CN107990891B - Underwater robot combined navigation method based on long baseline and beacon online calibration - Google Patents

Underwater robot combined navigation method based on long baseline and beacon online calibration Download PDF

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CN107990891B
CN107990891B CN201610944484.4A CN201610944484A CN107990891B CN 107990891 B CN107990891 B CN 107990891B CN 201610944484 A CN201610944484 A CN 201610944484A CN 107990891 B CN107990891 B CN 107990891B
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王轶群
李波
刘健
赵宏宇
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Shenyang Institute of Automation of CAS
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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
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S5/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
    • G01S5/18—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using ultrasonic, sonic, or infrasonic waves
    • G01S5/22—Position of source determined by co-ordinating a plurality of position lines defined by path-difference measurements

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Abstract

The invention relates to a combined navigation method based on long-baseline acoustic positioning and beacon online calibration, which realizes underwater navigation positioning of an underwater robot. The invention comprises the following steps: the method comprises the steps that the single beacon slope distance measurement values of the AUV at different moments are utilized to realize online calibration of the positions of the long-baseline beacons; and establishing a Kalman filter based on long-baseline acoustic positioning and inertial navigation data fusion, and calculating the position estimation of the integrated navigation. The method can effectively fuse long baseline positioning and inertial navigation data, and has higher navigation positioning precision; the method is convenient to transplant and can be suitable for underwater navigation and positioning of various underwater vehicles.

Description

Underwater robot combined navigation method based on long baseline and beacon online calibration
Technical Field
The invention relates to the technical field of underwater robots, in particular to an underwater robot combined navigation method based on long baseline acoustic positioning and beacon online calibration technology for an unmanned autonomous underwater robot (AUV), and the AUV underwater high-precision combined navigation positioning is realized.
Background
In marine applications, underwater robots play an increasingly important role. Underwater robots are divided into two categories: one is a remote control type cabled underwater Robot (ROV) and the other is an unmanned autonomous underwater robot (AUV). The ROV needs to be supported by a mother ship on the water surface, is limited by the length of a cable, and has a limited working distance which is only hundreds of meters generally; the AUV carries energy and can be far away from the mother ship, and the movement distance reaches dozens of kilometers or even hundreds of kilometers. Therefore, the research of the AUV is more and more emphasized by various countries, and the development of the AUV represents the development direction of the underwater robot in the future. The underwater integrated navigation and positioning technology is a bottleneck of key and restriction of AUV development. Because of the particularity of the underwater environment, the differential GPS navigation positioning can not be directly used like the land, so the current underwater navigation positioning mainly comprises two types: inertial navigation data and underwater acoustic positioning navigation data. The positioning precision of the inertial navigation data is high in short voyage, but the navigation precision of the system is reduced by accumulated navigation errors along with the increase of the voyage; the underwater acoustic positioning navigation data has the highest positioning accuracy with a long baseline, the length of a matrix of a long baseline positioning system (LBL for short) is generally thousands of meters, more than 3 beacons (generally 4 beacons are laid, wherein 1 beacon is a backup beacon) need to be laid on the seabed, and the position of the AUV is determined by measuring the distance between the AUV and the beacons. The long baseline positioning has the advantages that the positioning accuracy is high, the positioning error is not accumulated along with the increase of time, and the problem of accumulated navigation error of an inertial navigation system is effectively solved, so that the long baseline data and the inertial navigation data are widely combined into a combined navigation system at present, and the high-accuracy underwater positioning is realized. However, the use of long baseline positioning also has some problems, firstly, the long baseline positioning is interfered by marine environment, noise often appears in the long baseline positioning, and the long baseline positioning cannot be directly used for navigation positioning; secondly, the calibration accuracy of the long-baseline beacon under the shallow water condition is higher, but the calibration accuracy of the long-baseline beacon under the deep water condition is not ideal. The long baseline needs to be laid with beacons and the mother ship needs to be used for beacon position calibration before use, underwater acoustic conditions in a deep water environment are complex, particularly more than 2000 meters, the calibration result of the long baseline beacon calibrated by the mother ship gradually declines along with the increase of the water depth, the positioning accuracy of the long baseline positioning system is affected, and the requirement of high-accuracy underwater positioning cannot be met. Under the deep water working condition, how to fuse the long baseline positioning data and the effective data of the inertial navigation data, namely, the long baseline positioning noise is inhibited, the navigation accumulated error is also inhibited, the high-precision underwater positioning is realized, and the method is a difficult problem of the deep water combination navigation key research; how to reduce the influence of the calibration error of the long-baseline beacon under the deep-water working condition on a navigation system is also an urgent technical problem to be solved in the deep-water combination navigation.
Disclosure of Invention
In order to solve the problems of filtering long baseline positioning and restraining accumulated errors of a navigation system and reduce the influence of beacon calibration errors on navigation positioning, the invention provides a novel combined navigation method based on long baseline acoustic positioning and beacon online calibration, which effectively integrates long baseline positioning and inertial navigation positioning data, interference of filtering long baseline positioning disturbance on combined navigation positioning is reduced, the accumulated errors of the navigation system are reduced, the beacon position is calibrated online, and the influence of calibration errors generated by a mother ship calibration beacon on navigation precision under a deep water condition is reduced.
The technical scheme adopted by the invention for realizing the purpose is as follows: the underwater robot combined navigation method based on the long baseline and the beacon online calibration comprises the following steps:
calibrating the position of each beacon on line by using the single beacon slope distance measured values of the AUV at different moments to obtain the calibration error of each beacon;
and estimating the AUV position of the integrated navigation according to the calibration error through a measurement equation based on the long-baseline acoustic positioning and a prediction equation of the inertial navigation.
The calibration error is obtained by
[dxi,dyi]T＝A-1B
Wherein the content of the first and second substances,
Figure BDA0001140836760000021
AUV positions at time t, time t +1 and time t +2 are (x)t,yt,zt),(xt+1,yt+1,zt+1) And (x)t+2,yt+2,zt+2) The skew distances from AUV to beacon i at time t, time t +1 and time t +2 are di_t,di_t+1,di_t+2；
Figure BDA0001140836760000031
the horizontal distances from AUV to beacon i at time t, time t +1 and time t +2 are ri_t,ri_t+1,ri_t+2(ii) a the horizontal distance estimates from AUV to beacon i at time t, time t +1 and time t +2 are respectively
Figure BDA0001140836760000032
The initial marker position of the parent ship of the beacon i is (x)i,yi,zi)。
The estimating of the position of the integrated navigation according to the calibration error by the long-baseline acoustic positioning-based metrology equation and the inertial navigation prediction equation comprises the steps of:
1) calculating the long baseline positioning position Z of the AUV at the time t according to the calibration errort(xt,yt)；
2) Establishing a measurement equation according to the AUV long baseline positioning position;
3) establishing a prediction equation of the combined navigation to obtain a predicted position X of the AUVt(xt,yt)；
4) Long baseline positioning position Z from AUVt(xt,yt) And predicting position Xt(xt,yt) And obtaining the AUV position of the combined navigation.
Calculating the long baseline positioning position Z of the AUV at the t moment according to the calibration errort(xt,yt) The method comprises the following steps: [ x ] oft,yt]T＝A-1(R-D)
Wherein the content of the first and second substances,
Figure BDA0001140836760000033
Figure BDA0001140836760000034
at time t the horizontal distance r from the AUV to beacons 1, 2, 31_t,r2_t,r3_t(ii) a The initial marker position of the parent ship of the beacon i is (x)i,yi,zi) (ii) a The position estimate of the beacons 1, 2, 3 is (x)1+dx1,y1+dy1)，(x2+dx2,y2+dy2) And (x)3+dx3,y3+dy3)，dxi,dyiCalibrating errors;
obtained by the above formulat,yt]TLong baseline positioning position Z as AUV at time tt(xt,yt) Is represented by a column vector of (a).
The measurement equation is
Figure BDA0001140836760000041
Wherein, VtIs the measurement noise of the beacon at time t;
Figure BDA0001140836760000042
a measurement expression representing time t;
Figure BDA0001140836760000043
true longitude and true latitude of the AUV at time t; ht＝[1,1]TIs a jacobian matrix that is a linearized approximation of the measurement equation.
The prediction equation is
Figure BDA0001140836760000044
Wherein u istPhi is the speed, heading, pitch and roll angles of the AUV at time t, Ut＝[ut,ψ]Is the system input matrix; Δ T is the time interval from time T-1 to time T; ht＝[1,1]TIs the Jacobian matrix of the measurement equation; the variance matrix of the system input noise is Qt；xt-1And yt-1Respectively representing the longitude and latitude positions of the AUV at the time t-1; pt,t-1A prediction variance representing the predicted AUV position at time t; pt-1An estimated variance representing the estimated AUV position at time t-1;
Figure BDA0001140836760000045
is an Euler transformation matrix; reIs the short axis radius of the earth; e is the ellipsoidal ellipticity of the earth's rotation.
The long baseline positioning position Z according to AUVt(xt,yt) And predicting position Xt(xt,yt) Obtaining a position estimate for the combined navigation is obtained by
Figure BDA0001140836760000046
Wherein, AUV estimates the position at the time t
Figure BDA0001140836760000051
And
Figure BDA0001140836760000052
respectively representing the longitude and latitude of the estimated position of the AUV at the time t; rtVariance matrix, h (X), representing the measurement noise at time tt)＝HtXt+Vt。
After the position of the integrated navigation is estimated, the navigation precision is estimated by calculating the position estimation variance of the integrated navigation:
Figure BDA0001140836760000053
wherein I is the identity matrix, PtAnd the position estimation variance of the AUV integrated navigation at the time t is shown.
The invention has the following beneficial effects and advantages:
1. compared with the traditional long baseline positioning and inertial navigation data, the method can effectively filter the interference of the wavelength baseline positioning noise on the integrated navigation by using the integrated navigation algorithm, simultaneously effectively inhibit the accumulated navigation error of the integrated navigation, and improve the AUV underwater positioning precision.
2. The traditional mother ship has the problem that the calibration precision is gradually reduced along with the increase of the water depth when the deep-water long-baseline beacon position is calibrated. The method takes the calibration position of the mother ship as an initial quantity, improves the beacon calibration precision through an online calibration technology, reduces the influence of the beacon calibration error on the navigation precision, and improves the underwater positioning precision of the AUV.
3. The application range is wide. The invention can be applied to AUV underwater navigation and can also be applied to underwater navigation of other submergible vehicles.
4. In order to effectively fuse long baseline positioning data and inertial navigation data and reduce the influence of beacon calibration errors on navigation positioning, the combined navigation technology based on the long baseline and the beacon online calibration technology are combined, so that the long baseline positioning data are effectively filtered, and the accumulated errors of a navigation system are inhibited; the long-baseline beacon position is calibrated on line, the influence of calibration errors on navigation precision is reduced, and the precision of AUV integrated navigation position estimation is improved.
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FIG. 1 is a schematic composition of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples.
The hardware requirement of the invention is that the AUV is provided with a depth meter to measure the depth, a Doppler to measure the current speed of the submersible, an attitude angle sensor to measure the current course angle, longitudinal inclination angle and roll angle of the submersible, an underwater acoustic distance meter to measure the distance from the AUV to the long baseline beacon, and 4 fixed long baseline beacons are arranged on the seabed.
As shown in fig. 1, when the AUV operates underwater, the speed measured by the doppler, the speed measured by the heading angle sensor, and the distance from the AUV to the beacon are automatically input into the integrated navigation algorithm, and the underwater position of the AUV is automatically calculated in real time.
4 acoustic beacons anchored on the sea bottom and an AUV, wherein the AUV is provided with an underwater acoustic distance meter, a Doppler log, an attitude angle sensor and a depth meter, wherein the attitude angle sensor measures the current course angle, the longitudinal inclination angle and the roll angle of the AUV, the Doppler log measures the current speed of the AUV, and the underwater acoustic distance meter measures the slant distance from the AUV to the acoustic beacons.
The method of the invention comprises two contents: firstly, calibrating each beacon position on line by using single beacon slope distance measured values of AUV at different moments; and secondly, establishing a Kalman filter based on data fusion of the long-baseline acoustic positioning and inertial navigation data, and calculating the position estimation of the integrated navigation.
1. On-line calibrating each beacon position by using single beacon slope distance measured values of AUV at different moments
The method utilizes the single beacon slope distance measured values of the AUV at different moments to calibrate each beacon position on line, and solves the problem that the calibration precision of the parent ship calibrating the long-baseline beacon is along with the waterThe calibration precision is reduced due to the increase of depth. The method is characterized in that the initial position calibrated by the mother ship is used as an initial quantity, a calibration error is calculated by using an online calibration algorithm, and then a calibration result with higher precision than the beacon position calibrated by the mother ship is obtained. Defining the initial target position of the parent ship of the beacon i (i is more than or equal to 1 and less than or equal to N, N is the total number of beacons) as (x)i,yi,zi) The variable is a known quantity; define the calibration error of beacon i as (dx)i,dyi0), which is the variable to be solved for; defining AUV position (x) at time t, time t +1 and time t +2t,yt,zt),(xt+1,yt+1,zt+1) And (x)t+2,yt+2,zt+2) The variable is a known quantity; defining the skew distances from AUV to beacon i at t moment, t +1 moment and t +2 moment as di_t,di_t+1,di_t+2The variable is a known quantity; then the calibration equation set of the beacon i, which is composed of the slope distance equations from the beacon i to the AUV at the time t, the time t +1 and the time t +2, is as follows:
Figure BDA0001140836760000071
the solution equation is:
[dxi,dyi]T＝A-1B
wherein the content of the first and second substances,
Figure BDA0001140836760000072
wherein, defining the horizontal distances r from AUV to beacon i at time t, time t +1 and time t +2i_t,ri_t+1,ri_t+2(ii) a Defining horizontal distance estimates AUV to beacon i at time t, time t +1 and time t +2
Figure BDA0001140836760000073
Then the horizontal distance and horizontal distance estimate are defined as follows:
Figure BDA0001140836760000074
2. establishing Kalman filter based on long-baseline acoustic positioning and inertial navigation data fusion
The function of establishing the Kalman filter based on the long-baseline acoustic positioning and inertial navigation data fusion is to comprehensively utilize the long-baseline acoustic positioning data, the Doppler measurement speed, the attitude angle measured by the attitude sensor and other inertial navigation data to obtain high-precision integrated navigation position estimation.
Step 1, calculating AUV long baseline positioning Z at time tt(xt,yt) The long baseline positioning solution equation is as follows:
[xt,yt]T＝A-1(R-D)
wherein the content of the first and second substances,
Figure BDA0001140836760000075
Figure BDA0001140836760000076
defining the horizontal distance from AUV to beacon 1, 2 and 3 at t as r1_t,r2_t,r3_tIs a known amount; the position estimate of the beacons 1, 2, 3 is (x)1+dx1,y1+dy1)，(x2+dx2,y2+dy2) And (x)3+dx3,y3+dy3) The variable dxi,dyiThe calculation result of the beacon position calibrated in the last step.
Step 2, defining AUV acoustic positioning Z at time tt(xt,yt) Wherein x istAnd ytRespectively representing the acoustic positioning positions of the AUV at the time t in the x direction and the y direction, and establishing a measurement equation of the long-baseline acoustic positioning:
Figure BDA0001140836760000081
wherein, VtIs the measurement noise of the beacon at the time t, which is zero mean Gaussian white noise, and the measurement noise variance matrix is RtThe variance matrix of the beacon noise is a device attribute;
Figure BDA0001140836760000083
the method is a measurement expression at the time t, and an accurate expression of the measurement expression cannot be obtained, wherein the accurate expression is a linear approximate expression of the measurement expression;
Figure BDA0001140836760000084
true longitude and true latitude of the AUV at time t are unknown quantities; definition Ht＝[1,1]TIs a Jacobian matrix that is a linearized approximation of the measurement equation and is a constant.
Step 3, defining the predicted position of AUV at the time t as Xt(xt,yt) Wherein x istAnd ytRespectively expressing longitude and latitude of the predicted AUV at the time t, and establishing a prediction equation of the integrated navigation:
Figure BDA0001140836760000082
wherein u istPsi, θ, phi are the speed, heading angle, pitch angle, and roll angle of the AUV at time t, which can be measured by doppler and attitude sensors, respectively, and are known quantities; then define Ut＝[ut,ψ]Is the system input matrix; Δ T is the time interval from time T-1 to time T, and is a known quantity; definition Ht＝[1,1]TIs the Jacobian matrix of the measurement equation, which is a constant; wtIs the system input noise at the time t, which is zero mean Gaussian white noise, and the variance matrix of the system input noise is defined as Qt，QtIs a device attribute of course angle sensor and Doppler, so the variance matrix Q of the input noisetIs a known amount; x is the number oft-1And yt-1The known quantities respectively represent the longitude and latitude positions of the AUV at the time t-1; pt,t-1Indicating predicted AUV position at time tThe predicted variance of (a), the unknown quantity; pt-1The estimated variance, which represents the estimated AUV position at time t-1, is a known quantity;
Figure BDA0001140836760000096
is an Euler transformation matrix, which transforms Doppler measured velocity based on AUV coordinate system into velocity of north-east coordinate system, which is a known quantity; reIs the short axis radius of the earth WGS-84 model, with the value of 6378137 meters; and e is the oblateness of the rotational ellipsoid of the earth WGS-84 model and takes the value of 1/298.257.
Calculating the predicted position of AUV at t time as X by using a prediction equation of combined navigationt(xt,yt)；
And 4, calculating the position estimation of the combined navigation: defining AUV estimated position at time t
Figure BDA0001140836760000091
Figure BDA0001140836760000095
And
Figure BDA0001140836760000092
respectively representing the longitude and latitude of the estimated position of the AUV at the time t; rtA variance matrix representing the measurement noise at time t, which is a device attribute; h (X)t) Represents a measurement expression h (X)t)＝HtXt+Vt；
Figure BDA0001140836760000093
And 5, calculating the position estimation variance of the combined navigation: definition PtThe position estimation variance and the navigation precision of AUV integrated navigation at the time t are estimated;
Figure BDA0001140836760000094
where I is the identity matrix. PtLarger indicates lower navigation accuracy.

Claims (2)

1. The underwater robot combined navigation method based on the long baseline and the beacon online calibration is characterized by comprising the following steps of:
calibrating the position of each beacon on line by using the single beacon slope distance measured values of the AUV at different moments to obtain the calibration error of each beacon;
the calibration error is obtained by
[dxi,dyi]T＝A-1B
Wherein the content of the first and second substances,
Figure FDA0003021861030000011
AUV positions at time t, time t +1 and time t +2 are (x)t,yt,zt),(xt+1,yt+1,zt+1) And (x)t+2,yt+2,zt+2) The skew distances from AUV to beacon i at time t, time t +1 and time t +2 are di_t,di_t+1,di_t+2；
Figure FDA0003021861030000012
the horizontal distances from AUV to beacon i at time t, time t +1 and time t +2 are ri_t,ri_t+1,ri_t+2(ii) a the horizontal distance estimates from AUV to beacon i at time t, time t +1 and time t +2 are respectively
Figure FDA0003021861030000013
The initial marker position of the parent ship of the beacon i is (x)i,yi,zi)；
Estimating the AUV position of the integrated navigation according to the calibration error and through a measurement equation based on the long-baseline acoustic positioning and a prediction equation of the inertial navigation;
the estimating of the position of the integrated navigation according to the calibration error by the long-baseline acoustic positioning-based metrology equation and the inertial navigation prediction equation comprises the steps of:
1) calculating the long baseline positioning position Z of the AUV at the time t according to the calibration errort(xt,yt) (ii) a The method comprises the following steps: [ x ] oft,yt]T＝C-1(R-D)
Wherein the content of the first and second substances,
Figure FDA0003021861030000021
Figure FDA0003021861030000022
at time t the horizontal distance r from the AUV to beacons 1, 2, 31_t,r2_t,r3_t(ii) a The initial marker position of the parent ship of the beacon i is (x)i,yi,zi) (ii) a The position estimate of the beacons 1, 2, 3 is (x)1+dx1,y1+dy1)，(x2+dx2,y2+dy2) And (x)3+dx3,y3+dy3)，dxi,dyiCalibrating errors;
obtained by the above formulat,yt]TLong baseline positioning position Z as AUV at time tt(xt,yt) A column vector representation of;
2) establishing a measurement equation according to the AUV long baseline positioning position;
the measurement equation is
Figure FDA0003021861030000023
Wherein, VtIs the measurement noise of the beacon at time t;
Figure FDA0003021861030000024
a measurement expression representing time t;
Figure FDA0003021861030000025
true longitude and true latitude of the AUV at time t; ht＝[1,1]TIs the Jacobian matrix of the measurement equation;
3) establishing a prediction equation of the combined navigation to obtain a predicted position X of the AUVt(xt,yt)；
The prediction equation is
Figure FDA0003021861030000026
Wherein u istPhi is the speed, heading, pitch and roll angles of the AUV at time t, Ut＝[ut,ψ]Is the system input matrix; Δ T is the time interval from time T-1 to time T; ht＝[1,1]TIs the Jacobian matrix of the measurement equation; the variance matrix of the system input noise is Qt；xt-1And yt-1Respectively representing the longitude and latitude positions of the AUV at the time t-1; pt,t-1A prediction variance representing the predicted AUV position at time t; pt-1An estimated variance representing the estimated AUV position at time t-1;
Figure FDA0003021861030000031
is an Euler transformation matrix; reIs the short axis radius of the earth; e is the ellipsoidal ellipticity of the earth's rotation;
4) long baseline positioning position Z from AUVt(xt,yt) And predicting position Xt(xt,yt) Obtaining AUV position of combined navigation by the following formula
Figure FDA0003021861030000032
Wherein, AUV estimates the position at the time t
Figure FDA0003021861030000033
Figure FDA0003021861030000034
And
Figure FDA0003021861030000035
respectively representing the longitude and latitude of the estimated position of the AUV at the time t; rtVariance matrix, h (X), representing the measurement noise at time tt)＝HtXt+Vt。
2. The underwater robot integrated navigation method based on long baseline and beacon online calibration as claimed in claim 1, wherein after the position of integrated navigation is estimated, the navigation accuracy is estimated by calculating the position estimation variance of integrated navigation:
Figure FDA0003021861030000036
wherein I is the identity matrix, PtAnd the position estimation variance of the AUV integrated navigation at the time t is shown.
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CN117146830B (en) * 2023-10-31 2024-01-26 山东科技大学 Self-adaptive multi-beacon dead reckoning and long-baseline tightly-combined navigation method

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104457754A (en) * 2014-12-19 2015-03-25 东南大学 SINS/LBL (strapdown inertial navigation systems/long base line) tight combination based AUV (autonomous underwater vehicle) underwater navigation positioning method
CN105628016A (en) * 2014-10-30 2016-06-01 中国科学院沈阳自动化研究所 Navigation positioning method based on ultra short base line
CN106017467A (en) * 2016-07-28 2016-10-12 中国船舶重工集团公司第七0七研究所 Inertia/underwater sound combined navigation method based on multiple underwater transponders

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CA2835239C (en) * 2011-05-06 2020-02-25 Richard J. Rikoski Systems and methods for synthetic aperture sonar

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105628016A (en) * 2014-10-30 2016-06-01 中国科学院沈阳自动化研究所 Navigation positioning method based on ultra short base line
CN104457754A (en) * 2014-12-19 2015-03-25 东南大学 SINS/LBL (strapdown inertial navigation systems/long base line) tight combination based AUV (autonomous underwater vehicle) underwater navigation positioning method
CN106017467A (en) * 2016-07-28 2016-10-12 中国船舶重工集团公司第七0七研究所 Inertia/underwater sound combined navigation method based on multiple underwater transponders

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
Improving Long Baseline Navigation for Autonomous Underwater Vehicles;Brian Bingham et al.;《Ocean Engineering》;20061231;第209-218页 *
一种利用单信标修正 AUV 定位误差的方法;张福斌等;《鱼雷技术》;20120229;第38-41页 *

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