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CN112729291B - SINS/DVL ocean current velocity estimation method for deep-submergence long-endurance submersible - Google Patents

SINS/DVL ocean current velocity estimation method for deep-submergence long-endurance submersible Download PDF

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CN112729291B
CN112729291B CN202011599375.6A CN202011599375A CN112729291B CN 112729291 B CN112729291 B CN 112729291B CN 202011599375 A CN202011599375 A CN 202011599375A CN 112729291 B CN112729291 B CN 112729291B
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CN112729291A (en
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刘锡祥
盛广润
刘贤俊
赵立业
黄永江
张玉鹏
赵苗苗
王子璇
蒲文浩
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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
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    • G01C21/203—Specially adapted for sailing ships

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Abstract

The invention discloses a SINS/DVL ocean current velocity estimation method for a deep-submergence long-range submersible, which comprises the following steps: 1. based on a SINS (strapdown inertial navigation System) under specific track motion, performing two-time navigation calculation output, namely SINS1 and SINS2, designing a high-pass filter to obtain the real horizontal speed of the SINS1, and combining the real horizontal speed with the SINS2 to realize SINS self-assisted navigation; 2. according to the Doppler effect principle, the deep-submerged long-range diving vehicle working in the middle water area measures the convection velocity by using the water tracking mode of a Doppler log DVL; 3. and estimating the ocean current velocity by using a least square estimation algorithm RLS in combination with high-precision navigation parameters provided by SINS self-assisted navigation and the convection velocity acquired by using the DVL. The invention can estimate the ocean current velocity information of the middle water area through the SINS/DVL under the conditions that navigation information sources are lacked and the ocean current velocity is difficult to acquire in real time.

Description

SINS/DVL ocean current velocity estimation method for deep-submergence long-endurance submersible
Technical Field
The invention belongs to the technical field of underwater navigation and positioning of manned submersible vehicles, and relates to a SINS/DVL ocean current velocity estimation method for a deep-submerged long-range submersible vehicle.
Background
Because of the attenuation of electromagnetic signals under water, the GPS cannot be used for navigation positioning in the middle water of the sea, and likewise, the doppler velocimeter DVL far from the sea bottom cannot be directly used for navigation. Navigation information sources are scarce in the middle water area, and only the inertial measurement unit IMU and the acoustic sensor are available. At present, especially for deep-submerged long-range underwater vehicles which execute long-time and long-distance submerged missions in large depth, how to utilize scarce navigation information sources in deep sea areas to carry out navigation research on middle-layer water areas has important scientific significance.
For a deep-submergence long-range submarine, the SINS/DVL combined navigation cannot be directly realized under the condition that the ocean current velocity of a middle-layer water area is unknown, and the ocean current velocity needs to be obtained in real time; even ocean current velocity information for mid-water areas may need to be observed in real time for military defense. Therefore, the importance of the ocean current velocity information is not self evident, but it is difficult to obtain the ocean current velocity in real time under the condition of middle water area with a lack of available navigation information sources. Therefore, under the technical conditions of the existing underwater navigation sensor, it is important to design a feasible and accurate estimation scheme for the ocean current velocity of the middle water area.
Patent document (application No.: CN 201910509338.2): an SINS self-assisted navigation method of a deep diving manned submersible. Firstly, SINS1 and SINS2 are obtained by carrying out SINS navigation solution twice, and then the horizontal velocity output by SINS1 is filtered and then is combined with SINS2 to construct velocity matching navigation. The key technology is that the actual motion information of the carrier is extracted by utilizing the submergence/surfacing motion characteristics of the carrier and the SINS error propagation characteristics of strapdown inertial navigation, and then the SINS self-assisted navigation is realized through Kalman filtering. The method is based on specific track motion, is only suitable for the navigation process of the submergence/ascent stage of the deep-loading submersible, but cannot provide an effective navigation method for the cruise task of the deep-submergence long-range submersible in the middle water area; and this method also cannot acquire ocean current velocity information.
