CN105182288A - Indoor-positioning-system-based RSSI Kalman filtering method - Google Patents
Indoor-positioning-system-based RSSI Kalman filtering method Download PDFInfo
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- G—PHYSICS
- 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
- G01S5/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
- G01S5/02—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves
- G01S5/0294—Trajectory determination or predictive filtering, e.g. target tracking or Kalman filtering
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- G—PHYSICS
- 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
- G01S5/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
- G01S5/02—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves
- G01S5/10—Position of receiver fixed by co-ordinating a plurality of position lines defined by path-difference measurements, e.g. omega or decca systems
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Abstract
Disclosed in the invention is an indoor-positioning-system-based received signal strength indication (RSSI) Kalman filtering method. The method comprises the following steps: step one, constructing a bluetooth Beacon environment of an indoor scene and establishing a signal strength map (SSMap); step two, selecting an actual measurement point, obtaining RSSI data from N bluetooth Beacons and storing the data into N channels; and step three, configuring a Kalman filter to carry out filtering processing on the received signal RSSI of the N channels. With the method, a defect that RSSI fluctuation dynamic ranges of all bluetooth Beacons received by a client are large due to the indoor electromagnetic environment influence can be overcome; and with control of the dynamic ranges of RSSI of all channels, the indoor positioning precision is effectively improved.
Description
Technical field
The present invention relates to indoor accurate position field, propose a kind of RSSI based on indoor locating system (the signal intensity instruction that ReceivedSignalStrengthIndication receives) kalman filter method.
Background technology
Indoor positioning refers to that in indoor environment, realize position locates, and mainly adopts a set of indoor location locating systems of the integrated formation of multiple technologies such as wireless telecommunications, architecture, inertial navigation location, thus realizes the monitoring position in the interior space such as personnel, object.The technology of common indoor wireless location has: Wi-Fi, bluetooth, infrared ray, ultra broadband, RFID (RadioFrequencyIdentification radio-frequency (RF) identification), ZigBee (purple honeybee) and ultrasound wave.But Wi-Fi signal is easily subject to other signal disturbing, infrared signal transmission is easily limited their application on traditional mobile terminal by the feature of the barrier such as wall interference apart from short; And ultra-wide band, RFID, ZigBee and ultrasonic signal are temporarily difficult to carry in existing mobile terminal.Thus comprehensive cost performance and feasibility analysis, Beijing Le Gaolehua company limited develops the indoor positioning navigation STARnet basic network system based on Bluetooth signal.Just need to launch tens Signal transmissions satellites to near-earth orbit as GPS navigation, STARnet system is also realize accurately location and navigation Service by laying the bluetooth Beacon covered comprehensively.User, after client has installed the software of indoor positioning, will receive the signal coming from neighbouring bluetooth Beacon and send, by analyzing and then calculate the exact position of user in indoor to the RSSI of this signal.But the RSSI of each Beacon that client receives is subject to the impact of complicated indoor electric magnetic environment, there is very large fluctuation, and then has influence on the estimation of client to indoor exact position, make whole positioning system stability decline.
Summary of the invention
The problem that the Bluetooth signal that the object of the invention is to receive to solve indoor positioning client is subject to indoor electric magnetic environment disturbance fluctuation dynamic range excessive, Kalman filtering process is carried out by the RSSI from each Beacon client received, reach the object reducing its dynamic range, and then improve the stability of indoor positioning.First the present invention needs the experiment scene having carried an indoor positioning, and this scene is evenly divided into coordinate grid; Then the mode mixed with Chain Network according to Star network arranges N number of bluetooth Beacon in this scene; First receive the signal from N number of bluetooth Beacon respectively by device clients each coordinate position in test scene and be made into SSMap (SignalStrengthMap signal intensity map) database, resolving relational expression according to SSMap database information acquisition system state variance and distance with signal intensity; Then enter the actual measurement stage, receive the signal from N number of Beacon in indoor optional position by device clients, after being divided by these signals RSSI N number of passage to store, design Kalman filter carries out filtering process; Finally the RSSI after Kalman filtering is calculated current device client apart from each Beacon position by distance and signal intensity relational expression, realize location by three-point fox method.
