CN107830872A - A kind of naval vessel strapdown inertial navigation system self-adaptive initial alignment methods - Google Patents
A kind of naval vessel strapdown inertial navigation system self-adaptive initial alignment methods Download PDFInfo
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- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
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Abstract
A kind of naval vessel strapdown inertial navigation system self-adaptive initial alignment methods, comprise the following steps：Naval vessel SINS starts working, and gathers the metrical information of fibre optic gyroscope and accelerometer；The initial position on naval vessel is provided according to the global positioning system carried on naval vessel, and collect optical fibre gyro, accelerometer information, the attitude information on naval vessel is determined using analytic expression coarse alignment algorithmic preliminaries, completes naval vessel SINS coarse alignment；Establish the Nonlinear Error Models of naval vessel SINS under high dynamic environment；The Nonlinear Filtering Formulae established under naval vessel high dynamic environment；Adaptive CKF algorithms based on VCE are estimated system noise in real time, while estimate the misalignment of system；Using the misalignment estimated in step 5 come the initial strapdown attitude matrix of update the system, accurate initial strap-down matrix is obtained, completes the fine alignment process under moving base.The present invention solves nonlinear problem and noise uncertain problem in initial Alignment of Inertial Navigation System, effectively lifting alignment combination property.
Description
Technical field
The present invention relates to navigation field, and in particular to a kind of initial alignment side of naval vessel fiber strapdown inertial navigation system
Method.
Background technology
Strapdown inertial navigation system (Strapdown Inertial Navigation System, SINS), which can utilize, to be added
Speedometer and the sensitive linear velocity and angular velocity information for arriving carrier itself of gyroscope, so as to provide speed, position and posture etc.
Navigation information.Due to SINS have round-the-clock, independence, it is anti-interference, disguised it is high, a variety of navigation informations, volume can be provided
The small, plurality of advantages such as price is low, is widely used in navigational field.And be initially aligned to naval vessel SINS and initial information is provided, it is
SINS carries out the premise of normal navigation work.The precision being initially aligned will directly influence the navigation accuracy of navigation system, therefore
Research is carried out to Initial Alignment Technique has important theoretical and practical significance.
At present, inertial navigation system is typically reduced to linear system when be initially aligned, but actual system
Exist it is non-linear, especially in the case of naval vessel high dynamic.It is approximately that linear system will reduce to handle by nonlinear system
The precision of filtering algorithm, or even filtering divergence can be caused.Therefore nonlinear filter has obtained increasing concern, wherein most
It is extended Kalman filter (Extended Kalman Filter, EKF) and Unscented kalman filtering device for what is commonly used
(Unscented Kalman Filter,UKF).But there is filtering accuracy in EKF and UKF in strong nonlinearity, high-dimensional system
It is not high and the problem of easily dissipate.In addition, above-mentioned wave filter also requires that the priori of system model is strict, it is known that but due to being
The influence of the drift of system device error itself, the high motor-driven caused dynamic error in naval vessel and external environment condition uncertain factor, this
One requires it is very inappeasable in actual applications.
To solve the uncertain problem of system, a variety of adaptive filter algorithms have been proposed in domestic and foreign scholars at present,
Such as Sage-Huge adaptive filter algorithms, adaptive factor regulation algorithm.Although estimation of these adaptive algorithms to system
Precision has improvement to a certain extent, but in practice, however it remains certain limitation.Such as：Sage-Huge is adaptive
Filtering algorithm can not use in process noise and simultaneously unknown measurement noise, one of amount known to its needs；Fade because
The sub- each iteration of adaptive-filtering intelligently has an optimal fading factor, it cannot be guaranteed that filter for more complicated system model
The best estimate of ripple device.