Disclosure of Invention
Aiming at the problems, the invention provides an SINS/DVL ocean current velocity estimation method for a deep-submergence long-range vehicle. Aiming at the current situation that the navigation information source of the middle water area is lack and the ocean current velocity is difficult to obtain, the SINS/DVL ocean current velocity estimation method of the deep-submerged long-range submersible is provided, and is characterized by comprising the following specific steps of:
(1) based on a SINS (strapdown inertial navigation System) under specific track motion, performing two-time navigation calculation output, namely SINS1 and SINS2, designing a high-pass filter to obtain the real horizontal speed of the SINS1, and combining the real horizontal speed with the SINS2 to realize SINS self-assisted navigation;
in the step 1, based on the strapdown inertial navigation system SINS under the specific trajectory motion, two navigation calculation outputs are performed, namely the SINS1 and the SINS2, a high-pass filter is designed to obtain the true horizontal velocity of the SINS1, and the high-pass filter is combined with the SINS2 to realize the SINS self-assisted navigation as follows:
s1.1: analyzing the track characteristic of the spiral submergence motion of the manned submersible vehicle and analyzing the SINS error propagation characteristic; the real horizontal speed of the SINS1 under the circular motion track is regarded as a high-frequency signal, and the horizontal speed error generated by the SINS1 working mechanism is regarded as a low-frequency signal;
the horizontal velocities output by SINS1 include: the true horizontal velocity and the horizontal velocity error are subjected to the existence of initial alignment error, installation error and navigation algorithm error to obtain an SINS system error model:
misalignment angle error equation:
Figure BDA0002868862780000021
the velocity error equation:
Figure BDA0002868862780000022
position error equation:
Figure BDA0002868862780000023
in the above formula, i represents an inertial coordinate system, n represents a navigation coordinate system, e represents a terrestrial coordinate system, and b represents a body coordinate system; phi is ═ phiN φE φD]TIs Euler misalignment angle, representing the rotation between the SINS calculated navigation coordinate system and the real navigation coordinate system n; delta Vn＝[δVN δVE δVD]TIs the speed error; δ L, δ λ, δ h are latitude, longitude and altitude errors, respectively; wherein the horizontal velocity error is delta VNAnd δ VEThree different vibration errors are involved, respectively:
hold down oscillation Ts：
Figure BDA0002868862780000024
Earth oscillation Te：
Figure BDA0002868862780000025
Foucault oscillation TF：
Figure BDA0002868862780000026
In the above, R is the earth radius, Ω is the earth rotation angular velocity, L is the latitude, and g is the gravitational acceleration;
the spiral submergence/upward floating of the deep submergence manned submersible is represented by circular track motion, and the speed of the deep submergence manned submersible in the horizontal direction is periodically changed; in the submergence or upward floating process of the submersible, the pull-down oscillation with the period T being 84.4 minutes in the horizontal speed error is considered; the change period of the real horizontal velocity is smaller than that of the Lagrange oscillation period, so that the real horizontal velocity is regarded as a high-frequency signal, and the horizontal velocity error is regarded as a low-frequency signal;
s1.2: designing a high-pass non-delay ZD-HPF digital filter, processing the horizontal speed output by the SINS1, filtering low-frequency speed error components, and further acquiring the real horizontal speed of the manned submersible;
setting the technical index (omega) of the digital high-pass filter according to the frequency difference characteristics of different signals to be preserved and filteredp,ωs,αp,αs) The filter is characterized in that the filter is a passband cut-off frequency, a stopband allowed maximum attenuation and a stopband allowed minimum attenuation respectively, the filter is converted into a corresponding simulation high-pass filter technical index through a bilinear transformation mapping relation through frequency, and the conversion formula is as follows:
Figure BDA0002868862780000031
technical index (omega) based on analog high-pass filterp,Ωs,αp,αs) By frequency conversion of the formula
Figure BDA0002868862780000032
Converted into an analog low-pass filter technical index (lambda)p,λs,αp,αs) For simple calculation, the passband boundary frequency of the normalized analog low-pass prototype system function G (p) is taken as lambdap1, the normalized stopband boundary frequency can be found as:
Figure BDA0002868862780000033
according to frequency-converted (lambda)p,λs,αp,αs) Calculating the order N of the filter:
Figure BDA0002868862780000034
Figure BDA0002868862780000035
Figure BDA0002868862780000036
designing a Butterworth filter, inquiring a normalized filter parameter table to obtain a normalized low-pass prototype system function G (p) corresponding to N, and obtaining a transfer function H of the analog high-pass filter by normalizationh(s) converting it into a digital low-pass filter H by means of a bilinear transformationl(z)；
Then the complementary idea is utilized to convert the digital high-pass filter into a digital high-pass filter H without time delayh(z)：
Hh(z)＝1-Hl(z) (12)
S1.3: combining the real horizontal velocity obtained in the step 1.2 with SINS2 through a kalman filter to realize SINS self-assisted navigation;
selecting an attitude misalignment angle, a speed error, a position error, a gyroscope constant zero offset and an accelerometer constant zero offset as state variables:
Figure BDA0002868862780000041
the state equation is:
Figure BDA0002868862780000042
wherein the system transfer matrix is:
Figure BDA0002868862780000043
a system interference matrix:
Figure BDA0002868862780000044
system noise vector:
Figure BDA0002868862780000045
horizontal velocity of SINS1 processed by high pass filter ZD-HPF obtained from S1.2
Figure BDA0002868862780000046
Figure BDA0002868862780000047
Selecting the horizontal velocity of the SINS2 output
Figure BDA0002868862780000048
And
Figure BDA0002868862780000049
the difference was measured as Z:
Figure BDA00028688627800000410
discretized kalman filter state equations and measurement equations:
Figure BDA0002868862780000051
solving is carried out according to a kalman filter, a state estimator is obtained to correct the output of the SINS2, the SINS self-assisted navigation is realized, and therefore a high-precision navigation solution is output
Figure BDA0002868862780000052
And
Figure BDA0002868862780000053
(2) according to the principle of Doppler effect, a deep-submerged long-range submersible working in a middle water area measures the convection velocity by using the water tracking mode of a Doppler log DVL;