A kind of RSSI kalman filter method based on indoor locating system of the present invention, comprises following step:
Step one, build indoor scene bluetooth Beacon environment and build SSMap;
Step 2, choose eyeball, obtain the RSSI data from N number of bluetooth Beacon, a point N number of passage stores;
Step 3, design Kalman filter carry out filtering process to the RSSI receiving the N number of passage of signal.
The invention has the advantages that:
Instant invention overcomes RSSI that client that indoor electromagnetic environmental impact brings receives each bluetooth Beacon to fluctuate the large shortcoming of dynamic range, by controlling the dynamic range of each passage RSSI, effectively improve the precision of indoor positioning.
Accompanying drawing explanation
Fig. 1 is system flowchart of the present invention;
Fig. 2 is the indoor scene figure that the present invention builds;
Fig. 3 is the SSMap distribution of minor30Beacon in whole indoor environment in the present invention;
Fig. 4 is the design sketch of mesh coordinate in the present invention (16,9) position the 1st passage bluetooth BeaconRSSI before and after Kalman filtering process;
Fig. 5 is the design sketch of mesh coordinate in the present invention (16,9) position the 2nd passage bluetooth BeaconRSSI before and after Kalman filtering process;
Fig. 6 is the design sketch of mesh coordinate in the present invention (16,9) position the 3rd passage bluetooth BeaconRSSI before and after Kalman filtering process;
Fig. 7 is the design sketch of mesh coordinate in the present invention (16,9) position the 4th passage bluetooth BeaconRSSI before and after Kalman filtering process;
Fig. 8 is the design sketch of mesh coordinate in the present invention (16,9) position the 5th passage bluetooth BeaconRSSI before and after Kalman filtering process;
Fig. 9 is the design sketch of mesh coordinate in the present invention (16,9) position the 6th passage bluetooth BeaconRSSI before and after Kalman filtering process;
Figure 10 is the design sketch of mesh coordinate in the present invention (16,9) position the 7th passage bluetooth BeaconRSSI before and after Kalman filtering process;
Figure 11 is the design sketch of mesh coordinate in the present invention (16,9) position the 8th passage bluetooth BeaconRSSI before and after Kalman filtering process.
Embodiment
Below in conjunction with drawings and Examples, the present invention is described in further detail.
The present invention is a kind of RSSI kalman filter method based on indoor locating system, and its process flow diagram as shown in Figure 1, comprises following step:
Step one, build indoor scene bluetooth Beacon environment and build SSMap.
Be specially:
The first step, builds indoor scene.
Assuming that this scene has M floor, and each floor is according to the uniform spatial distribution layout of W × L × H, and wherein: W is the width of floor, L is floor length, and H is floor clear height; Then the surface level of every one deck is divided into A × B coordinate grid according to length and width proportional spacing; N number of bluetooth Beacon with identical signal transmission power altogether is equally spaced disposed according to Star network and the mode that Chain Network mixes in the roof of every one deck scene, and in the broadcast singal of each bluetooth Beacon, contains the majorID recording its number of plies information and the minorID recording its positional information.
Second step, builds SSMap.
Carrying a height is h
1equipment receiving platform, is placed in every one deck A × B coordinate net center of a lattice successively by platform, then divide N number of passage to carry out the data acquisition of RSSI and record to N number of bluetooth Beacon respectively; Meanwhile, require at least to need t at the RSSI data acquisition of same coordinate grid
1sampling time accumulation with the data accumulation of 100 obtaining this single passage in coordinate position place; Like this, effectively can set up SSMap by the RSSI database of A × B × N × M × 100.
3rd step, computing system state variance and observational variance.
