CN110940340A - Multi-sensor information fusion method based on small UUV platform - Google Patents

Multi-sensor information fusion method based on small UUV platform Download PDF

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CN110940340A
CN110940340A CN201911335132.9A CN201911335132A CN110940340A CN 110940340 A CN110940340 A CN 110940340A CN 201911335132 A CN201911335132 A CN 201911335132A CN 110940340 A CN110940340 A CN 110940340A
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state
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gps
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任申真
吴文轩
许文正
黄天骁
刘维
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Zhongke Marine (suzhou) Marine Technology Co Ltd
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Zhongke Marine (suzhou) Marine 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
    • G01C21/20—Instruments for performing navigational calculations
    • G01C21/203—Specially adapted for sailing ships
    • 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
    • G01C21/005—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 with correlation of navigation data from several sources, e.g. map or contour matching

Abstract

The invention provides a multi-sensor information fusion method based on a small UUV platform, which comprises two stages: the first stage is as follows: building an SINS/DVL/GPS/MCP integrated navigation system model, and building a filtering state equation and a measurement equation; and a second stage: and performing optimal estimation on the state information by using the improved self-adaptive AUKF to obtain accurate navigation information. The navigation sensor adopted by the invention is provided with a Strapdown Inertial Navigation System (SINS), a Doppler log (DVL), a GPS and a Magnetic Compass (MCP), and the improved self-adaptive lossless Kalman filtering (AUKF) is introduced into the integrated navigation system, so that the navigation precision and the self-adaptive capacity of the small UUV are greatly improved, and the influence of the error of the inertial sensor and the interference of an underwater complex environment on the navigation precision can be obviously eliminated.