in the step 2, according to the principle of the doppler effect, the deep submersible vehicle working in the middle water area utilizes the water tracking mode of the doppler log DVL, and the method for measuring the convection velocity is as follows:
s2.1: according to the Doppler effect principle, under the condition of DVL speed measurement based on single beam configuration, the carrier speed v is solvedx；
The Doppler velocimeter DVL transmits ultrasonic waves to the seabed through an ultrasonic transducer arranged on a carrier, the speed of the carrier is measured according to the Doppler effect, the Doppler effect refers to the physics phenomenon that the frequency receiving frequency is different when a transmitting sound source moves relative to a medium, and the frequency f of the transmitting sound wave is known0Angle alpha, propagation velocity c0As long as the Doppler shift f is measureddThe size of (d) gives the carrier velocity:
Figure BDA0002868862780000054
s2.2: the speed measurement of the DVL configured by four beams is realized, the water tracking mode of the DVL working in the middle water area is selected, and the convection velocity is solved
Figure BDA0002868862780000055
Assuming forward and backward beams of a dual-beam DVL systemThe transmission frequencies are all f0Difference f in Doppler shift between forward and backward directionsd1And fd2Comprises the following steps:
Figure BDA0002868862780000056
from the above formula, one can obtain:
Figure BDA0002868862780000057
the velocity in the dual beam configuration DVL is obtained from equation (21):
Figure BDA0002868862780000058
the velocity under the four-beam DVL system is known from equation (24):
Figure BDA0002868862780000061
under the condition of a middle water area where the deep-diving long-endurance submersible is positioned, the distance from the submersible to the sea bottom far exceeds the range of DVL speed measurement, so that the bottom tracking mode of the DVL cannot be used, and the DVL can only work in the water tracking mode to measure the speed of relative water flow
Figure BDA0002868862780000062
(3) Combining high-precision navigation parameters provided by SINS self-assisted navigation and convection velocity obtained by DVL, and estimating the ocean current velocity by using a least square estimation algorithm RLS;
in the step 3, the method for estimating the ocean current velocity by using the least square estimation algorithm RLS in combination with the high-precision navigation parameters provided by the SINS self-assisted navigation and the convection velocity obtained by using the DVL is as follows:
s3.1: high-precision navigation solution for arranging SINS self-assisted navigation output obtained in step 1
Figure BDA0002868862780000063
And the measured convection velocity of the DVL obtained in step 2
Figure BDA0002868862780000064
Preparing data serving as ocean current estimation;
s3.2: modeling actual DVL measurements to solve for ocean current velocities
Figure BDA0002868862780000065
For the purpose, build and SINS self-assisted navigation output
Figure BDA0002868862780000066
A relational relation;
through analyzing the DVL velocity measurement principle, the establishment of a DVL velocity measurement model is as follows:
Figure BDA0002868862780000067
in the above-mentioned manner,
Figure BDA0002868862780000068
is the DVL measurement, i.e., the convective velocity; δ k represents the DVL scale factor error,
Figure BDA0002868862780000069
Vdrepresenting an error-free ideal value measured in the DVL coordinate system; delta VdRepresenting the random measurement error of the DVL, during the estimation of the ocean current,
Figure BDA00028688627800000610
and
Figure BDA00028688627800000611
and also Vc nThe relation of (A) is as follows:
Figure BDA00028688627800000612
from step 1, the attitude matrix
Figure BDA00028688627800000613
And velocity
Figure BDA00028688627800000614
Is a navigation solution output by the SINS self-assisted navigation; vc n＝[VcN VcE VcD]TThen representing the projection of the ocean current velocity under the navigation system n;
Figure BDA00028688627800000615
the coordinate transformation matrix from the DVL instrument coordinate d system to the submersible vehicle b system can be obtained by the following formula:
Figure BDA00028688627800000616
wherein I is a 3-order identity matrix;
Figure BDA00028688627800000617
indicates the installation error angle between the d series and the b series,
Figure BDA00028688627800000618
αγ、αθin order to horizontally install the error angle,
Figure BDA00028688627800000619
the course installation error angle;
formula (28) is substituted for formula (27):
Figure BDA0002868862780000071
and (4) sorting the relational expression, namely: velocity of ocean currents Vc nAnd the installation error angle
Figure BDA0002868862780000072
Moving to the right of the equation, the known term is transferred to the other side of the equation, resulting in:
Figure BDA0002868862780000073
s3.3: estimating a Recursive Least Square, and estimating a horizontal ocean current velocity by using an RLS algorithm;
since the SINS self-assisted navigation outputs horizontal speed and the depth meter outputs height information, only the horizontal ocean current speed V needs to be consideredc nIn order to estimate the unknown horizontal ocean current velocity Vc nAnd an installation error angle α, setting a state variable:
Figure BDA0002868862780000074
the system state equation is:
Figure BDA0002868862780000075
according to equation (29) of step 3.2, the quantity is selected as:
Figure BDA0002868862780000076
wherein, the attitude matrix
Figure BDA0002868862780000077
Figure BDA0002868862780000078
Further, the system measurement equation:
Z2＝H2X2+V2 (35)
wherein
Figure BDA0002868862780000079
Estimating a Recursive Least Square to estimate a Recursive Least Square (RLS) algorithm to estimate an installation error angle and a horizontal ocean current velocity Vc nThe calculation formula is as follows:
Figure BDA00028688627800000710
by the calculation formula, the mounting angle error and the ocean current velocity information V can be estimatedc n。
As a further improvement of the present invention, in order to accurately measure the three-dimensional velocity of the carrier and reduce the influence of the carrier due to the bumpiness and the undulation, it is generally necessary to simultaneously emit one ultrasonic beam in each of the four directions, i.e., the front, the back, the left, and the right of the carrier, so as to form a four-beam Janus orthogonal configuration. While only three beams are required to provide three components of velocity, it is relatively easier for a four-beam configuration planar array antenna to produce four beams; and one more measurement equation can improve the accuracy and the measurement redundancy of the DVL speed calculation and realize the improvement of the system reliability.
