CN106840150B - A kind of mixed processing method to fail for DVL in integrated navigation - Google Patents

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

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CN106840150B
CN106840150B CN201710055425.6A CN201710055425A CN106840150B CN 106840150 B CN106840150 B CN 106840150B CN 201710055425 A CN201710055425 A CN 201710055425A CN 106840150 B CN106840150 B CN 106840150B
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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

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Abstract

The invention discloses a kind of mixed processing methods to fail for DVL in integrated navigation, when DVL is effective, it acquires SINS and resolves information and DVL measurement information composition tables of data, linear prediction model is established using Partial Least Squares Regression, subtract each other DVL measurement information and Partial Least-Squares Regression Model prediction acquired results to obtain nubbin again, and as training objective, corresponding prediction model is obtained using support vector regression training；When DVL failure, established Partial Least-Squares Regression Model and support vector regression model is utilized to predict that DVL measures linear segment and nubbin respectively, and using sum of the two as the DVL measurement information predicted, in the case of guaranteeing DVL intermittent failure, the reliability of SINS/DVL integrated navigation result.The present invention is modeled using Partial Least Squares Regression and support vector regression, and uses dual model hybrid predicting, effectively increases the accuracy of prediction result.

Description

A kind of mixed processing method to fail for DVL in integrated navigation
Technical field
The present invention relates to integrated navigation fields, and in particular to a kind of Doppler anemometer suitable for processing integrated navigation The method of (Doppler Velocity Log, DVL) intermittent failure.
Background technique
Strapdown inertial navigation system (Strapdown Inertial Navigation System, SINS) is with its reliability High, good concealment advantage is widely adopted.But the constant error as existing for gyro and accelerometer, SINS calculation result miss Difference can accumulate at any 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 aircraft such as AUV), introducing Doppler anemometer (Doppler Velocity Log, DVL) and constituting SINS/DVL integrated navigation is A kind of common 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 water flow.Due to the non-intellectual of water-bed environment and variability, when water-bed landform generation acute variation or the bottom are deposited In the strong substance of the wave absorbtions such as mud class, DVL sound wave can not be returned, so that navigational speed information can not be obtained.For between this kind of DVL The case where having a rest property fails, it is necessary to a kind of processing method is studied, so that the navigation accuracy of SINS/DVL integrated navigation system is in DVL The demand of aircraft is still met in failure certain time.
Current existing integrated navigation system sensor failure processing method, mostly uses single prediction model to estimate to lose Imitate the 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 to fail for DVL in SINS/DVL integrated navigation system is studied, so that place It is meaningful that the navigation system anti-DVL out-of-service time after reason, which is extended,.
Summary of the invention
Technical problem: the present invention proposes a kind of mixed processing method to fail for DVL in integrated navigation, and this method can mention The navigation accuracy of SINS/DVL integrated navigation system when high DVL fails, and effectively extend the anti-DVL failure duration of navigation system.
Technical solution: a kind of mixed processing method to fail for DVL in integrated navigation of the invention, including following step It is rapid:
A, when DVL is effective, observation SINS first resolves information and DVL measurement information, constitutes tables of data, and utilization is partially minimum Two, which multiply recurrence, establishes Partial Least-Squares Regression Model, is predicted with the Partial Least-Squares Regression Model, then by the DVL The prediction result of measurement information and the Partial Least-Squares Regression Model is subtracted each other, using obtained nubbin as training objective, Corresponding support vector regression model is obtained using support vector regression training；
B, when DVL fails, the Partial Least-Squares Regression Model and support vector regression mould established in the step a are utilized Type predicts that DVL measures linear segment and nubbin respectively, and using sum of the two as the DVL measurement information predicted, finally It is used for prediction result to resolve gained with SINS and carries out information fusion, to realize the SINS/DVL combination under the failure of DVL intermittence Navigation.
Further, in the method for the present invention, the establishment process of tables of data in step a are as follows:
When DVL is effective, information is resolved to SINS
Figure BDA0001218915020000021
It is measured with DVL Information
Figure BDA0001218915020000022
It is observed, obtains N number of sample point, constitute tables of data, the tables of data includes argument data table With dependent variable tables of data；
The argument data table are as follows:
Figure BDA0001218915020000023
Institute Stating dependent variable tables of data is
