CN116295538B - USBL (universal serial bus) installation error calibration method based on improved particle filtering - Google Patents
USBL (universal serial bus) installation error calibration method based on improved particle filtering Download PDFInfo
- Publication number
- CN116295538B CN116295538B CN202310589081.2A CN202310589081A CN116295538B CN 116295538 B CN116295538 B CN 116295538B CN 202310589081 A CN202310589081 A CN 202310589081A CN 116295538 B CN116295538 B CN 116295538B
- Authority
- CN
- China
- Prior art keywords
- representing
- usbl
- matrix
- state
- time
- 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
Links
- 239000002245 particle Substances 0.000 title claims abstract description 66
- 238000000034 method Methods 0.000 title claims abstract description 41
- 238000009434 installation Methods 0.000 title claims abstract description 32
- 238000001914 filtration Methods 0.000 title claims abstract description 29
- 239000013598 vector Substances 0.000 claims abstract description 16
- 238000012545 processing Methods 0.000 claims abstract description 13
- 238000009499 grossing Methods 0.000 claims abstract description 7
- 239000004973 liquid crystal related substance Substances 0.000 claims description 46
- 239000011159 matrix material Substances 0.000 claims description 46
- 238000005259 measurement Methods 0.000 claims description 41
- 238000012546 transfer Methods 0.000 claims description 17
- 238000005070 sampling Methods 0.000 claims description 6
- 206010033307 Overweight Diseases 0.000 claims description 3
- 238000012952 Resampling Methods 0.000 claims description 3
- 238000004364 calculation method Methods 0.000 claims description 3
- 238000010606 normalization Methods 0.000 claims 1
- 230000007704 transition Effects 0.000 claims 1
- 230000017105 transposition Effects 0.000 claims 1
- 238000012360 testing method Methods 0.000 description 4
- 238000010586 diagram Methods 0.000 description 2
- 230000001105 regulatory effect Effects 0.000 description 2
- 238000010276 construction Methods 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000000717 retained effect Effects 0.000 description 1
Classifications
-
- H—ELECTRICITY
- H03—ELECTRONIC CIRCUITRY
- H03H—IMPEDANCE NETWORKS, e.g. RESONANT CIRCUITS; RESONATORS
- H03H17/00—Networks using digital techniques
- H03H17/02—Frequency selective networks
- H03H17/0211—Frequency selective networks using specific transformation algorithms, e.g. WALSH functions, Fermat transforms, Mersenne transforms, polynomial transforms, Hilbert transforms
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C25/00—Manufacturing, calibrating, cleaning, or repairing instruments or devices referred to in the other groups of this subclass
- G01C25/005—Manufacturing, calibrating, cleaning, or repairing instruments or devices referred to in the other groups of this subclass initial alignment, calibration or starting-up of inertial devices
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/004—Artificial life, i.e. computing arrangements simulating life
- G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Mathematical Physics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Computing Systems (AREA)
- Life Sciences & Earth Sciences (AREA)
- Data Mining & Analysis (AREA)
- Pure & Applied Mathematics (AREA)
- Mathematical Analysis (AREA)
- Computer Hardware Design (AREA)
- Health & Medical Sciences (AREA)
- Algebra (AREA)
- Artificial Intelligence (AREA)
- Biomedical Technology (AREA)
- Biophysics (AREA)
- Computational Linguistics (AREA)
- Mathematical Optimization (AREA)
- Evolutionary Computation (AREA)
- General Health & Medical Sciences (AREA)
- Molecular Biology (AREA)
- General Engineering & Computer Science (AREA)
- Software Systems (AREA)
- Manufacturing & Machinery (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Navigation (AREA)
Abstract
The invention discloses a USBL (universal serial bus) installation error calibration method based on improved particle filtering, which belongs to the technical field of underwater robot combined positioning, and is based on two azimuth information and diagonal information output by a USBL and position information under a navigation system provided by a satellite positioning system; the absolute position of the transponder under the navigation system is firstly constructed into a system state space model; based on the constructed system state space model, carrying out state estimation based on an improved particle filter algorithm to obtain an optimal state estimation value; and carrying out smoothing variable structure filtering processing on the basis of the obtained optimal state estimation value. The method realizes the anti-interference robust filter estimation of the state quantity, and finally obtains a relatively accurate state vector.
