.65m × 3.6m), wherein each regional site places 4 AP, (wherein 1 is alternatively
Reference point AP) and 1 anchor node.AP comes from intelligence stone science and technology and provides iBeacon signal transmitting base station BrightBeacon, anchor section
Point comes from the remote control terminal CloudBeacon of intelligence stone science and technology.21 points are chosen altogether as target point to be measured, Mei Gedian
3s is gathered, signal collecting device chooses millet 2S (Android 5.0.2) at target point.
Sample data is acquired to target area first, to obtain the ambient parameter average in 3 regions (wherein：RSSI
Reference distance corresponding to value is 1m, and form is (Pr (d0), n)), its result is as shown in table 1：
Each regional environment mean parameter of table 1
According to the data in table 1, Shadowing models Mean Method, Shadowing extended model Mean Methods are selected
(extending Mean Method), with set forth herein method tested in terms of ranging accuracy.Before assignment test, to reality
Required parameters are tested to be initialized.Specific initial parameter value is as shown in table 2：
The initial value of table 2 is set
In experimentation, 10 points are randomly selected to target area first, each point collection 3s, and according to each point
The AP information of acquisition carries out equalization processing.Due to set forth herein method be based on RSSI rangings, choose average most for this
4 big AP points, calculated, and the performance in terms of ranging is carried out pair using Mean Method, extension Mean Method respectively
Than its result is as shown in Figure 5.
As shown in Figure 5, the multizone environment self-adaption indoor positioning side of RSSI rangings is improved based on Kalman filtering algorithm
Method, its maximum error amount are 1.36m, mean error 0.524m；Its worst error value for extending Mean Method is 2.57m, is put down
Equal error 0.647m；The worst error value of Mean Method is 2.29m, mean error 0.802m, and this experimental result illustrates ranging
Error maximum is Mean Method.Moreover, the localization method and Mean Method of RSSI rangings are improved based on Kalman filtering algorithm
With extension Mean Method compare, mean error reduces 34.66% and 19.01% respectively, this result also illustrate have compared with
The method of small range error is advantageous to improve the precision of indoor positioning.
On this basis, it is the progressive mechanism of production for verifying the discussion too low problem of precision, from tri- regions choosings of A, B, C
Take 21 points to be determined to carry out experimental design and tested, then the indoor positioning test result obtained is as shown in Figure 6.
It will be appreciated from fig. 6 that the mean error based on Kalman filtering algorithm improvement RSSI distance-measuring and positioning methods is 1.0005m,
Extend the average localization error 1.1785m of Mean Method, and the average localization error 1.2895m of Mean Method, compare and
Speech, error have dropped 15.1% and 22.41% respectively.And at 21 in point to be determined, changed at this based on Kalman filtering algorithm
The error rate that entering RSSI distance-measuring and positioning methods has 15 points is respectively less than other two kinds, while the reliability of this method is up to
71.43%.And then at No. 13 points, 3 kinds of methods all reach the maximum of respective error, wherein average position error is maximum,
And it is less than 0.5 meter in No. 5 points, the error that RSSI distance-measuring and positioning methods are improved based on Kalman filtering algorithm.In addition, expand
Exhibition average position error is almost approached with being improved the error of RSSI distance-measuring and positioning methods based on Kalman filtering algorithm.This knot
Fruit, it is meant that when environment locally changes when target area, indoor positioning error can become big, but be based on Kalman filtering
The timing of algorithm improvement RSSI distance-measuring and positioning methods carries out data acquisition to the region and updates ambient parameter storehouse K, effectively weakens
Local environment changes the influence to indoor positioning precision, the characteristics of so as to embodying good environment self-adaption.
Described above is only the preferred embodiment of the present invention, it is noted that for the ordinary skill people of the art
For member, under the premise without departing from the principles of the invention, some improvements and modifications can also be made, these improvements and modifications
It should be regarded as protection scope of the present invention.
