CN113670302A - Inertia/ultra-short baseline combined navigation method under influence of motion effect - Google Patents

Inertia/ultra-short baseline combined navigation method under influence of motion effect Download PDF

Info

Publication number
CN113670302A
CN113670302A CN202111019606.6A CN202111019606A CN113670302A CN 113670302 A CN113670302 A CN 113670302A CN 202111019606 A CN202111019606 A CN 202111019606A CN 113670302 A CN113670302 A CN 113670302A
Authority
CN
China
Prior art keywords
representing
ultra
matrix
error
navigation
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN202111019606.6A
Other languages
Chinese (zh)
Other versions
CN113670302B (en
Inventor
张涛
张亮
夏茂栋
张佳宇
刘射德
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Southeast University
Original Assignee
Southeast University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Southeast University filed Critical Southeast University
Priority to CN202111019606.6A priority Critical patent/CN113670302B/en
Publication of CN113670302A publication Critical patent/CN113670302A/en
Application granted granted Critical
Publication of CN113670302B publication Critical patent/CN113670302B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • 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
    • G01C21/1652—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 with ranging devices, e.g. LIDAR or RADAR
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation

Landscapes

  • Engineering & Computer Science (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Automation & Control Theory (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Navigation (AREA)

Abstract

The invention discloses an inertia/ultra-short baseline combined navigation method under the influence of a motion effect, which aims at the problem of skew distance calculation error caused by the change of the positions of sound signals at the transmitting and receiving moments of an aircraft under the motion condition, establishes a new skew distance measurement model according to the round-trip delay of the sound signals between an ultra-short baseline hydrophone array and a responder and by combining the known underwater sound velocity and the known inertial navigation positions at the transmitting and receiving moments of the sound signals, designs an inertia/ultra-short baseline combined navigation method based on recursive filtering, and effectively improves the inertia/ultra-short baseline combined navigation positioning precision under the dynamic environment.

