CN104808174A - Wireless positioning system of nuclear power station based on Kalman filter and dead reckoning - Google Patents

Wireless positioning system of nuclear power station based on Kalman filter and dead reckoning Download PDF

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CN104808174A
CN104808174A CN201410693459.4A CN201410693459A CN104808174A CN 104808174 A CN104808174 A CN 104808174A CN 201410693459 A CN201410693459 A CN 201410693459A CN 104808174 A CN104808174 A CN 104808174A
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mobile terminal
kalman filter
mems sensor
data
angle
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CN104808174B (en
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卫民
孙燕军
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Liu Donghong
Ma Yamei
Wang Ping
Xi Wei
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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
    • G01S5/04—Position of source determined by a plurality of spaced direction-finders
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • 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
    • G01C21/04—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by terrestrial means
    • G01C21/08—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by terrestrial means involving use of the magnetic field of the earth
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • 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
    • G01C21/10—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration
    • G01C21/12—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning
    • G01C21/16—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation

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  • Engineering & Computer Science (AREA)
  • Remote Sensing (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Automation & Control Theory (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Environmental & Geological Engineering (AREA)
  • General Life Sciences & Earth Sciences (AREA)
  • Geology (AREA)
  • Navigation (AREA)
  • Position Fixing By Use Of Radio Waves (AREA)

Abstract

The invention relates to a wireless positioning system of a nuclear power station based on a Kalman filter and dead reckoning. The system is basically composed of a special mobile terminal, a smart phone, a positioning server and a positioning graphic user interface client, wherein the special mobile terminal is internally provided with a WIFI module, an MEMS sensor and an industrial special sensor, and the MEMS sensor of the special mobile terminal mainly comprises an accelerometer, a gyroscope and a magnetometer. The wireless network environment on which the system relies is necessary. The MEMS sensor in the special mobile terminal provides sensor data, the moving direction and distance of a wearer of the mobile terminal are calculated by Kalman filtering, and a calculation result, wireless signal intensity indication and terrestrial magnetism data are used to calculate the position and locus of the wearer of the mobile terminal by Kalman filtering again. The system can raise different alarm for the special working environment of the nuclear power station, including stay overtime, stationary overtime, and fall off.

Description

Based on the nuclear power station wireless location system of Kalman filter and dead reckoning
Technical field
The invention belongs to wireless communication technology field, be specifically related to a kind of nuclear power station wireless location system based on Kalman filter and dead reckoning.
Background technology
Indoor positioning technologies based on WIFI (IEEE 802.11 series standard) is applied at nuclear power station, is still in initial stage at present at home.Because nuclear power station is to the particular/special requirement of electromagnetic environment, and the singularity of building topological structure etc., all make the wireless network environment of nuclear power station greatly be different from common commercial environment.
Based on the indoor positioning technologies of WIFI, when adopting fingerprint comparison method to position, rely on the foundation of its fingerprint base to a great extent.At present, set up fingerprint base and mainly comprise based on signal propagation model and field exploring two kinds, wherein:
(1) signal is utilized to carry out in atmosphere propagating come out model and relevant parameter based on signal propagation model method, to each coordinate in locating area, calculate certain access point (Access Point, AP) value that the signal intensity to this coordinate indicates (Received Signal Strength Indicator, RSSI).But affect the factor existence (wall of such as various thickness, the wall being with door or door opening and furniture etc.) that signal is propagated because indoor are various, needs consider that the situation doing special processing is a lot.In addition, wireless signal is after reflection, diffraction, scattering, and simple propagation model is difficult to these situations to be described clearly.Nuclear power station is built the complicacy of topological structure and is adopted the singularity of building materials more to make based on signal propagation model method difficult.
(2) field exploring method builds fingerprint base based on field exploring signal strength values, being do not need indoor propagation model, thus estimating the parameter of propagation model with regard to not needing.But need field level signal exploration locating area being carried out to off-line phase before locating in real time, the RSSI intensity level in each reference point locations that collection locating area is chosen is as fingerprint characteristic.When adopting common fingerprint comparison method to position, the density choosing reference point has a significant impact positioning precision.But once exploration point increases, all corresponding increase of its quantities and complexity.Especially in the complicated responsive each factory building of nuclear power station, carry out on-site land survey, owing to implementing from nuclear power station capital construction, wireless coverage, to load nuclear fuel and prepare generating, all have regular hour window, the exploration work choosing a large amount of exploration point hardly may.
As can be seen here, adopting fingerprint comparison method to carry out indoor positioning in nuclear power plant environment, there is limitation in its practicality.
In addition, development in recent years utilizes the inertial navigation system of MEMS (MEMS (micro electro mechanical system)) sensor also to have application rapidly in indoor positioning.Inertial navigation belongs to the air navigation aid that (Dead Reckoning, DR) formula is inferred in boat position, and its principle is, carries out double integral solve azimuth information to the measured value of accelerometer.Adopt the inertial navigation system of high precision Inertial Measurement Unit, when it is applied to location, positioning precision is also very high.And high-precision Inertial Measurement Unit, widely use because the reasons such as cost, volume, power consumption there is no method.In general, adopt low cost such as the INS errors based on MEMS sensor to accumulate in time, when calculating double integral, will very large position excursion be brought, cannot work independently for a long time.We will adopt following individual's boat position to infer (Personal Dead Reckoning, PDR) algorithm.
It has been generally acknowledged that the walking movement of people is two dimensional motion, its ultimate principle is plane geometry method.When the sampling period is very short, the walking of people is rectilinear motion, learns a certain moment positional information, just can release the displacement of previous moment or subsequent time positional information and sampling period one skilled in the art.Usually, known initial position (x 0, y 0), the stepping type of our oriented rear supposition,
x k = x 0 + Σ i = 1 k D i sin ψ i
y k = y 0 + Σ i = 1 k D i cos ψ i k＝1，2，3，...
Two key factors are had: travel distance D and course angle ψ in this hypothetical system.Course angle is defined as the angle in direction of travel and magnetic north direction herein.Similarly, we can have supposition stepping type forward.
Utilize Geomagnetic signal to position more and more receiving publicity in recent years.Geomagnetic field has good stability.But Partial Perimeter environment (neighbouring iron ore, ferrous metal material is as reinforcing bar in iron content plant equipment, buildings etc.) can produce the interference of certain limit to magnetic field, make field signal be distorted.Although surrounding enviroment produce interference to magnetic field, when various erection of equipment in a buildings is complete, after working environment is stable, in buildings, each point is also stable containing Geomagnetic signal that is noisy, distortion.
Newton's algorithm list of references: Hagan, Martin T., Demuth, Howard B., Beale, Mark, Neural Network Design, PWS Publishing Company, Boston 1995.
Direction calculating list of references: J.L.Marins, X.Yun, E.R.Bachmann, R.B.McGhee, and M.J.Zyda, " An Extended Kalman Filtter for Quaternion-basedOrientation Estimation Using MARG Sensors ", in Proc.IEEE/RSJ Int.Conf.Intell.Robots Syst., Maui, HI, Oct.2001, pp.2003-2011.
Summary of the invention
The invention provides a kind of nuclear power station wireless location system based on Kalman filter and dead reckoning.This positioning system completes location Calculation and not only utilizes wireless signal strength data (RSSI value), also uses Geomagnetic signal data.The present invention utilizes the result of calculation of individual dead reckoning (PDR), the state of the Kalman filter of " prediction " its structure, introduce new observed reading again, the artificial neural network through constructing in advance is that observation equation calculates " correction " its state outcome.The object of the invention is to overcome the defect adopting separately above-mentioned various technology to have, thus avoid aforesaid limitation.System of the present invention also special providing for nuclear power station particular job environment is stayed time-out, static time-out, is fallen and various safety, the accident alarm function such as to fall.
The element of this positioning system comprises: the smart mobile phone (Android platform, iOS platform or Windows Phone) of the Specialised mobile terminal of built-in WIFI module and MEMS sensor and industry sensor special, the built-in WIFI module of having installed specific application services and MEMS sensor, location-server and positioning pattern user interface client.In Specialised mobile terminal, MEMS sensor mainly comprises accelerometer, gyroscope, magnetometer.
This positioning system wireless network environment run of relying is absolutely necessary, but does not belong to native system and comprise within scope.
Can before real-time working, first need to carry out exploration work in this positioning system.Some exploration point image data are chosen according to indoor topological structure and layout in locating area.Each exploration point needs the data gathering all directions four direction.Image data comprises: exploration point coordinate (x, y), exploration point place's wireless signal strength data (RSSI value), and Geomagnetic signal data.These data genaration radial primary function network is utilized (to belong to the one of artificial neural network, gain the name because its activation function adopts radial basis function) RBF, realize the signal strength values RSSI of locating area coordinate of ground point to access point AP and the mapping of Geomagnetic signal.By this mapping, generate other finger print data necessary, to reduce exploration workload.
Positioning system of the present invention as shown in Figure 1; Its course of work and data stream are as shown in Figure 3.The step of concrete location is as follows.
Step 1: by the output valve of each moment MEMS sensor of Specialised mobile terminal, calculate the motion morphology of Specialised mobile terminal wearer, comprise static, mobile, fall.Now can judge whether trigger alerts according to various alarm conditions.
Step 2: by the output valve of each moment MEMS sensor of Specialised mobile terminal, calculates travel distance D.This positioning system adopts two key factors in PDR algorithm to be travel distance D and course angle ψ.We calculate the step number of walking with algorithm realization passometer, then estimate step-length, namely obtain distance D.With the large position excursion that such method can avoid double integral to bring.Step-size estimation consider because have: (1) usually, the step-length of people is the 37%-45% of height; (2) height information takes from employee database, when height information lacks, is averaged height by sex; (3) step-length changes with variation in pace speed; (4) time powerful walking (now the output valve of accelerometer depart from average more), step-length can larger (being tending towards 45%); (5) the non-common factors such as indoor (especially indoor in workspace) walking is not considered to jump, run fast, leg step-length discrepancy in left and right is very large.
Step 3: by the output valve of each moment MEMS sensor of Specialised mobile terminal, calculates course angle ψ, pitching angle theta and roll angle for convenience of calculating, the wearing mode (such as: be worn on loins, front panel points to the front of wearer) of our special specifications Specialised mobile terminal in nuclear power station.Like this, course angle is the angle in Specialised mobile terminal front panel direction and magnetic north direction.We are by first asking the hypercomplex number relevant to course angle, the angle of pitch and roll angle, then are converted to course angle, the angle of pitch and roll angle by hypercomplex number.Fig. 2 is the schematic diagram solving corresponding hypercomplex number.In figure bg, bm represents vector acceleration and the magnetic field vector of the carrier coordinate system of MEMS sensor respectively; eg, em represents vector acceleration and the magnetic field vector of inertial coordinates system respectively.By bg, bm and eg, em calculates relevant hypercomplex number q=(a, b, c, d) through Newton's algorithm, this hypercomplex number q with gyroscope survey data (p, q, r) together as the observed reading of Kalman filter calculate estimated value ( ) and ( ).The conversion formula utilizing quaternary to count to Eulerian angle can obtain course angle ψ, pitching angle theta and roll angle (Newton's algorithm sees reference document: Hagan, Martin T., Demuth, Howard B., Beale, Mark, Neural Network Design, PWS Publishing Company, Boston 1995.Direction calculating sees reference document: J.L.Marins, X.Yun, E.R.Bachmann, R.B.McGhee, and M.J.Zyda, " An Extended Kalman Filter for Qua ternion-basedOrientation Estima tion Using MARG Sensors ", in Proc.IEEE/RSJ Int.Conf.Intell.Robots Syst., Maui, HI, Oct.2001, pp.2003-2011.)
Step 4: build Kalman filter.Its dynamic model is, using position coordinates (x, y) as state vector, the state equation of Kalman filter is
x k+1＝x k+D sinψ
y k+1＝y k+D cosψ
Wherein D and ψ obtains through step 2 and step 3.Its observation model is, a series of RSSI value obtained with Specialised mobile terminal measurement and the magnetometer output valve of Specialised mobile terminal, for observed quantity, utilize aforementioned radial basis function RBF, and the observation equation of Kalman filter is,
z m，k＝RBF m，k(x k，y k)+n m，k
Wherein z m, kthe RSSI value (1≤m≤M) of m the AP observed, or measure to magnetometer data (M+1≤m≤M+3); n m, kfor the random noise of Gaussian distributed, its average is zero, and standard deviation is determined (i.e. the standard deviation of survey data) by the variability of survey data.The position coordinates of Specialised mobile terminal wearer is calculated by this Kalman filter.
Accompanying drawing explanation
Fig. 1 is a wireless indoor positioning system schematic diagram based on WIFI simplified.
Fig. 2 is the schematic diagram solving corresponding hypercomplex number.
Fig. 3 is the native system course of work and data flow diagram.
Embodiment
Being exemplary below by way of the embodiment be described with reference to the drawings, only for explaining the present invention, and can not limitation of the present invention being interpreted as.
Specific embodiment of the invention not only comprises every real-time computation process during location, also comprises on-site land survey and the process (being commonly referred to training process or learning process) by survey data structure RBF network.For keeping consistency, do not lose integrality, this process number is step 0 by we simultaneously.Concrete implementation step is as follows.
Step 0: on-site land survey, utilizes survey data to construct RBF network.
Some exploration point image data are chosen according to indoor topological structure and layout in locating area.Each exploration point needs the data gathering all directions four direction.Image data comprises: exploration point coordinate (x, y), exploration point place wireless signal strength data, and Geomagnetic signal data.These data genaration radial primary function network is utilized (to belong to the one of artificial neural network, gain the name because its activation function adopts radial basis function) RBF, realize the signal strength values RSSI of locating area coordinate of ground point to access point AP and the mapping of Geomagnetic signal.
Step 1: by the output valve of each moment MEMS sensor of Specialised mobile terminal, calculate the motion morphology of Specialised mobile terminal wearer, comprise static, mobile, fall.Now can judge whether trigger alerts according to various alarm conditions.
Step 2: by the output valve of each moment MEMS sensor of Specialised mobile terminal, calculates travel distance D.This positioning system adopts two key factors in PDR algorithm to be travel distance D and course angle ψ.We calculate the step number of walking with algorithm realization passometer, then estimate step-length, namely obtain distance D.With the large position excursion that such method can avoid double integral to bring.Step-size estimation consider because have: (1) usually, the step-length of people is the 37%-45% of height; (2) height information takes from employee database, when height information lacks, is averaged height by sex; (3) step-length changes with variation in pace speed; (4) time powerful walking (now the output valve of accelerometer depart from average more), step-length can larger (being tending towards 45%); (5) the non-common factors such as indoor (especially indoor in workspace) walking is not considered to jump, run fast, leg step-length discrepancy in left and right is very large.
Step 3: by the output valve of each moment MEMS sensor of Specialised mobile terminal, calculates course angle ψ, pitching angle theta and roll angle for convenience of calculating, the wearing mode (such as: be worn on loins, front panel points to the front of wearer) of our special specifications Specialised mobile terminal in nuclear power station.Like this, course angle is the angle in Specialised mobile terminal front panel direction and magnetic north direction.We are by first asking the hypercomplex number relevant to course angle, the angle of pitch and roll angle, then are converted to course angle, the angle of pitch and roll angle by hypercomplex number.Fig. 2 is the schematic diagram solving corresponding hypercomplex number.In figure bg, bm represents vector acceleration and the magnetic field vector of the carrier coordinate system of MEMS sensor respectively; eg, em represents vector acceleration and the magnetic field vector of inertial coordinates system respectively.By bg, bm and eg, em calculates relevant hypercomplex number q=(a, b, c, d) through Newton's algorithm, this hypercomplex number q with gyroscope survey data (p, q, r) together as the observed reading of Kalman filter calculate estimated value ( ) and ( ).The conversion formula utilizing quaternary to count to Eulerian angle can obtain course angle ψ, pitching angle theta and roll angle
Step 4: build Kalman filter.Its dynamic model is, using position coordinates (x, y) as state vector, the state equation of Kalman filter is
x k+1＝x k+D sinψ
y k+1＝y k+D cosψ
Wherein D and ψ obtains through step 2 and step 3.Its observation model is, a series of RSSI value obtained with Specialised mobile terminal measurement and the magnetometer output valve of Specialised mobile terminal, for observed quantity, utilize aforementioned radial basis function RBF, and the observation equation of Kalman filter is,
z m，k＝RBF m，k(x k，y k)+n m，k
Wherein z m, kthe RSSI value (1≤m≤M) of m the AP observed, or measure to magnetometer data (M+1≤m≤M+3); n m, kfor the random noise of Gaussian distributed, its average is zero, and standard deviation is determined (i.e. the standard deviation of survey data) by the variability of survey data.The position coordinates of Specialised mobile terminal wearer is calculated by this Kalman filter.
The content that instructions is not described in detail belongs to the known prior art of professional and technical personnel in the field.
The foregoing is only the preferred embodiments of the present invention, be not limited to the present invention, to those skilled in the art, the present invention can have various change and change.All do within spirit of the present invention and principle any amendment, equivalent replacement, improvement etc., within the protection domain that all should belong to claim of the present invention.

Claims (6)

1., based on a nuclear power station wireless location system for Kalman filter and dead reckoning, it is characterized in that, the element of this system comprises:
Specialised mobile terminal, is worn on it detected personnel, built-in WIFI module and MEMS sensor and industry sensor special (MEMS sensor mainly comprises accelerometer, gyroscope, magnetometer);
Smart mobile phone, has installed specific application services, built-in WIFI module and MEMS sensor machine (Android platform, iOS platform or Windows Phone);
Location-server, receives the various signal datas of Specialised mobile terminal and smart mobile phone collection, calculates position result in real time, according to various alarm conditions trigger alerts;
Positioning pattern user interface client, in order to real-time display position result, presents various warning information.
2. positioning system as claimed in claim 1, it is characterized in that, the location algorithm adopted comprises the following steps:
Step 0: on-site land survey, in locating area, choosing some exploration point image data according to indoor topological structure and layout, (image data comprises: exploration point coordinate (x, y), exploration point place wireless signal strength data, and Geomagnetic signal data), these data genaration radial primary function network is utilized (to belong to the one of artificial neural network, gain the name because its activation function adopts radial basis function) RBF, realize the signal strength values RSSI of locating area coordinate of ground point to access point AP and the mapping of Geomagnetic signal;
Step 1: by the output valve of each moment MEMS sensor of Specialised mobile terminal, calculate the motion morphology of Specialised mobile terminal wearer, comprise static, mobile, fall.Now can judge whether trigger alerts according to various alarm conditions;
Step 2: by the output valve of each moment MEMS sensor of Specialised mobile terminal, calculates the step number of walking, then estimates step-length, namely obtain distance D with algorithm realization passometer;
Step 3: by the output valve of each moment MEMS sensor of Specialised mobile terminal, first ask the hypercomplex number relevant to course angle, the angle of pitch and roll angle, then be converted to course angle, the angle of pitch and roll angle by hypercomplex number.Calculate relevant hypercomplex number q=(a, b, c, d) through Newton's algorithm, this hypercomplex number q calculates estimated value as the observed reading of Kalman filter together with gyroscope survey data (p, q, r) with the conversion formula utilizing quaternary to count to Eulerian angle can obtain course angle ψ, pitching angle theta and roll angle ;
Step 4: build Kalman filter, (1) dynamic model is, using position coordinates (x, y) as state vector, the state equation of Kalman filter is
x k+1＝x k+D sinψ
y k+1＝y k+D cosψ
Wherein D and ψ obtains through step 2 and step 3; (2) observation model is, a series of RSSI value obtained with Specialised mobile terminal measurement and the magnetometer output valve of Specialised mobile terminal, for observed quantity, utilize aforementioned radial basis function RBF, and the observation equation of Kalman filter is,
z m，k＝RBF m，k(x k，y k)+n m，k
Wherein z m, kthe RSSI value (1≤m≤M) of m the AP observed, or measure to magnetometer data (M+1≤m≤M+3); n m, kfor the random noise of Gaussian distributed, its average is zero, and standard deviation is determined (i.e. the standard deviation of survey data) by the variability of survey data, is calculated the position coordinates of Specialised mobile terminal wearer by this Kalman filter.
3. positioning system as claimed in claim 2, is characterized in that, in described location algorithm step 0, its radial primary function network RBF generated achieves the signal strength values RSSI of locating area coordinate of ground point to access point AP and the mapping of Geomagnetic signal.
4. positioning system as claimed in claim 2, it is characterized in that, in described location algorithm step 4, D and ψ in the state equation of its Kalman filter is the result of calculation of PDR algorithm.
5. positioning system as claimed in claim 2, is characterized in that, in described location algorithm step 4, and the random noise n in the observation equation of its Kalman filter m, kgaussian distributed, its average is zero, and standard deviation is the standard deviation of survey data.
6. positioning system as claimed in claim 2, is characterized in that, in described location algorithm step 4, the observation equation of its Kalman filter make use of the radial basis function RBF of structure in step 0.
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CN109764865A (en) * 2019-01-25 2019-05-17 北京交通大学 A kind of indoor orientation method based on MEMS and UWB

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