Description
Technical Field
The invention belongs to the technical field of underwater robot navigation and positioning, and particularly relates to a USBL (universal serial bus) installation error calibration method based on improved particle filtering.
Background
An ultra-short baseline positioning system (USBL) is an underwater positioning device that utilizes acoustic principles, is capable of providing relatively accurate positional information for an underwater robot, and errors do not accumulate over time. The Strapdown Inertial Navigation System (SINS) is an autonomous navigation system based on an Inertial Measurement Unit (IMU), and has the characteristics of high updating frequency and comprehensive navigation data, but errors of the SINS are accumulated with time. Therefore, the SINS/USBL-based integrated navigation system can provide absolute position information for underwater machines.
In order to realize the high-precision SINS/USBL integrated navigation function, the installation error angle between the IMU and the USBL is estimated first, and then the installation error angle is compensated to the integrated navigation system to improve the navigation precision. Considering that the calibration model of the USBL is nonlinear, the commonly used USBL installation error angle calibration algorithm is an extended kalman filter, or an improved algorithm thereof.
Considering the complexity of the underwater environment, the traditional calibration algorithm based on the extended Kalman filtering has the problems of poor robustness, low calibration precision and the like, and severely restricts the navigation precision of the SINS/USBL.
Disclosure of Invention
Aiming at the problems, the invention provides a USBL installation error calibration method based on improved particle filtering, which comprises the steps of firstly, constructing a state space model, and constructing a nonlinear state equation and a measurement equation according to USBL output information; second, improved particle filtering algorithms are introduced. The traditional particle filter algorithm is improved by designing Euclidean distance and weight factors, and optimal estimation of state vectors is realized; and finally, designing a smooth variable structure filter based on the state vector estimation to realize anti-interference robust filter estimation of the state quantity, and finally obtaining a relatively accurate state vector (installation error angle information).
In order to achieve the above purpose, the technical scheme of the invention is as follows:
a USBL installation error calibration method based on improved particle filtering is based on the known quantity of USBL output information:wherein->And->Two azimuth information respectively representing USBL output,/->Skew information representing USBL output; position information under navigation system provided by satellite positioning system>The method comprises the steps of carrying out a first treatment on the surface of the Absolute position of transponder in navigation system +.>The method is characterized by comprising the following steps of:
s1, constructing a system state space model;
s2, carrying out state estimation based on an improved particle filtering algorithm on the basis of the system state space model constructed in the step S1 to obtain an optimal state estimation value;
s3, performing smoothing variable structure filtering processing on the basis of the optimal state estimated value obtained in the step S2.