Description

Inertia/ultra-short baseline combined navigation method under influence of motion effect
Technical Field
The invention belongs to the technical field of underwater navigation based on an inertia/ultra-short baseline combined navigation method, and particularly relates to an inertia/ultra-short baseline combined navigation method under the influence of a motion effect.
Background
Inertial Navigation Systems (INS) have been widely used for autonomous Navigation and positioning of underwater vehicles due to their advantages of good autonomy, strong concealment, high precision in a short time, and the like. The inertial navigation can provide all-round information of attitude, speed and position in all weather, has incomparable advantages of other navigation sensors, and becomes the first choice for navigation and positioning of the underwater vehicle. Although inertial navigation technology is mature day by day, the characteristic that the positioning error is accumulated over time and needs to be readjusted periodically to ensure a certain precision cannot be changed, so that systematic error calibration methods of inertial navigation are explored in all countries of the world.
Because the transmission distance of the sound wave under water is long and the signal attenuation loss is small, the underwater sound navigation is widely applied to navigation and positioning of underwater vehicles. The ultra-short baseline positioning system has higher portability and independence because the deployment of a base array is not required to be realized, and is widely applied to underwater vehicles. The complexity of the underwater environment, the diversity of combat missions, and the maneuverability of underwater vehicles, have resulted in a single ultra-short baseline navigation position or inertial navigation that has not met the needs of vehicle navigation. The inertial navigation and the ultra-short baseline can be mutually assisted and supplemented in principle and application, the research of the combined navigation system which takes the inertial navigation as a core and is assisted by the ultra-short baseline positioning technology becomes an important research direction of the current underwater vehicle, and the combined navigation system adopting the inertial/ultra-short baseline has important theoretical significance and practical value for the high-precision long-time navigation of the underwater vehicle.
In the ultra-short baseline positioning, the traditional slope distance calculation mode ignores the change of the position at the moment of receiving and transmitting the acoustic signal, and can bring certain errors to the slope distance calculation. Therefore, considering the motion characteristics in a dynamic environment is important for improving the accuracy of inertial/ultra-short baseline combined navigation.
Disclosure of Invention
In view of the above problems, the present invention aims to provide an inertial/ultra-short baseline integrated navigation method under the influence of motion effect, which unifies the error states of signal receiving and sending moments through a state transition matrix, and establishes a new inertial/ultra-short baseline integrated navigation measurement model by using the round-trip delay of acoustic signals between a transponder and a hydrophone matrix as an observed quantity, so as to achieve the purpose of overcoming the influence of slant-range calculation errors on positioning accuracy due to motion.
The invention provides an inertia/ultra-short baseline combined navigation method under the influence of motion effect, which comprises the following steps:
(1) establishing an inertial/ultra-short baseline combined navigation state equation model;
in the step (1), establishing an inertial/ultra-short baseline integrated navigation state equation model:
taking the position error, the speed error and the attitude error of inertial navigation as system state quantities to obtain:
Figure BDA0003240930890000021
wherein [ phi ]E φN φU]TError of misalignment angle [ delta V ] representing attitudeE δVN δVU]TIndicates the speed error, [ delta L delta lambda delta h]TIndicates the position error, [ epsilon ]x εy εz]TRepresenting the zero-bias of the gyroscope,
Figure BDA0003240930890000022
represents the zero offset of the accelerometer;
thus, the state equation for the combined navigation is:
Figure BDA0003240930890000023
f (t) represents a state transition matrix, is set according to a strapdown inertial navigation error equation, and W (t) represents a process noise matrix;
(2) Establishing an azimuth angle measurement equation model of the inertia/ultra-short baseline integrated navigation;
in the step (2), an azimuth measurement equation model of the inertial/ultra-short baseline integrated navigation is established, which specifically comprises the following steps:
the measurement equation expression of the azimuth angle is as follows:
Figure BDA0003240930890000024
wherein the content of the first and second substances,
Figure BDA0003240930890000025
Δ α and Δ β represent errors in azimuth angle, αusbl，βusblIndicating the azimuth angle, alpha, from the ultra-short baseline measurementins，βinsIndicating the azimuth calculated using inertial navigation and transponder position,
Figure BDA0003240930890000026
Figure BDA0003240930890000027
[*]1:2the first two rows of the matrix are represented,
Figure BDA0003240930890000028
a mounting error matrix representing the ultra-short baseline,
Figure BDA0003240930890000029
representing a coordinate transformation matrix from a navigation system to a carrier system,
Figure BDA00032409308900000210
representing a coordinate transformation matrix, p, from a terrestrial coordinate system to a navigation systemeIs a relative position vector under the earth system, expressed as:
Figure BDA00032409308900000211
wherein R isNIs the curvature radius of the mortise-unitary ring, e is the elliptical eccentricity,
Figure BDA00032409308900000212
to be the position of the transponder,
Figure BDA00032409308900000213
the position of inertial navigation output;
Ceexpressed as:
Figure BDA00032409308900000214
wherein R isNh＝RN+h，[L λ h]Respectively representing longitude, latitude and altitude;
BSis shown as
Figure BDA0003240930890000031
Wherein S isa＝sin(a),SaCos (a), L or λ;
H1expressed as:
Figure BDA0003240930890000032
wherein the content of the first and second substances,
Figure BDA0003240930890000033
the relative position of the transponder under a matrix coordinate system is shown, and r represents the slant distance;
(3) establishing an inertia/ultra-short baseline combined navigation slope distance measurement equation model based on state recursion in the state-based step (3), specifically:
The inertial navigation positions of the acoustic signal corresponding to the transmitting time k and the receiving time k + i are
Figure BDA0003240930890000034
And
Figure BDA0003240930890000035
the corresponding error state of inertial navigation is
Figure BDA0003240930890000036
And
Figure BDA0003240930890000037
unifying the error states at different moments through a state transition matrix to obtain:
Figure BDA0003240930890000038
wherein the content of the first and second substances,
Figure BDA0003240930890000039
under the condition of considering inertial navigation errors, the corresponding slope distance obtained by utilizing inertial navigation positions at the moment of receiving and sending and the position of a transponder is expressed as follows:
Figure BDA00032409308900000310
wherein r is1Representing the true distance, r, between the transponder and the position of the ship at the moment of signal transmission2Representing the true distance between the transponder and the location of the vessel at the time of signal reception,
Figure BDA00032409308900000311
is represented by r1The error of (a) is detected,
Figure BDA00032409308900000312
is represented by r2The error of (a) is detected,
Figure BDA00032409308900000313
which represents the position error at the time k,
Figure BDA00032409308900000314
represents the position error at time k + i, Ce,k+iC representing time k + ieThe values of the matrix are then compared to each other,
Figure BDA00032409308900000315
representing time of k + i
Figure BDA0003240930890000041
Matrix value, Bs,k+iB representing time k + iSThe values of the matrix are then compared to each other,
Figure BDA0003240930890000042
Figure BDA0003240930890000043
p representing time k + ieMatrix value, Ce,kC representing time keThe values of the matrix are then compared to each other,
Figure BDA0003240930890000044
indicating time k
Figure BDA0003240930890000045
Matrix value, Bs,kB representing time kSThe values of the matrix are then compared to each other,
Figure BDA0003240930890000046
Figure BDA0003240930890000047
p representing time keA matrix value;
the round trip slope from the ultra short baseline measurement is expressed as:
ct＝r1+r2-δr
where δ r represents the error of the ultra-short baseline measurement;
therefore, the new round-trip-slope-based measurement model is as follows:
Figure BDA0003240930890000048
wherein H2-1＝[01×6 HPD1 01×6]，
Figure BDA0003240930890000049
H2-1＝[01×6 HPD2 01×6]，
Figure BDA00032409308900000410
Figure BDA00032409308900000411
{*}7:9Representing columns 7-9 of the vector,
Figure BDA00032409308900000412
An inertia/ultra-short baseline combination navigation slope distance measurement equation model of state recursion;
(4) and (4) updating and feeding back the error by using a Kalman filtering method according to the state equation and the measurement equation established in the steps (1) to (3).
As a further improvement of the invention, in the step (4), the method specifically comprises the following steps:
and (3) integrating the steps (2) and (3), wherein the combined navigation measurement equation of the inertia/ultra-short baseline is as follows:
Z＝HX+V＝[Hα Hr]TX+[V1 V2]T
according to a new measurement equation, the step of updating the Kalman filtering is as follows:
(a) and (3) state one-step prediction:
Figure BDA00032409308900000413
wherein the content of the first and second substances,
Figure BDA0003240930890000051
representing the state estimate at time k-1, Fk-1State transition from k-1 to k
Moving the matrix;
(b) state one-step prediction mean square error:
Figure BDA0003240930890000052
wherein Q isk-1Representing the process noise matrix, Pk-1Represents the root mean square error at time k-1;
(c) filtering gain:
Figure BDA0003240930890000053
wherein HkA measurement matrix representing time k, RkRepresenting a measurement noise matrix;
(d) and (3) state estimation:
Figure BDA0003240930890000054
wherein z iskRepresenting an observed quantity;
(e) state estimation mean square error: pk＝Pk-KkHkPk|(k-1)。
Has the advantages that:
the inertia/ultra-short baseline combined navigation method based on recursive filtering established according to the steps can overcome the influence of slant range calculation errors caused by movement on the positioning accuracy in a dynamic environment, and has higher positioning accuracy in the dynamic environment compared with the traditional method.
Drawings
FIG. 1 is a schematic diagram of an inertial/ultra-short baseline integrated navigation algorithm based on recursive filtering.
Detailed Description
The invention is described in further detail below with reference to the following detailed description and accompanying drawings:
the invention aims to provide an inertia/ultra-short baseline combined navigation method under the influence of a motion effect, which unifies the error state of signal receiving and sending moments through a state transition matrix, establishes a new inertia/ultra-short baseline combined navigation measurement model by using the round-trip delay of an acoustic signal between a transponder and a hydrophone matrix as an observed quantity, and achieves the purpose of overcoming the influence of slant range calculation errors on positioning accuracy caused by motion.
Aiming at the influence of slant range calculation errors in a dynamic environment on navigation positioning precision, the invention designs an inertia/ultra-short baseline combined navigation method based on recursive filtering, wherein a schematic diagram of an inertia/ultra-short baseline combined navigation algorithm based on recursive filtering is shown in FIG. 1. The specific method comprises the following steps:
step 1: establishing an inertial/ultra-short baseline integrated navigation state equation model, which specifically comprises the following steps:
taking the position error, the speed error and the attitude error of inertial navigation as system state quantities to obtain:
Figure BDA0003240930890000055
wherein [ phi ]E φN φU]TError of misalignment angle [ delta V ] representing attitude E δVN δVU]TIndicates the speed error, [ delta L delta lambda delta h]TIndicates the position error, [ epsilon ]x εy εz]TRepresenting the zero-bias of the gyroscope,
Figure BDA0003240930890000061
indicating zero offset of the accelerometer.
Thus, the state equation for the combined navigation is:
Figure BDA0003240930890000062
wherein, F (t) represents a state transition matrix and is set according to a strapdown inertial navigation error equation, and W (t) represents a process noise matrix.
Step 2: establishing an azimuth angle measurement equation model of inertial/ultra-short baseline integrated navigation, which specifically comprises the following steps:
the measurement equation expression of the azimuth angle is as follows:
Figure BDA0003240930890000063
wherein the content of the first and second substances,
Figure BDA0003240930890000064
Δ α and Δ β represent errors in azimuth angle, αusbl，βusblIndicating ultra-short baseline measurementsThe azimuth angle of arrival. Alpha is alphains，βinsIndicating the azimuth calculated using inertial navigation and transponder position.
Figure BDA0003240930890000065
Figure BDA0003240930890000066
[*]1:2Representing the first two rows of the matrix.
Figure BDA0003240930890000067
A mounting error matrix representing the ultra-short baseline,
Figure BDA0003240930890000068
representing a coordinate transformation matrix from a navigation system to a carrier system,
Figure BDA0003240930890000069
a coordinate transformation matrix from the terrestrial coordinate system to the navigation system is represented. p is a radical ofeIs a relative position vector under the earth system, expressed as:
Figure BDA00032409308900000610
wherein R isNIs the curvature radius of the mortise-unitary ring, e is the elliptical eccentricity,
Figure BDA00032409308900000611