Description

Multi-sensor information fusion method based on small UUV platform
Technical Field
The invention relates to the technical field of sensors and navigation, in particular to a multi-sensor information fusion method based on a small UUV platform.
Background
An Underwater Unmanned Vehicle (UUV) is a Vehicle that travels Underwater, Unmanned, operated by manual remote control, or automatically controlled. UUV plays an important role in marine environment investigation, underwater resource exploration, pipeline detection, salvage and other aspects. The high-precision underwater navigation positioning technology is used as a precondition of UUV underwater operation and a technical guarantee of safe operation, determines whether the UUV underwater operation can be safely operated and returned, is particularly important in numerous key technologies, and is often used as an important index for measuring the maturity and the practicability of the UUV.
The Strapdown Inertial Navigation System (SINS) mainly uses inertial devices (an accelerometer and a gyroscope) to measure the acceleration and the angular velocity of a carrier relative to an inertial space, and then obtains navigation information of the current carrier according to initial conditions and integration. However, after the UUV works underwater for a period of time, navigation information errors are accumulated and increased along with the increase of time, so that the UUV is difficult to work alone for a long time, and the navigation accuracy of the small UUV cannot be met.
In addition, the common UKF used by UUV in the prior art has three problems, namely, the first problem of self-adaptation. The UKF has higher precision compared with the EKF, but in practice, when the UKF filter selects an initial value, if the initial value has error interference, the optimal result is influenced. Second, robustness is poor. In actual operation, the precision and stability of the filter are reduced under the driving or interference of noise. Thirdly, for the structural problem of the system model, when the system model has large errors, the filter eliminates noise through state optimal estimation and scales the covariance by using an adaptive factor, but the effect is not ideal.
Disclosure of Invention
In order to solve the problems in the prior art, the invention provides a multi-sensor information fusion method based on a small UUV platform, which can greatly improve the navigation precision and the self-adaptive capacity of the small UUV and obviously eliminate the influence of the error of an inertial sensor and the interference of an underwater complex environment on the navigation precision.
In order to achieve the purpose, the invention adopts the following technical scheme:
the multi-sensor information fusion method based on the small UUV platform comprises two stages:
the first stage is as follows: building an SINS/DVL/GPS/MCP integrated navigation system model, and building a filtering state equation and a measurement equation;
and a second stage: and performing optimal estimation on the state information by using the improved self-adaptive AUKF to obtain accurate navigation information.
Further, the first stage specifically comprises the following steps:
step 1: respectively building up an SINS, DVL and MCP error model;
step 2: establishing a state equation and an observation equation of unscented Kalman filtering (AUKF):
according to an error model of the inertial sensor, defining a state variable of the fiber inertial navigation as
Figure BDA0002330743040000021
The state variable of DVL is XDVL＝[δVd,δΔ,δc]Heading angle error delta psi of magnetic compassMCPIs a state variable of the system, i.e. XMCP＝δψMCP；
The state equation of the SINS/DVL/GPS/MCP combined navigation system is as follows:
Figure BDA0002330743040000022
wherein X ═ XINS,XDVL,XMCP]TA state variable representing the state of the system,
Figure BDA0002330743040000023
and G represents a state transition matrix and a noise input matrix, respectively;
the measurement equation is expressed as: Z-HX + V
Wherein the content of the first and second substances,
Figure BDA0002330743040000024
the measurement information is:
Figure BDA0002330743040000031
wherein L isINS,λINS,hINSRespectively representing longitude, latitude and altitude of inertial navigation measurement;
LGPS,λGPS,hGPSrespectively representing longitude, latitude and altitude measured by the GPS;
Ve,INS,Vn,INS,Vu,INSrespectively representing east, north and sky velocity values of inertial navigation measurement;
Ve,DVL,Vn,DVL,Vu,DVLrespectively representing the east, north and sky speed values measured by the Doppler log;
Figure BDA0002330743040000032
respectively representing the heading angles measured by the inertial navigation system and the magnetic compass.
Further, the second stage specifically includes the following steps:
step S1: selecting a reasonable adaptive factor ak，akConstructed according to the following formula:
Figure BDA0002330743040000033
wherein the residual error
Figure BDA0002330743040000034
P is a covariance matrix.
Step S2: and (3) state noise adaptive processing: if the observation equation is linear, assuming that under the interference of additive noise, the covariance of the state during filtering is expressed as:
Figure BDA0002330743040000035
the innovation generated in the observation equation can be expressed as:
Figure BDA0002330743040000036
under optimal estimation conditions, dkFor Gaussian white noise with an expected value of 0, the variance is taken for two sides to obtain:
Figure BDA0002330743040000041
the scale factor is introduced according to the above equation:
Figure BDA0002330743040000042
a can be represented roughly
Figure BDA0002330743040000043
And
Figure BDA0002330743040000044
the state noise covariance of the system model can be expressed by the following equation:
Figure BDA0002330743040000045
the scaling factor a may be a number greater or less than 1, which may adaptively adjust QkOnly when
Figure BDA0002330743040000046
And
Figure BDA0002330743040000047
when stable, a will be a fixed value of 1.