The present invention is based on oneself of variance components estimate (Variance Component Estimation, VCE) by introducing
Volume Kalman filtering (Cubature Kalman Filter, CKF) is adapted to, suppression system is non-linear and uncertain problem pair
The influence of the initial alignment precisions of naval vessel SINS and navigation accuracy, the initial alignment of high accuracy on naval vessel is completed, is led in high precision for naval vessel
Boat lays the foundation.
The content of the invention
Can solve that inertial navigation system is non-linear and system noise uncertainty is asked simultaneously it is an object of the invention to provide one kind
The naval vessel strapdown inertial navigation system self-adaptive initial alignment methods of topic.
The purpose of the present invention is through the following steps that to realize：
Step 1：Naval vessel SINS starts working, and gathers the metrical information of fibre optic gyroscope and accelerometer in SINS；
Step 2：The initial position on naval vessel, and the light collected are provided according to the global positioning system carried on naval vessel
Fine gyro, accelerometer information, the attitude information on naval vessel is determined using analytic expression coarse alignment algorithmic preliminaries, complete naval vessel SINS's
Coarse alignment；
Step 3：Establish the Nonlinear Error Models of naval vessel SINS under high dynamic environment；
Step 4：The Nonlinear Filtering Formulae established under naval vessel high dynamic environment；
Step 5：System noise is estimated in real time using the adaptive CKF algorithms based on VCE, while estimates system
Misalignment；
Step 6：Using the misalignment estimated in step 5 come the initial strapdown attitude matrix of update the system, essence is obtained
True initial strap-down matrix, so as to complete the fine alignment process under moving base.
Further, the adaptive CKF algorithms based on VCE, its step are：
1) three groups of puppet observed quantities are defined first；
2) it is theoretical according to residual error, the system residual vector of three groups of puppet observed quantities is calculated respectively；
3) CKF filterings are performed, calculate the variance matrix of system residual vector；
4) redundant obser ration part of separate three groups of measurements vector is obtained；
5) redundant obser ration part of calculating process noise variance component and measuring noise square difference component；
6) measuring noise square difference battle array and process-noise variance battle array are obtained；
7) step 3)-step 6) is repeated.
The present invention has the advantages that：By building VCE adaptive CKF algorithms, while solves high dynamic machine
The state-noise of the nonlinear problem of Ship System and system and the measurement uncertain problem of noise statisticses, have under rotating ring border
Effect improves stability, the robustness of alignment filtering algorithm, and compared with traditional CKF algorithms, after the inventive method, course is lost
Faster, precision is higher for quasi- angle error convergence rate, and the initial combination property that is aligned gets a promotion.
Brief description of the drawings
Fig. 1 is the flow chart of the inventive method；
Fig. 2 utilizes the present invention and the pitching misalignment correlation curve of misalignment during conventional method under the conditions of being first group；
Fig. 3 utilizes the present invention and the rolling misalignment correlation curve of misalignment during conventional method under the conditions of being first group；
Fig. 4 utilizes the present invention and the course misalignment correlation curve of misalignment during conventional method under the conditions of being first group；
Fig. 5 utilizes the present invention and the pitching misalignment correlation curve of misalignment during conventional method under the conditions of being second group；
Fig. 6 utilizes the present invention and the rolling misalignment correlation curve of misalignment during conventional method under the conditions of being second group；
Fig. 7 utilizes the present invention and the course misalignment correlation curve of misalignment during conventional method under the conditions of being second group；
Fig. 8 utilizes the present invention and the pitching misalignment correlation curve of misalignment during conventional method under the conditions of being the 3rd group；
Fig. 9 utilizes the present invention and the rolling misalignment correlation curve of misalignment during conventional method under the conditions of being the 3rd group；
Figure 10 utilizes the present invention and the course misalignment correlation curve of misalignment during conventional method under the conditions of being the 3rd group.
Embodiment
Below in conjunction with accompanying drawing, the present invention is described in detail.