Further, the specific method of step S1 is:
firstly, a state space model of a USBL installation error calibration system is established, wherein the state space model comprises a state equation and a measurement equation:
representation ofkState vector of time of day->Representing a state transfer function>Representing a system noise matrix>Representation ofkTime measurement matrix->Representing the measurement transfer function, +.>Representation ofkMeasuring a noise matrix at the moment;
kstate vector of time of dayThe definition is as follows:
wherein, the liquid crystal display device comprises a liquid crystal display device,respectively represent the USBL under the carrier coordinate systemx、y、zMounting error angles in three directions, superscriptTRepresenting a transpose of the matrix;
ktime measurement matrixThe definition is as follows:
considering that the installation error angle is constant, its derivative is 0, so the state transfer function is expressed as follows:
measuring transfer functionIs represented by the expression:
wherein, the liquid crystal display device comprises a liquid crystal display device,indicating the relative position of the transponder in the acoustic coordinate system, corner markaThe acoustic coordinate system is represented as follows:
wherein, the corner marknIndicating navigation coordinate system, corner markbThe coordinate system of the carrier is represented,representing position information in a navigation coordinate system provided by a satellite positioning system as a known quantity; />Representing the absolute position of the transponder at the navigational coordinates, which is a known quantity; />Representing a posture transfer matrix of a carrier coordinate system and a navigation coordinate system; />Representing the installation error attitude matrix of the acoustic coordinate system and the carrier system, namely the parameter to be calibrated, < ->And state vector->The relationship of (2) is expressed as follows:
further, the specific method of step S2 is as follows:
s21, initializing parameters
Definition of the definitionA priori probability density function representing a state vector, wherein the state vector prior probability density function at zero time is used in initializing parameters>Generating a sample particle population->Since the number of the initialized particle sets is m=120, the number of the initialized particle sets is set to be m=120i=1, 2,; initializing the weight of the individual particles to +.>；
S22, importance sampling processing
The weights of the particles are calculated by adopting a Euclidean distance-based method, and the formula is as follows:
wherein, the liquid crystal display device comprises a liquid crystal display device,representation ofkTime of day (time)iWeight of individual particles->Representing the measurement noise covariance matrix->Representation ofkTime measurement matrix->Representation ofkTime of day (time)iState estimation values of individual particles, in order to avoid the case that the weight value of the particles is 0, the weight minimum value is set to 0.0000001;
weighting and normalizing the weight value of each particle obtained by calculation based on the Euclidean distance method:
wherein, the liquid crystal display device comprises a liquid crystal display device,representing the weighted and normalized productskTime of day (time)iWeight of individual particles->Represents the weight adjustment factor introduced, satisfy->；
S23, resampling processing
First, high-weight particles close to USBL measured values at the previous moment are reserved; secondly, randomly sampling particles with small weights in a measurement error range by taking USBL measured values at the previous moment as the mean value in an initializing mode, and finally reconstructing M=120 particle weight sets；
S24, calculating an optimal state estimated value
Wherein, the liquid crystal display device comprises a liquid crystal display device,representing the processed particles after the improved particle filtering algorithmkState estimation value of time.
Further, the specific method of step S3 is as follows:
s31, according to the processing of the improved particle filtering algorithmkState estimation value of timeCorresponding pre-measurement and prior information are calculated, and the method specifically comprises the following steps:
wherein, the liquid crystal display device comprises a liquid crystal display device,representation ofkPredicted measurement value at +1, +.>Representation ofkA priori innovation at time +1, +.>Representing the measurement function->Representation ofkMeasurement information at +1;
s32, calculating a smoothing variable structure filter gain
Wherein, the liquid crystal display device comprises a liquid crystal display device,representation ofkSmooth variable structure filter gain at +1 time, < ->Diagonal element matrix representing a matrix>Representation ofkA priori information about the moment->Representation ofkA priori innovation at time +1, +.>Representing the saturation function of the device,representing the regulatory factor->Representing a smooth boundary layer, expressed as follows:
wherein, the liquid crystal display device comprises a liquid crystal display device,representing a measurement transfer matrix; />Representing a covariance matrix; />Representation ofkA measurement noise covariance matrix at +1 moment;
s33, state estimation and covariance matrix update
Wherein, the liquid crystal display device comprises a liquid crystal display device,representation ofkState estimation value at +1 time, +.>Representation ofkCovariance matrix at +1, +.>Representing a unit matrix with a diagonal of 1.
Compared with the prior art, the invention has the following advantages:
(1) Aiming at the problem of calibration of the installation error angle of the IMU and the USBL in the underwater navigation system, the particle filter algorithm is introduced into the calibration of the installation error of the USBL, and the calibration method of the installation error angle of the USBL based on the particle filter algorithm is designed on the basis of a nonlinear space model.
(2) Aiming at the problem of particle depletion in the traditional particle filtering algorithm, the Euclidean distance and the weight factor are introduced, and the diversity and the effectiveness of particles in the particle filtering algorithm are improved.
(3) According to the method, the smooth variable structure filtering algorithm is introduced into the particle filtering algorithm aiming at the problem of influence of the outside on the USBL installation error calibration system, and the anti-interference capability of the system is further improved by designing the gain matrix.