Wherein, T1At the time of to observe Partial Least Squares Regression sample point, and in T1- 1 moment and T1- 2 moment DVL have Effect,
Figure BDA0001218915020000025
With
Figure BDA0001218915020000026
Respectively T1East orientation speed that -2 moment SINS are resolved, north orientation speed and Course angle,
Figure BDA0001218915020000027
With
Figure BDA0001218915020000028
Respectively T1East orientation speed, the north orientation speed that -1 moment SINS is resolved And course angle,
Figure BDA0001218915020000029
With
Figure BDA00012189150200000210
Respectively T1East orientation speed that moment SINS is resolved, north orientation speed and Course angle,
Figure BDA00012189150200000211
For T1Moment DVL measures the east orientation speed of velocity projections to navigation system,
Figure BDA00012189150200000212
For T1Moment DVL measures speed The north orientation speed that degree projection is to navigation；
In the step a, DVL measurement information is
Figure BDA00012189150200000213
The prediction of the Partial Least-Squares Regression Model As a result it is
Figure BDA00012189150200000214
The nubbin that the two is subtracted each other, i.e. training objective are
Figure BDA00012189150200000215
Have:
Figure BDA0001218915020000031
Wherein, T2At the time of to observe support vector regression sample point,
Figure BDA0001218915020000033
For T2Moment DVL measures velocity projections extremely The east orientation speed of navigation system,For T2Moment DVL measures the north orientation speed of velocity projections to navigation system,
Figure BDA0001218915020000035
Respectively T2Moment Partial Least-Squares Regression Model prediction gained east orientation speed, north orientation speed,
Figure BDA0001218915020000036
For T2Moment remnants east orientation speed,
Figure BDA0001218915020000037
For T2Moment remnants north orientation speed；
In the step a, SINS is resolved into parameterIt is inputted as training,
Figure BDA0001218915020000039
As training objective, M sample point is observed, obtains support vector regression mould using support vector regression training Type；
Wherein,
Figure BDA00012189150200000310
With
Figure BDA00012189150200000311
Respectively T2East orientation speed that moment SINS is resolved, north orientation speed Degree and course angle.
Further, predict that DVL measures the detailed process of linear segment and nubbin in the method for the present invention, in step b Are as follows:
When DVL failure, the SINS at this failure moment and its moment early period is resolved into informationInput is inclined Least square regression model, model export DVL and measure linear segment, and the SINS of failure moment is resolved informationSupport vector regression model is inputted, model exports DVL and measures nubbin；
Wherein, T0For DVL failure moment,With
Figure BDA00012189150200000315
Respectively T0- 2 moment SINS resolved Obtained east orientation speed, north orientation speed and course angle,
Figure BDA00012189150200000316
With
Figure BDA00012189150200000317
Respectively T0- 1 moment SINS Obtained east orientation speed, north orientation speed and course angle is resolved,
Figure BDA00012189150200000318
With
Figure BDA00012189150200000319
Respectively T0Moment SINS solution Obtained east orientation speed, north orientation speed and course angle.
The utility model has the advantages that compared with prior art, the present invention having the advantage that
(1) prior art is inputted only with the relevant information of failure moment as prediction, and the present invention utilizes failure moment And the SINS before failure resolves input of the information as prediction model, which considers the trend of velocity variations, thus Be conducive to overcome failure moment accidentalia to result bring adverse effect, and there is multiple correlation for this independent variable The case where, it is modeled using Partial Least Squares Regression, since it carries out regression modeling to multivariable for multivariable, ensure that The robustness of model is conducive to the reliability for improving prediction result.
(2) prior art mostly uses single model to be predicted, the present invention utilizes Partial Least-Squares Regression Model and branch It holds vector regression model and carries out hybrid predicting, Partial Least Squares Regression belongs to linear regression model (LRM), predicts the existing portion remnants Divide and further predicted by support vector regression, support vector regression belongs to nonlinear regression model (NLRM), therefore dual model is utilized to carry out Complementation overcomes the drawbacks of Individual forecast model precision of estimation result declines with the growth of DVL out-of-service time to a certain extent, To improve the accuracy of prediction result.
Detailed description of the invention
Fig. 1 is the mixed processing method functional block diagram to fail for DVL in integrated navigation；
Fig. 2 is using the velocity error simulation curve figure after the mentioned mixed processing method of the present invention.
Specific embodiment
Below with reference to embodiment and Figure of description, the present invention is further illustrated.
A kind of mixed processing method to fail for DVL in integrated navigation of the invention, using Partial Least Squares Regression and Support vector regression associated prediction DVL measurement information, the specific steps are as follows:
A, when DVL is effective, information is resolved to SINS and DVL is measured Information is observed, and obtains N number of sample point, constitutes argument data table
Figure BDA0001218915020000043
With Dependent variable tables of data
