CN113932815B - Robustness optimization Kalman filtering relative navigation method, device, equipment and storage medium - Google Patents

Robustness optimization Kalman filtering relative navigation method, device, equipment and storage medium Download PDF

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CN113932815B
CN113932815B CN202111217468.2A CN202111217468A CN113932815B CN 113932815 B CN113932815 B CN 113932815B CN 202111217468 A CN202111217468 A CN 202111217468A CN 113932815 B CN113932815 B CN 113932815B
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unmanned aerial
aerial vehicle
kalman filtering
robustness
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CN113932815A (en
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王础
陈浩
李娜
张彤
张依漪
王佳佳
侯健
马静
王凤敏
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Beijing Jinghang Computing Communication Research Institute
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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
    • G01C21/20—Instruments for performing navigational calculations
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/10—Internal combustion engine [ICE] based vehicles
    • Y02T10/40—Engine management systems

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  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Automation & Control Theory (AREA)
  • Physics & Mathematics (AREA)
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Abstract

The invention relates to a relative navigation method, a relative navigation device, relative navigation equipment and relative navigation storage medium for robustness optimization Kalman filtering; the method comprises the following steps: determining a Kalman filtering state sequence and an observation sequence; establishing a Kalman filtering model corresponding to the state sequence and the observation sequence; filtering by adopting an established Kalman filtering model; in the Kalman filtering process, a Kalman gain matrix is adoptedFor improving the robustness of Kalman filtering; wherein, the liquid crystal display device comprises a liquid crystal display device,a posterior prediction error covariance matrix estimated for Kalman filtering at a kth time point; i is an identity matrix; h k For observing the transfer matrix; v (V) k Is an observation noise matrix; lambda (lambda) 1 Lambda is the first robustness parameter 2 Is a second robustness parameter. Compared with the traditional Kalman filtering algorithm, the method improves the robustness of errors of measured data and reduces the deviation between a calculation result and a true value.

