CN106840150A - A kind of mixed processing method for DVL failures in integrated navigation - Google Patents

A kind of mixed processing method for DVL failures in integrated navigation Download PDF

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CN106840150A
CN106840150A CN201710055425.6A CN201710055425A CN106840150A CN 106840150 A CN106840150 A CN 106840150A CN 201710055425 A CN201710055425 A CN 201710055425A CN 106840150 A CN106840150 A CN 106840150A
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dvl
moment
orientation speed
sins
regression model
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CN106840150B (en
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程向红
朱倚娴
胡杰
周玲
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Southeast University
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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
    • 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

Abstract

The invention discloses a kind of mixed processing method for DVL failures in integrated navigation, when DVL is effective, collection SINS resolves information and DVL measurement informations constitute tables of data, linear prediction model is set up using PLS, DVL measurement informations and Partial Least-Squares Regression Model prediction acquired results are subtracted each other again obtains nubbin, and as training objective, corresponding forecast model is obtained using support vector regression training；When DVL fails, set up Partial Least-Squares Regression Model and support vector regression model is utilized to predict that DVL measures linear segment and nubbin respectively, and using both sums as the DVL measurement informations predicted, so as in the case of ensureing DVL intermittent failures, the reliability of SINS/DVL integrated navigation results.The present invention is modeled using PLS and support vector regression, and uses dual model hybrid predicting, effectively increases the accuracy for predicting the outcome.

