CN109579850B - Deepwater intelligent navigation method based on auxiliary inertial navigation to water velocity - Google Patents

Deepwater intelligent navigation method based on auxiliary inertial navigation to water velocity Download PDF

Info

Publication number
CN109579850B
CN109579850B CN201910038063.9A CN201910038063A CN109579850B CN 109579850 B CN109579850 B CN 109579850B CN 201910038063 A CN201910038063 A CN 201910038063A CN 109579850 B CN109579850 B CN 109579850B
Authority
CN
China
Prior art keywords
dvl
velocity
speed
navigation
inertial 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.)
Active
Application number
CN201910038063.9A
Other languages
Chinese (zh)
Other versions
CN109579850A (en
Inventor
何波
吕鹏飞
郭佳
沈钺
沙启鑫
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.)
Ocean University of China
Original Assignee
Ocean University of China
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 Ocean University of China filed Critical Ocean University of China
Priority to CN201910038063.9A priority Critical patent/CN109579850B/en
Publication of CN109579850A publication Critical patent/CN109579850A/en
Application granted granted Critical
Publication of CN109579850B publication Critical patent/CN109579850B/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
    • G01C21/20—Instruments for performing navigational calculations
    • G01C21/203—Specially adapted for sailing ships
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/005—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 with correlation of navigation data from several sources, e.g. map or contour matching
    • 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

Landscapes

  • Engineering & Computer Science (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Automation & Control Theory (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Position Fixing By Use Of Radio Waves (AREA)
  • Navigation (AREA)

Abstract

The invention discloses a deepwater intelligent navigation method based on water velocity assisted inertial navigation, which is to take measures from the aspects of acoustic information preprocessing, a water velocity assisted inertial navigation system and the like so as to solve the related problems of poor acoustic information reliability brought by acoustic measurement and marine environment, large navigation accumulated error when an AUV is too far away from the sea bottom and DVL is not used for assisting the ground velocity and the like. Specifically, the method comprises the steps that (1) rudder fin propeller change information is introduced based on a motion constraint thought, and acoustic information is preprocessed to improve the reliability of the acoustic information; (2) introducing a virtual log scheme, designing a delay-free HMM/KF filter, and comprehensively processing the speeds of the DVL and the INS so as to reduce DVL speed errors and INS long-period errors caused by the bump, the swing, the acceleration, the deceleration and the turning of the AUV; (3) and the graph optimization algorithm is adopted, the error level is controlled by utilizing nonlinear optimization, marginalization and optimization are carried out in a recursion mode, and the composition of real-time property is realized so as to improve the navigation precision.

Description

Deepwater intelligent navigation method based on auxiliary inertial navigation to water velocity
Technical Field
The invention relates to the field of intelligent navigation of underwater vehicles, in particular to a deepwater intelligent navigation method based on water velocity assisted inertial navigation.
Background
The ocean is the second big space behind the relay land of the four tactical spaces developed by human beings, is a strategic development base of energy, biological resources, metal resources and water resources, and has a great supporting effect on the development of the economic society. The extensive and deep exploration and development of oceans become one of the development subjects of the 21 st century, the nation also refers to the unprecedented strategic heights of 'concerning oceans, knowing oceans and slightly oceans', plans such as 'bivalve and one sea', 'transparent oceans' and 'healthy oceans' are deeply developed, and a '21 st century silk-on-sea' ocean environment safety guarantee system is constructed.
As an important assistant for exploring and developing oceans by human beings, an Autonomous Underwater Vehicle (AUV) plays a role in ocean development no better than that of an artificial satellite in space exploration, and the AUV with high-performance Underwater operation capability becomes comprehensive manifestation of national ocean competitiveness. The AUV completes the predetermined tasks of marine scientific research, resource investigation, emergency search and rescue and the like without leaving the underwater navigation technology, and the accuracy of navigation positioning information is the bottleneck problem for determining the development and application of the AUV.
In the stage of underwater navigation of the AUV, a Doppler Velocimeter (DVL) is often used to provide Velocity information to assist an Inertial Navigation System (INS) navigation, the sensor technology is mature, the volume, power consumption and cost meet the requirements of the AUV, and the information of different sensors makes up for the deficiency, so that the navigation accuracy can be improved. However, due to the particularity of the marine environment and the seawater medium, the acoustic signal needs to be interacted with the external environment, and the measurement result has incomplete dependence.
At present, the combined navigation of the DVL-assisted INS is generally realized by taking the ground speed provided by the DVL as external observation information, estimating attitude, speed and position errors of the INS by a filtering algorithm, and correcting in an open loop or feedback manner to finally obtain more accurate attitude, speed and position navigation information. However, when the deep-water AUV performs a task, it is difficult to work in the upper ocean layer and the middle ocean layer, and it is not possible to ensure that the ultrasonic beam emitted by the DVL reaches the seabed. Limited by volume, weight and power consumption, the AUV cannot assemble DVLs at great depths, and except for working at deep ocean levels 1-300 meters from the bottom, the AUV will always be in convection mode, providing only water velocity, not ground velocity.
Disclosure of Invention
The invention provides a deepwater intelligent navigation method based on water velocity assisted inertial navigation, which is to take measures from the aspects of acoustic information preprocessing, a water velocity assisted inertial navigation system and the like so as to solve the problems of poor acoustic information reliability brought by acoustic measurement and marine environment, large navigation accumulated error during DVL water velocity assistance and the like.
The invention is realized by adopting the following technical scheme, and a deepwater intelligent navigation method based on the water velocity assisted inertial navigation comprises the following steps:
step A, acoustic information preprocessing based on motion constraint and model assistance comprises the following steps:
a1, acquiring navigation related sensor information of an AUV (underwater vehicle), wherein the sensor information comprises the speed, acceleration, angle and position information of an INS (inertial navigation system), the speed information of a DVL (dynamic velocity indicator), the rudder fin paddle change time information and the positioning information of a GPS (global positioning system);
step A2, taking the current movement speed and acceleration of the AUV and the change time interval of the rudder fin paddle as input, taking the speed at the next moment of DVL and the acceleration information at the next moment of INS as observed quantities, establishing a DVL speed active tracking model to inhibit the measurement noise of an acoustic system, and providing model output when the DVL speed fails and jumps;
step B, constructing a virtual log, and optimizing the output of the active tracking model based on the DVL speed in the step A, wherein the optimization comprises the following steps:
b1, designing a DVL and INS velocity measurement equation comprehensive operation by using the high-precision part of the DVL on the water velocity and the inertial navigation velocity;
step B2, designing a non-delay HMM/KF filter, filtering out speed errors of corresponding frequency bands of the inertial navigation speed and the DVL speed, and outputting the optimized water velocity;
and C, realizing autonomous navigation positioning based on the obtained optimized water aligning speed.
Further, in the step a2, the established DVL velocity active tracking model is:
Figure GDA0003527794770000021
wherein V is the speed of DVL, a is the acceleration output by INS, alpha is the reciprocal of the change time of rudder fin paddle, and is calculated by AUV control strategy, when the carrier always makes uniform linear motionWhen the time constant is infinite, the maneuvering time constant is infinite;
Figure GDA0003527794770000023
the velocity tracking model can inhibit acoustic system measurement noise, detect failure and jump in acoustic auxiliary information and provide model output when DVL fails and jumps.
Further, in step B1, the DVL and INS velocity equation is as follows:
Figure GDA0003527794770000022
wherein, VDVLAnd VINSThe output speeds of the DVL to the water speed and the inertial navigation are respectively; vcurrentAnd VAUVOcean current velocity and AUV actual velocity respectively; delta VDVL(H) And δ VINS(L) high frequency component error of DVL and low frequency component error of inertial navigation, respectively, wherein the ocean current velocity VcurrentThe method is a first-order Markov process, the relevant time is several hours, the high-precision parts of the DVL to the water velocity and the inertial navigation velocity are fully utilized, and the DVL and INS velocity measurement equation are designed to be comprehensively calculated to obtain the initial high-precision water velocity.
Further, the step B2 is implemented by:
the method comprises the following steps of constructing a velocity comprehensive processing HMM model of an inertial navigation INS and a DVL, and comprising the following two random processes:
Figure GDA0003527794770000031
in which ξk，νkFor the high-frequency band component needing to be filtered, the system state matrix A and the observation matrix H meet the following conditions:
Figure GDA0003527794770000032
analyzing the characteristics of the HMM model and constructing a simple two-dimensional HMM/KF filtering model, wherein the difference equation is as follows:
Figure GDA0003527794770000033
converting the filter in the form of a difference equation into a digitally filtered form in the frequency domain:
Figure GDA0003527794770000034
and further obtaining a mode, a phase and a cut-off frequency of the frequency response of the HMM/KF filter, filtering frequency domain error components of the DVL and the inertial navigation respectively, obtaining an optimized high-precision water velocity, and realizing the high-precision function of the virtual log, wherein the virtual log comprehensively considers the velocities of the DVL and the INS, the obtained water velocity precision is higher than the ground velocity of the INS, and the virtual log has higher frequency and is input into a navigation system.
Further, the step C is implemented in the following manner: and when the AUV is underwater, based on the water velocity of DVL, the INS angle and the sampling time interval, the method utilizes nonlinear optimization to control errors and carries out marginalization and optimization in a recursion mode, thereby realizing composition in real time and improving navigation accuracy.
Compared with the prior art, the invention has the advantages and positive effects that:
the method combines the motion constraint thought, uses an active tracking method, simultaneously utilizes rudder fin pulp and acceleration information of the AUV, performs primary processing on the measured value of the acoustic sensor, and filters partial interference noise; by constructing a virtual log, after the DVL water velocity and the inertial navigation velocity are comprehensively processed, respective interference components are filtered by using a delay-free filter, and an optimized water velocity is output; the method avoids the defect that the consistency and the precision of the map are difficult to ensure based on a recursive Bayes state estimation method, uses a navigation method based on map optimization, controls the error level by utilizing nonlinear optimization, effectively controls the accumulated error, realizes the real-time composition and improves the navigation precision.
Drawings
FIG. 1 is a schematic diagram of an AUV main sensor according to an embodiment of the present invention;
FIG. 2 is a schematic block diagram of a navigation method according to an embodiment of the present invention;
FIG. 3 is a flow chart of HMM/KF prediction update according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of the optimization algorithm shown in FIG. 2;
wherein: 1. an ultra-short baseline transducer; 2. a global positioning system GPS; 3. an inertial navigation system INS; 4. a Doppler velocimeter DVL; 5. depth gauge IPS.
Detailed Description
In order to make the above objects, features and advantages of the present invention more clearly understood, the present invention will be further described with reference to the accompanying drawings and examples. It should be noted that the embodiments and features of the embodiments of the present application may be combined with each other without conflict.
The deep water intelligent navigation method based on the auxiliary inertial navigation to the water velocity is shown in fig. 2 and comprises the following steps:
firstly, preprocessing acoustic information based on motion constraint and model assistance specifically comprises the following steps:
(1) acquiring navigation related sensor information of an underwater vehicle AUV (autonomous underwater vehicle), wherein FIG. 1 is a schematic diagram of the installation position of a main sensor, and the sensor information comprises speed, acceleration, angle and position information of an inertial navigation INS, speed information of a DVL (dynamic velocity indicator), rudder fin propeller change time information and positioning information of a GPS (global positioning system);
(2) the scheme introduces a motion constraint thought, and adopts a current statistical model to preprocess the DVL speed, wherein the motion constraint thought is derived from a dynamic model: in the process of diving/diving, the AUV generally moves ahead slowly with a determined 'preset track', which belongs to a motion constraint, and when the motion constraint and tracking idea is applied to the preprocessing of acoustic positioning and speed information, the speed of the AUV also has the characteristic of 'motion constraint', and has initiative, specifically:
the method comprises the following steps of taking the current movement speed and acceleration of the AUV and the rudder fin paddle change time interval as input, taking the speed at the next moment of the DVL and the acceleration information at the next moment of the INS as observed quantities, and establishing a DVL speed active tracking model:
Figure GDA0003527794770000041
wherein V is the velocity of the DVL; a is the acceleration output by the INS; alpha is the reciprocal of the rudder fin paddle change time, is calculated by an AUV control strategy, and when the carrier always makes uniform linear motion, the maneuvering time constant of the carrier is an infinite value;
Figure GDA0003527794770000043
the velocity tracking model can inhibit the measurement noise of an acoustic system, detect failure and jump in acoustic auxiliary information and provide model output when the DVL fails and jumps, namely the DVL is about to the water velocity;
step two, optimizing the output based on the DVL speed active tracking model in the step one:
(1) constructing a virtual log, fully utilizing the high-precision parts of the DVL to the water velocity and the inertial navigation velocity, and designing the comprehensive operation of the DVL and the INS velocity measurement equation:
the speed measurement error of the water velocity measured by DVL can be divided into constant coefficient error related to AUV speed, sideslip error related to AUV turning and rotary maneuvering, and the like. Combining the doppler effect velocity measurement principle of the DVL, it can be known that the velocity measurement error is mainly reflected as a high-frequency component, while the velocity measurement error of the INS is mainly reflected as a low-frequency component of over eighty minutes, and the velocity measurement equations of the DVL and the INS are as follows:
Figure GDA0003527794770000042
wherein, VDVLAnd VINSThe output speeds of the DVL to the water speed and the inertial navigation are respectively; vcurrentAnd VAUVAre respectively provided withOcean current velocity and AUV actual velocity; delta VDVL(H) And δ VINS(L) high frequency component error of DVL and low frequency component error of inertial navigation, respectively, wherein the ocean current velocity VcurrentThe method is a first-order Markov process, the relevant time is several hours, the high-precision parts of the DVL to the water velocity and the inertial navigation velocity are fully utilized, and the DVL and INS velocity measurement equation are designed to carry out comprehensive operation to obtain a preliminary high-precision water velocity;
(2) designing a delay-free HMM/KF filter, filtering out speed errors of corresponding frequency bands of the inertial navigation speed and the DVL speed, and outputting an optimal water velocity;
classic digital filter and moving average filter have time delay, can't satisfy the requirement of real-time output navigation information, and this scheme designs a no delay HMM/KF digital filter, and its theory of operation is: establishing an HMM/KF filtering equation through the observation value sequence and a known HMM model, and solving the current value of the optimal state in the required frequency band from the current observation value and the predicted value at the previous moment;
the method comprises the following steps of constructing a velocity comprehensive processing HMM model of an inertial navigation INS and a DVL, and mainly comprising the following two random processes:
Figure GDA0003527794770000051
in which ξk，νkFor the high-frequency band component needing to be filtered, the system state matrix A and the observation matrix H meet the following conditions:
Figure GDA0003527794770000052
as shown in fig. 3, the HMM model characteristics are analyzed and a simple two-dimensional HMM/KF filtering model is constructed, whose difference equation is as follows:
Figure GDA0003527794770000053
a classical digital filtering form that converts a filter in the form of a difference equation into the frequency domain is as follows:
Figure GDA0003527794770000054
the mode, the phase and the cut-off frequency of the frequency response of the HMM/KF filter are obtained by analyzing the above formula, so that respective frequency domain error components of the DVL and the inertial navigation can be filtered, the optimized high-precision DVL is obtained, the water velocity is output, and the high-precision function of the virtual log is realized. And the virtual log comprehensively considers the speeds of the DVL and the INS, the accuracy of the obtained water velocity is higher than the ground velocity of the INS, the obtained water velocity has higher frequency, and the obtained water velocity is input into a navigation system.
And step three, realizing autonomous navigation positioning based on the obtained optimized DVL water velocity, as shown in fig. 4, wherein the scheme is based on a graph optimization method, utilizes nonlinear optimization to control the error level, and effectively controls the accumulated error:
when the AUV is on the water surface, the position correction is carried out by combining the GPS position, when the AUV is underwater, because the GPS has no signal, only the water velocity, the INS angle and the sampling time interval of the DVL are used, the nonlinear optimization control error is utilized, and the marginalization and optimization are carried out in a recursion mode, so that the composition of real-time property is realized to improve the navigation precision:
according to the obtained high-precision intelligent navigation on the water speed, the time interval and the angle, firstly, a belief network model is adopted to obtain X, L, U, Z variable joint probability density as follows:
Figure GDA0003527794770000061
wherein X represents a pose point, L represents a landmark position, U is a control quantity, Z is a measurement value, and P (X)0) Representing the prior probability, P (x)i|xi-1,ui) For control input uiProbability of motion under the conditions, P (z)k|xik,ljk) Representing the measurement probability. The above equation is converted to an equivalent least squares problem based on Maximum A Posteriori (MAP) estimation, and can be in negative log formObtaining:
Figure GDA0003527794770000062
according to the fact that the noise terms of the motion equation and the measurement equation are normal distribution noise with the mean value of zero, the motion probability model and the measurement probability model of the AUV can be written as follows:
Figure GDA0003527794770000063
Figure GDA0003527794770000064
wherein f (-) and h (-) are motion and measurement equations of a noise-free term respectively, the mean values of the noise terms are both 0, ΛiAnd ΓkThe covariance of the noise terms, which are the equation of motion and the equation of measurement, respectively, can be obtained by substituting the least squares equation
Figure GDA0003527794770000065
The above formula is developed by first order taylor to the standard least squares representation:
Figure GDA0003527794770000066
the above formula synthesizes two column vectors of X and L into a theta vector, A is composed of a Jacobian matrix of a motion equation at an X point, a Jacobian matrix of a measurement equation at the X point and a Jacobian matrix of the measurement equation at a L point, and b is composed of a state prediction error and a measurement prediction error. Solving the least squares uses the following equation:
Aθ＝b
considering that real-time calculation is needed in the AUV operation process, the A can be rotated by Givens to obtain the position information more quickly, and the updated and decomposed matrix is as follows
Figure GDA0003527794770000067
Multiplying both sides of the least square equation by Q according to the above formulaTTo obtain
Figure GDA0003527794770000068
Wherein the number of rows of d is made equal to the number of rows of R, Q being an orthogonal matrixTQ is a unit array, and the above formula can be simplified
Rθ＝d
Because R is an upper triangular matrix, on the premise of knowing previous results, a corresponding solution can be obtained in one step by a back substitution method, and the method is very effective for solving the AUV in real time. Setting a threshold value according to the actual navigation condition, carrying out variable rearrangement when the threshold value is exceeded so as to ensure the sparsity of the matrix and reduce the calculation complexity, recalculating the whole solution after carrying out nonlinear optimization by a Gauss-Newton method, a Levenberg-Marquardt method or a Dog-Leg method, and realizing the composition of real-time property so as to improve the navigation precision.
The above description is only a preferred embodiment of the present invention, and not intended to limit the present invention in other forms, and any person skilled in the art may apply the above modifications or changes to the equivalent embodiments with equivalent changes, without departing from the technical spirit of the present invention, and any simple modification, equivalent change and change made to the above embodiments according to the technical spirit of the present invention still belong to the protection scope of the technical spirit of the present invention.

Claims (4)

1. The deepwater intelligent navigation method based on the auxiliary inertial navigation to the water velocity is characterized by comprising the following steps of:
step A, acoustic information preprocessing based on motion constraint and model assistance comprises the following steps:
a1, acquiring navigation related sensor information of an AUV (underwater vehicle), wherein the sensor information comprises the speed, acceleration, angle and position information of an INS (inertial navigation system), the speed information of a DVL (dynamic velocity indicator), the rudder fin paddle change time information and the positioning information of a GPS (global positioning system);
step A2, taking the current movement speed and acceleration of the AUV and the change time interval of the rudder fin paddle as input, taking the speed at the next moment of DVL and the acceleration information at the next moment of INS as observed quantities, establishing a DVL speed active tracking model to inhibit the measurement noise of an acoustic system, and providing model output when the DVL speed fails and jumps;
the established DVL speed active tracking model is as follows:
Figure FDA0003517979410000011
wherein V is the speed of DVL, a is the acceleration output by INS, and alpha is the reciprocal of the change time of rudder fin paddle;
Figure FDA0003517979410000012
average acceleration in the current update period provided for inertial navigation;
step B, constructing a virtual log, and optimizing the output of the active tracking model based on the DVL speed in the step A, wherein the optimization comprises the following steps:
b1, designing a DVL and INS velocity measurement equation comprehensive operation by using the high-precision part of the DVL on the water velocity and the inertial navigation velocity;
step B2, designing a non-delay HMM/KF filter, filtering out speed errors of corresponding frequency bands of the inertial navigation speed and the DVL speed, and outputting the optimized water velocity;
and C, realizing autonomous navigation positioning based on the obtained optimized water aligning speed.
2. The deep water intelligent navigation method based on the assisted inertial navigation on the water velocity as claimed in claim 1, characterized in that: in step B1, the velocity measurement equation of DVL and INS is:
Figure FDA0003517979410000013
wherein, VDVLAnd VINSThe output speeds of the DVL to the water speed and the inertial navigation are respectively; vcurrentAnd VAUVOcean current velocity and AUV actual velocity respectively; delta VDVL(H) And δ VINS(L) high frequency component error of DVL and low frequency component error of inertial navigation, respectively, wherein the ocean current velocity VcurrentIs a first order markov process.
3. The deep water intelligent navigation method based on the assisted inertial navigation on the water velocity as claimed in claim 2, characterized in that: the step B2 is implemented by the following steps:
the method comprises the following steps of constructing a velocity comprehensive processing HMM model of an inertial navigation INS and a DVL, and comprising the following two random processes:
Figure FDA0003517979410000021
in which ξk，νkFor the high-frequency band component needing to be filtered, the system state matrix A and the observation matrix H meet the following conditions:
Figure FDA0003517979410000022
analyzing the characteristics of the HMM model and constructing a simple two-dimensional HMM/KF filtering model, wherein the difference equation is as follows:
Figure FDA0003517979410000023
converting the filter in the form of a difference equation into a digitally filtered form in the frequency domain:
Figure FDA0003517979410000024
and further, a mode, a phase and a cut-off frequency of the frequency response of the HMM/KF filter are obtained, frequency domain error components of the DVL and the inertial navigation are filtered, and the optimized high-precision water velocity is obtained.
4. The deep water intelligent navigation method based on the assisted inertial navigation on water velocity as claimed in claim 3, characterized in that: the step C is realized by adopting the following mode:
and when the AUV is underwater, based on the water velocity of the DVL, the INS angle and the sampling time interval, performing marginalization and optimization in a recursion mode by utilizing a nonlinear optimization control error, and realizing real-time composition so as to improve the navigation precision.
CN201910038063.9A 2019-01-16 2019-01-16 Deepwater intelligent navigation method based on auxiliary inertial navigation to water velocity Active CN109579850B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201910038063.9A CN109579850B (en) 2019-01-16 2019-01-16 Deepwater intelligent navigation method based on auxiliary inertial navigation to water velocity

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201910038063.9A CN109579850B (en) 2019-01-16 2019-01-16 Deepwater intelligent navigation method based on auxiliary inertial navigation to water velocity

Publications (2)

Publication Number Publication Date
CN109579850A CN109579850A (en) 2019-04-05
CN109579850B true CN109579850B (en) 2022-04-29

Family

ID=65916548

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201910038063.9A Active CN109579850B (en) 2019-01-16 2019-01-16 Deepwater intelligent navigation method based on auxiliary inertial navigation to water velocity

Country Status (1)

Country Link
CN (1) CN109579850B (en)

Families Citing this family (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110906933B (en) * 2019-11-06 2021-10-22 中国海洋大学 AUV (autonomous Underwater vehicle) auxiliary navigation method based on deep neural network
CN112230296B (en) * 2019-12-17 2021-07-23 东南大学 Gravity-related time reciprocal determination method
CN111174774B (en) * 2020-01-21 2023-04-21 河海大学 Navigation information fusion method and system under certain depth water level mode
CN111829512B (en) * 2020-06-08 2024-04-09 中国航天空气动力技术研究院 AUV navigation positioning method and system based on multi-sensor data fusion
CN112254718B (en) * 2020-08-04 2024-04-09 东南大学 Motion constraint assisted underwater integrated navigation method based on improved Sage-Husa self-adaptive filtering

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP2515140A1 (en) * 2011-04-21 2012-10-24 iXBlue Industries S.A.S. Method for global acoustic positioning of a naval or submarine target
CN103278163A (en) * 2013-05-24 2013-09-04 哈尔滨工程大学 Nonlinear-model-based SINS/DVL (strapdown inertial navigation system/doppler velocity log) integrated navigation method
CN103429986A (en) * 2011-02-17 2013-12-04 希创唐纳惯性公司 Inertial navigation sculling algorithm
CN106908086A (en) * 2017-04-14 2017-06-30 北京理工大学 A kind of modification method of Doppler log range rate error

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103429986A (en) * 2011-02-17 2013-12-04 希创唐纳惯性公司 Inertial navigation sculling algorithm
EP2515140A1 (en) * 2011-04-21 2012-10-24 iXBlue Industries S.A.S. Method for global acoustic positioning of a naval or submarine target
CN103278163A (en) * 2013-05-24 2013-09-04 哈尔滨工程大学 Nonlinear-model-based SINS/DVL (strapdown inertial navigation system/doppler velocity log) integrated navigation method
CN106908086A (en) * 2017-04-14 2017-06-30 北京理工大学 A kind of modification method of Doppler log range rate error

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
A Pretreatment Method for the Velocity of DVL Based on the Motion Constraint for the Integrated SINS/DVL;Zhao, Li-Ye等;《Applied Sciences》;20160311;第6卷(第3期);第1-15页 *
AUV组合导航系统容错关键技术研究;朱倚娴;《中国优秀博硕士学位论文全文数据库(博士) 工程科技Ⅱ辑》;20190115(第12期);第C036-11页 *
Shallow-sea application of an intelligent fusion module for low-cost sensors in AUV;Jia Guo等;《Ocean Engineering》;20180115;第148卷;第386-400页 *

Also Published As

Publication number Publication date
CN109579850A (en) 2019-04-05

Similar Documents

Publication Publication Date Title
CN109579850B (en) Deepwater intelligent navigation method based on auxiliary inertial navigation to water velocity
CN109443379B (en) SINS/DV L underwater anti-shaking alignment method of deep-sea submersible vehicle
CN109737956B (en) SINS/USBL phase difference tight combination navigation positioning method based on double transponders
CN109459040B (en) Multi-AUV (autonomous Underwater vehicle) cooperative positioning method based on RBF (radial basis function) neural network assisted volume Kalman filtering
CN102323586B (en) UUV (unmanned underwater vehicle) aided navigation method based on current profile
CN107990891B (en) Underwater robot combined navigation method based on long baseline and beacon online calibration
CN111596333B (en) Underwater positioning navigation method and system
CN111580518B (en) Unmanned ship layered obstacle avoidance method based on improved drosophila optimization and dynamic window method
CN110274591B (en) ADCP (advanced deep submersible vehicle) assisted SINS (strapdown inertial navigation system) navigation method of deep submersible manned submersible
CN103090884A (en) SINS (Strapdown Inertial Navigation System)-based method for restraining velocity measuring error of DVL (Doppler Velocity Log)
CN111076728A (en) DR/USBL-based deep submersible vehicle combined navigation method
CN112556697A (en) Shallow coupling data fusion navigation method based on federated structure
CN112710304B (en) Underwater autonomous vehicle navigation method based on adaptive filtering
RU2467914C1 (en) Method of ship navigability control and device to this end
Stanway Water profile navigation with an acoustic Doppler current profiler
CN115201799A (en) Time-varying Kalman filtering tracking method for sonar
CN110333369B (en) UUV DVL speed measurement system based on water surface GPS correction and self-adaptive denoising method
CN110703205B (en) Ultra-short baseline positioning method based on self-adaptive unscented Kalman filtering
Kato et al. Underwater navigation for long-range autonomous underwater vehicles using geomagnetic and bathymetric information
Stanway Dead reckoning through the water column with an acoustic Doppler current profiler: Field experiences
CN115031726A (en) Data fusion navigation positioning method
Zhang et al. Ocean current-aided localization and navigation for underwater gliders with information matching algorithm
Cohen et al. LiBeamsNet: AUV velocity vector estimation in situations of limited DVL beam measurements
CN109813316A (en) A kind of underwater carrier tight integration air navigation aid based on terrain aided
Sun et al. Acoustic robust velocity measurement algorithm based on variational Bayes adaptive Kalman filter

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