Description

Robustness optimization Kalman filtering relative navigation method, device, equipment and storage medium
Technical Field
The invention relates to the technical field of Kalman filtering and navigation, in particular to a relative navigation method, device, equipment and storage medium for optimizing Kalman filtering in robustness.
Background
Kalman is an algorithm that uses a linear system state equation to optimally estimate the state of the system from system observations. At present, the robustness improving method of the Kalman filtering model is mainly based on the steps of processing sensing data and information fusion.
In the aspect of sensor data processing, the model output precision is improved mainly by detecting abnormal values of sensor data. And mining and identifying data with significant differences or changes to other sensing data based on the sensor prior information and the sensing data distribution characteristics.
In the aspect of information fusion, the accuracy of the output result of the model is improved through data acquired by different sensors. The data fusion methods commonly used at present comprise centralized fusion, distributed fusion and hybrid fusion. The centralized fusion aims at directly inputting the original data of each sensor into the filtering model in parallel for processing. The data of each sensor is preprocessed by the distributed fusion, and intermediate information is formed and then is input into a filtering model for processing. The hybrid fusion is to input the original data and the preprocessed data into a filtering model for processing.
In the current robustness improving method, a mechanism for optimizing a filtering algorithm step is lacking, and the problem that an unmodeled error is generated due to the difference between a Kalman filtering model and an actual scene, so that a calculation result of the filtering model has a larger deviation from a true value is not solved.
Disclosure of Invention
In view of the above analysis, the present invention aims to provide a method, an apparatus, a device and a storage medium for relative navigation of Kalman filtering with optimized robustness, which solve the problem of deviation of calculation results of Kalman filtering models.
The technical scheme provided by the invention is as follows:
the invention discloses a robustness optimization Kalman filtering relative navigation method, which comprises the following steps:
determining a Kalman filtering state sequence and an observation sequence;
establishing a Kalman filtering model corresponding to the state sequence and the observation sequence;
filtering by adopting an established Kalman filtering model;
in the course of the Kalman filtering process,
using Kalman gain matricesFor improving the robustness of Kalman filtering; wherein (1)>A posterior prediction error covariance matrix estimated for Kalman filtering at a kth time point; i is an identity matrix; h k For observing the transfer matrix; v (V) k Is an observation noise matrix; lambda (lambda) 1 Lambda is the first robustness parameter 2 Is a second robustness parameter;
the state sequence isThe observation sequence is->The Kalman filter model is:
X k+1 ＝A k X k +G k W k ；
Y k ＝H k X k +V k ；
wherein N represents the total length of the sequence, A k For state transition matrix, H k For observing the transfer matrix; g k Is a system noise coefficient matrix; system noise matrix W k Satisfies a gaussian distribution N (0,I); observation noise matrix V k Satisfy Gaussian distribution N (0, sigma) k )；∑ k Is covariance matrix;
the Kalman filtering process includes:
initializing an initial value X of a state vector 0 And the initial value of variance of the observed noise 0 ；
PredictionAnd +.>
Calculation of Kalman gain matrix
EstimatedAnd +.>
Wherein,,a priori state estimation value at the moment k; />A posterior state estimation value at the moment k; />A covariance matrix of the posterior prediction error is obtained;
a first robustness parameter lambda 1 Is calculated by the following steps:
calculating the residual vector x k ：
Based on the residual vector x k Calculate { x } k Covariance matrix Σ 0 ：
Wherein;
calculating a residual matrix R k ：Definition r k Is R k A vector of diagonal elements;
calculating a first robustness parameter lambda 1 ：
A second robustness parameter lambda 2 Is calculated by the following steps:
calculating the residual vector x' k ：
Based on the residual vector x' k Calculate { x' k Covariance matrix Σ }, of' 0 ：
Wherein the method comprises the steps of
Calculating a residual matrix R' k ：R′ k ＝∑′ 0 -V k The method comprises the steps of carrying out a first treatment on the surface of the Definition r' k Is R'. k A vector of diagonal elements;
calculating a second robustness parameter lambda 2 ：
State sequence in Kalman filtering model during multi-unmanned aerial vehicle relative navigation filteringState value X of (2) k A 12-dimensional state value vector representing a kth point in time, including relative heading angular velocities of unmanned aerial vehicle a and unmanned aerial vehicle B, relative heading angular accelerations of unmanned aerial vehicle a and unmanned aerial vehicle C, relative position vectors of unmanned aerial vehicle a and unmanned aerial vehicle B, relative position vectors of unmanned aerial vehicle a and unmanned aerial vehicle C, and velocity vectors of unmanned aerial vehicles A, B and C;
when the relative navigation filtering of multiple unmanned aerial vehicles is carried out, the observation sequence in the Kalman filtering modelY in (3) k The 6-dimensional observation value vector representing the kth time point comprises a relative distance length of unmanned aerial vehicle A and unmanned aerial vehicle B, a relative distance length of unmanned aerial vehicle B and unmanned aerial vehicle C, a relative distance length of unmanned aerial vehicle A and unmanned aerial vehicle C, a difference value of speed sizes of unmanned aerial vehicle A and unmanned aerial vehicle B, a difference value of speed sizes of unmanned aerial vehicle B and unmanned aerial vehicle C and a difference value of speed sizes of unmanned aerial vehicle A and unmanned aerial vehicle C.
