CN108663051A - A kind of modeling of passive integrated navigation system and information fusion method under water - Google Patents
A kind of modeling of passive integrated navigation system and information fusion method under water Download PDFInfo
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- CN108663051A CN108663051A CN201810409363.9A CN201810409363A CN108663051A CN 108663051 A CN108663051 A CN 108663051A CN 201810409363 A CN201810409363 A CN 201810409363A CN 108663051 A CN108663051 A CN 108663051A
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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
- G01C21/20—Instruments for performing navigational calculations
- G01C21/203—Specially adapted for sailing ships
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Abstract
The underwater passive integrated navigation system modeling of the present invention and information fusion method, include the following steps：Step 1）Set the initial estimation covariance matrix of each subfilter and senior filter to integrated navigation system initial informationTimes, step 2）Each subfilter and senior filter complete independently time renewal process；Step 3）Each subfilter is completed to measure renewal process according to newest measurement information；Step 4）After obtaining the partial estimation value of each subfilter and the estimated value of senior filter, then carry out optimal data fusion；Step 5）By the optimal estimation value of acquisition, each subfilter and senior filter filtering estimated value and covariance matrix are set according to specified information distribution mode.The present invention have it is easy to implement, guarantee can be provided for integrated navigation reliability and accuracy.Have the advantages that easy to implement.
Description
Technical field
The present invention relates to underwater navigation field more particularly to a kind of underwater passive integrated navigation system modelings and information fusion
Method.
Background technology
Ocean is the key areas of human survival and development, contains abundant resource.In recent years, marine resources are opened
Send out and utilize the grand strategy target for having become every country.Since China human mortality is numerous, per capita resources are relatively deficient, to sea
Ocean carries out comprehensively deep understanding and exploitation protection has a very important significance.Autonomous Underwater Vehicle belongs to underwater machine
One kind of device people can rely on the dynamical system of itself and navigation system smoothly to complete task.Submarine navigation device has
The advantages that good concealment, mobility strong, working range is wide, plays very important work during marine resources development
With.
For submarine navigation device, navigation accuracy is its important technical indicator.High-precision navigation system can be held
It is continuous that accurate location information is provided so that submarine navigation device can carry out underwater operation for a long time.Wherein, inertial navigation system
The navigation informations such as position, speed and the posture of carrier can be provided as a kind of common navigation equipment.Inertial navigation system exists
Do not have external information it is modified in the case of, position error can be with accumulated time, it is difficult to meet oceangoing voyage journey navigation require.It proposes
The integrated navigation system being combined with inertial navigation system using Doppler log, sonar positioning system, lavish gift instrument etc. is utilized
The auxiliary information that various navigation equipments provide is modified the position error of inertial navigation, and then improves the positioning accuracy of system.
With the development of underwater navigation technology, the navigation information of multiple sensors is subjected to effective integration, and then improve submarine navigation device
Positioning accuracy and information fusion technology field an important research direction.
Invention content
Present invention aims to overcome that above-mentioned the deficiencies in the prior art, provides a kind of underwater passive integrated navigation system and build
Mould and information fusion method, are specifically realized by following technical scheme：
The underwater passive integrated navigation system modeling and information fusion method, it is characterised in that specifically include following step
Suddenly：
It is initial that step 1) sets the initial estimation covariance matrix of each subfilter and senior filter to integrated navigation system
The γ of informationiTimes, i=1,2 ..., N, m；
Each subfilter of step 2) and senior filter complete independently time renewal process, subfilter are divided into linear son filtering
Device and nonlinearities filter, linear subfilter carries out the time renewal process of state variable by Kalman filter, non-
Linear subfilter obtains state by the state renewal process of nonlinear algorithm；
Each subfilter of step 3) is completed to measure renewal process according to newest measurement information, and linear subfilter passes through card
Thalmann filter carries out measurement update, and non-linear subfilter obtains updated state by Gaussian particle nonlinear algorithm
Distributed constant；
After step 4) obtains the partial estimation value of each subfilter and the estimated value of senior filter, then carry out optimal data
Fusion；
Each subfilter and main filtering is arranged by the optimal estimation value of acquisition, according to specified information distribution mode in step 5)
Device filters estimated value and covariance matrix.
The underwater passive integrated navigation system modeling and the further design of information fusion method are, in step 1)
γiMeet information conservation principle, i.e. β1+…+βN+βm=1, wherein γi=βi -1, βiIndicate information sharing scheme.