Compared with the prior art, the invention has the beneficial technical effects that: the invention relates to a multi-sensor information fusion method based on a small UUV platform, which is characterized in that a navigation sensor is provided with a Strapdown Inertial Navigation System (SINS), a Doppler log (DVL), a GPS, a Magnetic Compass (MCP) and the like, a combined navigation system model of the SINS/DVL/GPS/MCP is built, a state equation and an observation equation of filtering are built, improved self-adaptive lossless Kalman filtering (AUKF) is introduced into the combined navigation system, a covariance matrix and a state noise covariance matrix of state information and observation information are adaptively adjusted, and the weight of the state information and the observation information in an optimal filtering result is balanced, so that the navigation precision and the self-adaptive capacity of the small UUV are greatly improved, and the influence of errors of the inertial sensor and the interference of an underwater complex environment on the navigation precision can be obviously eliminated.
Drawings
FIG. 1 is a flow chart of an integrated navigation system according to an embodiment of the present invention;
FIG. 2 is a graph of simulation errors obtained using a common UKF algorithm;
FIG. 3 is a graph of simulation error obtained using the modified AUKF algorithm.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the drawings of the embodiments of the present invention. It is to be understood that the embodiments described are only a few embodiments of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the described embodiments of the invention, are within the scope of the invention.
The technical scheme of the invention is further explained in detail by combining the drawings in the specification.
The multi-sensor information fusion method based on the small UUV platform comprises two stages:
the first stage is as follows: building an SINS/DVL/GPS/MCP integrated navigation system model, and building a filtering state equation and a measurement equation; for the long-time diving operation of the UUV, the navigation error is continuously increased along with the accumulation of time, and the high-precision navigation cannot be met, so that an auxiliary sensor is necessary to be used;
as shown in fig. 1, UUV is equipped with inertial navigation system (SINS) using high frequency Inertial Measurement Unit (IMU) to calculate position, velocity and attitude information, which is the main navigation system of UUV; in addition, a Doppler log (DVL) is adopted to measure the speed of the carrier relative to the bottom, so as to provide speed information for the SINS and inhibit the increase of the position error; the magnetic compass MCP provides heading information for the combined navigation system and the DVL; the GPS provides water surface position information; therefore, the increase of navigation errors can be effectively inhibited, and the navigation precision is improved;
the first stage specifically comprises the following steps:
step 1: respectively building up an SINS, DVL and MCP error model;
1) the SINS error model, the error equation for inertial navigation attitude may be described as:
Figure BDA0002330743040000051
wherein psin＝[ψeψnψu]TThree attitude angles, epsilon, representing inertial navigationn＝[εeεnεu]TIs the random drift of the gyroscope and,
Figure BDA0002330743040000052
representing the angular velocity of rotation of the earth in a navigational coordinate system, has
Figure BDA0002330743040000053
Figure BDA0002330743040000054
Representing the angular velocity of rotation of the navigational coordinate system relative to the terrestrial coordinate system, has
Figure BDA0002330743040000055
L and lambda are respectively the latitude and longitude of the point where the carrier is located;
the velocity error equation for inertial navigation can be described as:
Figure BDA0002330743040000061
in the formula, δ Vn＝[δVeδVnδVu]TThe speed error is indicated in the form of a speed error,
Figure BDA0002330743040000062
indicating the deviation of the accelerometer under the navigation system. The error of the inertial device can be seen as constant and with zero mean white noise interference. The error models for gyroscopes and accelerometers can generally be reduced to separate models
Figure BDA0002330743040000063
And
Figure BDA0002330743040000064
and the white noise component is wgAnd wa；
According to
Figure BDA0002330743040000065
And
Figure BDA0002330743040000066
the position error of inertial navigation can be expressed as:
Figure BDA0002330743040000067
wherein δ L, δ λ and δ h represent latitude, longitude and altitude errors, respectively;
2) an error model of Doppler log (DVL), according to the working principle of Doppler log, the Doppler log measures the drift angle and velocity of UUV relative to seabed or water layer, and the measured error mainly includes drift angle error delta and velocity offset error delta VdAnd scale factor error δ c. Wherein, δ Δ and δ VdCan be described by a first-order Markov process, where δ c is a random constant, and then the corresponding Doppler log error equation is:
Figure BDA0002330743040000068
in the formula (I), the compound is shown in the specification,
Figure BDA0002330743040000069
and
Figure BDA00023307430400000610
respectively, the relevant time;
3) magnetic Compass (MCP) error model, whose error model (T) can be described approximately by a first order Markov process, according to the operating principle of the magnetic compassMCPIs a correlation time, wMCPRandom white noise):
Figure BDA0002330743040000071
step 2: establishing a state equation and an observation equation of unscented Kalman filtering (AUKF):
according to an error model of the inertial sensor, defining a state variable of the fiber inertial navigation as
Figure BDA0002330743040000072
The state variable of DVL is XDVL＝[δVd,δΔ,δc]Heading angle error delta psi of magnetic compassMCPIs a state variable of the system, i.e. XMCP＝δψMCP；
The state equation of the SINS/DVL/GPS/MCP combined navigation system is as follows:
Figure BDA0002330743040000073
wherein X ═ XINS,XDVL,XMCP]TA state variable representing the state of the system,
Figure BDA0002330743040000074
and G represents a state transition matrix and a noise input matrix, respectively;
the measurement equation is expressed as: Z-HX + V
Wherein the content of the first and second substances,
Figure BDA0002330743040000075
the measurement information is:
Figure BDA0002330743040000076
wherein L isINS,λINS,hINSRespectively representing longitude, latitude and altitude of inertial navigation measurement;
LGPS,λGPS,hGPSrespectively representing longitude, latitude and altitude measured by the GPS;
Ve,INS,Vn,INS,Vu,INSrespectively representing east, north and sky velocity values of inertial navigation measurement;
Ve,DVL,Vn,DVL,Vu,DVLrespectively representing the east, north and sky speed values measured by the Doppler log;
Figure BDA0002330743040000081
respectively representing the heading angles measured by the inertial navigation system and the magnetic compass.
And a second stage: the method comprises the following steps of performing optimal estimation on state information by using an improved self-adaptive AUKF to obtain accurate navigation information:
step S1: selecting a reasonable adaptive factor ak: adaptive factor akRelated to the covariance matrix and residual error of the system, the initial value is 1, and the value range is that a is more than or equal to 0kLess than or equal to 1; if a iskThe weight ratio between the system model prediction information and the measurement information can be balanced if the value is reasonable;
akconstructed according to the following formula:
Figure BDA0002330743040000082
wherein the residual error
Figure BDA0002330743040000083
P is a covariance matrix;
according to the above formula, akWhen there is an error disturbance in the state value, akIf the weight is less than 1, the weight of the predicted value in the calculation of the optimal estimated value is as small as possible; when the predicted value changes obviously abnormally, akWill be close to 0, i.e. the predicted value at this timeThe weight is 0 in calculating the optimal estimated value, so akCan use VkAnd
Figure BDA0002330743040000084
adaptive modulation
Figure BDA0002330743040000085
Step S2: and (3) state noise adaptive processing: when the model error and the external noise ratio of the system are large, if attenuation is performed only by means of the above adaptive factors, the obtained optimal state estimation value is not ideal, and further adaptive processing needs to be performed on state noise to obtain an accurate state value;
if the observation equation is linear, assuming under the interference of additive noise, the covariance of the state during filtering can be expressed as:
Figure BDA0002330743040000086
covariance
Figure BDA0002330743040000091
And calculated by UKF filter formula
Figure BDA0002330743040000092
Plays an important role in the variance prediction of the system. Is mainly based on
Figure BDA0002330743040000093
And
Figure BDA0002330743040000094
judging whether the state noise covariance needs scaling or not according to the change of the state noise variance; the innovation generated in the observation equation can be expressed as:
Figure BDA0002330743040000095
under optimal estimation conditions, dkTo expect forGaussian white noise with a value of 0, the variance is taken for both sides to obtain:
Figure BDA0002330743040000096
the scale factor is introduced according to the above equation:
Figure BDA0002330743040000097
a can be represented roughly
Figure BDA0002330743040000098
And
Figure BDA0002330743040000099
the state noise covariance of the system model can be expressed by the following equation:
Figure BDA00023307430400000910
the scaling factor a may be a number greater or less than 1, which may adaptively adjust QkOnly when
Figure BDA00023307430400000911
And
Figure BDA00023307430400000912
when stable, a will be a fixed value of 1.
Experiments and simulations of the above method:
the experimental use of a small UUV of a certain type, the type and performance index of the built-in main navigation sensors SINS and DVL are shown in tables 1 and 2. Lake testing is carried out in water areas near longitude 120.5604 and latitude 31.8921, before the UUV is launched, power-on self-inspection is carried out on the UUV, a propeller and a rudder of the UUV can work normally, and then optical fiber inertial navigation is started to finish north-seeking work (2-3 min).
Table 1: inertial navigation performance meter
Figure BDA00023307430400000913
Figure BDA0002330743040000101
Table 2: DVL Performance Table
Figure BDA0002330743040000102
MATLAB simulation is carried out on the method by using the acquired navigation data, and simulation results obtained by respectively using the UKF and the improved AUKF algorithm are shown in FIGS. 2 and 3.
Fig. 2 and 3 are graphs of velocity and position error simulations using the conventional UKF algorithm and the modified UKF algorithm, respectively. It can be seen from the comparison between fig. 2 and fig. 3 that the position and speed errors obtained by using the improved AUKF algorithm are small, because in the actual working environment, various noise interferences exist, and have a certain time-varying property, while the improved AUKF algorithm has a certain adaptive capacity and a good state estimation effect, which indicates that the improved AUKF algorithm has a certain superiority in the multi-sensor fusion work.
The invention relates to a multi-sensor information fusion method based on a small UUV platform, which is characterized in that a navigation sensor is provided with a Strapdown Inertial Navigation System (SINS), a Doppler log (DVL), a GPS, a Magnetic Compass (MCP) and the like, a combined navigation system model of the SINS/DVL/GPS/MCP is built, a state equation and an observation equation of filtering are built, improved self-adaptive lossless Kalman filtering (AUKF) is introduced into the combined navigation system, a covariance matrix and a state noise covariance matrix of state information and observation information are adaptively adjusted, and the weight of the state information and the observation information in an optimal filtering result is balanced, so that the navigation precision and the self-adaptive capacity of the small UUV are greatly improved, and the influence of errors of the inertial sensor and the interference of an underwater complex environment on the navigation precision can be obviously eliminated.
While the foregoing is directed to the preferred embodiment of the present invention, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention as defined in the appended claims.