With reference to Fig. 1~4, the present invention is a kind of naval vessel SINS self-adaptive initial alignment methods based on CKF and VCE methods,
Embodiment is：
Step 1：Naval vessel SINS starts working, and gathers the metrical information of fibre optic gyroscope and accelerometer in SINS；
Step 2：The initial position on naval vessel, and the light collected are provided according to the global positioning system carried on naval vessel
Fine gyro, accelerometer information, the attitude information on naval vessel is determined using analytic expression coarse alignment algorithmic preliminaries, complete naval vessel SINS's
Coarse alignment；
Step 3：Establish the Nonlinear Error Models of naval vessel SINS under high dynamic environment：
Wherein,It is the direction cosine matrix between carrier system b to navigational coordinate system n；It is that carrier system b navigates to calculating
Direction cosine matrix between coordinate system n '；It is the direction cosines square between navigational coordinate system n to calculating navigational coordinate system n '
Battle array；It is the error in measurement of gyroscope；WithIt is n systems relative inertness coordinate system i turning rate and its measurement respectively
Error；With δ fbIt is acceleration measuring value and measurement error respectively；It is earth rotation angular speed,It is earth rotation angle
Rate error；It is the position speed calculated,It is position rate error；With δ vnIt is speed and velocity error respectively；
For accelerometer measures noise vector；For latitude；δ λ andRespectively longitude and latitude error；RMAnd RNRespectively earth
Noon face and prime plane radius of curvature；α=[αx αy αz]TIt is Eulerian angles, CωFor intermediate variable, and have
Step 4：The Nonlinear Filtering Formulae established under naval vessel high dynamic environment, expression formula are：
Involved state vector isIn formula,
δλ,For longitude and latitude error, δ vx,δvyFor east orientation north orientation velocity error, αx,αy,αzFor misalignment, εx,εy,εzFor gyroscope constant value
Drift andFor accelerometer bias；
Involved noise vector is wk=[02×1 wax way wgx wgy wgz 05×1]TIn formula, wax,way,wazFor acceleration
Count measurement noise, wgx,wgy,wgzFor the measurement noise of gyroscope；
Systematic perspective is measured as the SINS installed on naval vessel and Doppler log surveys the difference of ship velocity, measures noise
For η=[ηx ηy]T, observational equation is：
Step 5：Consider the uncertain factor in the case of naval vessel high dynamic, the adaptive CKF algorithms based on VCE are to system
Noise is estimated in real time, while estimates the misalignment of system；
1) three groups of puppet observed quantities are defined first：
2) it is theoretical according to residual error, the system residual vector of three groups of puppet observed quantities is calculated respectively, and expression formula is：
3) CKF filtering amounts are performed, calculate the variance matrix of system residual vector, calculation expression is respectively：
4) redundant obser ration part of separate three groups of measurements vector is obtained, is respectively：
5) redundant obser ration part of process-noise variance component and measuring noise square difference component is：
6) understand that the component of variance factor can be by residual vector and corresponding redundant observation according to Helmert variance components estimates
Component obtains according to equation below：
Then in any k moment, observation vector lz(k) calculation expression of variance of unit weight is：
Wherein i=1,2 ..., p, p be Δ (k) dimension.
The measuring noise square difference battle array R and process-noise variance battle array Q that can obtain system be respectively：
Step 6：Using the misalignment estimated in step (5) come the initial strapdown attitude matrix of update the system, you can
Accurate initial strap-down matrix is obtained, so as to complete the fine alignment process under moving base.
The effect of the present invention is verified by the following method：
Simulating, verifying is carried out to inventive algorithm using laboratory simulations, emulation primary condition sets as follows：
The initial parameter of table 1 is set
Simulation result is as shown in Fig. 2~Fig. 5, more clearly to analyze the superiority of the present invention, by the present invention and tradition
It is table 2 that CKF filtering algorithms arrange to course error under three groups of misalignment angular dimensions.