to be the position of the transponder,
Figure BDA00032409308900000612
and the position of inertial navigation output.
CeExpressed as:
Figure BDA00032409308900000613
wherein R isNh＝RN+h，[L λ h]Respectively, longitude, latitude, and altitude.
BSIs shown as
Figure BDA00032409308900000614
Wherein S isa＝sin(a),SaCos (a), L or λ.
H1Expressed as:
Figure BDA0003240930890000071
wherein the content of the first and second substances,
Figure BDA0003240930890000072
representing the relative position of the transponder in the base coordinate system. r represents the slope distance.
And step 3: establishing an inertia/ultra-short baseline combined navigation slope distance measurement equation model based on state recursion, specifically;
the inertial navigation positions of the acoustic signal corresponding to the transmitting time k and the receiving time k + i are
Figure BDA0003240930890000073
And
Figure BDA0003240930890000074
the corresponding error state of inertial navigation is
Figure BDA0003240930890000075
And
Figure BDA0003240930890000076
by unifying the error states at different times through the state transition matrix, the following can be obtained:
Figure BDA0003240930890000077
wherein the content of the first and second substances,
Figure BDA0003240930890000078
under the condition of considering inertial navigation errors, the corresponding slope distance calculated by using the inertial navigation position at the moment of receiving and sending and the position of the transponder can be expressed as follows:
Figure BDA0003240930890000079
wherein r is1Representing the true distance, r, between the transponder and the position of the ship at the moment of signal transmission2Representing the true distance between the transponder and the location of the vessel at the time of signal reception,
Figure BDA00032409308900000710
is represented by r1The error of (a) is detected,
Figure BDA00032409308900000711
is represented by r2The error of (2).
Figure BDA00032409308900000712
Which represents the position error at the time k,
Figure BDA00032409308900000713
representing the position error at time k + i. Ce,k+iC representing time k + ieThe values of the matrix are then compared to each other,
Figure BDA00032409308900000714
representing time of k + i
Figure BDA00032409308900000715
Matrix value, BS,k+iB representing time k + iSThe values of the matrix are then compared to each other,
Figure BDA00032409308900000716
Figure BDA00032409308900000717
p representing time k + ieThe matrix values. Ce,kC representing time keThe values of the matrix are then compared to each other,
Figure BDA00032409308900000718
indicating time k
Figure BDA00032409308900000719
Matrix value, BS,kB representing time kSThe values of the matrix are then compared to each other,
Figure BDA0003240930890000081
Figure BDA0003240930890000082
p representing time k eThe matrix values.
The round trip slope from the ultra short baseline measurement is expressed as:
ct＝r1+r2-δr
where δ r represents the error of the ultra-short baseline measurement.
Therefore, the new round-trip-slope-based measurement model is as follows:
Figure BDA0003240930890000083
wherein H2-1＝[01×6 HPD1 01×6]，
Figure BDA0003240930890000084
H2-1＝[01×6 HPD201×6]，
Figure BDA0003240930890000085
Figure BDA0003240930890000086
{*}7:9Columns 7-9 of the vector are shown.
Figure BDA0003240930890000087
And 4, step 4: updating and feeding back errors by using a Kalman filtering method according to the state equation and the measurement equation established in the step 1-3, specifically;
and (3) integrating the steps 2 and 3, wherein the combined navigation measurement equation of the inertia/ultra-short baseline is as follows:
Z＝HX+V＝[HΔHr]TX+[V1V2]T
according to a new measurement equation, the step of updating the Kalman filtering is as follows:
(a) and (3) state one-step prediction:
Figure BDA0003240930890000088
wherein the content of the first and second substances,
Figure BDA0003240930890000089
representing the state estimate at time k-1, Fk-1Representing the state transition matrix from time k-1 to time k.
(b) State one-step prediction mean square error:
Figure BDA00032409308900000810
wherein Q isk-1Representing the process noise matrix, Pk-1Representing the root mean square error at time k-1.
(c) Filtering gain:
Figure BDA00032409308900000811
wherein HkA measurement matrix representing time k, RkRepresenting the measured noise matrix.
(d) And (3) state estimation:
Figure BDA00032409308900000812
wherein z iskRepresents the observed quantity.
(e) State estimation mean square error: pk＝Pk-KkHkPk|(k-1)。
The above description is only one of the preferred embodiments of the present invention, and is not intended to limit the present invention in any way, but any modifications or equivalent variations made in accordance with the technical spirit of the present invention are within the scope of the present invention as claimed.

