CN109186595A - A kind of indoor and outdoor combined navigation device based on STM32 - Google Patents

A kind of indoor and outdoor combined navigation device based on STM32 Download PDF

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Publication number
CN109186595A
CN109186595A CN201811178887.8A CN201811178887A CN109186595A CN 109186595 A CN109186595 A CN 109186595A CN 201811178887 A CN201811178887 A CN 201811178887A CN 109186595 A CN109186595 A CN 109186595A
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mems
navigation
module
measurement
moment
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王晨琳
刘海颖
韩金龙
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Gewu Zhihang Shenzhen Technology Co ltd
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Gewu Perception (shenzhen) Technology Co Ltd
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    • 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
    • G01C21/165—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 combined with non-inertial navigation instruments
    • 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
    • G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
    • G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
    • G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
    • G01S19/42—Determining position
    • G01S19/45—Determining position by combining measurements of signals from the satellite radio beacon positioning system with a supplementary measurement
    • G01S19/47—Determining position by combining measurements of signals from the satellite radio beacon positioning system with a supplementary measurement the supplementary measurement being an inertial measurement, e.g. tightly coupled inertial
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/02—Services making use of location information
    • H04W4/024—Guidance services
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/30—Services specially adapted for particular environments, situations or purposes
    • H04W4/33—Services specially adapted for particular environments, situations or purposes for indoor environments, e.g. buildings
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W48/00—Access restriction; Network selection; Access point selection
    • H04W48/08—Access restriction or access information delivery, e.g. discovery data delivery
    • H04W48/10—Access restriction or access information delivery, e.g. discovery data delivery using broadcasted information
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W48/00—Access restriction; Network selection; Access point selection
    • H04W48/16—Discovering, processing access restriction or access information

Abstract

The invention discloses a kind of indoor and outdoor combined navigation device based on STM32, the device select suitable integrated navigation strategy by the data interaction between client, server end, positioning terminal, and continuous reliable positioning service is provided under various scenes.Wherein positioning terminal is by pre-processing MEMS sensor data, GPS data with STM32 data processor, these pretreated navigation informations are sent to server end again, the Wi-Fi information sent in conjunction with client, select suitable integrated navigation strategy, Recursive Filtering resolving is carried out using optimal Kalman filter, the optimal estimation of error state is obtained, the inertial navigation result for providing consecutive tracking service is modified.Client receives the indoor and outdoor integrated navigation that server end resolves as a result, realizing the pattern switching of outdoor map and indoor map in friendly user interface, provides more comfortable navigation Service for user.

Description

A kind of indoor and outdoor combined navigation device based on STM32
Technical field
The present invention relates to navigation field, in particular to a kind of indoor and outdoor combined navigation device based on STM32.
Background technique
With the fast development of positioning and airmanship, traditional single navigation mode is difficult to meet navigation system to accurate The requirement of degree, reliability and real-time, integrated navigation information fusion technology are increasingly becoming the Main way of airmanship research.Letter Breath fusion is also referred to as data fusion, is most proposed earlier than early 1970s by laboratory director federation of U.S. Department of Defense.Information The navigation information of isomorphism or isomery from different navigation source is associated and is merged by fusion, is reached information and is gone pseudo- and distillation Purpose, finally obtain accurate, complete navigation information.Integrated navigation system is exactly a kind of multi-sensor Information Fusion System, The defect that each navigation system has itself that can not avoid when working independently, and for integrated navigation system, difference may be implemented and lead Mutual supplement with each other's advantages between boat system helps to promote dynamic locating accuracy, guarantees system stable operation, improves System Error-tolerance Property.
Existing navigation system can be divided into outdoor navigation system and two kinds of indoor navigation system.Outdoor navigation system mostly uses GPS, CNSS, GLONASS etc. more mature Satellite Navigation Technique, however satellite caused by the environment such as high building, trees, interior is believed Number masking and interrupt mean that it can not obtain reliably lasting navigation information under these circumstances.Indoor navigation system can adopt With location technologies such as Wi-Fi, bluetooth, radio frequency identification (RFID), ultra wide band (UWB), pedestrian's reckonings (PDR).However it is different Location technology there is different advantage and short slab again.Location technology based on WIFI, bluetooth, RFID depends on the geometry of signal Distribution, and be easily protected from environmental, absolute fix precision is low；Location technology based on UWB needs to be equipped with corresponding hardware device, It is at high cost；And there is accumulative miss although positioning result is continuous and has stronger independence in the location technology based on PDR Difference and absolute location information can not be obtained.
Summary of the invention
The technical problem to be solved by the present invention is to provide a kind of can guarantee, and successional positioning service is provided under various scenes A kind of indoor and outdoor combined navigation device based on STM32.
The technical solution adopted by the present invention to solve the technical problems is: a kind of indoor and outdoor integrated navigation based on STM32 Device, it is characterised in that: including positioning terminal, server end and the client of solid physically, the positioning terminal includes GPS positioning module, MEMS inertial navigation module, wireless communication module and STM32 data processing module, the GPS positioning module, MEMS Inertial navigation module, wireless communication module are connected with STM32 data processing module respectively, the positioning terminal and server end channel radio Letter connection, the user terminal and server end wireless communication connect；
The positioning terminal is realized by STM32 data processing module to GPS positioning module, MEMS inertial navigation module, wireless The data processing and control of communication module, module is sent to server end to the navigation information that positioning terminal obtains by wireless communication It is handled；
Wi-Fi net information of the client to obtain surrounding, that is, scan and collect the AP signal of surrounding, get AP The MAC Address broadcast out, and mac address information is sent to server end；
The server end is used to retrieve the geographical location of each AP and the navigation information for combining positioning terminal to send obtains It navigation results and is sent to client out and is shown by client.
Further, the MEMS inertial navigation module includes accelerometer and gyroscope, pass through accelerometer and gyroscope Output judge that pedestrian is in static or movement, when stationary state, MEMS inertial navigation module application ZUPT and ZARU correct INS The navigation error of mechanization；When motion state, the thought of step-size estimation is corrected in MEMS inertial navigation module application PDR algorithm The navigation error of INS mechanization；
The INS mechanization equation is as follows:
Wherein rnAnd vnRespectively position and speed vector,For b system to the direction cosine matrix of n system, fbIt is specific force, WithRespectively angular speed antisymmetric matrix and n system to i system rotation antisymmetric matrix,WithRespectively rotational-angular velocity of the earth The rotation antisymmetric matrix of antisymmetric matrix and n system to ECEF system, gnFor local gravity vector；
The discretization state and measurement equation of filter in MEMS inertial navigation module are as follows:
Wherein XkFor k moment INS error state vector, ZkFor the measurement vector at k moment, ΦK, k-1For k-1 to the k moment System Matrix of shifting of a step, Wk-1For the system noise acoustic matrix at k-1 moment, HkFor the measurement matrix at k moment, VkFor the measurement at k moment Noise.
Measurement information when stationary state is based on ZUPT and ZARU, measurement equation are as follows:
ZΨ=ΨINS-Ψ0=HΨX+VΨ；
WhereinFor under carrier system INS resolve velocity vectors,It is zero velocity vectors, HvFor rate measurement matrix, Vv Noise, Ψ are measured for rateINSFor the pedestrian advancing direction that INS mechanization resolves, Ψ0To judge that the motion state of pedestrian is The pedestrian advancing direction of static previous moment, HΨFor angle measurement matrix, VΨFor angle measurement noise,
Measurement information when motion state is the step-size estimation based on pedestrian in PDR algorithm, step-size estimation formula are as follows:
The three axis pseudo rate vectors established based on this are as follows:
Rate measurement equation when the pedestrian movement's state finally established are as follows:
Wherein s is step-size estimation value, azmaxAnd azminRespectively the maximum value and minimum value of vertical acceleration, K are constant. Assuming that pedestrian's forward speed is constant in a short time, then vf=s/ts, tsFor the time interval of each step,For under carrier system Consider three axis pseudo rate vectors of lateral and vertical rate constraint.
Further, server end is according to locating since there are cumulative errors for the navigation results of MEMS inertial navigation module The different integrated navigation strategy of environmental selection, in outdoor environment, using MEMS/GPS integrated navigation strategy；Environment indoors When, it is otherwise independent using MEMS using MEMS/Wi-Fi integrated navigation strategy if Wi-Fi information meets Wi-Fi location condition Navigation strategy.
Further, state vector of the outdoor navigation using filter under MEMS/GPS integrated navigation strategy are as follows:
xI=[δ r1×3 δv1×3 ε1×3 d1×3 b1×3]T；
Wherein this state vector is INS error state vector, δ r1×3For three-dimensional position error, δ v1×3For three-dimensional velocity mistake Difference, ε1×3For 3 d pose error angle, d1×3For accelerometer bias error, b1×3For gyroscopic drift error；
The discretization state equation of junction filter is identical as MEMS inertial navigation module, measurement equation are as follows:
Zp=rMEMS-rGPS=HpX+Vp；
Wherein rMEMSFor the three dimensional local information that MEMS module resolves, rGPSFor the three dimensional local information that GPS is resolved, HpFor position Set measurement matrix, VpNoise is measured for position；
The state vector of filter under indoor MEMS/Wi-Fi integrated navigation strategy are as follows:
xs=[δ r1×3 δv1×3 ε1×3 d1×3 b1×3 δxw]T；
Wherein δ xw=bRSSFor RSS (received signal strength) deviation of Wi-Fi net；
The discretization state and measurement equation of indoor navigation junction filter are as follows:
Wherein XsIt (k) is the state vector of k moment junction filter, ZρIt (k) is the pseudo range measurement vector at k moment, Φs(k, It k-1 is) the state Matrix of shifting of a step at k-1 to k moment, WsIt (k-1) is the system noise acoustic matrix at k-1 moment, HρIt (k) is the k moment Measurement matrix, VρIt (k) is the measurement noise at k moment；
Its measurement is that the pseudorange based on Wi-Fi and the pseudorange based on MEMS are poor, shown in formula specific as follows:
Wherein dMEMS, kFor pseudorange of the pedestrian to k-th of AP that MEMS is resolved, dWi-Fi, kIt is resolved according to the RSS value of Wi-Fi Pseudorange of the pedestrian to k-th of AP；
Pseudo-range information dMEMS, kThe pedestrian that the location information and MEMS of the AP retrieved by server-side database resolves Location information obtains, and concrete operation formula is as follows:
Wherein λMEMS,hMEMSThe pedestrian position information (longitude, latitude, height) resolved for MEMS module, λAP, k,hAP, kFor the location information (longitude, latitude, height) of k-th of AP, M is radius of curvature of the earth, and N is that earth curvature is vertical Radius；
Kalman's optimal filter processing is carried out to the dynamic model of above-mentioned filter, recurrence equation group is as follows:
By above-mentioned recurrence equation group, the optimal estimation P of error state is calculatedk, MEMS navigation results are corrected, And then obtain that precision is higher, more reliable integrated navigation result.
The beneficial effects of the present invention are:
1, the present invention is by pre-processing MEMS sensor data, GPS data with STM32 data processor, then These pretreated navigation informations are sent to server end, the Wi-Fi information sent in conjunction with client carries out corresponding group Filtering processing is closed, centralized data processing is avoided and requires data processing speed high, the big problem of system complex realization difficulty, Improve the overall execution efficiency of navigation device；
2, the present invention selects suitable integrated navigation by the data interaction between client, server end, positioning terminal Strategy is modified the inertial navigation result for providing consecutive tracking service, and avoiding current indoor and outdoor navigation device cannot protect Demonstrate,prove the problem of providing successional positioning service under various scenes；
3, the present invention applies MEMS/Wi-Fi integrated navigation technology, devises the tight integration filter based on pseudorange to realize Indoor navigation, the airmanship for avoiding the utilization of indoor navigation field is single, the technological deficiency of poor reliability.
Detailed description of the invention
Fig. 1 is a kind of indoor and outdoor combined navigation device schematic diagram based on STM32 of the present invention.