First all RSSI data calling the SSMap under i passage are saved as Pow matrix, by the variance of all RSSI data under trying to achieve this passage of var function in Matlab, with Q (i) record, then square the observational variance of system is set up for reference value by scope error scale division value, with R (i) record, finally i is traveled through from 1 to N.
Step 2, choose eyeball, obtain the RSSI data from N number of bluetooth Beacon, a point N number of passage stores.
Be specially:
In indoor scene A × B coordinate grid, choose the eyeball of arbitrary coordinate position, received by client and divide other RSSI data from N number of bluetooth Beacon.Require at least to need t at the RSSI data acquisition of this eyeball
2sampling time accumulation to obtain g RSSI data accumulation under this single passage in eyeball position; Then calculate the fluctuation maximal value of the RSSI data under each passage of this eyeball, fluctuation minimum value and fluctuation dynamic range, pass to the process of follow-up Kalman filter together with RSSI data itself with xml file layout.
Step 3, design Kalman filter carry out filtering process to the RSSI receiving the N number of passage of signal.
Be specially:
The first step, kalman filter models is set up.The design of Kalman filter is divided into prediction and the correction of system state equation.First list the state equation of system, first start with from system state equation general expression:
X(i,k+1)＝AX(i,k)+W(i,k)(1)
S(i,k)＝CX(i,k)+V(i,k)(2)
In formula, X (i, k) and X (i, k+1) is system state vector, represents the RSSI estimated value that eyeball is to be optimized in k moment and k+1 reception signal i-th passage respectively respectively; S (i, k) is systematic observation vector, represents the observed reading of eyeball RSSI in k reception signal i-th passage; And A is system matrix, C is system output matrix; W (i, k) and V (i, k) is respectively in the system state noise of k moment i-th passage and observation noise, and approximately thinks that W (i, k) and V (i, k) is separate zero-mean white noise sequence, satisfied:
E[W(i,k)]＝E[V(i,k)]＝0(3)
E[W(i,k)W(i,k)
T]＝Q(i)(4)
E[V(i,k)V(i,k)
T]＝R(i)(5)
State equation thus, the forecasting process of Kalman filter equation can be listed:
P(i,k|k-1)＝AP(i,k-1|k-1)A
T+Q(i)(6)
In formula, X (i, k|k-1) utilizes the result of k-1 moment status predication in the k moment in i-th passage,
be utilize the result of k-1 moment state optimization in the k moment in i-th passage, U (i, k) is the state controlled quentity controlled variable in k moment i-th passage, and P (i, k|k-1) is
corresponding covariance matrix, P (i, k-1|k-1) is
corresponding covariance matrix.
Further, the trimming process of Kalman filter equation can be listed:
K(i,k)＝P(i,k|k-1)C
T[CP(i,k|k-1)C
T+R(i)]
-1(8)
P(i,k|k)＝[I(i)-K(i,k)C]P(i,k|k-1)(10)
In formula, K (i, k) is the kalman gain in i-th passage, and its effect makes Posterior estimator error covariance minimum.
Carry out in the problem of filtering process specific to indoor positioning to bluetooth BeaconRSSI, because system has t to the process of same eyeball Received signal strength
2the accumulation of sampling time g RSSI data, does not thus change in same eyeball position system status parameters, and this just means that system matrix A and system output matrix C is unit matrix, and thus system prediction process can be rewritten as:
P(i,k|k-1)＝P(i,k-1|k-1)+Q(i)(11)
System compensation process can be rewritten as:
K(i,k)＝P(i,k|k-1)[P(i,k|k-1)+R(i)]
-1(13)
P(i,k|k)＝[I(i)-K(i,k)]P(i,k|k-1)(15)
Second step, the program of Kalman filter realizes.The present invention Karman_Filter.m program achieves the process to the RSSI signal card Kalman Filtering that given position equipment any in the indoor environment of building receives.First the N number of passage each g group RSSI data transformations utilizing the xmlread function in Matlab to be received by equipment is that the matrix data z form of [N × g] reads; Afterwards initialization is carried out to matrix data, it should be noted that the meaning of each initial value of program here:
The timing node of letter k representative system state change, the channel coding of alphabetical i representative observation, the numerical value that each channel coding is transmitted indicates the RSSI of the bluetooth Beacon arrived from this channel reception.
