CN109506653A - Based on the indoor positioning pedestrian's method for improving particle filter under NLOS environment - Google Patents
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- 230000004927 fusion Effects 0.000 claims abstract description 7
- 239000011159 matrix material Substances 0.000 claims description 9
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- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/20—Instruments for performing navigational calculations
- G01C21/206—Instruments for performing navigational calculations specially adapted for indoor navigation
Abstract
The invention discloses indoor pedestrian's localization methods based on improvement particle filter under a kind of NLOS environment, the hardware configuration of this method has mobile node, reference mode and wireless sensor gateway, mobile node is responsible for communicating with reference mode, obtain the RSSI value of reference mode, and RSSI value is transferred to host computer through gateway and is handled, mobile node includes the nine axis inertial navigation modules with three axis accelerometer, three-axis gyroscope and three axle magnetometer, for doing pedestrian's dead reckoning algorithm；By the position of RSSI location estimation mobile node, direction and the acceleration information of mobile node are obtained by inertial navigation, estimates the position of mobile node, use particle filter fusion RSSI positioning and inertial navigation to obtain the coordinate of mobile node indoor positioning.This method, which uses, there is the particle filter mode of more preferable filter effect to merge to channel loss model the non-linear environment of non-gaussian, and make can still provide in a nlos environment is accurately positioned, and improve positioning accuracy.
Description
Technical field
The present invention relates to indoor pedestrian's localization methods based on improvement particle filter under a kind of NLOS environment.
Background technique
It is existing to be based on RSSI (received signal strength indication received signal strength indicator device)
Indoor positioning solution have based on fingerprint recognition and based on two class of channel model.The first scheme passes through building fingerprint base
Realize positioning, this method needs to take a substantial amount of time and energy in the integrated data figure of off-line phase building RSSI,
And calibration need to be re-started when environment changes.Second scheme is by channel loss model, according to the RSSI value of measurement
Estimate the distance between receiver and transmitter, the position of recipient is then estimated according to algorithm for estimating.
In order to improve the positioning accuracy of second scheme, usually it is merged with inertial navigation system, existing solution
Certainly scheme mostly uses greatly the mode of Kalman filtering to be merged, and Kalman filtering to the filter effect of nonlinear system not
Obviously.And signal can generate the NLOS such as reflection, refraction (Non-Line of Sight non-view when propagating in dynamic environment
Away from) circulation way, so that the RSSI value received and actual value is generated deviation, will affect positioning accuracy.
Summary of the invention
Technical problem to be solved by the invention is to provide the indoor rows based on improvement particle filter under a kind of NLOS environment
People's localization method, this method, which uses, has the particle filter mode of more preferable filter effect to RSSI the non-linear environment of non-gaussian
Positioning system is merged with pedestrian's dead reckoning system, and constantly updates path-loss factor η using the mode of iteration, is made
This localization method can still provide for being accurately positioned in a nlos environment, improve positioning accuracy.