Drawings
FIG. 1 is a flow chart of a USBL installation error calibration method based on improved particle filtering as described in the present invention;
FIG. 2 is a diagram of a motion profile of a test carrier;
fig. 3 shows the result of the USBL installation error angle estimated by the method of the present invention.
Detailed Description
The present invention is further illustrated in the following drawings and detailed description, which are to be understood as being merely illustrative of the invention and not limiting the scope of the invention.
A USBL installation error calibration method based on improved particle filtering is based on the known quantity of USBL output information:wherein->And->Two azimuth information respectively representing USBL output,/->Skew information representing USBL output; position information under navigation system provided by satellite positioning system>The method comprises the steps of carrying out a first treatment on the surface of the Absolute position of transponder in navigation system +.>The method specifically comprises the following steps:
s1, constructing a system state space model, wherein the state space model construction is a foundation for realizing a filtering method. Therefore, first, a state space model (including a state equation and a measurement equation) of the USBL installation error calibration system is established as follows:
representation ofkState vector of time of day->Representing a state transfer function>Representing a system noise matrix>Representation ofkTime measurement matrix->Representing the measurement transfer function, +.>Representation ofkMeasuring a noise matrix at the moment;
kstate vector of time of dayThe definition is as follows:
wherein, the liquid crystal display device comprises a liquid crystal display device,respectively represent the USBL under the carrier coordinate systemx、y、zMounting error angles in three directions, superscriptTRepresenting a transpose of the matrix;
ktime measurement matrixThe definition is as follows:
considering that the installation error angle is constant, its derivative is 0, so the state transfer function is expressed as follows:
measuring transfer functionIs represented by the expression:
wherein, the liquid crystal display device comprises a liquid crystal display device,indicating the relative position, angle, of the transponder in the acoustic coordinate systemLabel (C)aThe acoustic coordinate system is represented as follows:
wherein, the corner marknIndicating navigation coordinate system, corner markbThe coordinate system of the carrier is represented,representing position information in a navigation coordinate system provided by a satellite positioning system as a known quantity; />Representing the absolute position of the transponder at the navigational coordinates, which is a known quantity; />Representing a posture transfer matrix of a carrier coordinate system and a navigation coordinate system; />Representing the installation error attitude matrix of the acoustic coordinate system and the carrier system, namely the parameter to be calibrated, < ->And state vector->The relationship of (2) is expressed as follows:
。
s2, carrying out state estimation based on an improved particle filter algorithm on the basis of the system state space model constructed in the step S1 to obtain an optimal state estimation value, wherein the method specifically comprises the following steps of:
s21, initializing parameters
Definition of the definitionA priori probability density function representing the state vector, which, among the initialization parameters,state vector prior probability density function using zero time>Generating a sample particle population->Since the number of the initialized particle sets is m=120, the number of the initialized particle sets is set to be m=120i=1, 2,; initializing the weight of the individual particles to +.>；
S22, importance sampling processing
The weights of the particles are calculated by adopting a Euclidean distance-based method, and the formula is as follows:
wherein, the liquid crystal display device comprises a liquid crystal display device,representation ofkTime of day (time)iWeight of individual particles->Representing the measurement noise covariance matrix->Representation ofkTime measurement matrix->Representation ofkTime of day (time)iState estimation values of individual particles, in order to avoid the case that the weight value of the particles is 0, the weight minimum value is set to 0.0000001;
weighting and normalizing the weight value of each particle obtained by calculation based on the Euclidean distance method:
wherein, the liquid crystal display device comprises a liquid crystal display device,representing the weighted and normalized productskTime of day (time)iWeight of individual particles->Represents the weight adjustment factor introduced, satisfy->In this embodiment, select +.>；
S23, resampling processing.
First, high weight particles (error within 1 m) that were close to the USBL measurement at the previous time are retained; secondly, randomly sampling particles with small weights (error is larger than 1M) in a measurement error range by taking USBL measured values at the previous moment as a mean value according to an initialization mode, and finally reconstructing M=120 particle weight sets；
S24, calculating an optimal state estimated value
Wherein, the liquid crystal display device comprises a liquid crystal display device,representing the processed particles after the improved particle filtering algorithmkState estimation value of time.