The invention also discloses a Kalman filtering relative navigation device with optimized robustness, which comprises a filtering interface module and a Kalman filtering module;
the filtering interface module is used for acquiring Kalman filtered input data and outputting a filtering result;
the Kalman filtering module adopts the Kalman filtering method with optimized robustness to carry out relative navigation filtering.
The invention also discloses an electronic device, comprising:
one or more processors;
a storage means for storing one or more programs;
the one or more programs, when executed by the one or more processors, cause the one or more processors to implement a robustness-optimized Kalman filter relative navigation method as described above.
The invention also discloses a computer readable medium having stored thereon a computer program which when executed by a processor implements a robustness optimizing Kalman filtering relative navigation method as described above.
The invention has the beneficial effects that:
compared with the traditional Kalman filtering algorithm, the method has the advantages that the robustness of the measured data errors is improved by optimizing the robustness of the gain matrix in the Kalman filtering algorithm step, the unmodeled errors generated by the difference between the Kalman filtering model and the actual scene are solved, and the deviation between the calculation result and the true value is reduced.
Drawings
The drawings are only for purposes of illustrating particular embodiments and are not to be construed as limiting the invention, like reference numerals being used to refer to like parts throughout the several views.
FIG. 1 is a flowchart of a Kalman filtering method according to an embodiment of the present invention;
FIG. 2 is a flowchart of a Kalman filtering process in an embodiment of the invention;
FIG. 3 is a flowchart of a method for calculating a first robustness parameter according to an embodiment of the present invention;
fig. 4 is a flowchart of a method for calculating a second robustness parameter according to an embodiment of the present invention.
Detailed Description
Preferred embodiments of the present invention are described in detail below with reference to the attached drawing figures, which form a part of the present application and, together with the embodiments of the present invention, serve to explain the principles of the invention.
The embodiment discloses a relative navigation method of Kalman filtering with optimized robustness, which comprises the following steps as shown in fig. 1:
step S101, determining a Kalman filtering state sequence and an observation sequence;
step S102, establishing a Kalman filtering model corresponding to the state sequence and the observation sequence;
step S103, filtering by adopting an established Kalman filtering model;
in the course of a conventional Kalman filtering process,
kalman gain matrix
To increase the robustness of Kalman filtering, the conventional Kalman gain matrix is improved in this embodiment,
using Kalman gain matricesWherein (1)>A Kalman filtered estimate for a kth time point; i is an identity matrix; h k For observing the transfer matrix; v (V) k Is an observation noise matrix; lambda (lambda) 1 Lambda is the first robustness parameter 2 Is a second robustness parameter. By determining a first robustness parameter lambda 1 And a second robustness parameter lambda 2 Brought into gain matrix K k And the variability of the actual scene is eliminated on the Kalman filtering model, so that the robustness of Kalman filtering is improved.
Specifically, the Kalman filtered state sequence isThe observation sequence is->
The Kalman filter model is:
X k+1 ＝A k X k +G k W k ；
Y k ＝H k X k +V k ；
wherein N represents the total length of the sequence, A k For state transition matrix, H k For observing the transfer matrix; g k Is a system noise coefficient matrix; system noise matrix W k Satisfies a gaussian distribution N (0,I); observation noise matrix V k Satisfy Gaussian distribution N (0, sigma) k )；∑ k Is a covariance matrix.
More specifically, the Kalman filtering process is shown in fig. 2, and includes:
step S201, initializing initial value X of State vector 0 And the initial value of variance of the observed noise 0 ；
Step S202, predictionAnd +.>
Step S203, calculating a Kalman gain matrix;
in the step, aiming at calculation errors caused by modeling differences between original Kalman filtering and actual application scenes, the method adoptsFor system noise matrix W k And an observation noise matrix V k Modeling and correction is performed on the covariance matrix of (c).
Step S204, estimationAnd +.>
Wherein,,a priori state estimation value at the moment k; />A posterior state estimation value at the moment k; />Is a posterior prediction error covariance matrix.
Specifically, a first robustness parameter lambda 1 As shown in fig. 3, includes:
step S301, calculating residual vector x k ：
Step S302, based on residual vector x k Calculate { x } k Covariance matrix Σ 0 ：
Wherein;
step S303, calculating a residual matrix R k ：Definition r k Is R k A vector of diagonal elements;
step S304, calculating a first robustness parameter lambda 1 ：
Specifically, the second robustness parameter lambda 2 As shown in fig. 4, includes:
step S401, calculating residual vector x' k ：
Step S402, based on the residual vector x' k Calculate { x' k Covariance matrix Σ }, of' 0 ：
Wherein the method comprises the steps of
Step S403, calculating a residual matrix R' k ：R′ k ＝∑′ 0 -V k The method comprises the steps of carrying out a first treatment on the surface of the Definition r' k Is R'. k A vector of diagonal elements;
step S404, calculating a second robustness parameter lambda 2 ：
According to a specific embodiment of the invention, kalman filtering is performed aiming at the relative navigation task of the unmanned aerial vehicle, so that the navigation result meets the relative navigation precision requirement.
Specifically, when performing the relative navigation filtering of multiple unmanned aerial vehicles, the determining the state sequence and the observation sequence of the Kalman filtering in step S101 in this embodiment specifically includes:
state sequence in Kalman filter modelState value X of (2) k A 12-dimensional state value vector representing a kth point in time; the 12-dimensional state value vector comprises relative course angular speeds of the unmanned aerial vehicle A and the unmanned aerial vehicle B in 1 dimension, relative course angular accelerations of the unmanned aerial vehicle A and the unmanned aerial vehicle C in 1 dimension, relative position vectors of the unmanned aerial vehicle A and the unmanned aerial vehicle B in 2 dimensions, relative position vectors of the unmanned aerial vehicle A and the unmanned aerial vehicle C in 2 dimensions and speed vectors of the unmanned aerial vehicles A, B and C in 2 dimensions;
observation sequence in Kalman filtering modelY in (3) k The 6-dimensional observation value vector representing the kth time point comprises a relative distance length of an unmanned aerial vehicle A and an unmanned aerial vehicle B, a relative distance length of the unmanned aerial vehicle B and an unmanned aerial vehicle C, a relative distance length of the unmanned aerial vehicle A and the unmanned aerial vehicle C, and an unmanned aerial vehicle A and an unmanned aerial vehicleDifference in speed B, difference in speed of unmanned aerial vehicle B and unmanned aerial vehicle C, and difference in speed of unmanned aerial vehicle a and unmanned aerial vehicle C.
By the Kalman filtering algorithm of the embodiment, the system noise matrix W is paired in the gain matrix k And an observation noise matrix V k Modeling and correction are carried out on covariance matrix of the model (C), so that robustness optimization is realized, robustness to measured data errors is improved, unmodeled errors caused by differences between a Kalman filtering model and an actual scene are solved, and deviation between a calculation result and a true value is reduced.
In order to further solve the problem that in the relative navigation task of the unmanned aerial vehicle, the measured data is influenced by random errors in the natural world and noise interference resistance, so that the state quantity calculation result has errors with the true value. In this embodiment, the recognition of the measurement data with large noise interference is performed to reduce the influence of the measurement data error on the Kalman filtering model result.
Specifically, the patent mainly calculates an observation value vector Y k Regarding the confidence coefficient of the Kalman filtering model, when the confidence coefficient is lower than a set confidence threshold value, the noise of the measured data is judged to be large, and the Kalman filtering model is invalid in the current calculation result. Therefore, the measurement data with larger noise interference is identified, and the influence of measurement data errors on the Kalman filtering model result is reduced.
More specifically, the present embodiment sets the observation vector Y k Confidence ρ of (1) k The definition is as follows:
the measurement value vector Y k Confidence ρ of (1) k The method comprises the following steps:
wherein a is a weight; p (Y) k |Y k-1 ，...，Y 1 ) For a given prior measurement value vector Y k-1 ，...，Y 1 On the condition of (1) for the measurement value vector Y k Probability density function values of Kalman filtering;
representing the value vector X when the kth state k The value is a conditional probability density function value of the Kalman filtering estimated value.
Preferably, the probability density function value p (Y k ) The calculation method of (1) is as follows: due to p (Y) k |Y k-1 ，...，Y 1 ) Having the following expression:
the present embodiment employs a sampling method to approximate p (Y k |Y k-1 ，...，Y 1 ). According to the Kalman filtering estimation method of the embodiment, the following can be obtained
Said p (Y) k |Y k-1 ，...，Y 1 ) The probability density function is expected to beVariance is P k ；
Wherein;a posterior state estimation value at the moment k;
P k by the formulaEstimating; wherein Q is the process excitation noise covariance;
to normal distributionSampling to obtain M samples->
Then p (Y) k |Y k-1 ，...，Y 1 ) By the formulaCalculating to obtain; wherein (1)>Is normal distribution->In the present embodiment, by adding the pair of observed value vectors Y k And carrying out confidence calculation based on the Kalman filtering model, identifying and mining the measurement data lower than a given threshold value, and reducing the influence of measurement data errors on the Kalman filtering model result.
In a specific scheme of the embodiment, a relative navigation device of Kalman filtering with optimized robustness is also disclosed, which comprises a filtering interface module and a Kalman filtering module;
the filtering interface module is used for acquiring Kalman filtered input data and outputting a filtering result;
the Kalman filtering module adopts the robustness optimization Kalman filtering relative navigation method to carry out filtering.
The specific technical details and effects in this scheme are the same as those in the previous embodiment, and will not be described in detail here.
In a specific scheme of this embodiment, an electronic device is also disclosed, including:
one or more processors;
a storage means for storing one or more programs;
the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the robustness-optimized Kalman filter relative navigation method as described above.
The specific technical details and effects in this scheme are the same as those in the previous embodiment, and will not be described in detail here.
In a specific aspect of this embodiment, a computer readable medium having stored thereon a computer program which when executed by a processor implements a robustness-optimized Kalman filter relative navigation method as described above is also disclosed.
The specific technical details and effects in this scheme are the same as those in the previous embodiment, and will not be described in detail here.
The present invention is not limited to the above-mentioned embodiments, and any changes or substitutions that can be easily understood by those skilled in the art within the technical scope of the present invention are intended to be included in the scope of the present invention.