In order to solve the above technical problems, based on the indoor pedestrian positioning side for improving particle filter under NLOS environment of the present invention
Method includes the following steps:
Step 1: mobile node, reference mode and wireless sensor network that indoor pedestrian's positioning is carried comprising pedestrian
It closes, wherein mobile node is responsible for communicating with reference mode, obtains the RSSI value of reference mode, and RSSI value is transmitted through gateway
It is handled to host computer, mobile node includes that nine axis with three axis accelerometer, three-axis gyroscope and three axle magnetometer are used
Property navigation module, the dead reckoning for pedestrian；
Step 2: RSSI is positioned: the propagation model based on RSSI Location Theory and test, no matter outdoor or indoor channel,
For mean receiving power with the logarithmic decrement of distance, the expression formula of RSSI and distance is as follows:
P (d)=P (d0)-10ηlog10(d/d0)+Xσ (1)
Wherein,ηFor path loss index, show the rate that path loss increases with distance；d0For near-earth reference distance；d
To transmit and receive distance；P (d) and p (d0) it is respectively when distance is d and d0When received signal strength, unit be decibel milli
Watt (dBm)；XσFor zero-mean gaussian distribution variables N (0, σ)；
Step 3: the distance between reference mode and mobile node d are derived according to formula (1),
Wherein, path loss index η is affected by the surrounding environment, and is 2~4 usually in outdoor environment, and in complexity
Then 4~6 in indoor environment；
Step 4: assuming to be disposed with N number of reference mode in environment indoors, and first reference mode coordinate is set as
Reference point (x1,y1,z1), any reference mode i (1≤i≤N) arrives mobile node (x, y, z*) distance square withIt indicates,
Formula is as follows:
Wherein, xi、yi、ziFor the coordinate of i-th of reference mode,
According to formula (3), as reference mode i ≠ 1, it is subtracted each other with reference point, obtains following formula:
Formula (4) is write as to the form of following matrix:
HX=b (5)
Since the height of mobile node is constant, i.e. z*It is constant, thus in formula (5) each symbol concrete meaning it is as follows:
For the estimated location of mobile node,
It can be found out by following formula using the solution of least square method:
X=(HTH)-1HTb (8)
Wherein, the transposed matrix of symbol T representing matrix H；
Step 5: inertial navigation: data are acquired using nine axis inertial navigation modules of mobile node, using step-length and step
Several products obtains the position of mobile node,
In formula, xt、ytFor coordinate of the t moment mobile node under X and Y coordinates system, xt+1、yt+1For t+1 moment movable joint
Coordinate of the point under X and Y coordinates system, SLtFor the step-length of t moment, θtFor the course angle of t moment mobile node；
Step 6: step number detects, step number detection is carried out by the way of peak detection, the step number usually detected is greater than real
Border step number, therefore define a step number and detect peak parameters Δ Tn, Δ TnTime interval between peak value, if in Δ TnIt is interior
There is more than two peak value, then the acceleration value using first peak value as the step of pedestrian and as subsequent step estimation, and
Ignore other peak values；
Step 7: step-size estimation, step-length is estimated using following empirical equation,
SL=K × (Amax-Amin)(14) (10)
Wherein, AmaxAnd AminAminIt is the minimum and maximum normal acceleration in single step, K is constant and is instructed by walking
It gets；
Step 8: pedestrian's course angle is obtained by the three axle magnetometer of nine axis inertial navigation modules；
Step 9: using particle filter fusion RSSI positioning and inertial navigation to obtain the position of mobile node, it is assumed that
Know the original state (x of mobile node0,y0), it is by particle structure designPopulation N=100, weight 1/N, often
A particle represents a possible positions of mobile nodes；
It is updated Step 10: particle structure is updated in formula (9), for the reasonability for verifying particle, needs each grain
Son is compared with observed quantity, i.e., the observation found out by possibility positions of mobile nodes information representated by each particle and by formula (8)
Position is compared, and the particle weight obtained closer to observation is bigger, on the contrary then smaller, it is assumed that be found out by formula (8)
Positions of mobile nodes is (a, b), then the weight of particle such as following formula:
In formula, k is constant, and M is sufficiently large constant, normalizes weight are as follows:
Step 11: determining positions of mobile nodes, the weight with each particle is updated according to particle, then the position of mobile node
It sets and is solved according to the weighted sum of each particle, it may be assumed that
To obtain the coordinate of mobile node indoor positioning.
Further, since the cadence of conventional mobile node is no more than 3Hz in the step 6, by Δ TnIt is set as
0.3s。
Further, in the step 10, particle, which updates, uses importance resampling mode, ignores the low particle of weight, multiple
The high particle of weight processed, and random quantity is added to the location information of some of particles, inhibit particle to fall into local optimum, adds
The particle for entering random quantity obtains corresponding weight when updating.