The invention discloses a SINS/DVL ocean current velocity estimation method for a deep-submergence long-range submersible, which comprises the following steps: 1. based on a SINS (strapdown inertial navigation System) under specific track motion, performing two-time navigation calculation output, namely SINS1 and SINS2, designing a high-pass filter to obtain the real horizontal speed of the SINS1, and combining the real horizontal speed with the SINS2 to realize SINS self-assisted navigation; 2. according to the Doppler effect principle, the deep-submerged long-range diving vehicle working in the middle water area measures the convection velocity by using the water tracking mode of a Doppler log DVL; 3. and estimating the ocean current velocity by using a least square estimation algorithm RLS in combination with high-precision navigation parameters provided by SINS self-assisted navigation and the convection velocity acquired by using the DVL. The invention can estimate the ocean current velocity information of the middle water area through the SINS/DVL under the conditions that navigation information sources are lacked and the ocean current velocity is difficult to acquire in real time.
Drawings
FIG. 1 is a diagram of an SINS self-assisted navigation framework according to an embodiment of the present invention;
FIG. 2 is a flow diagram of a delay-free high pass filter (ZD-HPF) design according to an embodiment of the invention;
FIG. 3 is a schematic diagram of a four beam configuration Doppler log test according to an embodiment of the invention;
FIG. 4 is a flow chart of the estimation of the ocean current velocity of the SINS/DVL of the deep-submerged long-range submersible of the present invention.
Detailed Description
The invention is described in further detail below with reference to the following detailed description and accompanying drawings:
the invention provides a SINS/DVL ocean current velocity estimation method for a deep-submergence long-range submersible. Aiming at the current situation that the navigation information source of the middle water area is lack and the ocean current velocity is difficult to obtain, a method for realizing the estimation of the installation error angle and the ocean current velocity by combining the high-precision navigation information output by SINS self-assisted navigation and the convection velocity obtained by using DVL and using RLS algorithm is provided.
As a specific implementation method, the invention provides a SINS/DVL ocean current velocity estimation method of a deep-submergence long-range vehicle, which is shown in a flow chart in fig. 4, and the specific steps are as follows:
step 1: the SINS based on the specific track motion carries out two times of navigation calculation output, namely the SINS1 and the SINS2, a high-pass filter is designed to obtain the real horizontal velocity of the SINS1, and the high-pass filter is combined with the SINS2 to realize SINS self-assisted navigation.
FIG. 1 is a diagram of an SINS self-assisted navigation framework, and the deep-diving manned submersible has the function of resisting flow by adopting a spiral diving scheme in the diving process and can be divided into circular motion in the horizontal direction and uniform linear motion in the vertical direction. The horizontal velocity error generated by the SINS working mechanism is considered as a low-frequency component, and the true horizontal velocity of the submersible spiral is considered as a high-frequency component. Based on the SINS working principle and the error propagation equation, the horizontal velocity output by the SINS1 can be processed by utilizing the difference of high/low frequencies, and the real horizontal velocity is obtained. Then, the horizontal velocity is combined with the SINS2 through a kalman filter to realize SINS self-assisted navigation.
Step 1.1: analyzing the track characteristic of the spiral submergence motion of the manned submersible vehicle and analyzing the SINS error propagation characteristic; the real horizontal speed of the SINS1 under the circular motion track is regarded as a high-frequency signal, and the horizontal speed error generated by the working mechanism of the SINS1 is regarded as a low-frequency signal.
The horizontal velocities output by SINS1 include: the true horizontal velocity and the horizontal velocity error are subjected to the existence of initial alignment error, installation error and navigation algorithm error to obtain an SINS system error model:
misalignment angle error equation:
Figure BDA0002868862780000091
the velocity error equation:
Figure BDA0002868862780000092
position error equation:
Figure BDA0002868862780000093
in the above formula, i represents an inertial coordinate system, n represents a navigation coordinate system, e represents a terrestrial coordinate system, and b represents a body coordinate system; phi is ═ phiN φE φD]TIs Euler misalignment angle, representing the rotation between the SINS calculated navigation coordinate system and the real navigation coordinate system n; delta Vn＝[δVN δVE δVD]TIs the speed error; δ L, δ λ, δ h are latitude, longitude and altitude errors, respectively; wherein the horizontal velocity error is delta VNAnd δ VEThree different vibration errors are involved, respectively:
hold down oscillation Ts：
Figure BDA0002868862780000094
Earth oscillation Te：
Figure BDA0002868862780000095
Foucault oscillation TF：
Figure BDA0002868862780000096
In the above, R is the earth radius, Ω is the earth rotation angular velocity, L is the latitude, and g is the gravitational acceleration.
The spiral submergence/upward floating of the deep submergence manned submersible is represented by circular track motion, and the speed of the deep submergence manned submersible in the horizontal direction is periodically changed; the submergence or ascension process of the submersible is generally not more than 3 hours, so that the invention considers the pull-down oscillation with the period T being 84.4 minutes in the horizontal speed error; the real horizontal velocity variation period is much smaller than the hula oscillation period, so the real horizontal velocity can be regarded as a high frequency signal, and the horizontal velocity error can be regarded as a low frequency signal.