Claims (3)

1. The multi-sensor information fusion method based on the small UUV platform is characterized by comprising two stages:
the first stage is as follows: building an SINS/DVL/GPS/MCP integrated navigation system model, and building a filtering state equation and a measurement equation;
and a second stage: and performing optimal estimation on the state information by using the improved self-adaptive AUKF to obtain accurate navigation information.
2. The small UUV platform-based multi-sensor information fusion method as claimed in claim 1, wherein the first stage specifically comprises the following steps:
step 1: respectively building up an SINS, DVL and MCP error model;
step 2: establishing a state equation and an observation equation of Unscented Kalman Filtering (UKF):
according to an error model of the inertial sensor, defining a state variable of the fiber inertial navigation as
Figure FDA0002330743030000011
The state variable of DVL is XDVL＝[δVd,δΔ,δc]Heading angle error delta psi of magnetic compassMCPIs a state variable of the system, i.e. XMCP＝δψMCP；
The state equation of the SINS/DVL/GPS/MCP combined navigation system is as follows:
Figure FDA0002330743030000012
wherein X ═ XINS,XDVL,XMCP]TA state variable representing the state of the system,
Figure FDA0002330743030000013
and G represents a state transition matrix and a noise input matrix, respectively;
the measurement equation is expressed as: Z-HX + V
Wherein the content of the first and second substances,
Figure FDA0002330743030000014
the measurement information is:
Figure FDA0002330743030000021
wherein L isINS,λINS,hINSRespectively representing longitude, latitude and altitude of inertial navigation measurement;
LGPS,λGPS,hGPSrespectively representing longitude, latitude and altitude measured by the GPS;
Ve,INS,Vn,INS,Vu,INSrespectively representing east, north and sky velocity values of inertial navigation measurement;
Ve,DVL,Vn,DVL,Vu,DVLrespectively representing the east, north and sky speed values measured by the Doppler log;
Figure FDA0002330743030000022
respectively representing the heading angles measured by the inertial navigation system and the magnetic compass.
3. The small UUV platform-based multi-sensor information fusion method as claimed in claim 1, wherein the second stage specifically comprises the following steps:
step S1: selecting a reasonable adaptive factor ak，akConstructed according to the following formula:
Figure FDA0002330743030000023
wherein the residual error
Figure FDA0002330743030000024
P is a partyA difference matrix.
Step S2: and (3) state noise adaptive processing: if the observation equation is linear, assuming that under the interference of additive noise, the covariance of the state during filtering is expressed as:
Figure FDA0002330743030000025
the innovation generated in the observation equation can be expressed as:
Figure FDA0002330743030000026
under optimal estimation conditions, dkFor Gaussian white noise with an expected value of 0, the variance is taken for two sides to obtain:
Figure FDA0002330743030000031
the scale factor is introduced according to the above equation:
Figure FDA0002330743030000032
a can be represented roughly
Figure FDA0002330743030000033
And
Figure FDA0002330743030000034
the state noise covariance of the system model can be expressed by the following equation:
Figure FDA0002330743030000035
the scaling factor a may be a number greater or less than 1, which may adaptively adjust QkOnly when
Figure FDA0002330743030000036
And
Figure FDA0002330743030000037
when stable, a will be a fixed value of 1.
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CN115060274A (en) * 2022-08-17 2022-09-16 南开大学 Underwater integrated autonomous navigation device and initial alignment method thereof
CN115079113A (en) * 2022-08-22 2022-09-20 国家海洋技术中心 Ground wave radar directional diagram measuring method and system based on unmanned ship

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