Xhat is [N × g] matrix, represents the posterior estimate to RSSI, namely in the k moment, in conjunction with measured value and the k-1 moment RSSI prior estimate of current RSSI, the estimated value obtained upgrades, corresponding to the X in state equation, by first the element zero setting of this matrix when initialization;
Xhatminus is [N × g] matrix, represents the prior estimate of RSSI, namely in the k-1 moment, to the estimation that k moment RSSI makes, corresponding to the X in state equation, by first the element zero setting of this matrix when initialization;
P is [N × g] matrix, represents the variance of Posterior estimator, by first the element zero setting of this matrix when initialization;
Pminus is [N × g] matrix, represents the variance of prior estimate, by first the element zero setting of this matrix when initialization;
K is [N × g] matrix, represents kalman gain amount, by first the element zero setting of this matrix when initialization;
Following the present invention first sets about process from i-th passage, is first value of input signal respective channel, then is 1 by P in this passage (i, 1) assignment by xhat (i, 1) assignment in i-th passage; In program so far, matrix data initialization completes.Then in same passage i, the predictive equation rewritten according to system and correction equation.
System prediction equation in code:
xhatminus(i,k)＝xhat(i,k-1)(16)
Pminus(i,k)＝P(i,k-1)+Q(i)(17)
System compensation equation in code:
K(i,k)＝Pminus(i,k)/(Pminus(i,k)+R(i))(18)
xhat(i,k)＝xhatminus(i,k)+K(i,k)*(z(i,k)-xhatminus(i,k)(19)
P(i,k)＝(1-K(i,k))*Pminus(i,k)(20)
K is traveled through from 2 to g, can show that i-th passage RSSI posterior estimate is along with the change of moment point, has namely reacted Kalman filtering effect; Finally i is traveled through from 1 to N, each passage RSSI Kalman filtering effect can be obtained; Mapping exports the design sketch of this eyeball position each passage bluetooth BeaconRSSI after Kalman filtering process.
3rd step, compares this eyeball place RSSI dynamic range change before and after Kalman filtering.Call judgeparaments.m file, the N number of passage RSSI data (unit dB) wherein before preinput configuration filtering, folinput configures filtered N number of passage RSSI data (unit dB); Before and after program output card Kalman Filtering, N number of passage divides other maximal value, minimum value and its dynamic range fluctuated.
Finally, the RSSI after Kalman filtering process is calculated distance corresponding to RSSI by distance and the relational expression of signal intensity.Obviously can calculate its distance upper limit and distance lower limit (respectively to should under passage RSSI maxima and minima) for the bluetooth Beacon of each passage, the space length resect in recycling surveying can be extrapolated eyeball and build scene middle distance position range.
Embodiment:
The present invention is a kind of RSSI kalman filter method based on indoor locating system, and by the platform that Beijing Le Gaolehua Technology Co., Ltd. carries, its specific embodiment is as follows:
Step one, build indoor scene bluetooth Beacon environment and build SSMap.
Be specially:
The first step, builds indoor scene.As shown in Figure 2, this scene areas adopts the space layout of 13m × 7.579m × 2.78m, first indoor scene surface level is divided into the two dimensional surface coordinate system of 26 × 16 according to the spacing of 0.5m × 0.5m, the basis of this coordinate system arranges 8 bluetooth Beacon in the top of scene, and its actual plane distribution parameter is as follows.