Step 1.2: as shown in figure 1, a high-pass non-delay ZD-HPF digital filter is designed to process the horizontal velocity output by SINS1, filter out low-frequency velocity error components and further acquire the real horizontal velocity of the manned submersible.
Fig. 2 is a flow chart of the design of the delay-free high-pass filter ZD-HPF, as shown in fig. 2: setting the technical index (omega) of the digital high-pass filter according to the frequency difference characteristics of different signals to be preserved and filteredp,ωs,αp,αs) The pass band cut-off frequency, the stop band cut-off frequency, the maximum attenuation allowed by the stop band, and the minimum attenuation allowed by the stop band, respectively. The mapping relation of bilinear transformation is converted into corresponding technical indexes of the analog high-pass filter through frequency conversion, and the conversion formula is as follows:
Figure BDA0002868862780000101
technical index (omega) based on analog high-pass filterp,Ωs,αp,αs) By frequency conversion of the formula
Figure BDA0002868862780000102
Converted into an analog low-pass filter technical index (lambda)p,λs,αp,αs) For simple calculation, the passband boundary frequency of the normalized analog low-pass prototype system function G (p) is taken as lambdap1, the normalized stopband boundary frequency can be found as:
Figure BDA0002868862780000103
according to frequency-converted (lambda)p,λs,αp,αs) Calculating the order N of the filter:
Figure BDA0002868862780000104
Figure BDA0002868862780000105
Figure BDA0002868862780000106
designing a Butterworth filter, inquiring a normalized filter parameter table to obtain a normalized low-pass prototype system function G (p) corresponding to N, and performing normalization to obtain a transfer function H of the analog high-pass filterh(s) converting it into a digital low-pass filter H by means of a bilinear transformationl(z)。
Then the complementary idea is utilized to convert the digital high-pass filter into a digital high-pass filter H without time delayh(z)：
Hh(z)＝1-Hl(z) (12)
Step 1.3: the SINS self-assisted navigation is realized by combining the true horizontal velocity obtained in step 1.2 with SINS2 through a kalman filter.
Selecting an attitude misalignment angle, a speed error, a position error, a gyroscope constant zero offset and an accelerometer constant zero offset as state variables:
Figure BDA0002868862780000111
the state equation is:
Figure BDA0002868862780000112
wherein the system transfer matrix is:
Figure BDA0002868862780000113
a system interference matrix:
Figure BDA0002868862780000114
system noise vector:
Figure BDA0002868862780000115
horizontal velocity of SINS1 processed by high pass Filter (ZD-HPF) obtained from step 1.2
Figure BDA0002868862780000116
Figure BDA0002868862780000117
Selecting the horizontal velocity of the SINS2 output
Figure BDA0002868862780000118
And
Figure BDA0002868862780000119
the difference was measured as Z:
Figure BDA00028688627800001110
discretized kalman filter state equations and measurement equations:
Figure BDA0002868862780000121
as shown in fig. 1, solving is performed according to a kalman filter to obtain a state estimator, and the output of the SINS2 is corrected to realize SINS self-assisted navigation, so that a high-precision navigation solution is output
Figure BDA0002868862780000122
And
Figure BDA0002868862780000123
step 2: according to the Doppler effect principle, a deep-submerged long-range submersible working in a middle water area measures the convection velocity by using the water tracking mode of DVL;
step 2.1: according to the Doppler effect principle, under the condition of DVL speed measurement based on single beam configuration, the carrier speed v is solvedx；
The doppler velocimeter DVL transmits ultrasonic waves to the sea floor through an ultrasonic transducer mounted on a carrier, and measures the carrier velocity according to the doppler effect. The doppler effect is a physical phenomenon that the frequency receiving frequencies of a transmitting sound source are different when the transmitting sound source moves relative to a medium. Frequency f of a generally known transmitted acoustic wave0Angle alpha, propagation velocity c0As long as the Doppler shift f is measureddThe carrier speed can be obtained by the following steps:
Figure BDA0002868862780000124
step 2.2: the speed measurement of the DVL configured by four beams is realized, the water tracking mode of the DVL working in the middle water area is selected, and the convection velocity is solved
Figure BDA0002868862780000125
Fig. 3 is a schematic diagram of a four-beam doppler velocity measurement system, and in practical applications, in order to accurately measure the three-dimensional velocity of a carrier and reduce the influence of the carrier due to the bumpiness and the undulation, it is generally required to simultaneously emit an ultrasonic beam in each of the four directions, i.e., the front, the back, the left, and the right, of the carrier, so as to form a four-beam Janus orthogonal arrangement. While only three beams are required to provide three components of velocity, it is relatively easier for a four-beam configuration planar array antenna to produce four beams; and one more measurement equation can improve the accuracy and the measurement redundancy of the DVL speed calculation and realize the improvement of the system reliability.
Suppose the beam transmitting frequency of the dual-beam DVL system in both forward and backward directions is f0Difference f in Doppler shift between forward and backward directionsd1And fd2Comprises the following steps:
Figure BDA0002868862780000126
from the above formula, one can obtain:
Figure BDA0002868862780000127
the velocity in the dual beam configuration DVL is obtained from equation (21):
Figure BDA0002868862780000128
the velocity under the four-beam DVL system is known from equation (24):
Figure BDA0002868862780000131
under the condition of the middle water area where the deep-diving long-endurance submersible is positioned, the distance from the submersible to the seabed far exceeds the range of DVL speed measurement, so that the bottom tracking mode of the DVL cannot be used, and the DVL can only work in the water tracking mode to measure the speed of relative water flow
Figure BDA0002868862780000132
And step 3: combining high-precision navigation parameters provided by SINS self-assisted navigation and convection velocity measured by DVL, and estimating the ocean current velocity by using a least square estimation algorithm RLS;
step 3.1: high-precision navigation solution for arranging SINS self-assisted navigation output obtained in step 1
Figure BDA0002868862780000133
And the measured convection velocity of the DVL obtained in step 2
Figure BDA0002868862780000134
Data are prepared for ocean current estimation.
Step 3.2: modeling actual DVL measurements to solve for ocean current velocity Vc nFor the purpose, build and SINS self-assisted navigation output
Figure BDA0002868862780000135
The correlation relation.
Through analyzing the DVL velocity measurement principle, the establishment of a DVL velocity measurement model is as follows:
Figure BDA0002868862780000136
in the above-mentioned manner,
Figure BDA0002868862780000137
is the DVL measurement, i.e., the convective velocity; δ k represents the DVL scale factor error,
Figure BDA0002868862780000138
Vdrepresenting an error-free ideal value measured in the DVL coordinate system; delta VdRepresenting the DVL random measurement error. In the course of the estimation of the ocean current,
Figure BDA0002868862780000139
and
Figure BDA00028688627800001310
and also Vc nThe relation of (A) is as follows:
Figure BDA00028688627800001311
from step 1, the attitude matrix
Figure BDA00028688627800001312
And velocity
Figure BDA00028688627800001313
Is a navigation solution output by the SINS self-assisted navigation; vc n＝[VcN VcE VcD]TThen representing the projection of the ocean current velocity under the navigation system n;
Figure BDA00028688627800001314
the coordinate transformation matrix from the DVL instrument coordinate d system to the submersible vehicle b system can be obtained by the following formula:
Figure BDA00028688627800001315
wherein I is a 3-order identity matrix;
Figure BDA00028688627800001316
indicates the installation error angle between the d series and the b series,
Figure BDA00028688627800001317
αγ、αθin order to horizontally install the error angle,
Figure BDA00028688627800001318
is a course installation error angle.
Formula (28) is substituted for formula (27):
Figure BDA0002868862780000141
and (4) sorting the relational expression, namely: velocity of ocean currents Vc nAnd the installation error angle
Figure BDA0002868862780000142
Moving to the right of the equation, the known term is transferred to the other side of the equation, resulting in:
Figure BDA0002868862780000143
step 3.3: estimating a Recursive Least Square (RLS) Square by using an RLS algorithm to estimate a horizontal ocean current velocity Vc n。
Since the SINS self-assisted navigation outputs horizontal speed and the depth meter outputs height information, only the horizontal ocean current speed V needs to be consideredc nIn order to estimate the unknown horizontal ocean current velocity Vc nAnd an installation error angle α, setting a state variable:
Figure BDA0002868862780000144
the system state equation is:
Figure BDA0002868862780000145
according to equation (29) of step 3.2, the quantity is selected as:
Figure BDA0002868862780000146
wherein, the attitude matrix
Figure BDA0002868862780000147
Figure BDA0002868862780000148
Further, the system measurement equation:
Z2＝H2X2+V2 (35)
wherein
Figure BDA0002868862780000149
Estimating a Recursive Least Square to estimate a Recursive Least Square, estimating an installation error angle and a horizontal ocean current velocity by an RLS algorithm, wherein the calculation formula is as follows:
Figure BDA00028688627800001410
by the calculation formula, the mounting angle error and the ocean current velocity information V can be estimatedc n。
The above description is only a preferred embodiment of the present invention, and is not intended to limit the present invention in any way, but any modifications or equivalent variations made according to the technical spirit of the present invention are within the scope of the present invention as claimed.