S3, considering external interference, the method carries out smoothing variable structure filtering processing on the basis of the optimal state estimated value obtained in the step S2, and the specific method is as follows:
s31, according to the processing of the improved particle filtering algorithmkState estimation value of timeCalculate the phaseThe predicted amount and a priori information of the response are as follows:
wherein, the liquid crystal display device comprises a liquid crystal display device,representation ofkPredicted measurement value at +1, +.>Representation ofkA priori innovation at time +1, +.>Representing the measurement function->Representation ofkMeasurement information at +1;
s32, calculating a smoothing variable structure filter gain
Wherein, the liquid crystal display device comprises a liquid crystal display device,representation ofkSmooth variable structure filter gain at +1 time, < ->Diagonal element matrix representing a matrix>Representation ofkA priori information about the moment->Representation ofkA priori innovation at time +1, +.>Representing the saturation function of the device,representing the regulatory factor->Representing a smooth boundary layer, expressed as follows:
wherein, the liquid crystal display device comprises a liquid crystal display device,representing a measurement transfer matrix; />Representing a covariance matrix; />Representation ofkA measurement noise covariance matrix at +1 moment;
s33, state estimation and covariance matrix update
Wherein, the liquid crystal display device comprises a liquid crystal display device,representation ofkState estimation value at +1 time, +.>Representation ofkCovariance matrix at +1, +.>Representing a unit matrix with a diagonal of 1.
In order to verify the effectiveness of the method, an USBL installation error calibration method experiment based on improved particle filtering is carried out. The test apparatus comprises: test boats, USBL sensing equipment, etc. Wherein figure 2 is a diagram of the motion profile of a test carrier, which is moving circumferentially around an underwater transponder. Fig. 3 shows the result of the USBL installation error angle estimated by the method of the present invention, and it can be seen from the figure that the method of the present invention can effectively estimate the installation error angle information of the USBL in three directions.
It should be noted that the foregoing merely illustrates the technical idea of the present invention and is not intended to limit the scope of the present invention, and that a person skilled in the art may make several improvements and modifications without departing from the principles of the present invention, which fall within the scope of the claims of the present invention.
Claims (2)
1. A USBL installation error calibration method based on improved particle filtering is based on the known quantity of USBL output information: α, β, U, wherein α and β respectively represent two azimuth information outputted from the USBL, and U represents the pitch information outputted from the USBL; position information under navigation system provided by satellite positioning systemAbsolute position of transponder in navigation system +.>The method is characterized by comprising the following steps of:
s1, constructing a system state space model;
s2, carrying out state estimation based on an improved particle filtering algorithm on the basis of the system state space model constructed in the step S1 to obtain an optimal state estimation value;
s3, carrying out smoothing variable structure filtering processing on the basis of the optimal state estimated value obtained in the step S2;
the specific method of the step S1 is as follows:
firstly, a state space model of a USBL installation error calibration system is established, wherein the state space model comprises a state equation and a measurement equation:
X k ＝F(X k-1 )+W k-1
Z k ＝H(X k )+V k
X k state vector representing time k，F(X k-1 ) Representing state transition functions, W k-1 Representing the system noise matrix, Z k Represents the measurement matrix at time k, H (X k ) Representing the measured transfer function, V k A measurement noise matrix representing the k moment;
state vector X at time k k The definition is as follows:
X k ＝[A x A y A z ] T
wherein A is x 、A y 、A z Respectively representing the installation error angles of USBL in the x, y and z directions under a carrier coordinate system, and the superscript T represents the transposition of the matrix;
measurement matrix Z at time k k The definition is as follows:
z k ＝[α β R] T
considering that the installation error angle is constant, its derivative is 0, so the state transfer function is expressed as follows:
F(X k-1 )＝0
measuring transfer function H (X) k ) Is represented by the expression:
wherein [ x ] a y a z a ] T The relative position of the transponder in the acoustic coordinate system is represented by the angle mark a, which is specifically represented as follows:
wherein the angle sign n represents a navigation coordinate system, the angle sign b represents a carrier coordinate system,representing position information in a navigation coordinate system provided by a satellite positioning system as a known quantity; />Representing the absolute position of the transponder at the navigational coordinates, which is a known quantity;representing a posture transfer matrix of a carrier coordinate system and a navigation coordinate system; />Representing the installation error attitude matrix of the acoustic coordinate system and the carrier system, namely the parameter to be calibrated, < ->And state vector X k The relationship of (2) is expressed as follows:
the specific method of step S2 is as follows:
s21, initializing parameters
Definition P (X) k ) The state vector prior probability density function P (X 0 ) Generating a population of sample particlesInitializing the number of particle sets to m=120, so i=1, 2,; initializing the weight of the individual particles to +.>
S22, importance sampling processing
The weights of the particles are calculated by adopting a Euclidean distance-based method, and the formula is as follows:
wherein, the liquid crystal display device comprises a liquid crystal display device,the weight of the ith particle at the k moment is represented, R represents the measurement noise covariance matrix, Z k Measurement matrix representing the moment k +.>The state estimation value of the ith particle at the k moment is represented, and in order to avoid the situation that the weight value of the particle is 0, the minimum value of the weight is set to be 0.0000001;
weighting and normalizing the weight value of each particle obtained by calculation based on the Euclidean distance method:
wherein, the liquid crystal display device comprises a liquid crystal display device,representing the weight of the ith particle at the k moment after the weighting and normalization treatment, wherein eta represents an introduced weight adjustment factor and satisfies 0 < eta < 1;
s23, resampling processing
First, high-weight particles close to USBL measured values at the previous moment are reserved; secondly, randomly sampling particles with small weights in a measurement error range by taking USBL measured values at the previous moment as the mean value in an initializing mode, and finally reconstructing M=120 particle weight sets
S24, calculating an optimal state estimated value
Wherein, the liquid crystal display device comprises a liquid crystal display device,the state estimation value at k time after the processing of the improved particle filter algorithm is shown.
2. The USBL installation error calibration method based on improved particle filtering of claim 1, wherein the specific method of step S3 is as follows:
s31, according to the state estimation value of the k moment processed by the improved particle filtering algorithmCorresponding pre-measurement and priori information are calculated, and the method is specifically as follows:
wherein, the liquid crystal display device comprises a liquid crystal display device,represents the predicted measurement value at time k+1, e k+1 Representing a priori information at time k+1, < ->Representing the measurement function, Z k+1 Measurement information indicating the time k+1;
s32, calculating a smoothing variable structure filter gain
K k+1 ＝H -1 diag[(|e k+1 |+ρ|e k |)·sat(Ω -1 e k+1 )]×[diag(e k+1 )] -1
Wherein K is k+1 Representation ofSmooth variable structure filter gain at time k+1, diag [. Cndot. ]]Matrix of diagonal elements representing a matrix of elements, e k Representing a priori information at time k, e k+1 Representing a priori information at time k+1, sat (·) represents the saturation function, ρ=0.5 represents the adjustment factor, Ω represents the smooth boundary layer, expressed as follows:
Ω＝((|e k+1 |+ρ|e k |) -1 HP k H T (HP k H T +R k+1 ) -1 ) -1
wherein H represents a measurement transfer matrix; p (P) k Representing a covariance matrix; r is R k+1 A measurement noise covariance matrix at the time k+1 is represented;
s33, state estimation and covariance matrix update
Wherein, the liquid crystal display device comprises a liquid crystal display device,representing the state estimate at time k+1, P k+1 The covariance matrix at time k+1 is represented, and I represents the identity matrix with a diagonal of 1.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310589081.2A CN116295538B (en) | 2023-05-24 | 2023-05-24 | USBL (universal serial bus) installation error calibration method based on improved particle filtering |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310589081.2A CN116295538B (en) | 2023-05-24 | 2023-05-24 | USBL (universal serial bus) installation error calibration method based on improved particle filtering |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN116295538A CN116295538A (en) | 2023-06-23 |
| CN116295538B true CN116295538B (en) | 2023-08-01 |
Family
ID=86832703
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310589081.2A Active CN116295538B (en) | 2023-05-24 | 2023-05-24 | USBL (universal serial bus) installation error calibration method based on improved particle filtering |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116295538B (en) |
Family Cites Families (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR101755307B1 (en) * | 2016-05-11 | 2017-07-10 | 한국해양과학기술원 | A position measurement error correcting method of underwater moving objects |
| CN108562287A (en) * | 2018-01-08 | 2018-09-21 | 哈尔滨工程大学 | A kind of Terrain-aided Underwater Navigation based on adaptively sampled particle filter |
| CN109613520B (en) * | 2018-12-14 | 2022-10-14 | 东南大学 | Ultra-short baseline installation error online calibration method based on filtering |
| CN110531618B (en) * | 2019-08-27 | 2020-07-31 | 河海大学 | Closed loop detection robot self-positioning error elimination method based on effective key frame |
| CN114666732B (en) * | 2022-03-15 | 2024-05-10 | 江苏科技大学 | Moving target positioning calculation and error evaluation method under noisy network |
| CN115855049B (en) * | 2023-02-07 | 2023-05-12 | 河海大学 | SINS/DVL navigation method based on particle swarm optimization robust filtering |
-
2023
- 2023-05-24 CN CN202310589081.2A patent/CN116295538B/en active Active
Also Published As
| Publication number | Publication date |
|---|---|
| CN116295538A (en) | 2023-06-23 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111156987B (en) | Inertia/astronomy combined navigation method based on residual compensation multi-rate CKF | |
| US8577637B2 (en) | System and method of magnetic compass calibration | |
| CN109163721A (en) | Attitude measurement method and terminal device | |
| US9417091B2 (en) | System and method for determining and correcting field sensors errors | |
| Narasimhappa et al. | Fiber-optic gyroscope signal denoising using an adaptive robust Kalman filter | |
| CN110514203B (en) | Underwater integrated navigation method based on ISR-UKF | |
| CN112798021B (en) | Inertial navigation system inter-travelling initial alignment method based on laser Doppler velocimeter | |
| CN110702113B (en) | Method for preprocessing data and calculating attitude of strapdown inertial navigation system based on MEMS sensor | |
| CN111750865B (en) | Self-adaptive filtering navigation method for difunctional deep sea unmanned submersible vehicle navigation system | |
| CN113155129B (en) | Holder attitude estimation method based on extended Kalman filtering | |
| CN111649747A (en) | IMU-based adaptive EKF attitude measurement improvement method | |
| CN110703205A (en) | Ultrashort baseline positioning method based on adaptive unscented Kalman filtering | |
| CN112284388B (en) | Unmanned aerial vehicle multisource information fusion navigation method | |
| CN116295538B (en) | USBL (universal serial bus) installation error calibration method based on improved particle filtering | |
| CN112197765B (en) | Method for realizing fine navigation of underwater robot | |
| CN110375773B (en) | Attitude initialization method for MEMS inertial navigation system | |
| CN110672127B (en) | Real-time calibration method for array type MEMS magnetic sensor | |
| CN110849364A (en) | Adaptive Kalman attitude estimation method based on communication-in-motion | |
| CN114858166B (en) | IMU attitude resolving method based on maximum correlation entropy Kalman filter | |
| CN115014321B (en) | Bionic polarization multisource fusion orientation method based on adaptive robust filtering | |
| CN110954081A (en) | Quick calibration device and method for magnetic compass | |
| CN113390421B (en) | Unmanned aerial vehicle positioning method and device based on Kalman filtering | |
| CN115451946A (en) | Indoor pedestrian positioning method combining MEMS-IMU and Wi-Fi | |
| Zhu et al. | A hybrid step model and new azimuth estimation method for pedestrian dead reckoning | |
| CN109489689B (en) | Satellite vector measurement error on-orbit estimation method based on alpha-beta filtering |
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 |