Further, in the step 2, in RSSI positioning, path loss index η changes with environment, can not be in dynamic
Fixed value is given in environment, then the dynamic in the form of iteration of the location information after using step 9 particle filter to merge updates
η；The form renewal of η is derived according to formula (1):
Wherein, d is the positions of mobile nodes (x after merging by particle filtert,yt) at a distance from reference mode, by
This finds out the path loss index η after updatingnew, and by the η in its alternate form (2), then that found out by formula (8) is NLOS
Positions of mobile nodes information under environment, finds out particle filter as the observation of particle filter for the location information and merges it
Positions of mobile nodes afterwards, such loop iteration obtain dynamic route loss index η.
Due to using above-mentioned technology based on the indoor pedestrian's localization method for improving particle filter under NLOS environment of the present invention
Scheme, i.e. this method indoor positioning include mobile node, reference mode and wireless sensor gateway, and wherein mobile node is negative
Duty is communicated with reference mode, obtains the RSSI value of reference mode, and RSSI value is transferred to host computer through gateway and is handled,
Mobile node includes the nine axis inertial navigation modules with three axis accelerometer, three-axis gyroscope and three axle magnetometer, for moving
The dead reckoning of dynamic node；By the position of RSSI location estimation mobile node, the side of mobile node is obtained by inertial navigation
To with acceleration information, estimate the position of mobile node, use particle filter fusion RSSI positioning and inertial navigation to be moved
The coordinate of dynamic node indoor positioning.This method uses the particle filter for having more preferable filter effect to the non-linear environment of non-gaussian
Mode merges channel loss model, and make can still provide in a nlos environment is accurately positioned, and improves positioning accuracy.
Specific embodiment
Included the following steps: under NLOS environment of the present invention based on the indoor positioning pedestrian's method for improving particle filter
Step 1: mobile node, reference mode and wireless sensor network that indoor pedestrian's positioning is carried comprising pedestrian
It closes, wherein mobile node is responsible for communicating with reference mode, obtains the RSSI value of reference mode, and RSSI value is transmitted through gateway
It is handled to host computer, mobile node includes that nine axis with three axis accelerometer, three-axis gyroscope and three axle magnetometer are used
Property navigation module, the dead reckoning for pedestrian；
Step 2: RSSI is positioned: the propagation model based on RSSI Location Theory and test, no matter outdoor or indoor channel,
For mean receiving power with the logarithmic decrement of distance, the expression formula of RSSI and distance is as follows:
P (d)=P (d0)-10ηlog10(d/d0)+Xσ (1)
Wherein, η is path loss index, shows the rate that path loss increases with distance；d0For near-earth reference distance；d
To transmit and receive distance；P (d) and p (d0) it is respectively when distance is d and d0When received signal strength, unit be decibel milli
Watt (dBm)；XσFor zero-mean gaussian distribution variables N (0, σ)；
Step 3: the distance between reference mode and mobile node d are derived according to formula (1),
Wherein, path loss index η is affected by the surrounding environment, and is 2~4 usually in outdoor environment, and in complexity
Then 4~6 in indoor environment；Therefore η is being calibrated using before formula (2) estimated distance d, needing to spend many energy, and
In dynamic environment, channel model constantly changes, and is difficult to estimate accurate η value, and therefore, how real time calibration η then becomes to weigh very much
It wants；
Step 4: assuming to be disposed with N number of reference mode in environment indoors, and the coordinate of first reference mode is set
For reference point (x1,y1,z1), any reference mode i (1≤i≤N) arrives mobile node (x, y, z*) distance square withTable
Show, formula is as follows:
Wherein, xi、yi、ziFor the coordinate of i-th of reference mode,
According to formula (3), as reference mode i ≠ 1, it is subtracted each other with reference point, obtains following formula:
Formula (4) is write as to the form of following matrix:
HX=b (5)
Since the height of mobile node is constant, i.e. z*It is constant, thus in formula (5) each symbol concrete meaning it is as follows:
For the estimated location of mobile node,
It can be found out by following formula using the solution of least square method:
X=(HTH)-1HTb (8)
Wherein, the transposed matrix of symbol T representing matrix H；
Step 5: inertial navigation: reckoning (Pedestrian Dead Reckoning, PDR) algorithm be it is a kind of with
Inertia sensing cell data is the location algorithm of core, is adopted using three axis accelerometer, three-axis gyroscope and three axle magnetometer
Collect data and constantly calculate to obtain the direction of motion and acceleration information, so that it is determined that specific location, utilizes mobile node
Three axis accelerometer, three-axis gyroscope and three axle magnetometer acquire data, movable joint is obtained using the product of step-length and step number
The position of point,
In formula, xt、ytFor coordinate of the t moment mobile node under X and Y coordinates system, xt+1、yt+1For t+1 moment movable joint
Coordinate of the point under X and Y coordinates system, SLtFor the step-length of t moment, θtFor the course angle of t moment mobile node；Formula (9) mainly needs
Solve the problems, such as three: step number detection, step-length is estimated and course angle；
Step 6: step number detects, step number detection is carried out by the way of peak detection, the step number usually detected is greater than real
Border step number, therefore define a step number and detect peak parameters Δ Tn, Δ TnTime interval between peak value, if in Δ TnIt is interior
There is more than two peak value, then the acceleration value using first peak value as the step of pedestrian and as subsequent step estimation, and
Ignore other peak values；
Step 7: step-size estimation, step-length is estimated using following empirical equation,
SL=K × (Amax-Amin)(14) (10)
Wherein, AmaxAnd AminAminIt is the minimum and maximum normal acceleration in single step, K is constant and is instructed by walking
It gets；
Step 8: pedestrian's course angle is obtained by the three axle magnetometer of nine axis inertial navigation modules；
Step 9: be strongly non-linear system since inertial navigation includes acceleration information, and particle filter is for non-thread
Property non-Gaussian environment have good filter effect, therefore use particle filter fusion RSSI positioning with inertial navigation to obtain
The position of mobile node, it is assumed that the original state (x of known mobile node0,y0), it is by particle structure designParticle
N=100, weight 1/N are counted, each particle represents a possible positions of mobile nodes；
It is updated Step 10: particle structure is updated in formula (9), for the reasonability for verifying particle, needs each grain
Son is compared with observed quantity, i.e., the observation found out by possibility positions of mobile nodes information representated by each particle and by formula (8)
Position is compared, and the particle weight obtained closer to observation is bigger, on the contrary then smaller, it is assumed that be found out by formula (8)
Positions of mobile nodes is (a, b), then the weight of particle such as following formula:
In formula, k is constant, and M is sufficiently large constant, normalizes weight are as follows:
Step 11: determining positions of mobile nodes, the weight with each particle is updated according to particle, then the position root of mobile node
It is solved according to the weighted sum of each particle, it may be assumed that
To obtain the coordinate of mobile node indoor positioning.
Preferably, since the cadence of conventional mobile node is no more than 3Hz in the step 6, by Δ TnIt is set as
0.3s。
Preferably, traditional particle filter uses sequential importance sampling, and which can generate the degeneration of particle, in order to
Avoid such case, in the step 10, particle, which updates, uses importance resampling mode, ignores the low particle of weight, replicates
The high particle of weight, and random quantity is added to the location information of some of particles, inhibit particle to fall into local optimum, is added
The particle of random quantity obtains corresponding weight when updating.The mode of resampling will appear a kind of limiting case, i.e. particle is concentrated
Only include a kind of particle and its duplication, causes serious particle tcam-exhaustion, added by the location information to some of particles
Enter random quantity, thus particle can be inhibited to fall into local optimum, and the particle that random quantity is added can the acquisition pair when updating
The weight answered, so that large effect will not be caused to positioning result.