Claims (2)

1. A SINS/DVL ocean current velocity estimation method for a deep-submergence long-endurance submersible is characterized by comprising the following specific steps:
(1) based on the two-time navigation resolving output of a Strapdown Inertial Navigation System (SINS) under the circular motion track motion, wherein the SINS1 and the SINS2 are respectively used, a high-pass filter is designed to obtain the real horizontal speed of the SINS1, and the high-pass filter is combined with the SINS2 to realize SINS self-assisted navigation;
in the step (1), based on the strapdown inertial navigation system SINS under the circular motion trajectory motion, performing two navigation calculation outputs, namely SINS1 and SINS2, designing a high-pass filter to obtain the true horizontal velocity of the SINS1, and combining the true horizontal velocity with the SINS2 to realize SINS self-assisted navigation as follows:
s1.1: analyzing the track characteristic of the spiral submergence motion of the manned submersible vehicle and analyzing the SINS error propagation characteristic; the real horizontal speed of the SINS1 under the circular motion track is regarded as a high-frequency signal, and the horizontal speed error generated by the SINS1 working mechanism is regarded as a low-frequency signal;
the horizontal velocities output by SINS1 include: the true horizontal velocity and the horizontal velocity error are subjected to the existence of initial alignment error, installation error and navigation algorithm error to obtain an SINS system error model:
misalignment angle error equation:
Figure FDA0003474140690000011
the velocity error equation:
Figure FDA0003474140690000012
position error equation:
Figure FDA0003474140690000013
in the above formula, i represents an inertial coordinate system, n represents a navigation coordinate system, e represents a terrestrial coordinate system, and b represents a body coordinate system; phi is ═ phiN φE φD]TIs Euler misalignment angle, representing the rotation between the SINS calculated navigation coordinate system and the real navigation coordinate system n; delta Vn＝[δVN δVE δVD]TIs the speed error; δ L, δ λ, δ h are latitude, longitude and altitude errors, respectively; wherein the horizontal velocity error is delta VNAnd δ VEThree different vibration errors are involved, respectively:
hold down oscillation Ts：
Figure FDA0003474140690000014
Earth oscillation Te：
Figure FDA0003474140690000021
Foucault oscillation TF：
Figure FDA0003474140690000022
In the above, R is the earth radius, Ω is the earth rotation angular velocity, L is the latitude, and g is the gravitational acceleration;
the spiral submergence/upward floating of the deep submergence manned submersible is represented by circular track motion, and the speed of the deep submergence manned submersible in the horizontal direction is periodically changed; in the submergence or upward floating process of the submersible, the pull-down oscillation with the period T being 84.4 minutes in the horizontal speed error is considered; the change period of the real horizontal velocity is smaller than that of the Lagrange oscillation period, so that the real horizontal velocity is regarded as a high-frequency signal, and the horizontal velocity error is regarded as a low-frequency signal;
s1.2: designing a high-pass non-delay ZD-HPF digital filter, processing the horizontal speed output by the SINS1, filtering low-frequency speed error components, and further acquiring the real horizontal speed of the manned submersible;
setting the technical index (omega) of the digital high-pass filter according to the frequency difference characteristics of different signals to be preserved and filteredp,ωs,αp,αs) The cut-off frequency of the pass band, the cut-off frequency of the stop band, the maximum attenuation allowed by the stop band and the minimum allowed by the stop bandAttenuation, which is obtained by converting the mapping relation of bilinear transformation into the corresponding technical index of the analog high-pass filter through frequency conversion, wherein the conversion formula is as follows:
Figure FDA0003474140690000023
technical index (omega) based on analog high-pass filterp,Ωs,αp,αs) By frequency conversion of the formula
Figure FDA0003474140690000024
Converted into an analog low-pass filter technical index (lambda)p,λs,αp,αs) For simple calculation, the passband boundary frequency of the normalized analog low-pass prototype system function G (p) is taken as lambdap1, the normalized stopband boundary frequency can be found as:
Figure FDA0003474140690000025
according to frequency-converted (lambda)p,λs,αp,αs) Calculating the order N of the filter:
Figure FDA0003474140690000026
Figure FDA0003474140690000031
Figure FDA0003474140690000032
designing a Butterworth filter, inquiring a normalized filter parameter table to obtain a normalized low-pass prototype system function G (p) corresponding to N, and normalizingObtaining the transfer function H of the analog high-pass filterh(s) converting it into a digital low-pass filter H by means of a bilinear transformationl(z)；
Then the complementary idea is utilized to convert the digital high-pass filter into a digital high-pass filter H without time delayh(z)：
Hh(z)＝1-Hl(z) (12)
S1.3: combining the real horizontal velocity obtained in the step 1.2 with SINS2 through a kalman filter to realize SINS self-assisted navigation;
selecting an attitude misalignment angle, a speed error, a position error, a gyroscope constant zero offset and an accelerometer constant zero offset as state variables:
Figure FDA0003474140690000033
the state equation is:
Figure FDA0003474140690000034
wherein the system transfer matrix is:
Figure FDA0003474140690000035
a system interference matrix:
Figure FDA0003474140690000036
system noise vector:
Figure FDA0003474140690000037
horizontal velocity of SINS1 processed by high pass filter ZD-HPF obtained from S1.2
Figure FDA0003474140690000041
Figure FDA0003474140690000042
Selecting the horizontal velocity of the SINS2 output
Figure FDA0003474140690000043
And
Figure FDA0003474140690000044
the difference was measured as Z:
Figure FDA0003474140690000045
discretized kalman filter state equations and measurement equations:
Figure FDA0003474140690000046
solving is carried out according to a kalman filter, a state estimator is obtained to correct the output of the SINS2, the SINS self-assisted navigation is realized, and therefore a high-precision navigation solution is output
Figure FDA0003474140690000047
And
Figure FDA0003474140690000048
(2) according to the principle of Doppler effect, a deep-submerged long-range submersible working in a middle water area measures the convection velocity by using the water tracking mode of a Doppler log DVL;