The underwater passive integrated navigation system modeling and the further design of information fusion method are that step 2) is specific
Include the following steps：
Step 2-1) from importance density function P (xk|z0:k) sampling obtain particle collectionM is total population, z0:kTable
Show kth time observation, xkIndicate kth particle；
Step 2-2) according to the weights of each particle of formula (5) calculating；
Wherein, ΣkIndicate covarianceFor mean value,For covariance, zkFor observation,For state value；
Step 2-3) weights of each particle are normalized according to formula (6)；
Wherein,The normalization of weights is indicated respectively.
Step 2-4) according to formula (7) estimation posterior probability density P (xk|z0:k) μkWith covariance Σk；
In formula, M indicates total sample number；
Approximate filtering probability distribution is calculated according to formula (8)；
P(xk|z0:k)≈N(xk；μk,Σk) (8)
The importance density function is calculated according to formula (9)
The underwater passive integrated navigation system models and the further design of information fusion method is, the step 4)
In, carry out optimal data fusion according to formula (1), formula (2)：
Wherein, PgBe global variable state covariance matrix,For subfilter state variable state covariance matrix,Be subfilter state,Indicate global state.
The underwater passive integrated navigation system models and the further design of information fusion method is, the step 5)
In, each subfilter and the filtering estimated value and its covariance matrix of senior filter are reset according to formula (3), formula (4), is counted
It obtains：
Wherein, QiComprehensive covariance matrix, QiiIt is the i-th subfilter covariance matrix, QmIt is senior filter covariance square
Battle array, Q are system covariance matrixes.
The underwater passive integrated navigation system models and the further design of information fusion method is, the step 2)
In, nonlinearities filter, using improvement Gaussian integration point.
Advantages of the present invention is as follows：
The present invention has easy to implement, can be that passive integrated navigation reliability and accuracy provide guarantee under water.Have
Advantage easy to implement.
Description of the drawings
Fig. 1 is underwater passive integrated navigation system structure chart.
Fig. 2 is that underwater passive integrated navigation system models schematic diagram.
Specific implementation mode
Below in conjunction with attached drawing, technical scheme of the present invention is described in detail.
Such as Fig. 1, the scale underwater vehicle combined navigation system that the present invention of the present embodiment uses is as shown, mainly by strapdown
Inertial navigation system, Doppler log, electronic compass, sonar positioning system and information fusion filtering device composition, strapdown are used
Property navigation system, Doppler log, electronic compass, sonar positioning system respectively with information fusion filtering device communicate to connect.Respectively
A navigation system provides corresponding navigation information, these navigation informations are merged using the method for filtering, and then obtains more
High-precision navigation information.
Submarine navigation device system measures the position of submarine navigation device, speed, appearance using Strapdown Inertial Navigation System as principle navigation system
State information；Doppler log provides velocity information for Strapdown Inertial Navigation System, and electronic compass provides course information, sonar positioning system
System provides location information, is combined, and then improve the positioning accuracy of integrated navigation system, disclosure satisfy that submarine navigation device height
The demand of precision navigation.
It includes following link to initially set up system state equation：
The foundation of SINS state equations：The analysis of Strapdown Inertial Navigation System error model takes the state of Strapdown Inertial Navigation System to become
Amount is：
XSINS=[δ L δ λ δ Vx δVy Tx φy φz εx εy εz]T
Establish the foundation of DVL state equations：The state variable of Doppler log is taken to be：
XDVL=[δ Vd δΔ δc]T
Obtaining Doppler log state equation is：
Establish electronic compass state equation：The state variable of electronic compass is taken to be：
XEC=δ ψEC
Then the state equation of electronic compass can be expressed as：
Establish sonar positioning system state equation
In sonar positioning system, receiver clock-offsets and clock correction drift are often provided in the form of unknown number, error mould
Type can be expressed as：
Wherein, δ t indicate the clock correction error of receiver；δtrIndicate the drift error of receiver clock-offsets；λrIndicate receiver
Correlation time；εrIndicate white noise vector.