| major | minor | X-coordinate | Y-coordinate |
| 14 | 29 | 12.60 | 7.30 |
| 14 | 30 | 6.50 | 7.30 |
| 14 | 31 | 1.00 | 7.30 |
| 14 | 32 | 3.35 | 3.70 |
| 14 | 33 | 9.67 | 3.70 |
| 14 | 34 | 12.70 | 0.30 |
| 14 | 35 | 6.50 | 0.30 |
| 14 | 36 | 0.30 | 0.30 |
It should be noted that these 8 Beacon have identical uuid numbering (i.e. F62D3F65-2FCB-AB76-00AB-681819202122), as the hardware number of the system of setting for this platform; Major14 represents the floor of indoor environment simultaneously, because the test environment of this project is all in same level (i.e. number of floor levels M=1), so major does not change; And minor is different, distinguish the foundation of each Beacon as client receiver.In addition each Beacon also can broadcast the peculiar information such as oneself uuid, major, minor and coordinate position sending Bluetooth signal to the interior space simultaneously.
Second step, builds SSMap.First building a height is 1.41m, takes up an area the test platform of 0.5m × 0.5m, ios device is positioned over Platform center; Then from (0 of coordinate system, 0) position starts, indicate 8 bluetooth Beacon by the frequency of 1 time per second of ios device and divide the Bluetooth signal RSSI being clipped to this device end, and the observation time accumulating 100s obtains each Beacon amounts to 100 × 8 data at this coordinate position place; Xml format file is made from the RSSI data of 8 bluetooth Beacon according to each coordinate (26 × 16) place in same method traversal grid.It is F62D3F65-2FCB-AB76-00AB-681819202122, major14, minor30 that Fig. 3 illustrates by uuid, is positioned at X-coordinate 6.50, the SSMap that the signal that Y-coordinate 7.30 position bluetooth Beacon produces is formed.
3rd step, computing system state variance and observational variance.First all RSSI data calling the SSMap under i passage are saved as Pow matrix, by the variance of all RSSI data under trying to achieve this passage of var function in Matlab, with Q (i) record; Then square the observational variance of system is set up, with R (i) record for reference value by scope error scale division value; Finally i is traveled through from 1 to 8.
Step 2, choose eyeball, obtain the RSSI data from 8 bluetooth Beacon, point 8 passages store.
Be specially:
In indoor scene, choose the eyeball that coordinate is (16,9), obtain the RSSI data from 8 bluetooth Beacon.Use ios device platform equally with the frequency of 1 instruction bluetooth BeaconRSSI per second, obtain 8 passages after carrying out the accumulation of 30s and divide other 30 groups of RSSI data, and be saved as xml file layout and pass to post-processed.It should be noted that the design standards of the bluetooth BeaconRSSI instruction software that Le Gaolehua company is developing is the frequency instruction bluetooth BeaconRSSI of 10 times per second, also can obtain 8 passages, 30 groups of RSSI data respectively by the accumulation of 3 seconds.Can only emulate deisgn product data by the measured data under current I OS equipment by device-restrictive the present invention at present, but the data simulation deisgn product data of the actual measurement of the present invention just also exist the delayed of input time, can't bring the change of data itself.
Step 3, design Kalman filter carry out filtering process to the RSSI receiving signal 8 passages.
Be specially:
The present invention Karman_Filter.m program achieves the RSSI signal card Kalman Filtering process to any given position.
The first step, before Kalman filtering, data are extracted.The each 30 groups of RSSI data transformations of 8 passages utilizing xmlread function to be received by ios device are that the matrix data z of [8 × 30] reads.
Second step, the initialization of matrix data.It should be noted that the meaning of each initial value of program here:
Xhat matrix is the posterior estimate to RSSI, and namely in the k moment, in conjunction with measured value and the k-1 moment RSSI prior estimate of current RSSI, the estimated value obtained upgrades, and corresponding to the X in state equation, is initialized as the null matrix of [8 × 30]; Then be first RSSI data of input signal at this passage by first of each passage element assignment;
xhat(i,1)＝z(i,1)(21)
Xhatminus matrix is the prior estimate of RSSI, namely in the k-1 moment, to the estimation that k moment RSSI makes, corresponding to the X in state equation, is initialized as the null matrix of [8 × 30];
P matrix is the variance of Posterior estimator, is initialized as the null matrix of [8 × 30]; Then be 1 by first of each passage element assignment;
P(i,1)＝1(22)
Pminus matrix is the variance of prior estimate, is initialized as the null matrix of [8 × 30];
K matrix is kalman gain amount, is initialized as the null matrix of [8 × 30];
3rd step, the predictive equation of rewriting and correction equation.In same passage i, rewrite iterative equation according to system state equation.