Claims (8)
1. one kind is based on the adaptive indoor orientation method of the improved multizone of RSSI rangings, it is characterised in that specifically according to following
Step is implemented：
Step 1：Target area is divided into more sub-regions environment according to doors structure feature, built based on ambient parameter storehouse
Shadowing extended models；
Step 2：Design and build the hardware system structure that the Shadowing extended models obtained with the step 1 match；
Step 3：The Shadowing extended models built by the step 1 and the hardware system knot obtained with the step 2
Structure, the renewal in ambient parameter storehouse is realized, in the process using Kalman filtering algorithm to the RSSI that is received at reference distance
Value is filtered；
Step 4：Maximum-likelihood estimation carries out location estimation.
2. one kind according to claim 1 is based on the adaptive indoor orientation method of the improved multizone of RSSI rangings, it is special
Sign is that the step 1 is specifically implemented according to following steps：
Step (1.1), will positioning overlay area split according to target area structure, formed n sub-regions, will positioning covering
Region represents with A, then A={ a1, a2, a3..., an}；
Step (1.2), set every sub-regions aiUnder same environment, wherein i=1,2 ..., n, then according to Shadowing moulds
Type, each aiThe path loss index n in other corresponding regionsiIt is expressed as：
ni={ ni1, ni2..., nin}
Similarly, RSSI value is at reference distanceThen path loss index N and
Reference distance d0Received signal power Pr (D corresponding to point0) represent as follows respectively：
Wherein, nijFor any one path loss index in N, signal projector AP affiliated areas a is representediWith tested point p places
Region ajBetween path loss index；Pr(d0ij) it is Pr (D0) in RSSI value at any one reference distance, represent belonging to AP
Region aiWith tested point p region ajIn reference distance d0Received signal power value corresponding to place, and nij=nji, Pr (d0ij)
=Pr (d0ji), constructing environment parameter library K=(Pr (D0), N),
So Shadowing extended models are：
3. one kind according to claim 2 is based on the adaptive indoor orientation method of the improved multizone of RSSI rangings, it is special
Sign is that the Shadowing models expression is：
Pr (d)=Pr (d0)-10nlg(d/d0)+Xδ
Wherein, Pr (d) represents the RSSI value received of receiving terminal；Pr(d0) it is reference distance d0Received corresponding to place
RSSI value；N is path loss index, n and environmental correclation；D represents the distance between receiving terminal and transmitting terminal；d0For with reference to away from
From；XsRepresent Gaussian random variable, XsAverage value is 0, and main reflection is when the timing of distance one, the change of received signal power.
4. one kind according to claim 2 is based on the adaptive indoor orientation method of the improved multizone of RSSI rangings, it is special
Sign is that the ambient parameter storehouse K is updated by following steps：
Step a, RSSI at reference distance is obtained in real time and carries out Kalman filtering filtering；
Step b, path loss index n corresponding to each AP is calculated by Shadowing extended models, and to the same area or
Meet each path loss index n equalizations processing in region；
Step c, ambient parameter storehouse K is updated.
5. one kind according to claim 1 is based on the adaptive indoor orientation method of the improved multizone of RSSI rangings, it is special
Sign is that the indoor positioning hardware system model being adapted with Shadowing extended models that the step 2 designs meets following
Condition：
LAN environment is built in area to be targeted, and AP and anchor node are arranged in the regional divided, each region is extremely
Arrange that 1 anchor node and 4 AP equipment, wherein AP equipment there must be 1 AP to serve as reference point AP less.Meanwhile the network environment
Ensure that anchor node device can be uploaded onto the server all AP information scanned in time by internet, in case ambient parameter
Storehouse updates, and obtains real-time ambient parameter storehouse when point to be determined gets periphery AP information, and from internet end, completes undetermined
Position determination at site,
By indoor positioning hardware system model and then obtain：If the hardware device in a region is represented with H, and relation be present：H=
{APij,Sij}；APijRepresent any one separate unit AP, SijThen represent that any one anchor node S, wherein i represent the affiliated area of equipment
Domain, j represent device numbering, i, j ∈ (1,2,3 ..., n).