Take clock correction error delta t and clock correction drift error δ trAs the state variable of Long baselines positioning system, can obtain：
XSN=[δ t δ tr]T
Then the state equation of Long baselines positioning system can be expressed as：
It includes following link to establish system measurements equation：
Establish SINS/DVL measurement equations：In SINS/DVL integrated navigation systems, Doppler log can provide load
The outer velocity information of body, it is poor that the velocity information of inertial reference calculation and outer velocity information are made, using speed difference as integrated navigation system
The measurement of system, then the measurement equation of system can be expressed as：
Establish SINS/EC measurement equations
In SINS/EC integrated navigation systems, electronic compass can obtain the course angle information of carrier, by this course angle
It is poor that the course angle information that information and inertial navigation estimation obtain is made, and using course angle difference as the measurement of integrated navigation system,
The course angle of middle electronic compass can be expressed as：ψE=ψt+δψE.The course angle obtained by inertial reference calculation can be expressed as：ψI
=ψt+δψI.And the measurement equation for obtaining SINS/EC integrated navigation systems is：
ZIE=[ψI-ψE]=[δ ψI-δψE]=[01×6 1 01×3 -1]XIE+VIE
Establish SINS/SN measurement equations：
In SINS/SN integrated navigations, using nonlinear pseudo away from equation as the measurement of system：
Enable the measurement Z=[R of system1m,R2m,…Rim], then measurement equation is：
ZIL(t)=h (XIL(t))+VIL(t)
In information integration program, using inertial navigation system as principle navigation system, by the speed of inertial navigation system output, position
It sets and posture information is as common reference information, using Doppler log, electronic compass, sonar positioning system as subsystem,
Each system filter model is established respectively.SINS/DVL, SINS/EC constitute linear subsystem, and linear Kalman filter may be used
Wave device is filtered estimation, and SINS/SN constitutes nonlinearities system, needs to be filtered using Gaussian integration point and estimate
Meter.
For SINS/DVL subsystems：
The state equation of SINS/DVL integrated navigation systems is：
The measurement equation of SINS/DVL integrated navigation systems is：
ZID=HIDXID+VID
For SINS/EC subsystems：
SINS/EC subsystem state equations are：
SINS/EC subsystem measurement equations are：
ZIE=[01×4 1 01×7 -1]XIE+VIE
For SINS/SN subsystems
The state equation of SINS/SN subsystem integrated navigation systems is：
The measurement equation of SINS/SN integrated navigation systems：ZIS=H (XIS)+VID
In integrated navigation system, realize that Doppler log, electronic compass and sonar are fixed using mixing Federated Filters
The combination of position system and inertial navigation system.The process of information exchange between senior filter and each subfilter, it is federal in conjunction with mixing
It is as follows can to summarize the mixing Federated Filtering based on integrated navigation system for filter design procedure：
Information is distributed：
Wherein, βiIndicate the distribution coefficient of information, and
Time updates：
A. linear system can carry out time update, including senior filter according to the following formula
B. nonlinearities system is according to distributionAnd PCpThe parameter distribution of state variable is updated according to the following formula
Measure update：
A. linear subsystem carries out measurement update according to the following formula
B. nonlinearities system is after obtaining newest measurement, according to formulaParticle weights are calculated to go forward side by side
Row normalized obtainsState variable is estimated according to following formula.
Data fusion：
The specific implementation process of the present embodiment is：
It is initial that step 1) sets the initial estimation covariance matrix of each subfilter and senior filter to integrated navigation system
The γ of informationi(i=1,2 ..., N, m) times.γiMeet information conservation principle, i.e. β1+…+βN+βm=1, wherein
Step 2), each subfilter and senior filter complete independently time renewal process.Wherein, linear subfilter passes through
Kalman filter carries out the time renewal process of state variable, and nonlinearities filter is updated by the state of nonlinear algorithm
Process obtains state.Nonlinearities filter is updated by Gaussian particle nonlinear filtering algorithm.
The step 2) is as follows：
Step 2-1) from importance density function P (xk|z0:k) sampling obtain particle collection
Step 2-2) calculate the weights of each particle.
Step 2-3) weights of each particle are normalized.
Step 2-4) according to following formula estimation posterior probability density P (xk|z0:k) μkWith covariance Σk。
In formula, M indicates total sample number.
The μ being calculated by above formulakWith covariance ΣkApproximation filters probability distribution
P(xk|z0:k)≈N(xk；μk,Σk)
For Gaussian particle filtering algorithm, the importance density functionIt can simply be chosen for
Each subfilter of step 3) is completed to measure renewal process according to newest measurement information.Wherein, linear subfilter
Measurement update is carried out by Kalman filter, and non-linear subfilter obtains updated state point by nonlinear algorithm
Cloth parameter.