System prediction equation in code:
xhatminus(i,k)＝xhat(i,k-1)(23)
Pminus(i,k)＝P(i,k-1)+Q(i)(24)
System compensation equation in code:
K(i,k)＝Pminus(i,k)/(Pminus(i,k)+R(i))(25)
xhat(i,k)＝xhatminus(i,k)+K(i,k)*(z(i,k)-xhatminus(i,k)(26)
P(i,k)＝(1-K(i,k))*Pminus(i,k)(27)
4th step, by k from 2 to 30 traversals.The effect of RSSI after Kalman filtering process in same passage i is obtained after traversal.
5th step, by i from 1 to 8 traversals.The effect of RSSI after Kalman filtering process in 8 passages is obtained after traversal.
6th step, mapping exports the design sketch of mesh coordinate (16,9) position each passage bluetooth BeaconRSSI after Kalman filtering process, shown in Fig. 4-11.
Fig. 4 is the design sketch of mesh coordinate in the present invention (16,9) position the 1st passage bluetooth BeaconRSSI before and after Kalman filtering process;
Fig. 5 is the design sketch of mesh coordinate in the present invention (16,9) position the 2nd passage bluetooth BeaconRSSI before and after Kalman filtering process;
Fig. 6 is the design sketch of mesh coordinate in the present invention (16,9) position the 3rd passage bluetooth BeaconRSSI before and after Kalman filtering process;
Fig. 7 is the design sketch of mesh coordinate in the present invention (16,9) position the 4th passage bluetooth BeaconRSSI before and after Kalman filtering process;
Fig. 8 is the design sketch of mesh coordinate in the present invention (16,9) position the 5th passage bluetooth BeaconRSSI before and after Kalman filtering process;
Fig. 9 is the design sketch of mesh coordinate in the present invention (16,9) position the 6th passage bluetooth BeaconRSSI before and after Kalman filtering process;
Figure 10 is the design sketch of mesh coordinate in the present invention (16,9) position the 7th passage bluetooth BeaconRSSI before and after Kalman filtering process;
Figure 11 is the design sketch of mesh coordinate in the present invention (16,9) position the 8th passage bluetooth BeaconRSSI before and after Kalman filtering process.
7th step, compares this eyeball place RSSI dynamic range change before and after Kalman filtering.Call judgeparaments.m file, each 30 the RSSI data (unit dB) of 8 passages wherein before preinput configuration filtering, folinput configures each 10 the RSSI data (unit dB) of filtered 8 passages; Before and after program output card Kalman Filtering, 8 passages divide other maximal value, minimum value and its dynamic range fluctuated.It should be noted that in this example and think that Kalman filtering is tend towards stability after 20 timing nodes (namely realizing RSSI stable reading after corresponding ideal equipment 2s), thus we do following table show into (namely from the 20th timing node to last) degree of optimization to system fluctuation dynamic range after Kalman filtering:
As can be seen from the above table, what equipment received obviously reduces in dynamic range after Kalman filtering process from 8 passage bluetooth BeaconRSSI, and this will greatly be conducive to us by the follow-up work of resolving relational implementation indoor accurate position of distance with RSSI.
Finally, the RSSI after Kalman filtering process is calculated distance corresponding to RSSI by distance and the relational expression of signal intensity.Obviously for each passage bluetooth Beacon we can calculate its distance upper limit and distance lower limit (respectively to should under passage RSSI maxima and minima), the space length resect in recycling surveying can be extrapolated eyeball and build scene middle distance position range.