Course misalignment angle error simulation result under 2 three groups of parameters of table
Using the inventive method it can be seen from Fig. 2~Figure 10, longitudinally, laterally with course estimation error can in 50s it is fast
Speed convergence, horizontal error angle are converged in less than 1 jiao point, and course error angle is converged in 5 jiaos points.Compared with traditional CKF algorithms, adopt
After inventive algorithm, faster, precision is higher for course misalignment error convergence speed.In summary, method provided by the invention,
Can solve simultaneously system initially alignment in it is non-linear and uncertain, quick height can be realized in the case of various parameters
Accurate alignment.
Claims (2)
1. a kind of naval vessel strapdown inertial navigation system self-adaptive initial alignment methods, it is characterised in that comprise the following steps：
Step 1：Naval vessel SINS starts working, and gathers the metrical information of fibre optic gyroscope and accelerometer in SINS；
Step 2：The initial position on naval vessel, and the optical fiber top collected are provided according to the global positioning system carried on naval vessel
Spiral shell, accelerometer information, the attitude information on naval vessel is determined using analytic expression coarse alignment algorithmic preliminaries, complete the thick right of naval vessel SINS
It is accurate；
Step 3：Establish the Nonlinear Error Models of naval vessel SINS under high dynamic environment；
Step 4：The Nonlinear Filtering Formulae established under naval vessel high dynamic environment；
Step 5：Adaptive CKF algorithms based on VCE are estimated system noise in real time, while estimate the misalignment of system
Angle；
Step 6：Using the misalignment estimated in step 5 come the initial strapdown attitude matrix of update the system, obtain accurate
Initial strap-down matrix, complete the fine alignment process under moving base.
A kind of 2. naval vessel strapdown inertial navigation system self-adaptive initial alignment methods as claimed in claim 1, it is characterised in that
The adaptive CKF algorithms based on VCE, its step are：
1) three groups of puppet observed quantities are defined first；
2) it is theoretical according to residual error, the system residual vector of three groups of puppet observed quantities is calculated respectively；
3) CKF filterings are performed, calculate the variance matrix of system residual vector；
4) redundant obser ration part of separate three groups of measurements vector is obtained；
5) redundant obser ration part of calculating process noise variance component and measuring noise square difference component；
6) measuring noise square difference battle array and process-noise variance battle array are obtained；
7) step 3)-step 6) is repeated.
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Cited By (5)
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| CN110398257A (en) * | 2019-07-17 | 2019-11-01 | 哈尔滨工程大学 | The quick initial alignment on moving base method of SINS system of GPS auxiliary |
| CN111044049A (en) * | 2019-12-30 | 2020-04-21 | 东南大学 | Improved UKF algorithm for unmanned ship alignment under severe sea conditions |
| CN111307114A (en) * | 2019-11-29 | 2020-06-19 | 哈尔滨工程大学 | Water surface ship horizontal attitude measurement method based on motion reference unit |
| CN114061621A (en) * | 2021-11-11 | 2022-02-18 | 东南大学 | Initial alignment method based on strong tracking of large misalignment angle of rotary modulation of moving machine base |
| CN117948986A (en) * | 2024-03-27 | 2024-04-30 | 华航导控(天津)科技有限公司 | Polar region factor graph construction method and polar region integrated navigation method |
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110398257A (en) * | 2019-07-17 | 2019-11-01 | 哈尔滨工程大学 | The quick initial alignment on moving base method of SINS system of GPS auxiliary |
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| CN111044049A (en) * | 2019-12-30 | 2020-04-21 | 东南大学 | Improved UKF algorithm for unmanned ship alignment under severe sea conditions |
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| CN114061621B (en) * | 2021-11-11 | 2023-11-17 | 东南大学 | Large-misalignment-angle initial alignment method based on rotation modulation of strong tracking movable machine base |
| CN117948986A (en) * | 2024-03-27 | 2024-04-30 | 华航导控(天津)科技有限公司 | Polar region factor graph construction method and polar region integrated navigation method |
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Application publication date: 20180323 |