After step 4) obtains the partial estimation value of each subfilter and the estimated value of senior filter, then carry out optimal data
Fusion；
Optimal data fusion is calculated according to the following formula
Each subfilter and main filtering is arranged by the optimal estimation value of acquisition, according to specified information distribution mode in step 5)
Device filters estimated value and covariance matrix.
According to specified information distribution mode reset each subfilter and senior filter filtering estimated value and its
Covariance matrix is calculated according to the following formula：
The foregoing is only a preferred embodiment of the present invention, but scope of protection of the present invention is not limited thereto,
Any one skilled in the art in the technical scope disclosed by the present invention, the change or replacement that can be readily occurred in,
It should be covered by the protection scope of the present invention.Therefore, protection scope of the present invention should be with scope of the claims
Subject to.
Claims (6)
1. a kind of underwater passive integrated navigation system modeling and information fusion method, it is characterised in that specifically comprise the following steps：
Step 1) sets the initial estimation covariance matrix of each subfilter and senior filter to integrated navigation system initial information
γiTimes, i=1,2 ..., N, m；
Each subfilter of step 2) and senior filter complete independently time renewal process, subfilter be divided into linear subfilter with
Nonlinearities filter, linear subfilter carries out the time renewal process of state variable by Kalman filter, non-linear
Subfilter obtains state by the state renewal process of nonlinear algorithm；
Each subfilter of step 3) is completed to measure renewal process according to newest measurement information, and linear subfilter passes through Kalman
Filter carries out measurement update, and non-linear subfilter obtains updated state by Gaussian particle nonlinear algorithm and is distributed
Parameter；
After step 4) obtains the partial estimation value of each subfilter and the estimated value of senior filter, then carries out optimal data and melt
It closes；
The optimal estimation value of acquisition is arranged each subfilter according to specified information distribution mode and senior filter is filtered by step 5)
Wave estimated value and covariance matrix.
2. underwater passive integrated navigation system modeling according to claim 1 and information fusion method, which is characterized in that step
It is rapid 1) in γiMeet information conservation principle, i.e. β1+…+βN+βm=1, whereinβiIndicate information sharing scheme.
3. underwater passive integrated navigation system modeling according to claim 1 and information fusion method, which is characterized in that step
It is rapid 2) to specifically comprise the following steps：
Step 2-1) from importance density function P (xk|z0:k) sampling obtain particle collectionM is total population, z0:kIndicate kth
Secondary observation, xkIndicate kth particle；
Step 2-2) according to the weights of each particle of formula (5) calculating；
Wherein, ΣkIndicate covarianceFor mean value,For covariance, zkFor observation,For state value；
Step 2-3) weights of each particle are normalized according to formula (6)；
Wherein,The normalization of weights is indicated respectively.
Step 2-4) according to formula (7) estimation posterior probability density P (xk|z0:k) μkWith covariance Σk；
In formula, M indicates total sample number；
Approximate filtering probability distribution is calculated according to formula (8)；
P(xk|z0:k)≈N(xk；μk,Σk) (8)
The importance density function is calculated according to formula (9)
4. underwater passive integrated navigation system modeling according to claim 1 and information fusion method, which is characterized in that institute
It states in step 4), optimal data fusion is carried out according to formula (1), formula (2)：
Wherein, PgBe global variable state covariance matrix,For subfilter state variable state covariance matrix,It is
Subfilter state,Indicate global state.
5. underwater passive integrated navigation system modeling according to claim 1 and information fusion method, which is characterized in that institute
It states in step 5), filtering estimated value and its association side of each subfilter and senior filter is reset according to formula (3), formula (4)
Poor matrix, is calculated：
Wherein, QiComprehensive covariance matrix, QiiIt is the i-th subfilter covariance matrix, QmBeing senior filter covariance matrix, Q is
System covariance matrix.
6. underwater passive integrated navigation system modeling according to claim 1 and information fusion method, which is characterized in that institute
It states in step 2), nonlinearities filter, using improvement Gaussian integration point.
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| CN111947651A (en) * | 2020-07-17 | 2020-11-17 | 中国人民解放军海军工程大学 | Underwater combined navigation information fusion method and system and autonomous underwater vehicle |
| CN113218386A (en) * | 2021-07-08 | 2021-08-06 | 深之蓝海洋科技股份有限公司 | Method and device for high-precision navigation of robot in liquid building |
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Application publication date: 20181016 |