Preferably, in the step 2, in RSSI positioning, path loss index η changes with environment, can not be in dynamic
Fixed value is given in environment, then the dynamic in the form of iteration of the location information after using step 9 particle filter to merge updates
η；The form renewal of η is derived according to formula (1):
Wherein, d is the positions of mobile nodes (x after merging by particle filtert,yt) at a distance from reference mode, by
This finds out the path loss index η after updatingnew, and by the η in its alternate form (2), then that found out by formula (8) is NLOS
Positions of mobile nodes information under environment, finds out particle filter as the observation of particle filter for the location information and merges it
Positions of mobile nodes afterwards, such loop iteration obtain dynamic route loss index η.
In this method, mobile node and reference mode can be used TI company production with wireless communication function
CC2530 module, inertial navigation module are mpu9050 compound chip, and mobile node is placed on pedestrian foot, fixed by fusion RSSI
Position system and inertial navigation system can get the location information of pedestrian indoors.This method is used to the non-linear environment of non-gaussian
The mode of particle filter with more preferable filter effect is positioned to RSSI and inertial navigation system merges, and makes what is obtained to determine
Position information is more accurate.The non line of sight such as reflection, refraction (NLOS) biography can be generated when propagating in dynamic environment due to RSSI signal
Broadcast mode makes the RSSI value received and actual value generate deviation, and then influences positioning accuracy, and the present invention has carried out place to this
Reason, makes to can still provide for being accurately positioned under NLOS environment.
Claims (4)
1. based on the indoor pedestrian's localization method for improving particle filter under a kind of NLOS environment, it is characterised in that this method includes such as
Lower step:
Step 1: mobile node, reference mode and wireless sensor gateway that indoor pedestrian's positioning is carried comprising pedestrian, wherein
Mobile node is responsible for communicating with reference mode, obtains the RSSI value of reference mode, and RSSI value is transferred to host computer through gateway
It is handled, mobile node includes the nine axis inertial navigation moulds with three axis accelerometer, three-axis gyroscope and three axle magnetometer
Block, the dead reckoning for pedestrian；
Step 2: RSSI is positioned: the propagation model based on RSSI Location Theory and test, no matter outdoor or indoor channel, it is average
Power is received with the logarithmic decrement of distance, the expression formula of RSSI and distance is as follows:
P (d)=P (d0)-10ηlog10(d/d0)+Xσ (1)
Wherein, η is path loss index, shows the rate that path loss increases with distance；d0For near-earth reference distance；D is to send
Receive distance；P (d) and p (d0) it is respectively when distance is d and d0When received signal strength, unit be decibel milliwatt (dBm)；
XσFor zero-mean gaussian distribution variables N (0, σ)；
Step 3: the distance between reference mode and mobile node d are derived according to formula (1),
Wherein, path loss index η is affected by the surrounding environment, and is 2~4 usually in outdoor environment, and in complicated interior
Then exist in environment
Step 4: assuming to be disposed with N number of reference mode in environment indoors, and the coordinate of first reference mode is set as referring to
Point (x1, y1, z1), any reference mode i (1≤i≤N) arrives mobile node (x, y, z*) distance square withIt indicates, formula is such as
Under:
Wherein, xi、yi、ziFor the coordinate of i-th of reference mode,
According to formula (3), as reference mode i ≠ 1, it is subtracted each other with reference point, obtains following formula:
Formula (4) is write as to the form of following matrix:
Since the height of mobile node is constant, i.e. z*It is constant, therefore formulaIn each symbol concrete meaning it is as follows:
For the estimated location of mobile node,
It can be found out by following formula using the solution of least square method:
X=(HTH)-1HTb (8)
Wherein, the transposed matrix of symbol T representing matrix H；
Step 5: inertial navigation: data are acquired using nine axis inertial navigation modules of mobile node, using multiplying for step-length and step number
Product obtains the position of mobile node,
In formula, xt、ytFor coordinate of the t moment mobile node under X and Y coordinates system, xt+1、yt+1It is t+1 moment mobile node in X