The present invention receives the excessive problem of bluetooth BeaconRSSI fluctuation dynamic range mainly for the device end run in indoor accurate position, design Kalman filter to RSSI process, effectively control its dynamic range, and then reach the object improving device end positioning precision.By instance analysis, the RSSI of each passage has had obvious improvement in dynamic range after Kalman filter.
Claims (1)
1., based on a RSSI kalman filter method for indoor locating system, comprise following step:
Step one, build indoor scene bluetooth Beacon environment and build SSMap;
Be specially:
The first step, builds indoor scene;
Assuming that this scene has M floor, and each floor is according to the uniform spatial distribution layout of W × L × H, wherein: W is the width of floor, L is floor length, H is floor clear height, then the surface level of every one deck is divided into A × B coordinate grid according to length and width proportional spacing, N number of bluetooth Beacon with identical signal transmission power altogether is equally spaced disposed according to Star network and the mode that Chain Network mixes in the roof of every one deck scene, and in the broadcast singal of each bluetooth Beacon, contain the majorID recording its number of plies information and the minorID recording its positional information,
Second step, builds SSMap;
Carrying a height is h
1equipment receiving platform, is placed in every one deck A × B coordinate net center of a lattice successively by platform, then divide N number of passage to carry out the data acquisition of RSSI and record to N number of bluetooth Beacon respectively; Meanwhile, require at least to need t at the RSSI data acquisition of same coordinate grid
1sampling time accumulation with the data accumulation of 100 obtaining this single passage in coordinate position place, finally, by the RSSI Database SSMap of A × B × N × M × 100;
3rd step, computing system state variance and observational variance;
First all RSSI data calling the SSMap under i passage are saved as Pow matrix, the variance of all RSSI data under obtaining this passage, with Q (i) record, then square the observational variance of system is set up for reference value by scope error scale division value, with R (i) record, finally i is traveled through from 1 to N;
Step 2, choose eyeball, obtain the RSSI data from N number of bluetooth Beacon, a point N number of passage stores;
Be specially:
In indoor scene A × B coordinate grid, choose the eyeball of arbitrary coordinate position, received by client and divide other RSSI data from N number of bluetooth Beacon, at least need t at the RSSI data acquisition of this eyeball
2sampling time accumulation to obtain g RSSI data accumulation under this single passage in eyeball position; Then calculate the fluctuation maximal value of the RSSI data under each passage of this eyeball, fluctuation minimum value and fluctuation dynamic range, pass to the process of follow-up Kalman filter together with RSSI data with xml file layout;
Step 3, design Kalman filter carry out filtering process to the RSSI receiving the N number of passage of signal;
Be specially:
The first step, kalman filter models is set up;
The state equation of system is:
X(i,k+1)＝AX(i,k)+W(i,k)(1)
S(i,k)＝CX(i,k)+V(i,k)(2)
In formula, X (i, k) and X (i, k+1) is system state vector, represents the RSSI estimated value that eyeball is to be optimized in k moment and k+1 reception signal i-th passage respectively respectively; S (i, k) is systematic observation vector, represents the observed reading of eyeball RSSI in k reception signal i-th passage; And A is system matrix, C is system output matrix; W (i, k) and V (i, k) is respectively in the system state noise of k moment i-th passage and observation noise, and establishes W (i, k) and V (i, k) to be separate zero-mean white noise sequence, meets:
E[W(i,k)]＝E[V(i,k)]＝0(3)
E[W(i,k)W(i,k)
T]＝Q(i)(4)
E[V(i,k)V(i,k)
T]＝R(i)(5)
Obtain the forecasting process of Kalman filter equation:
P(i,k|k-1)＝AP(i,k-1|k-1)A
T+Q(i)(6)
In formula, X (i, k|k-1) utilizes the result of k-1 moment status predication in the k moment in i-th passage,