Description

A kind of mixed processing method for DVL failures in integrated navigation
Technical field
The present invention relates to integrated navigation field, and in particular to Doppler anemometer a kind of integrated navigation suitable for treatment The method of (Doppler Velocity Log, DVL) intermittent failure.
Background technology
Strapdown inertial navigation system (Strapdown Inertial Navigation System, SINS) is with its reliability The high, advantage of good concealment is widely adopted.But due to the constant error that gyro and accelerometer are present, SINS calculation results are missed Difference can be accumulated with the time, therefore it is necessary to introduce secondary navigation system composition integrated navigation.For unmanned surface vehicle (Unmanned Surface Vehicle, USV), autonomous underwater dive device (Autonomous Underwater Vehicle, The ROV such as AUV), introducing Doppler anemometer (Doppler Velocity Log, DVL) and constituting SINS/DVL integrated navigations is A kind of conventional navigation mode.
DVL using the Doppler frequency shift for being reflected back sound wave measure the relatively water-bed absolute velocity of the water surface or submarine navigation device or Speed of the person with respect to current.Due to the non-intellectual of water-bed environment and polytropy, when water-bed landform generation acute variation or the bottom are deposited In the strong material of the wave absorbtions such as mud class, DVL sound waves cannot be returned, so as to navigational speed information cannot be obtained.For between this kind of DVL The situation of having a rest property failure, it is necessary to study a kind of processing method so that the navigation accuracy of SINS/DVL integrated navigation systems is in DVL The demand of ROV is still met in failure certain hour.
Current existing integrated navigation system sensor failure processing method, estimates to lose using single forecast model more The effect due measurement information of moment sensor or direct estimation navigation error.Due to the presence of model error, Individual forecast model The precision of estimated result easily declines with the growth of sensor failure time.
Based on this, a kind of mixed processing method of DVL failures in integrated navigation system for SINS/DVL is studied so that place It is highly significant that navigation system anti-DVL out-of-service times after reason have extended.
The content of the invention
Technical problem：The present invention proposes a kind of mixed processing method for DVL failures in integrated navigation, and the method can be carried The navigation accuracy of SINS/DVL integrated navigation systems when DVL high fails, and the effectively anti-DVL failures duration of extension navigation system.
Technical scheme：A kind of mixed processing method for DVL failures in integrated navigation of the invention, including following step Suddenly：
A, when DVL is effective, SINS is observed first and resolves information and DVL measurement informations, tables of data is constituted, using partially minimum Two multiply recurrence sets up Partial Least-Squares Regression Model, is predicted with the Partial Least-Squares Regression Model, then by the DVL Measurement information and predicting the outcome for the Partial Least-Squares Regression Model are subtracted each other, the nubbin that will be obtained as training objective, Corresponding support vector regression model is obtained using support vector regression training；
B, when DVL fails, using in the step a set up Partial Least-Squares Regression Model and support vector regression mould Type predicts that DVL measures linear segment and nubbin respectively, and using both sums as the DVL measurement informations predicted, finally To predict the outcome carry out information fusion for resolving gained with SINS, to realize that the SINS/DVL under the intermittent failures of DVL is combined Navigation.
Further, in the inventive method, the process of setting up of tables of data is in step a：
When DVL is effective, information is resolved to SINS With DVL measurement informationsIt is observed, obtains N number of sample point, constitutes data Table, the tables of data includes argument data table and dependent variable tables of data；
The argument data table is：
The dependent variable tables of data is
Wherein, T1To observe the moment of PLS sample point, and in T1- 1 moment and T1- 2 moment DVL have Effect,WithRespectively T1- 2 moment SINS resolve obtain east orientation speed, north orientation speed and Course angle,WithRespectively T1- 1 moment SINS resolves east orientation speed, the north orientation speed for obtaining And course angle,WithRespectively T1Moment SINS resolve obtain east orientation speed, north orientation speed and Course angle,It is T1Moment DVL measures the east orientation speed of velocity projections to system of navigating,It is T1Moment DVL measures speed The north orientation speed that degree projection is to navigation；
In the step a, DVL measurement informations areThe prediction knot of the Partial Least-Squares Regression Model It is reallyThe two subtracts each other the nubbin for obtaining, i.e. training objectiveHave：
Wherein, T2To observe the moment of support vector regression sample point,It is T2Moment DVL measures velocity projections extremely The east orientation speed of navigation system,It is T2Moment DVL measures the north orientation speed of velocity projections to system of navigating,Respectively T2Moment Partial Least-Squares Regression Model prediction gained east orientation speed, north orientation speed,For T2Moment remnants east orientation speed,It is T2Moment remnants north orientation speed；
In the step a, SINS is resolved into parameterIt is input into as training,As training objective, M sample point is observed, support vector regression mould is obtained using support vector regression training Type；
Wherein,WithRespectively T2Moment SINS resolves east orientation speed, the north orientation speed for obtaining Degree and course angle.
Further, predict that DVL measures the idiographic flow of linear segment and nubbin in the inventive method, in step b For：
When DVL fails, the SINS at this failure moment and its early stage moment is resolved into information Input Partial Least-Squares Regression Model, model output DVL measures linear segment, and the SINS at moment of failing is resolved into informationInput support vector regression Model, model output DVL measures nubbin；
Wherein, T0For DVL fails the moment,WithRespectively T0- 2 moment SINS are resolved East orientation speed, north orientation speed and the course angle for obtaining,WithRespectively T0- 1 moment SINS is solved East orientation speed, north orientation speed and the course angle for obtaining,WithRespectively T0Moment SINS is resolved East orientation speed, north orientation speed and the course angle for obtaining.
Beneficial effect：The present invention compared with prior art, with advantages below：
(1) prior art is input into only with the relevant information at failure moment as prediction, and the present invention is utilized and failed the moment And the SINS before failure resolves input of the information as forecast model, the mode input considers the trend of velocity variations, so that Be conducive to the harmful effect for overcoming failure moment accidentalia to bring result, and there is multiple correlation for this independent variable Situation, be modeled using PLS, regression modeling is carried out to multivariable because it is directed to multivariable, it is ensured that The robustness of model, is conducive to improving the reliability for predicting the outcome.
(2) it is predicted using single model more than prior art, the present invention utilizes Partial Least-Squares Regression Model and branch Holding vector regression model carries out hybrid predicting, and PLS belongs to linear regression model (LRM), the remaining portion that its prediction is present Divide and further predicted by support vector regression, support vector regression belongs to nonlinear regression model (NLRM), therefore is carried out using dual model Complementation, overcomes Individual forecast model precision of estimation result the drawbacks of the growth of DVL out-of-service times declines to a certain extent, So as to improve the accuracy for predicting the outcome.
Brief description of the drawings
Fig. 1 is the mixed processing method theory diagram for DVL failures in integrated navigation；
Fig. 2 is to carry the velocity error simulation curve figure after mixed processing method using the present invention.
Specific embodiment
With reference to embodiment and Figure of description, the present invention is further illustrated.
A kind of mixed processing method for DVL failures in integrated navigation of the invention, using PLS and Support vector regression associated prediction DVL measurement informations, comprise the following steps that：
A, when DVL is effective, to SINS resolve information With DVL measurement informationsIt is observed, obtains N number of sample point, constitutes from change Amount tables of data With dependent variable tables of data
Wherein, T1To observe the moment of PLS sample point, and in T1- 1 moment and T1- 2 moment DVL have Effect,WithRespectively T1- 2 moment SINS resolve obtain east orientation speed, north orientation speed and Course angle,WithRespectively T1- 1 moment SINS resolves east orientation speed, the north orientation speed for obtaining And course angle,WithRespectively T1Moment SINS resolve obtain east orientation speed, north orientation speed and Course angle,It is T1Moment DVL measures the east orientation speed of velocity projections to system of navigating,It is T1Moment DVL measures speed The north orientation speed that degree projection is to navigation,
Forecast model is set up using PLS, by DVL measurement informationsA most young waiter in a wineshop or an inn partially Multiply forecast of regression model acquired resultsSubtract each other and obtain nubbinAs train mesh Mark,
Wherein, T2To observe the moment of support vector regression sample point,It is T2Moment DVL measures velocity projections extremely The east orientation speed of navigation system,It is T2Moment DVL measures the north orientation speed of velocity projections to system of navigating,Respectively T2Moment Partial Least-Squares Regression Model prediction gained east orientation speed, north orientation speed,For T2Moment remnants east orientation speed,It is T2Moment remnants north orientation speed,
SINS is resolved into parameterIt is input into as training,As training mesh Mark, observes M sample point, and support vector regression model is obtained using support vector regression training,
Wherein,WithRespectively T2Moment SINS resolves east orientation speed, the north orientation speed for obtaining Degree and course angle；
B, when DVL fails, using above-mentioned steps set up Partial Least-Squares Regression Model and support vector regression model Prediction DVL measurement informations, by moment T of failing0And its early stage moment T0- 1 and T0- 2 SINS resolves informationInput is partially most A young waiter in a wineshop or an inn multiplies regression model, and model output DVL measures linear segment, by moment T of failing0SINS resolve informationInput support vector regression model, model output DVL measures nubbin,
Wherein, T0For DVL fails the moment,WithRespectively T0- 2 moment SINS are resolved East orientation speed, north orientation speed and the course angle for obtaining,WithRespectively T0- 1 moment SINS is solved East orientation speed, north orientation speed and the course angle for obtaining,WithRespectively T0Moment SINS is resolved East orientation speed, north orientation speed and the course angle for obtaining,
Using linear segment and nubbin sum as the failure moment T for being predicted0DVL measurement informations because DVL is straight Connect measurement obtain be the speed of carrier system, and be used for SINS carry out information fusion be navigation system speed, therefore prediction DVL measurement informations be its projection to navigation system speed, will finally predict the outcome for SINS resolve gained carry out letter Breath fusion, to realize the SINS/DVL integrated navigations under the intermittent failures of DVL.
Feasibility of the invention is verified by following emulation：
(1) DVL auxiliary SINS, constitutes SINS/DVL integrated navigation systems；
(2) 0.03 °/h of gyroscope Random Constant Drift, random white noise 0.03 °/√ h, the random constant value biasing of accelerometer 0.2mg, random white noise 0.2mg, DVL velocity measurement error is the 0.5% of headway；
(3) the inertial sensor data update cycle is 10ms, and filtering cycle is 1s, simulation time 20min；
(4) when DVL is effective, 1000 samples (i.e. N=1000) of observation are observed with setting up Partial Least-Squares Regression Model 1200 samples (i.e. M=1200) are setting up support vector regression model；
(5) within 300s~420s time periods, DVL is made to fail, failure duration 120s.
By Computer Simulation, the velocity error after mixed processing method is carried using the present invention as shown in Figure 2.By Fig. 2 In correlation curve it is visible, the DVL out-of-service times section in, using the present invention carry the east orientation speed error after mixed processing method dimension Hold in ± 0.01m/s, north orientation speed error is maintained in ± 0.018m/s, and after being predicted only with PLS East orientation speed error is up to 0.022m/s, and north orientation speed error is up to 0.042m/s, and comparing to obtain, using the present invention Velocity error after carried mixed processing method is smaller, thus illustrates, institute's extracting method of the present invention energy compared with Individual forecast model Effectively improve the accuracy for predicting the outcome.
Above-described embodiment is only the preferred embodiment of the present invention, it should be pointed out that：For the ordinary skill of the art For personnel, under the premise without departing from the principles of the invention, some improvement and equivalent can also be made, these are to the present invention Claim be improved with the technical scheme after equivalent, each fall within protection scope of the present invention.

Claims (3)

1. it is a kind of in integrated navigation DVL failure mixed processing method, it is characterised in that the method is comprised the following steps：
A, when DVL is effective, first observe SINS resolve information and DVL measurement informations, constitute tables of data, using offset minimum binary Partial Least-Squares Regression Model is set up in recurrence, is predicted with the Partial Least-Squares Regression Model, then measures the DVL Information and predicting the outcome for the Partial Least-Squares Regression Model are subtracted each other, and the nubbin that will be obtained is utilized as training objective Support vector regression training obtains corresponding support vector regression model；
B, when DVL fails, using the Partial Least-Squares Regression Model and support vector regression model point set up in the step a Not Yu Ce DVL measure linear segment and nubbin, and using both sums as the DVL measurement informations predicted, finally will be pre- Surveying result is used to carry out information fusion with SINS resolving gained, to realize the SINS/DVL integrated navigations under the intermittent failures of DVL.
2. it is according to claim 1 it is a kind of in integrated navigation DVL failure mixed processing method, it is characterised in that In the step a, the process of setting up of tables of data is：
When DVL is effective, information is resolved to SINS With DVL measurement informationsIt is observed, obtains N number of sample point, constitutes data Table, the tables of data includes argument data table and dependent variable tables of data；
The argument data table is：
X = [ v S I N S ( T 1 - 2 ) e , v S I N S ( T 1 - 2 ) n , H S I N S ( T 1 - 2 ) , v S I N S ( T 1 - 1 ) e , v S I N S ( T 1 - 1 ) n , H S I N S ( T 1 - 1 ) , v S I N S ( T 1 ) e , v S I N S ( T 1 ) n , H S I N S ( T 1 ) ] N × 9 The dependent variable tables of data is
Wherein, T1To observe the moment of PLS sample point, and in T1- 1 moment and T1- 2 moment DVL are effective,WithRespectively T1- 2 moment SINS resolve east orientation speed, north orientation speed and the course for obtaining Angle,WithRespectively T1- 1 moment SINS resolves east orientation speed, north orientation speed and the boat for obtaining To angle,WithRespectively T1Moment SINS resolves east orientation speed, north orientation speed and the course for obtaining Angle,It is T1Moment DVL measures the east orientation speed of velocity projections to system of navigating,It is T1Moment DVL measures speed and throws The north orientation speed that shadow is to navigating；
In the step a, DVL measurement informations areThe Partial Least-Squares Regression Model predict the outcome forThe two subtracts each other the nubbin for obtaining, i.e. training objectiveHave：
δv ( T 2 ) e = v D V L ( T 2 ) e - v ^ P L S R ( T 2 ) e
δv ( T 2 ) n = v D V L ( T 2 ) n - v ^ P L S R ( T 2 ) n
Wherein, T2To observe the moment of support vector regression sample point,It is T2Moment DVL measures velocity projections East orientation speed,It is T2Moment DVL measures the north orientation speed of velocity projections to system of navigating,Respectively It is T2Moment Partial Least-Squares Regression Model prediction gained east orientation speed, north orientation speed,It is T2Moment remnants east orientation speed,It is T2Moment remnants north orientation speed；
In the step a, SINS is resolved into parameterIt is input into as training,Make It is training objective, observes M sample point, support vector regression model is obtained using support vector regression training；
Wherein,WithRespectively T2Moment SINS resolve obtain east orientation speed, north orientation speed and Course angle.
3. a kind of mixed processing method for DVL failures in integrated navigation according to claim 1 and 2, its feature exists In prediction DVL measures linear segment and the idiographic flow of nubbin is in the step b：
When DVL fails, the SINS at this failure moment and its early stage moment is resolved into information Input Partial Least-Squares Regression Model, model output DVL measures linear segment, and the SINS at moment of failing is resolved into informationInput support vector regression Model, model output DVL measures nubbin；
Wherein, T0For DVL fails the moment,WithRespectively T0- 2 moment SINS are resolved and obtained East orientation speed, north orientation speed and course angle,WithRespectively T0- 1 moment SINS is resolved East orientation speed, north orientation speed and the course angle for obtaining,WithRespectively T0Moment, SINS was resolved East orientation speed, north orientation speed and the course angle for arriving.
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