Claims (4)

1. A relative navigation method of Kalman filtering with optimized robustness is characterized by comprising the following steps:
determining a Kalman filtering state sequence and an observation sequence;
establishing a Kalman filtering model corresponding to the state sequence and the observation sequence;
filtering by adopting an established Kalman filtering model;
in the course of the Kalman filtering process,
using Kalman gain matricesFor improving the robustness of Kalman filtering; wherein (1)>A posterior prediction error covariance matrix estimated for Kalman filtering at a kth time point; i is an identity matrix; h k For observing the transfer matrix; v (V) k Is an observation noise matrix; lambda (lambda) 1 Lambda is the first robustness parameter 2 Is a second robustness parameter;
the state sequence isThe observation sequence is->The Kalman filter model is:
X k+1 ＝A k X k +G k W k ；
Y k ＝H k X k +V k ；
wherein N represents the total length of the sequence, A k For state transition matrix, H k For observing the transfer matrix; g k Is a system noise coefficient matrix; system noise matrix W k Satisfies a gaussian distribution N (0,I); observation noise matrix V k Satisfy a Gaussian distribution N (0, Σ k )；Σ k Is covariance matrix;
the Kalman filtering process includes:
initializing an initial value X of a state vector 0 And an initial value of covariance Σ of the observed noise 0 ；
PredictionAnd +.>
Calculation of Kalman gain matrix
EstimatedAnd +.>
Wherein,,first at time kChecking a state estimation value; />A posterior state estimation value at the moment k;
a first robustness parameter lambda 1 Is calculated by the following steps:
calculating the residual vector x k ：
Based on the residual vector x k Calculate { x } k Covariance matrix Σ 0 ：
Wherein;
calculating a residual matrix R k ：Definition r k Is R k A vector of diagonal elements;
calculating a first robustness parameter lambda 1 ：
A second robustness parameter lambda 2 Is calculated by the following steps:
calculating the residual vector x' k ：
Based on the residual vector x' k Calculate { x' k Covariance matrix Σ' 0 ：
Wherein the method comprises the steps of
Calculating a residual matrix R' k ：R′ k ＝Σ′ 0 -V k The method comprises the steps of carrying out a first treatment on the surface of the Definition r' k Is R'. k A vector of diagonal elements;
calculating a second robustness parameter lambda 2 ：
State sequence in Kalman filtering model during multi-unmanned aerial vehicle relative navigation filteringState value X of (2) k A 12-dimensional state value vector representing a kth point in time, including relative heading angular velocities of unmanned aerial vehicle a and unmanned aerial vehicle B, relative heading angular accelerations of unmanned aerial vehicle a and unmanned aerial vehicle C, relative position vectors of unmanned aerial vehicle a and unmanned aerial vehicle B, relative position vectors of unmanned aerial vehicle a and unmanned aerial vehicle C, and velocity vectors of unmanned aerial vehicles A, B and C;
when the relative navigation filtering of multiple unmanned aerial vehicles is carried out, the observation sequence in the Kalman filtering modelY in (3) k The 6-dimensional observation value vector representing the kth time point comprises a relative distance length of unmanned aerial vehicle A and unmanned aerial vehicle B, a relative distance length of unmanned aerial vehicle B and unmanned aerial vehicle C, a relative distance length of unmanned aerial vehicle A and unmanned aerial vehicle C, a difference value of speed sizes of unmanned aerial vehicle A and unmanned aerial vehicle B, a difference value of speed sizes of unmanned aerial vehicle B and unmanned aerial vehicle C and a difference value of speed sizes of unmanned aerial vehicle A and unmanned aerial vehicle C.
2. The robustness optimization Kalman filtering relative navigation device is characterized by comprising a filtering interface module and a Kalman filtering module;
the filtering interface module is used for acquiring Kalman filtered input data and outputting a filtering result;
the Kalman filtering module adopts the Kalman filtering relative navigation method with optimized robustness as claimed in claim 1 to carry out the filtering of relative navigation.
3. An electronic device, comprising:
one or more processors;
a storage means for storing one or more programs;
the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the robustness-optimized Kalman filter relative navigation method of claim 1.
4. A computer readable medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements a robustness-optimized Kalman filter relative navigation method as claimed in claim 1.
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Families Citing this family (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114741659B (en) * 2022-04-24 2023-01-31 江苏集萃清联智控科技有限公司 Adaptive model on-line reconstruction robust filtering method, device and system

Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2013149203A (en) * 2012-01-23 2013-08-01 Nippon Telegr & Teleph Corp <Ntt> Optimal model estimation device, method and program
CN105824897A (en) * 2016-03-14 2016-08-03 湖南大学 Mixed recommendation system and method based on Kalman filtering
CN108490472A (en) * 2018-01-29 2018-09-04 哈尔滨工程大学 A kind of unmanned boat Combinated navigation method based on Fuzzy adaptive filtering
CN108663068A (en) * 2018-03-20 2018-10-16 东南大学 A kind of SVM method for adaptive kalman filtering applied in initial alignment
CN109163720A (en) * 2018-08-27 2019-01-08 广西科技大学 Kalman filter tracking method based on fading memory exponent
CN109597864A (en) * 2018-11-13 2019-04-09 华中科技大学 Instant positioning and map constructing method and the system of ellipsoid boundary Kalman filtering
CN110112770A (en) * 2019-04-17 2019-08-09 河海大学 A kind of generator dynamic state estimator method based on adaptive H ∞ volume Kalman filtering
CN110196443A (en) * 2019-06-06 2019-09-03 中国人民解放军战略支援部队信息工程大学 A kind of fault-tolerance combined navigation method and system of aircraft

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20170371306A1 (en) * 2016-06-27 2017-12-28 Ecole Polytechnique Federale De Lausanne (Epfl) System and Method for Dispatching an Operation of a Distribution Feeder with Heterogeneous Prosumers

Patent Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2013149203A (en) * 2012-01-23 2013-08-01 Nippon Telegr & Teleph Corp <Ntt> Optimal model estimation device, method and program
CN105824897A (en) * 2016-03-14 2016-08-03 湖南大学 Mixed recommendation system and method based on Kalman filtering
CN108490472A (en) * 2018-01-29 2018-09-04 哈尔滨工程大学 A kind of unmanned boat Combinated navigation method based on Fuzzy adaptive filtering
CN108663068A (en) * 2018-03-20 2018-10-16 东南大学 A kind of SVM method for adaptive kalman filtering applied in initial alignment
CN109163720A (en) * 2018-08-27 2019-01-08 广西科技大学 Kalman filter tracking method based on fading memory exponent
CN109597864A (en) * 2018-11-13 2019-04-09 华中科技大学 Instant positioning and map constructing method and the system of ellipsoid boundary Kalman filtering
CN110112770A (en) * 2019-04-17 2019-08-09 河海大学 A kind of generator dynamic state estimator method based on adaptive H ∞ volume Kalman filtering
CN110196443A (en) * 2019-06-06 2019-09-03 中国人民解放军战略支援部队信息工程大学 A kind of fault-tolerance combined navigation method and system of aircraft

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
Adaptive H2/H∞ filter for integrated navigation system;Liu et al.;Proceeding of the 11th world congress on intelligent control and automation;2155-2159 *
基于UKF的实时弹道解算参数稳健设计;李为峰 等;飞行器测控学报(第4期);266-273 *

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