in the step (2), according to the principle of Doppler effect, the deep-diving long-range underwater vehicle working in the middle water area utilizes the water tracking mode of a Doppler log DVL, and the method for measuring the convection velocity comprises the following steps:
s2.1: according to the Doppler effect principle, under the condition of DVL speed measurement based on single beam configuration, the carrier speed v is solvedx；
The Doppler velocimeter DVL transmits ultrasonic waves to the seabed through an ultrasonic transducer arranged on a carrier, the speed of the carrier is measured according to the Doppler effect, the Doppler effect refers to the physics phenomenon that the frequency receiving frequency is different when a transmitting sound source moves relative to a medium, and the frequency f of the transmitting sound wave is known0Angle alpha, propagation velocity c0As long as the Doppler shift f is measureddThe size of (d) gives the carrier velocity:
Figure FDA0003474140690000049
s2.2: the speed measurement of the DVL configured by four beams is realized, the water tracking mode of the DVL working in the middle water area is selected, and the convection velocity is solved
Figure FDA00034741406900000410
Suppose the beam transmitting frequency of the dual-beam DVL system in both forward and backward directions is f0Difference f in Doppler shift between forward and backward directionsd1And fd2Comprises the following steps:
Figure FDA00034741406900000411
from the above formula, one can obtain:
Figure FDA0003474140690000051
the velocity in the dual beam configuration DVL is obtained from equation (21):
Figure FDA0003474140690000052
the velocity under the four-beam DVL system is known from equation (24):
Figure FDA0003474140690000053
under the condition of a middle water area where the deep-diving long-endurance submersible is positioned, the distance from the submersible to the sea bottom far exceeds the range of DVL speed measurement, so that the bottom tracking mode of the DVL cannot be used, and the DVL can only work in the water tracking mode to measure the speed of relative water flow
Figure FDA0003474140690000054
(3) Combining high-precision navigation parameters provided by SINS self-assisted navigation and convection velocity obtained by DVL, and estimating the ocean current velocity by using a least square estimation algorithm RLS;
in the step (3), the method for estimating the ocean current velocity by using the least square estimation algorithm RLS in combination with the high-precision navigation parameters provided by the SINS self-assisted navigation and the convection velocity obtained by using the DVL is as follows:
s3.1: high-precision navigation solution for arranging SINS self-assisted navigation output obtained in step 1
Figure FDA0003474140690000055
And the measured convection velocity of the DVL obtained in step 2
Figure FDA0003474140690000056
Preparing data serving as ocean current estimation;
s3.2: modeling actual DVL measurements to solve for ocean current velocities
Figure FDA0003474140690000057
For the purpose, build and SINS self-assisted navigation output
Figure FDA0003474140690000058
A relational relation;
through analyzing the DVL velocity measurement principle, the establishment of a DVL velocity measurement model is as follows:
Figure FDA0003474140690000059
in the above-mentioned manner,
Figure FDA00034741406900000510
is the DVL measurement, i.e., the convective velocity; δ k represents the DVL scale factor error,
Figure FDA00034741406900000511
Vdrepresenting an error-free ideal value measured in the DVL coordinate system; delta VdRepresenting the random measurement error of the DVL, during the estimation of the ocean current,
Figure FDA00034741406900000512
and
Figure FDA00034741406900000513
also provided are
Figure FDA00034741406900000514
The relation of (A) is as follows:
Figure FDA00034741406900000515
from step 1, the attitude matrix
Figure FDA0003474140690000061
And velocity
Figure FDA0003474140690000062
Is a navigation solution output by the SINS self-assisted navigation;
Figure FDA0003474140690000063
then representing the projection of the ocean current velocity under the navigation system n;
Figure FDA0003474140690000064
the coordinate transformation matrix from the DVL instrument coordinate d system to the submersible vehicle b system can be obtained by the following formula:
Figure FDA0003474140690000065
wherein I is a 3-order identity matrix;
Figure FDA0003474140690000066
indicates the installation error angle between the d series and the b series,
Figure FDA0003474140690000067
αγ、αθin order to horizontally install the error angle,
Figure FDA0003474140690000068
the course installation error angle;
formula (28) is substituted for formula (27):
Figure FDA0003474140690000069
and (4) sorting the relational expression, namely: velocity of ocean current
Figure FDA00034741406900000610
And the installation error angle
Figure FDA00034741406900000611
Moving to the right of the equation, the known term is transferred to the other side of the equation, resulting in:
Figure FDA00034741406900000612
s3.3: estimating a Recursive Least Square, and estimating a horizontal ocean current velocity by using an RLS algorithm;
since the SINS self-assisted navigation outputs horizontal speed and the depth meter outputs height information, only the horizontal ocean current speed needs to be considered
Figure FDA00034741406900000613
To estimate the unknown horizontal ocean current velocity
Figure FDA00034741406900000614
And an installation error angle α, setting a state variable:
Figure FDA00034741406900000615
the system state equation is:
Figure FDA00034741406900000616
according to equation (29) of step 3.2, the quantity is selected as:
Figure FDA00034741406900000617
wherein, the attitude matrix
Figure FDA00034741406900000618
Figure FDA00034741406900000619
The system measurement equation:
Z2＝H2X2+V2(35)
wherein
Figure FDA0003474140690000071
Estimating a Recursive Least Square to estimate a Recursive Least Square (RLS) algorithm to estimate an installation error angle and a horizontal ocean current velocity
Figure FDA0003474140690000072
The calculation formula is as follows:
Figure FDA0003474140690000073
by the calculation formula, the mounting angle error and the ocean current speed information can be estimated
Figure FDA0003474140690000074
2. The method of claim 1, wherein the four directions of the carrier are simultaneously transmitted with an ultrasonic beam to form a four-beam Janus orthogonal configuration.
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