Wherein, T1At the time of to observe Partial Least Squares Regression sample point, and in T1- 1 moment and T1- 2 moment DVL have Effect,
Figure BDA0001218915020000045
WithRespectively T1East orientation speed that -2 moment SINS are resolved, north orientation speed and Course angle,
Figure BDA0001218915020000047
With
Figure BDA0001218915020000048
Respectively T1East orientation speed, the north orientation speed that -1 moment SINS is resolved And course angle,
Figure BDA0001218915020000049
WithRespectively T1East orientation speed that moment SINS is resolved, north orientation speed and Course angle,
Figure BDA00012189150200000411
For T1Moment DVL measures the east orientation speed of velocity projections to navigation system,
Figure BDA00012189150200000412
For T1Moment DVL measures speed The north orientation speed that degree projection is to navigation,
Prediction model is established using Partial Least Squares Regression, by DVL measurement informationMinimum two partially Multiply forecast of regression model acquired results
Figure BDA0001218915020000052
Subtract each other to obtain nubbinAs train mesh Mark,
Figure BDA0001218915020000054
Figure BDA0001218915020000055
Wherein, T2At the time of to observe support vector regression sample point,
Figure BDA0001218915020000056
For T2Moment DVL measures velocity projections extremely The east orientation speed of navigation system,
Figure BDA0001218915020000057
For T2Moment DVL measures the north orientation speed of velocity projections to navigation system,
Figure BDA0001218915020000058
Respectively T2Moment Partial Least-Squares Regression Model prediction gained east orientation speed, north orientation speed,
Figure BDA0001218915020000059
For T2Moment remnants east orientation speed,
Figure BDA00012189150200000510
For T2Moment remnants north orientation speed,
SINS is resolved into parameter
Figure BDA00012189150200000511
It is inputted as training,
Figure BDA00012189150200000512
As training Target observes M sample point, obtains support vector regression model using support vector regression training,
Wherein,
Figure BDA00012189150200000513
WithRespectively T2East orientation speed that moment SINS is resolved, north orientation speed Degree and course angle；
B, when DVL fails, the Partial Least-Squares Regression Model and support vector regression model established using above-mentioned steps DVL measurement information is predicted, by failure moment T0And its moment early period T0- 1 and T0- 2 SINS resolves information
Figure BDA00012189150200000515
Input is inclined Least square regression model, model exports DVL and measures linear segment, by failure moment T0SINS resolve information
Figure BDA00012189150200000516
Support vector regression model is inputted, model exports DVL and measures nubbin,
Wherein, T0For DVL failure moment,
Figure BDA00012189150200000517
With
Figure BDA00012189150200000518
Respectively T0- 2 moment SINS resolved Obtained east orientation speed, north orientation speed and course angle,
Figure BDA00012189150200000519
With
Figure BDA00012189150200000520
Respectively T0- 1 moment SINS Obtained east orientation speed, north orientation speed and course angle is resolved,
Figure BDA00012189150200000521
WithRespectively T0Moment SINS solution Obtained east orientation speed, north orientation speed and course angle,
By the sum of linear segment and nubbin as the failure moment T predicted0DVL measurement information because DVL is straight Connect measurement obtain be carrier system speed, and be used to and SINS carry out information fusion be navigation system speed, therefore predict DVL measurement information be its projection to navigation system speed, finally by prediction result be used for SINS resolve gained carry out letter Breath fusion, to realize the SINS/DVL integrated navigation under the failure of DVL intermittence.
Feasibility of the invention is verified by emulation as follows:
(1) DVL assists SINS, constitutes SINS/DVL integrated navigation system；
(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 are the 0.5% of route speed；
(3) the inertial sensor data update cycle is 10ms, filtering cycle 1s, simulation time 20min；
(4) when DVL is effective, 1000 samples (i.e. N=1000) is observed to establish Partial Least-Squares Regression Model, are observed 1200 samples (i.e. M=1200) are to establish support vector regression model；
(5) within 300s~420s period, DVL is enabled to fail, fail duration 120s.
It is as shown in Figure 2 using the velocity error after the mentioned mixed processing method of the present invention by Computer Simulation.By Fig. 2 In correlation curve as it can be seen that in DVL out-of-service time section, using the east orientation speed error dimension after the mentioned mixed processing method of the present invention It holds in ± 0.01m/s, north orientation speed error maintains in ± 0.018m/s, and after being predicted only with Partial Least Squares Regression East orientation speed error is up to 0.022m/s, and north orientation speed error is up to 0.042m/s, and comparing can obtain, using the present invention Velocity error after mentioned mixed processing method is smaller, thus illustrates, the mentioned method energy compared with Individual forecast model of the present invention Effectively improve the accuracy of prediction result.
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, without departing from the principle of the present invention, several improvement and equivalent replacement can also be made, these are to the present invention Claim improve with the technical solution after equivalent replacement, each fall within protection scope of the present invention.