With the coordinate under Y-coordinate system, SLtFor the step-length of t moment, θtFor the course angle of t moment mobile node；
Step 6: step number detects, step number detection is carried out by the way of peak detection, the step number usually detected is greater than practical step
Number, therefore define a step number and detect peak parameters Δ Tn, Δ TnTime interval between peak value, if in Δ TnInterior appearance
More than two peak value, then the acceleration value using first peak value as the step of pedestrian and as subsequent step estimation, and ignores
Other peak values；
Step 7: step-size estimation, step-length is estimated using following empirical equation,
SL=K × (Amax-Amin)(1/4) (10)
Wherein, AmaxAnd AminAminIt is the minimum and maximum normal acceleration in single step, K is constant and is obtained by ambulation training
It arrives；
Step 8: pedestrian's course angle is obtained by the three axle magnetometer of nine axis inertial navigation modules；
Step 9: using particle filter fusion RSSI positioning and inertial navigation to obtain the position of mobile node, it is assumed that known shifting
Original state (the x of dynamic node0, y0), it is by particle structure designPopulation N=100, weight 1/N, each particle
Represent a possible positions of mobile nodes；
Be updated Step 10: particle structure is updated in formula (9), for verify particle reasonability, need by each particle with
Observed quantity compares, i.e., the observation position found out by possibility positions of mobile nodes information representated by each particle and by formula (8)
It is compared, the particle weight obtained closer to observation is bigger, on the contrary then smaller, it is assumed that the movement found out by formula (8)
Node location is (a, b), then the weight of particle such as following formula:
In formula, k is constant, and M is sufficiently large constant, normalizes weight are as follows:
Step 11: determine positions of mobile nodes, according to particle update and each particle weight, then the position of mobile node according to
The weighted sum of each particle is solved, it may be assumed that
To obtain the coordinate of mobile node indoor positioning.
2. based on the indoor pedestrian's localization method for improving particle filter, feature under NLOS environment according to claim 1
It is: since the cadence of conventional mobile node is no more than 3Hz in the step 6, by Δ TnIt is set as 0.3s.
3. special based on the indoor pedestrian's localization method for improving particle filter under NLOS environment according to claim 1 or 2
Sign is: in the step 10, particle, which updates, uses importance resampling mode, ignores the low particle of weight, and duplication weight is high
Particle, and to the location information of some of particles be added random quantity, inhibit particle fall into local optimum, random quantity is added
Particle obtains corresponding weight when updating.
4. special based on the indoor pedestrian's localization method for improving particle filter under NLOS environment according to claim 1 or 2
Sign is: in the step 2, in RSSI positioning, path loss index η changes with environment, can not give in dynamic environment
Fixed value, then location information dynamic update η in the form of iteration after using step 9 particle filter to merge；According to formula (1)
Derive the form renewal of η:
Wherein, d is the positions of mobile nodes (x after merging by particle filtert, yt) at a distance from reference mode, thus find out
Path loss index η after updatenew, and by the η in its alternate form (2), then what is found out by formula (8) is under NLOS environment
Positions of mobile nodes information, using the location information as the observation of particle filter find out particle filter fusion after movement
Node location, such loop iteration obtain dynamic route loss index η.
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| CN112333818A (en) * | 2020-10-27 | 2021-02-05 | 中南民族大学 | Multi-source fusion indoor positioning system and method based on self-adaptive periodic particle filtering |
| CN112729301A (en) * | 2020-12-10 | 2021-04-30 | 深圳大学 | Indoor positioning method based on multi-source data fusion |
| CN112762928A (en) * | 2020-12-23 | 2021-05-07 | 重庆邮电大学 | ODOM and DM landmark combined mobile robot containing laser SLAM and navigation method |
| CN113091748A (en) * | 2021-04-12 | 2021-07-09 | 北京航空航天大学 | Indoor self-calibration navigation positioning method |
| CN113155131A (en) * | 2021-04-14 | 2021-07-23 | 浙江工业大学 | Particle filter-based iBeacon and PDR fusion indoor positioning method |
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