Claims (2)

1. An inertia/ultra-short baseline combined navigation method under the influence of motion effect is characterized by comprising the following steps:
(1) establishing an inertial/ultra-short baseline combined navigation state equation model;
in the step (1), establishing an inertial/ultra-short baseline integrated navigation state equation model:
taking the position error, the speed error and the attitude error of inertial navigation as system state quantities to obtain:
Figure FDA0003240930880000011
wherein [ phi ]E φN φU]TError of misalignment angle [ delta V ] representing attitudeE δVN δVU]TIndicates the speed error, [ delta L delta lambda delta h]TIndicates the position error, [ epsilon ]x εy εz]TRepresenting the zero-bias of the gyroscope,
Figure FDA0003240930880000012
represents the zero offset of the accelerometer;
thus, the state equation for the combined navigation is:
Figure FDA0003240930880000013
f (t) represents a state transition matrix, is set according to a strapdown inertial navigation error equation, and W (t) represents a process noise matrix;
(2) establishing an azimuth angle measurement equation model of the inertia/ultra-short baseline integrated navigation;
in the step (2), an azimuth measurement equation model of the inertial/ultra-short baseline integrated navigation is established, which specifically comprises the following steps:
the measurement equation expression of the azimuth angle is as follows:
Figure FDA0003240930880000014
wherein the content of the first and second substances,
Figure FDA0003240930880000015
Δ α and Δ β represent errors in azimuth angle, αusbl，βusblIndicating the azimuth angle, alpha, from the ultra-short baseline measurementins，βinsIndicating the azimuth calculated using inertial navigation and transponder position,
Figure FDA0003240930880000016
Figure FDA0003240930880000017
[*]1：2The first two rows of the matrix are represented,
Figure FDA0003240930880000018
a mounting error matrix representing the ultra-short baseline,
Figure FDA0003240930880000019
representing a coordinate transformation matrix from a navigation system to a carrier system,
Figure FDA00032409308800000110
representing a coordinate transformation matrix, p, from a terrestrial coordinate system to a navigation systemeIs a relative position vector under the earth system, expressed as:
Figure FDA00032409308800000111
wherein R isNIs the curvature radius of the mortise-unitary ring, e is the elliptical eccentricity,
Figure FDA00032409308800000113
to be the position of the transponder,
Figure FDA00032409308800000112
the position of inertial navigation output;
Ceexpressed as:
Figure FDA0003240930880000021
wherein R isNh＝RN+h，[L λ h]Respectively representing longitude, latitude and altitude;
BSis shown as
Figure FDA0003240930880000022
Wherein S isa＝sin(a)，SaCos (a), L or λ;
H1expressed as:
Figure FDA0003240930880000023
wherein the content of the first and second substances,
Figure FDA0003240930880000024
the relative position of the transponder under a matrix coordinate system is shown, and r represents the slant distance;
(3) establishing an inertia/ultra-short baseline combined navigation slope distance measurement equation model based on state recursion in the state-based step (3), specifically:
the inertial navigation positions of the acoustic signal corresponding to the transmitting time k and the receiving time k + i are
Figure FDA0003240930880000025
And
Figure FDA0003240930880000026
the corresponding error state of inertial navigation is
Figure FDA0003240930880000027
And
Figure FDA0003240930880000028
unifying the error states at different moments through a state transition matrix to obtain:
Figure FDA0003240930880000029
wherein the content of the first and second substances,
Figure FDA00032409308800000210
under the condition of considering inertial navigation errors, the corresponding slope distance obtained by utilizing inertial navigation positions at the moment of receiving and sending and the position of a transponder is expressed as follows:
Figure FDA00032409308800000211
wherein r is1Representing the true distance, r, between the transponder and the position of the ship at the moment of signal transmission 2Representing the true distance between the transponder and the location of the vessel at the time of signal reception,
Figure FDA00032409308800000212
is represented by r1The error of (a) is detected,
Figure FDA0003240930880000031
is represented by r2The error of (a) is detected,
Figure FDA0003240930880000032
which represents the position error at the time k,
Figure FDA0003240930880000033
represents the position error at time k + i, Ce，k+iC representing time k + ieThe values of the matrix are then compared to each other,
Figure FDA0003240930880000034
representing time of k + i
Figure FDA0003240930880000035
Matrix value, BS，k+iB representing time k + iSThe values of the matrix are then compared to each other,
Figure FDA0003240930880000036
Figure FDA0003240930880000037
Figure FDA0003240930880000038
p representing time k + ieMatrix value, Ce，kC representing time keThe values of the matrix are then compared to each other,
Figure FDA0003240930880000039
indicating time k
Figure FDA00032409308800000310
Matrix value, BS，kB representing time kSThe values of the matrix are then compared to each other,
Figure FDA00032409308800000311
Figure FDA00032409308800000312
Figure FDA00032409308800000313
p representing time keA matrix value;
the round trip slope from the ultra short baseline measurement is expressed as:
ct＝r1+r2-δr
where δ r represents the error of the ultra-short baseline measurement;
therefore, the new round-trip-slope-based measurement model is as follows:
Figure FDA00032409308800000314
wherein H2-1＝[01×6 HPD1 01×6]，
Figure FDA00032409308800000315
H2-1＝[01×6 HPD2 01×6]，
Figure FDA00032409308800000316
Figure FDA00032409308800000317
{*}7：9Representing columns 7-9 of the vector,
Figure FDA00032409308800000318
an inertia/ultra-short baseline combination navigation slope distance measurement equation model of state recursion;
(4) and (4) updating and feeding back the error by using a Kalman filtering method according to the state equation and the measurement equation established in the steps (1) to (3).
2. The method of claim 1, wherein the combined inertial/ultra-short baseline navigation under the influence of motion effect comprises: in the step (4), the method specifically comprises the following steps:
and (3) integrating the steps (2) and (3), wherein the combined navigation measurement equation of the inertia/ultra-short baseline is as follows:
Z＝HX+V＝[Hα Hr]TX+[V1 V2]T
According to a new measurement equation, the step of updating the Kalman filtering is as follows:
(a) and (3) state one-step prediction:
Figure FDA0003240930880000041
wherein the content of the first and second substances,
Figure FDA0003240930880000042
representing the state estimate at time k-1, Fk-1A state transition matrix representing the time k-1 to the time k;
(b) state one-step prediction mean square error:
Figure FDA0003240930880000043
wherein Q isk-1Representing the process noise matrix, Pk-1Represents the root mean square error at time k-1;
(c) filtering gain:
Figure FDA0003240930880000044
wherein HkA measurement matrix representing time k, RkRepresenting a measurement noise matrix;
(d) and (3) state estimation:
Figure FDA0003240930880000045
wherein z iskRepresenting an observed quantity;
(e) state estimation mean square error: pk＝Pk-KkHkPk|(k-1)。
CN202111019606.6A 2021-09-01 2021-09-01 Inertial/ultrashort baseline integrated navigation method under influence of motion effect Active CN113670302B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202111019606.6A CN113670302B (en) 2021-09-01 2021-09-01 Inertial/ultrashort baseline integrated navigation method under influence of motion effect