Fig. 2 is MEMS inertial navigation module navigation data process flow diagram of the invention.
Fig. 3 is server end work flow diagram of the invention.
Specific embodiment
The present invention is further described with reference to the accompanying drawings and detailed description.
A kind of indoor and outdoor combined navigation device based on STM32 as shown in Figure 1, it is characterised in that: including solid in body Positioning terminal, server end and client on body, the positioning terminal includes GPS positioning module, MEMS inertial navigation module, wireless Communication module and STM32 data processing module, the GPS positioning module, MEMS inertial navigation module, wireless communication module respectively and The connection of STM32 data processing module, the positioning terminal and server end wireless communication connect, the user terminal and server end Wireless communication connection；
The positioning terminal is realized by STM32 data processing module to GPS positioning module, MEMS inertial navigation module, wireless The data processing and control of communication module, module is sent to server end to the navigation information that positioning terminal obtains by wireless communication It is handled；
Wi-Fi net information of the client to obtain surrounding, that is, scan and collect the AP signal of surrounding, get AP The MAC Address broadcast out, and mac address information is sent to server end；
The server end is used to retrieve the geographical location of each AP and the navigation information for combining positioning terminal to send obtains It navigation results and is sent to client out and is shown by client.
STM32 described above is based on aiming at what the Embedded Application for requiring high-performance, low cost, low-power consumption specially designed Kernel single-chip microcontroller, the MEMS inertial navigation module are a kind of based on MEMS (MEMS, Micro-Electro-Mechanical System) the miniature inertial navigation system of sensor technology, the AP signal are wireless network access point signal.
Specifically: when client enters the room environment, client opens Wi-Fi, can scan and the AP for collecting surrounding is (wireless Access point) signal, regardless of whether encryption, if having connected, the MAC Address that AP is broadcast out can be got；Client with There are data interaction, clients, and these Wi-Fi net information are sent to server end for server end, and server-side retrieval is each out The geographical location of AP, the navigation information sent in conjunction with positioning terminal runs corresponding Integrated Navigation Algorithm, finally by integrated navigation As a result it is sent to client.Client receives the indoor and outdoor integrated navigation of server end transmission as a result, realizing on a user interface The pattern switching of outdoor map and indoor map.
On the basis of the above, MEMS inertial navigation module navigation data process flow is as shown in Fig. 2, positioning terminal is fixed on human body (such as tying up on leg) carries out step detection with accelerometer, and gyroscope carries out zero-speed detection.When not detecting step and gyro The standard deviation that three axis of instrument observes data is less than threshold values, judges that pedestrian remains static；When detecting step and three axis of gyroscope The standard deviation for observing data is greater than threshold values, judges that pedestrian is kept in motion.When stationary state, MEMS inertial navigation module application Zero-speed rate and constant pedestrian advancing direction that ZUPT and ZARU is provided are used to establish the measurement equation of filter, with this school The navigation error of positive INS mechanization；When motion state, the thought of step-size estimation is obtained in MEMS inertial navigation module application PDR algorithm The forward speed for obtaining pedestrian navigation comprehensively considers pedestrian navigation and does not consider lateral and vertical velocity actual conditions generally, establishes Three axis pseudo rate vectors are used to establish the measurement equation of filter, and the navigation error of INS mechanization is corrected with this.
ZUPT described above represents the update of zero-speed rate, and the ZARU represents the update of zero angle rate, and the INS includes three axis Accelerometer and three-axis gyroscope, the PDR represent pedestrian's reckoning；
INS mechanization equation is as follows:
Wherein rnAnd vnRespectively position and speed vector,For b system to the direction cosine matrix of n system, fbIt is specific force, WithRespectively angular speed antisymmetric matrix and n system to i system rotation antisymmetric matrix,WithRespectively rotational-angular velocity of the earth The rotation antisymmetric matrix of antisymmetric matrix and n system to ECEF system, gnFor local gravity vector,
The discretization state and measurement equation of filter in MEMS inertial navigation module are as follows:
Wherein XkFor k moment INS error state vector, ZkFor the measurement vector at k moment, ΦK, k-1For k-1 to the k moment System Matrix of shifting of a step, Wk-1For the system noise acoustic matrix at k-1 moment, HkFor the measurement matrix at k moment, VkFor the measurement at k moment Noise.
Measurement information when stationary state is based on ZUPT and ZARU, measurement equation are as follows:
ZΨ=ΨINS-Ψ0=HΨX+VΨ；
WhereinFor under carrier system INS resolve velocity vectors,It is zero velocity vectors, HvFor rate measurement matrix, Vv Noise, Ψ are measured for rateINSFor the pedestrian advancing direction that INS mechanization resolves, Ψ0To judge that the motion state of pedestrian is The pedestrian advancing direction of static previous moment, HΨFor angle measurement matrix, VΨFor angle measurement noise,
Measurement information when motion state is the step-size estimation based on pedestrian in PDR algorithm, step-size estimation formula are as follows:
The three axis pseudo rate vectors established based on this are as follows:
Rate measurement equation when the pedestrian movement's state finally established are as follows:
Wherein s is step-size estimation value, azmaxAnd azminRespectively the maximum value and minimum value of vertical acceleration, K are constant. Assuming that pedestrian's forward speed is constant in a short time, then vf=s/ts, tsFor the time interval of each step,For under carrier system Consider three axis pseudo rate vectors of lateral and vertical rate constraint.
On the basis of the above, it is contemplated that there are cumulative errors for the navigation results of MEMS inertial navigation module, according to locating environment Selecting different integrated navigation strategies is the effective means for obtaining the navigation results of high-precision, high reliability.In outdoor environment, adopt With MEMS/GPS integrated navigation strategy；It is that indoor positioning request and surrounding are sent to server end that client, which opens Wi-Fi, Wi-Fi net information, received server-side information and the geographical location for passing through AP around database retrieval, when Wi-Fi signal number is big In 3 and be in different latitudes, then use MEMS/Wi-Fi integrated navigation strategy, otherwise use the independent navigation strategy of MEMS, tool Body are as follows:
Outdoor navigation uses the state vector of filter under MEMS/GPS integrated navigation strategy are as follows:
xI=[δ r1×3 δv1×3 ε1×3 d1×3 b1×3]T；
Wherein this state vector is INS error state vector, δ r1×3For three-dimensional position error, δ v1×3For three-dimensional velocity mistake Difference, ε1×3For 3 d pose error angle, d1×3For accelerometer bias error, b1×3For gyroscopic drift error；
The discretization state equation of junction filter is identical as MEMS inertial navigation module, measurement equation are as follows:
Zp=rMEMS-rGPS=HpX+Vp；
Wherein rMEMSFor the three dimensional local information that MEMS module resolves, rGPSFor the three dimensional local information that GPS is resolved, HpFor position Set measurement matrix, VpNoise is measured for position；
The state vector of filter under indoor MEMS/Wi-Fi integrated navigation strategy are as follows:
xs=[δ r1×3 δv1×3 ε1×3 d1×3 b1×3 δxw]T；
Wherein δ xw=bRSSFor RSS (received signal strength) deviation of Wi-Fi net；
The discretization state and measurement equation of indoor navigation junction filter are as follows:
Wherein XsIt (k) is the state vector of k moment junction filter, ZρIt (k) is the pseudo range measurement vector at k moment, Φs(k, It k-1 is) the state Matrix of shifting of a step at k-1 to k moment, WsIt (k-1) is the system noise acoustic matrix at k-1 moment, HρIt (k) is the k moment Measurement matrix, VρIt (k) is the measurement noise at k moment；
Its measurement is that the pseudorange based on Wi-Fi and the pseudorange based on MEMS are poor, shown in formula specific as follows:
Wherein dMEMS, kFor pseudorange of the pedestrian to k-th of AP that MEMS is resolved, dWi-Fi, kIt is resolved according to the RSS value of Wi-Fi Pseudorange of the pedestrian to k-th of AP；
Pseudo-range information dMEMS, kThe pedestrian that the location information and MEMS of the AP retrieved by server-side database resolves Location information obtains, and concrete operation formula is as follows:
Wherein λMEMS,hMEMSThe pedestrian position information (longitude, latitude, height) resolved for MEMS module, λAp, k,hAP, kFor the location information (longitude, latitude, height) of k-th of AP, M is radius of curvature of the earth, and N is that earth curvature is vertical Radius；
Kalman's optimal filter processing is carried out to the dynamic model of above-mentioned filter, recurrence equation group is as follows:
By above-mentioned recurrence equation group, the optimal estimation P of error state is calculatedk, MEMS navigation results are corrected, And then obtain that precision is higher, more reliable integrated navigation result.
Above-mentioned about Kalman's optimal filter recurrence equation group is the prior art, referring to Yu Jixiang, edits " Kalman filtering And its application in inertial navigation " aviation specialized teaching material editing group, 1984.
Particular embodiments described above has carried out further in detail the purpose of the present invention, technical scheme and beneficial effects It describes in detail bright, it should be understood that the above is only a specific embodiment of the present invention, is not intended to restrict the invention, it is all Within the spirit and principles in the present invention, any modification, equivalent substitution, improvement and etc. done should be included in guarantor of the invention Within the scope of shield.

Claims (4)

1. a kind of indoor and outdoor combined navigation device based on STM32, it is characterised in that: whole including the positioning of solid physically End, server end and client, the positioning terminal include GPS positioning module, MEMS inertial navigation module, wireless communication module and STM32 data processing module, the GPS positioning module, MEMS inertial navigation module, wireless communication module are respectively and at STM32 data Module connection is managed, the positioning terminal and server end wireless communication connect, and the user terminal and server end wireless communication connect It connects；
The positioning terminal is realized by STM32 data processing module to GPS positioning module, MEMS inertial navigation module, wireless communication The data processing and control of module, module is sent to server end progress to the navigation information that positioning terminal obtains by wireless communication Processing；
Wi-Fi net information of the client to obtain surrounding, that is, scan and collect the AP signal of surrounding, gets AP broadcast MAC Address out, and mac address information is sent to server end；
The server end is used to retrieve the geographical location of each AP and the navigation information of positioning terminal transmission is combined to obtain and leads Boat result is simultaneously sent to client and is shown by client.
2. a kind of indoor and outdoor combined navigation device based on STM32 as described in claim 1, it is characterised in that: the MEMS Inertial navigation module includes accelerometer and gyroscope, judges that pedestrian is in static or fortune by the output of accelerometer and gyroscope Dynamic, when stationary state, MEMS inertial navigation module application ZUPT and ZARU correct the navigation error of INS mechanization；Motion state When, the thought of step-size estimation corrects the navigation error of INS mechanization in MEMS inertial navigation module application PDR algorithm；
The INS mechanization equation is as follows:
Wherein rnAnd vnRespectively position and speed vector,For b system to the direction cosine matrix of n system, fbIt is specific force,With Respectively angular speed antisymmetric matrix and n system to i system rotation antisymmetric matrix,WithRespectively rotational-angular velocity of the earth is opposed Claim the rotation antisymmetric matrix of battle array and n system to ECEF system, gnFor local gravity vector；
The discretization state and measurement equation of filter in MEMS inertial navigation module are as follows:
Wherein XkFor k moment INS error state vector, ZkFor the measurement vector at k moment, ΦK, k-1For the system one at k-1 to k moment Walk transfer matrix, Wk-1For the system noise acoustic matrix at k-1 moment, HkFor the measurement matrix at k moment, VkFor the measurement noise at k moment；
Measurement information when stationary state is based on ZUPT and ZARU, measurement equation are as follows:
ZΨ=ΨINS-Ψ0=HΨX+VΨ；
WhereinFor under carrier system INS resolve velocity vectors,It is zero velocity vectors, HvFor rate measurement matrix, VvFor speed Rate measures noise, ΨINSFor the pedestrian advancing direction that INS mechanization resolves, Ψ0To judge that the motion state of pedestrian is static Previous moment pedestrian advancing direction, HΨFor angle measurement matrix, VΨFor angle measurement noise；
Measurement information when motion state is the step-size estimation based on pedestrian in PDR algorithm, step-size estimation formula are as follows:
The three axis pseudo rate vectors established based on this are as follows:
Rate measurement equation when the pedestrian movement's state finally established are as follows:
Wherein s is step-size estimation value, az maxAnd az minRespectively the maximum value and minimum value of vertical acceleration, K are constant.Assuming that Pedestrian's forward speed is constant in a short time, then vf=s/ts, tsFor the time interval of each step,To consider under carrier system Lateral and vertical rate constraint three axis pseudo rate vectors.
3. a kind of indoor and outdoor combined navigation device based on STM32 as described in claim 1, it is characterised in that: due to MEMS The navigation results of inertial navigation module are there are cumulative errors, the server end integrated navigation strategy different according to locating environmental selection, In outdoor environment, using MEMS/GPS integrated navigation strategy；Indoors when environment, if Wi-Fi information meets Wi-Fi positioning When condition, using MEMS/Wi-Fi integrated navigation strategy, the independent navigation strategy of MEMS is otherwise used.
4. a kind of indoor and outdoor combined navigation device based on STM32 as claimed in claim 3, it is characterised in that: outdoor navigation Using the state vector of filter under MEMS/GPS integrated navigation strategy are as follows:
xI=[δ r1×3 δv1×3 ε1×3 d1×3 b1×3]T；
Wherein this state vector is INS error state vector, δ r1×3For three-dimensional position error, δ v1×3For three-dimensional velocity error, ε1×3For 3 d pose error angle, d1×3For accelerometer bias error, b1×3For gyroscopic drift error；
The discretization state equation of junction filter is identical as MEMS inertial navigation module, measurement equation are as follows:
Zp=rMEMS-rGPS=HpX+Vp；
Wherein rMEMSFor the three dimensional local information that MEMS module resolves, rGPSFor the three dimensional local information that GPS is resolved, HpFor position quantity Survey matrix, VpNoise is measured for position；
The state vector of filter under indoor MEMS/Wi-Fi integrated navigation strategy are as follows:
xs=[δ r1×3 δv1×3 ε1×3 d1×3 b1×3 δxw]T；
Wherein δ xw=bRSSFor RSS (received signal strength) deviation of Wi-Fi net；
The discretization state and measurement equation of indoor navigation junction filter are as follows:
Wherein XsIt (k) is the state vector of k moment junction filter, ZρIt (k) is the pseudo range measurement vector at k moment, Φs(k, k-1) For the state Matrix of shifting of a step at k-1 to k moment, WsIt (k-1) is the system noise acoustic matrix at k-1 moment, HρIt (k) is the measurement at k moment Matrix, VρIt (k) is the measurement noise at k moment；
Its measurement is that the pseudorange based on Wi-Fi and the pseudorange based on MEMS are poor, shown in formula specific as follows:
Wherein dMEMS, kFor pseudorange of the pedestrian to k-th of AP that MEMS is resolved, dWi-Fi, kThe pedestrian resolved according to the RSS value of Wi-Fi To the pseudorange of k-th of AP；
Pseudo-range information dMEMS, kThe pedestrian position that the location information and MEMS of the AP retrieved by server-side database resolves Information acquisition, concrete operation formula are as follows:
Wherein λMEMS,hMEMSThe pedestrian position information (longitude, latitude, height) resolved for MEMS module, λAP, k, hAP, kFor the location information (longitude, latitude, height) of k-th of AP, M is radius of curvature of the earth, and N is earth curvature perpendicular radii；
Kalman's optimal filter processing is carried out to the dynamic model of above-mentioned filter, recurrence equation group is as follows:
By above-mentioned recurrence equation group, the optimal estimation P of error state is calculatedk, MEMS navigation results are corrected, in turn Obtain that precision is higher, more reliable integrated navigation result.
CN201811178887.8A 2018-10-10 2018-10-10 A kind of indoor and outdoor combined navigation device based on STM32 Pending CN109186595A (en)

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