6. one kind according to claim 1 is based on the adaptive indoor orientation method of the improved multizone of RSSI rangings, it is special
Sign is that the step 3 is specially：
The indoor positioning hardware system model obtained with reference to the step 2, the setting signal transmitter at the surrounding of overlay area
AP, heart position sets anchor node in the zone, and is placed in "On" state, then anchor node is circulated in constant duration t and swept
Neighbouring AP information is retouched, while these information are sent to server by LAN, server is according to the anchor node received
ID, signal projector AP MAC judge respective affiliated area and position coordinates, meanwhile, calculated according to Shadowing extended models
With renewal N and received signal power Pr (D0)。
7. one kind according to claim 6 is based on the adaptive indoor orientation method of the improved multizone of RSSI rangings, it is special
Sign is, is calculated according to Shadowing extended models and updates N and received signal power Pr (D0) be specially：
Assuming that there is m signal projector AP in a certain subregion or recombination region, then the zone routing loss index n calculating process
It is as follows：
In above formula, i represents i-th of region signal projector AP, according to path loss index n calculating process, it is necessary to anchor section
Point obtains the information such as surrounding AP UUID, RSSI in real time, carries out region recognition and parameter calculates, and the environment that real-time update is current
Parameter library K, so as to reach to the adaptive of environment, realize the purpose that indoor positioning precision improves；
Assuming that Shadowing extended models are discrete control system, moreover, the system can be retouched with the linear random differential equation
State, then the process equation of Kalman filtering and observational equation are as follows：
xk=Axk-1+Buk-1+qk-1
yk=Hxk+rk
In above formula, xkIt is k moment RSSI value to be optimized, uk-1It is controlled quentity controlled variable of the k-1 moment to system, A and B are systematic parameters,
ykIt is k moment rssi measurement values, H is measurement parameter, is represented in more measuring systems with matrix, qk-1And rkNoise is represented respectively,
According to Probability Statistics Theory, qk-1And rkWhite Gaussian noise is typically considered, it is 0 this key property that both, which have average,
Therefore, both covariances are expressed as：
In above formula, QkAnd RkThe covariance matrix of system noise and measurement noise is represented respectively,
Therefore, Kalman filter is expressed as following two processes, i.e.,：
(1) time updates：
(2) state updates：
In above formula,Represent the k moment system mode values of prediction；Represent the new error of process noise Q predictions；KkRepresent karr
Graceful gain；Represent k moment system optimal state values.
8. one kind according to claim 1 is based on the adaptive indoor orientation method of the improved multizone of RSSI rangings, it is special
Sign is that the step 4 is specially：
Assuming that target location is p (x0,y0), then n are received in point p, wherein, n>Positional representation where=4, AP information is
AP(xi,yi), i ∈ 1,2 ..., n, then the distance between target and each AP di, i ∈ 1,2 ..., n, calculating process is as follows：
In above formula, mathematic interpolation is done with other respectively using last, obtains following equation：
Make x=(x0,y0)T, then above formula be expressed as with matrix form Ax=b：
Matrix is solved by least square method, obtained：
X=(ATA)-1ATb
By x=(x0,y0)T=(ATA)-1ATB can determine point to be determined p (x0,y0) location coordinate information, according to target area
Coordinate diagram and p (x0,y0) point coordinates realizes location estimation.
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| CN113645565B (en) * | 2021-07-08 | 2022-10-04 | 南京邮电大学 | Indoor positioning method based on hexagonal closest packing structure |
| CN113435057A (en) * | 2021-07-12 | 2021-09-24 | 西安工程大学 | Reliability evaluation method for spinning machine performance degradation |
| CN113435057B (en) * | 2021-07-12 | 2023-01-13 | 西安工程大学 | Reliability evaluation method for spinning machine performance degradation |
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