Claims (2)

1. a kind of mixed processing method to fail for DVL in integrated navigation, which is characterized in that method includes the following steps:
A, when DVL is effective, observation SINS first resolves information and DVL measurement information, constitutes tables of data, utilizes offset minimum binary Partial Least-Squares Regression Model is established in recurrence, is predicted with the Partial Least-Squares Regression Model, then measures the DVL Information and the prediction result of the Partial Least-Squares Regression Model are subtracted each other, and using obtained nubbin as training objective, are utilized Support vector regression training obtains corresponding support vector regression model；The establishment process of tables of data are as follows:
When DVL is effective, information is resolved to SINS
Figure FDA0002134808970000011
It is measured with DVL Information
Figure FDA0002134808970000012
It is observed, obtains N number of sample point, constitute tables of data, the tables of data includes argument data Table and dependent variable tables of data；
The argument data table are as follows:
The dependent variable tables of data is
Figure FDA0002134808970000014
Wherein, T1At the time of to observe Partial Least Squares Regression sample point, and in T1- 1 moment and T1- 2 moment DVL are effective,With
Figure FDA0002134808970000016
Respectively T1East orientation speed, north orientation speed and the course that -2 moment SINS are resolved Angle,
Figure FDA0002134808970000017
With
Figure FDA0002134808970000018
Respectively T1East orientation speed, north orientation speed and the boat that -1 moment SINS is resolved To angle,
Figure FDA0002134808970000019
With
Figure FDA00021348089700000110
Respectively T1East orientation speed, north orientation speed and the course that moment SINS is resolved Angle,
Figure FDA00021348089700000111
For T1Moment DVL measures the east orientation speed of velocity projections to navigation system,
Figure FDA00021348089700000112
For T1Moment DVL measures speed and throws The north orientation speed that shadow is to navigation；
In the step a, DVL measurement information is
Figure FDA00021348089700000113
The prediction result of the Partial Least-Squares Regression Model is
Figure FDA00021348089700000114
The nubbin that the two is subtracted each other, i.e. training objective areHave:
Figure FDA00021348089700000116
Wherein, T2At the time of to observe support vector regression sample point,For T2Moment DVL measures velocity projections to navigation The east orientation speed of system,
Figure FDA0002134808970000022
For T2Moment DVL measures the north orientation speed of velocity projections to navigation system,
Figure FDA0002134808970000023
Respectively T2Moment Partial Least-Squares Regression Model prediction gained east orientation speed, north orientation speed,For T2Moment remnants east orientation Speed,
Figure FDA0002134808970000025
For T2Moment remnants north orientation speed；
In the step a, SINS is resolved into parameter
Figure FDA0002134808970000026
It is inputted as training,
Figure FDA0002134808970000027
Make For training objective, M sample point is observed, obtains support vector regression model using support vector regression training；
Wherein,With
Figure FDA0002134808970000029
Respectively T2East orientation speed that moment SINS is resolved, north orientation speed and Course angle；
B, when DVL fails, the Partial Least-Squares Regression Model and support vector regression model established in the step a point is utilized Not Yu Ce DVL measure linear segment and nubbin, finally will be pre- and using sum of the two as the DVL measurement information predicted It surveys result to be used to resolve information progress information fusion with SINS, to realize the SINS/DVL integrated navigation under the failure of DVL intermittence.
2. a kind of mixed processing method to fail for DVL in integrated navigation according to claim 1, which is characterized in that Predict that DVL measures the detailed process of linear segment and nubbin in the step b are as follows:
When DVL failure, the SINS at this failure moment and its moment early period is resolved into information
Figure FDA00021348089700000210
Input is inclined Least square regression model, model export DVL and measure linear segment, and the SINS of failure moment is resolved information
Figure FDA00021348089700000211
Support vector regression model is inputted, model exports DVL and measures nubbin；
Wherein, T0For DVL failure moment,
Figure FDA00021348089700000212
With
Figure FDA00021348089700000213
Respectively T0- 2 moment SINS resolve to obtain East orientation speed, north orientation speed and course angle,
Figure FDA00021348089700000214
WithRespectively T0- 1 moment SINS resolved Obtained east orientation speed, north orientation speed and course angle,
Figure FDA00021348089700000216
With
Figure FDA00021348089700000217
Respectively T0Moment, SINS was resolved East orientation speed, north orientation speed and the course angle arrived.
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