be utilize the result of k-1 moment state optimization in the k moment in i-th passage, U (i, k) is the state controlled quentity controlled variable in k moment i-th passage, and P (i, k|k-1) is
corresponding covariance matrix, P (i, k-1|k-1) is
corresponding covariance matrix;
List the trimming process of Kalman filter equation:
K(i,k)＝P(i,k|k-1)C
T[CP(i,k|k-1)C
T+R(i)]
-1(8)
P(i,k|k)＝[I(i)-K(i,k)C]P(i,k|k-1)(10)
In formula, K (i, k) is the kalman gain in i-th passage;
If system matrix A and system output matrix C is unit matrix, then system prediction process is:
P(i,k|k-1)＝P(i,k-1|k-1)+Q(i)(11)
System compensation process is:
K(i,k)＝P(i,k|k-1)[P(i,k|k-1)+R(i)]
-1(13)
P(i,k|k)＝[I(i)-K(i,k)]P(i,k|k-1)(15)
Second step, the program of Kalman filter realizes;
First the N number of passage each g group RSSI data transformations received by equipment is that the matrix data z form of [N × g] reads; Afterwards initialization is carried out to matrix data, if:
K: the timing node of representative system state change;
I: the channel coding of representative observation, the numerical value that each channel coding is transmitted indicates the RSSI of the bluetooth Beacon arrived from this channel reception;
Xhat: be [N × g] matrix, represent the posterior estimate to RSSI, namely in the k moment, in conjunction with measured value and the k-1 moment RSSI prior estimate of current RSSI, the estimated value obtained upgrades, corresponding to the X in state equation, by first the element zero setting of this matrix when initialization;
Xhatminus: be [N × g] matrix, represents the prior estimate of RSSI, namely in the k-1 moment, to the estimation that k moment RSSI makes, corresponding to the X in state equation, by first the element zero setting of this matrix when initialization;
P: be [N × g] matrix, represents the variance of Posterior estimator, by first the element zero setting of this matrix when initialization;
Pminus: be [N × g] matrix, represents the variance of prior estimate, by first the element zero setting of this matrix when initialization;
K: be [N × g] matrix, represents kalman gain amount, by first the element zero setting of this matrix when initialization;
For i-th passage, be first value of input signal respective channel by xhat (i, 1) assignment in i-th passage, then be 1 by P in this passage (i, 1) assignment, matrix data initialization completes;
In same passage i, system prediction equation:
xhatminus(i,k)＝xhat(i,k-1)(16)
Pminus(i,k)＝P(i,k-1)+Q(i)(17)
System compensation equation:
K(i,k)＝Pminus(i,k)/(Pminus(i,k)+R(i))(18)
xhat(i,k)＝xhatminus(i,k)+K(i,k)*(z(i,k)-xhatminus(i,k)(19)
P(i,k)＝(1-K(i,k))*Pminus(i,k)(20)
K is traveled through from 2 to g, draw the change of i-th passage RSSI posterior estimate along with moment point, namely Kalman filtering effect has been reacted, finally i is traveled through from 1 to N, obtain each passage RSSI Kalman filtering effect, mapping exports the design sketch of this eyeball position each passage bluetooth BeaconRSSI after Kalman filtering process;
3rd step, compares this eyeball place RSSI dynamic range change before and after Kalman filtering;
According to the N number of passage RSSI data before filtering, filtered N number of passage RSSI data, before and after output card Kalman Filtering, N number of passage divides other maximal value, minimum value and its dynamic range fluctuated;
Finally, RSSI after Kalman filtering process is calculated distance corresponding to RSSI by distance and the relational expression of signal intensity, bluetooth Beacon for each passage calculates its distance upper limit and distance lower limit, respectively to should RSSI maxima and minima under passage, obtain eyeball and building scene middle distance position range.
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Application publication date: 20151223 Assignee: Beijing Qiangwang Technology Co.,Ltd. Assignor: BEIHANG University Contract record no.: X2023990000710 Denomination of invention: A Method of RSSI Kalman filter Filtering Based on Indoor Positioning System Granted publication date: 20170929 License type: Common License Record date: 20230714 |