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202111019606.6A CN113670302B (en) 2021-09-01 2021-09-01 Inertial/ultrashort baseline integrated navigation method under influence of motion effect

Publications (2)

Publication Number Publication Date
CN113670302A true CN113670302A (en) 2021-11-19
CN113670302B CN113670302B (en) 2023-12-05

Family

ID=78548015

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202111019606.6A Active CN113670302B (en) 2021-09-01 2021-09-01 Inertial/ultrashort baseline integrated navigation method under influence of motion effect

Country Status (1)

Country Link
CN (1) CN113670302B (en)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116337115A (en) * 2023-05-31 2023-06-27 山东大学 Sonar-based method and system for calibrating inertial sensor

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20110068975A1 (en) * 2009-09-21 2011-03-24 Zietz John M Gnss ultra-short baseline heading determination system and method
CN105157701A (en) * 2015-09-01 2015-12-16 北京理工大学 Method for distributing underwater reference position
CN110132308A (en) * 2019-05-27 2019-08-16 东南大学 A kind of USBL fix error angle scaling method determined based on posture

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20110068975A1 (en) * 2009-09-21 2011-03-24 Zietz John M Gnss ultra-short baseline heading determination system and method
CN105157701A (en) * 2015-09-01 2015-12-16 北京理工大学 Method for distributing underwater reference position
CN110132308A (en) * 2019-05-27 2019-08-16 东南大学 A kind of USBL fix error angle scaling method determined based on posture

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
TAO ZHANG: "A Student’s T-Based Measurement Uncertainty Filter for SINS/USBL Tightly Integration Navigation System", IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, vol. 70, no. 9, pages 8627 - 8637 *
童金武: "超短基线安装误差对定位精度影响分析及其标定技术研究", 导航定位与授时, vol. 7, no. 2, pages 18 - 26 *

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116337115A (en) * 2023-05-31 2023-06-27 山东大学 Sonar-based method and system for calibrating inertial sensor
CN116337115B (en) * 2023-05-31 2023-08-29 山东大学 Sonar-based method and system for calibrating inertial sensor

Also Published As

Publication number Publication date
CN113670302B (en) 2023-12-05

Similar Documents

Publication Publication Date Title
CN109324330B (en) USBL/SINS tight combination navigation positioning method based on mixed derivative-free extended Kalman filtering
CN109737956B (en) SINS/USBL phase difference tight combination navigation positioning method based on double transponders
CN106643709B (en) Combined navigation method and device for offshore carrier
CN108594272B (en) Robust Kalman filtering-based anti-deception jamming integrated navigation method
CN111829512B (en) AUV navigation positioning method and system based on multi-sensor data fusion
CN106767793A (en) A kind of AUV underwater navigation localization methods based on SINS/USBL tight integrations
CN108594271B (en) Composite layered filtering-based anti-deception-interference combined navigation method
CN104316045A (en) AUV (autonomous underwater vehicle) interactive auxiliary positioning system and AUV interactive auxiliary positioning method based on SINS (strapdown inertial navigation system)/LBL (long base line)
CN109631884B (en) Passive underwater navigation method based on single buoy
CN111380518B (en) SINS/USBL tight combination navigation positioning method introducing radial velocity
CN111323012B (en) INS-assisted DVL speed measurement error compensation method under high dynamic environment of carrier
CN113794497B (en) Mobile satellite communication antenna terminal with anti-interference positioning function
CN113701751B (en) Navigation device based on multi-beam antenna
CN111854747B (en) DVL-assisted SINS (strapdown inertial navigation system) coarse alignment method under large-mobility condition of carrier
CN111578944B (en) Underwater glider positioning method based on single beacon
CN111142144B (en) Underwater acoustic positioning and timing buoy and underwater positioning method
CN111735455A (en) Improved Gaussian distance iterative algorithm based butt joint recovery integrated navigation method
CN113670302B (en) Inertial/ultrashort baseline integrated navigation method under influence of motion effect
CN111366151A (en) Information fusion method for ship navigation in polar region
CN113155134B (en) Underwater acoustic channel tracking and predicting method based on inertia information assistance
Zhao et al. A MFKF based SINS/DVL/USBL integrated navigation algorithm for unmanned underwater vehicles in polar regions
CN112083425B (en) SINS/LBL (strapdown inertial navigation system/location based language) tightly-integrated navigation method introducing radial velocity
JP2666006B2 (en) Underwater vehicle position measurement device
CN113820733B (en) Motion carrier navigation method and device based on directional antenna and Doppler information
CN115542363A (en) Attitude measurement method suitable for vertical downward-looking aviation pod

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant