CN110296701A - Inertia and satellite combined guidance system gradation type failure recall fault-tolerance approach - Google Patents

Inertia and satellite combined guidance system gradation type failure recall fault-tolerance approach Download PDF

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CN110296701A
CN110296701A CN201910612635.XA CN201910612635A CN110296701A CN 110296701 A CN110296701 A CN 110296701A CN 201910612635 A CN201910612635 A CN 201910612635A CN 110296701 A CN110296701 A CN 110296701A
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information
fault
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satellite
backtracking
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CN110296701B (en
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奔粤阳
王坤
赵玉新
吴磊
李倩
周广涛
姜南
陈海南
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Harbin Engineering University
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/10—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration
    • G01C21/12—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning
    • G01C21/16—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation
    • G01C21/165—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation combined with non-inertial navigation instruments
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • 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
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
    • G01S19/01—Satellite radio beacon positioning systems transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
    • G01S19/13—Receivers
    • G01S19/23—Testing, monitoring, correcting or calibrating of receiver elements
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
    • G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
    • G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
    • G01S19/42—Determining position
    • G01S19/45—Determining position by combining measurements of signals from the satellite radio beacon positioning system with a supplementary measurement
    • G01S19/46—Determining position by combining measurements of signals from the satellite radio beacon positioning system with a supplementary measurement the supplementary measurement being of a radio-wave signal type

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

Abstract

The present invention is to provide a kind of inertia and satellite combined guidance system gradation type failure to recall fault-tolerance approach.Carry out initial Alignment of Inertial Navigation System；It acquires and slides store sensor data；Gradation type fault detection and identification are carried out to satellite navigation location information using state card side and residual error card side mixing detection method, when satellite navigation fault-free, incision inertia/combinations of satellites navigation mode merges satellite navigation output information；When satellite navigation is in gradation type failure, carries out inertial navigation backtracking algorithm and Kalman recalls algorithm, obtain attitude matrix, speed and the position for not merging -1 moment of kth satellite gradation type fault message；Satellite navigation fault point is traced back to, pure inertial navigation retrospect is carried out and resolves, posture, speed and location information after satellite historical failure information is isolated in recursion to kth sampling instant, output.The present invention can effectively judge satellite navigation gradation type failure, and carry out inertial navigation and Kalman's backtracking algorithm to failure progress fault-tolerant processing, avoid historical failure information pollution.

Description

Gradual fault backtracking fault tolerance method for inertial and satellite combined navigation system
Technical Field
The invention relates to a fault tolerance method for an inertia integrated navigation system, in particular to a gradual fault tolerance method for the inertia/satellite integrated navigation system.
Background
Since the inertial integrated navigation system integrates a plurality of kinds of sensor information, the accuracy of the sensor information determines the system accuracy. The invention researches an inertial navigation backtracking and Kalman backtracking algorithm by utilizing stored gyroscope and accelerometer information when a satellite navigation has a gradual-change fault, and carries out pure inertial navigation fast backtracking processing after historical fault information is removed, so that fault tolerance is effectively carried out on the historical gradual-change fault information of the satellite navigation, and the precision and the reliability of a navigation result are improved.
In a published article, for example, in an article of 'SINS/BD tightly combined navigation system fault detection algorithm research and implementation' of 'navigation and control' volume 17, 4 th in 2018, volume 17, chi, yan chi and minzui, an improved chi-square fault detection algorithm is mentioned, gradual-change type faults are identified by using a new-information dynamic change characteristic, and fault satellites are isolated in real time, so that the influence of fault information on inertial navigation precision is effectively reduced. According to the simulation result, the method can quickly monitor the fault satellite, quickly avoid the pollution of fault information by setting different early warning threshold values, but can only isolate the gradual-change type fault in real time, cannot further process historical fault information, and cannot eliminate the influence of the historical gradual-change type fault information on the system.
Also, Zhang Huaqiang, Li Dongxing and Zhang Guoqiang (hybrid x's) published in the Chinese journal of inertia technology2Application of detection method in fault detection of integrated navigation system (A.W.)2Assays using two states χ2The detection method is used for detecting the sensor information gradual-change type fault through parallel operation, and the fault diagnosis result of the integrated navigation system is determined by the parallel operation result, so that the false alarm rate can be effectively reduced, the gradual-change type fault information can be accurately isolated, the fault diagnosis result of the integrated navigation system can be effectively improved, and the reliability of the system can be improved. According to the simulation result of an author, after the satellite navigation information simulation fails for 100-110 s, although the gradual-change type fault of the satellite navigation information can be effectively detected due to the introduction of fault information, after the fault is detected, further fault-tolerant processing is not performed on the history gradual-change type fault information fused before, the system is still polluted by the fault information within a certain period of time, and the steady-state influence of the history gradual-change type fault on the subsequent system cannot be eliminated.
The published articles describe and explore the gradual-change fault tolerance of the combined navigation system, but do not simultaneously achieve the purpose of further fault tolerance processing on historical fault information after the gradual-change fault is detected, so that the research on a method for reliably and quickly rejecting the historical fault and recalculating the navigation information in the fault time interval has innovativeness and practical engineering value.
Disclosure of Invention
The invention aims to provide a retrospective fault tolerance method which can effectively judge the satellite navigation gradual-change type fault, carry out fault tolerance processing on the fault and avoid the inertia of a historical fault information pollution system and the gradual-change type fault of a satellite integrated navigation system.
The purpose of the invention is realized as follows:
step 1, an inertial integrated navigation system binds initial navigation parameters and performs initial alignment of the inertial navigation system;
step 2, collecting sensor data, and storing [ t ] in a sliding mannerk-M,tk]The sensor data in the h/T time period mainly includes gyroscope information ωbAccelerometer information fbAnd satellite navigation latitude, longitude, altitude informationWherein, tkIs the kth sampling time, M is the backtracking number, h is the backtracking time, TsB represents a carrier coordinate system for a sampling period;
step 3, according to the satellite navigation position information, data analysis processing is carried out by using a state chi-square and residual chi-square mixed detection method, detection and identification are carried out on the gradual-change type fault, and when the satellite navigation has no gradual-change type fault, the step 4 is carried out; when the satellite navigation is in the gradual change type fault, performing step 5;
step 4, the system is switched into an inertia/satellite integrated navigation mode, time updating and measurement updating are carried out by using a Kalman filtering algorithm, and satellite navigation information of the kth sampling moment is subjected toFusing, and outputting the corrected body position, speed and posture information;
step 5, an attitude matrix from the k sampling moment carrier coordinate system b of the inertial navigation system to the navigation coordinate system nSpeed informationAnd location informationGyroscope information at kth sampling time stored by slidingAnd accelerometer informationPerforming inertial navigation backtracking algorithm to obtain an organism attitude matrix at the k-1 sampling momentSpeed of rotationAnd position
Step 6, performing a Kalman filter backtracking algorithm, and resolving position information by inertial navigation at the k-1 th sampling momentPosition information of satellite navigation using sliding stored k-1 th sampling timeObtaining Kalman backtracking measurement information at the k-1 sampling moment:
step 7, estimating the state of the Kalman filter at the kth sampling momentSum state estimation mean square error PkPerforming time backtracking update, and recurrently predicting the state at the k-1 th sampling moment by one stepSum-state one-step prediction mean square error Pk-1/k：
Wherein,Φk/k-1a state one-step transition matrix of the Kalman filter from the k-1 sampling moment to the k sampling moment is obtained;Γk-1driving a matrix for the system noise at the k-1 th sampling moment; qk-1A system noise matrix at the k-1 sampling moment;
and 8, performing Kalman measurement backtracking update by backtracking measurement information and time backtracking update:
Pk-1＝(I-Kk-1Hk-1)Pk-1/k
wherein, Kk-1Filtering a gain matrix at the k-1 th sampling moment; hk-1A k-1 sampling moment measurement matrix is obtained; rk-1Measuring a noise matrix at the k-1 sampling moment; i is an identity matrix;
step 9, obtaining error estimator according to Kalman backtracking algorithmFeeding back the position information corresponding to the k-1 sampling moment of inertial navigation backtracking calculation to obtain an attitude matrix which is not fused with the gradual change type fault information of the satellite at the k-1 sampling momentSpeed of rotationAnd position
Step 10, backtracking to the satellite navigation fault point, and storing the fault point in [ t ]k-M,tk]And (3) carrying out forward tracing resolving on the information of the gyroscope and the accelerometer from the fault point to the kth sampling time in the time period, recurrently obtaining the information of the gyroscope and the accelerometer to the kth sampling time, and outputting the body attitude, the speed and the position information after isolating the historical fault information of the satellite.
The invention provides a new method which is suitable for an inertial integrated navigation system, can effectively judge the satellite navigation gradual change type fault, carries out inertial navigation and Kalman backtracking algorithm, carries out fault-tolerant processing on the fault and avoids the pollution of historical fault information to the system.
The advantages of the invention are mainly reflected in that:
aiming at the problem of gradual-change type faults of an inertia/satellite combined navigation system, the invention provides an inertial navigation and Kalman backtracking algorithm suitable for gradual-change type fault tolerance processing, which can effectively eliminate historical fault information and effectively improve the precision and reliability of the combined navigation system.
Drawings
Fig. 1 is a flowchart of a gradual fault backtracking fault tolerance method applicable to an inertial/satellite integrated navigation system according to the present invention.
Detailed Description
The invention is described in more detail below by way of example.
Step 1, starting an inertial integrated navigation system, and binding initial navigation parameters: constant offset epsilon for inertial measurement unit gyroscopex,εy,εzAnd a constant offset of the accelerometer +x,▽y,▽zError in satellite navigation position measurementInitial latitudeInitial longitude λ0Initial height h0(ii) a Carrying out initial alignment of the inertial navigation system;
step 2, collecting sensor data, and storing [ t ] in a sliding mannerk-M,tk],(M＝h/Ts) Sensor data in a time period, mainly gyroscope information omegabAccelerometer information fbAnd satellite navigation latitude, longitude, altitude informationWherein, tkIs the kth sampling time, M is the backtracking number, h is the backtracking time, TsB represents a carrier coordinate system for a sampling period;
step 3, according to the satellite navigation position information, data analysis processing is carried out by using a state chi-square and residual chi-square mixed detection method, detection and identification are carried out on the gradual-change type fault, and when the satellite navigation has no fault, the step 4 is carried out; when the satellite navigation is in the gradual change type fault, performing step 5;
step 4, the system is switched into an inertia/satellite integrated navigation mode, time updating and measurement updating are carried out by using a Kalman filtering algorithm, and satellite navigation output information at the kth sampling moment is subjected toFusing, and outputting the corrected body position, speed and posture information;
step 5, an attitude matrix from the k sampling moment carrier coordinate system b of the inertial navigation system to the navigation coordinate system nSpeed informationAnd location informationGyroscope information at kth sampling time stored by slidingAccelerometer informationBacktracking the inertial navigation attitude to obtain an attitude matrix at the k-1 sampling moment:
wherein,x representsThe anti-symmetric matrix of (a) is,projecting the rotation angular rate of the carrier coordinate system b relative to the navigation coordinate system n at the kth sampling moment in the carrier coordinate system b;projecting the earth rotation angular rate at the kth sampling moment in a navigation coordinate system n;projecting the rotation angular speed of the navigation coordinate system n relative to the earth coordinate system e at the kth sampling moment in the navigation coordinate system n; rMIs the local earth meridian principal radius of curvature; rNThe main curvature radius of the local earth unitary fourth of twelve earthly branches;for the body speed at the kth sampling timeThe east, north and sky projection of the degree under the navigation coordinate system n;
step 6, obtaining the attitude matrix of the k-1 sampling momentBacktracking and calculating the body speed at the k-1 sampling moment:
wherein,the projection of the local gravity acceleration under the navigation coordinate system n at the kth sampling moment;
step 7, speed information of the k-1 sampling moment is obtainedBacktracking and calculating the body position at the k-1 sampling moment:
step 8, performing a Kalman filtering backtracking algorithm, and obtaining position information solved by inertial navigation backtracking at the k-1 th sampling momentPosition information of satellite navigation using sliding stored k-1 th sampling timeObtaining Kalman backtracking measurement information at the k-1 sampling moment:
9, estimating the state of the Kalman filter at the kth sampling momentSum state estimation mean square error PkPerforming time backtracking update, and recurrently predicting the state at the k-1 th sampling moment by one stepSum-state one-step prediction mean square error Pk-1/k：
Wherein,Φk/k-1a state one-step transition matrix of the Kalman filter from the k-1 sampling moment to the k sampling moment is obtained;Γk-1driving a matrix for the system noise at the k-1 th sampling moment; qk-1For the system noise matrix, Q, at the k-1 th sampling instantk-1＝Qk＝…＝Q0Is a constant value matrix;
step 10, performing the Kalman measurement backtracking update by backtracking measurement information and time backtracking update:
Pk-1＝(I-Kk-1Hk-1)Pk-1/k (13)
wherein, Kk-1Filtering a gain matrix at the k-1 th sampling moment; hk-1A k-1 sampling moment measurement matrix is obtained; rk-1Measuring the noise matrix, R, for the k-1 th sampling instantk-1＝Rk＝…＝R0Is a constant value matrix; i is an identity matrix;
step 11, obtaining error estimator according to Kalman backtracking algorithmFeeding back the position information corresponding to the k-1 sampling moment of inertial navigation backtracking calculation to obtain an attitude matrix which is not fused with the gradual change type fault information of the satellite at the k-1 sampling momentSpeed of rotationAnd position
Step 12, performing inertial navigation and Kalman backtracking algorithms alternately to backtrack to a satellite navigation fault point; using storage in [ t ]k-M,tk]And carrying out forward tracing and resolving on the gyroscope and accelerometer information from a fault point to the kth sampling time in a time period, recurrently obtaining the k sampling time, outputting body attitude, speed and position information after isolating the historical fault information of the satellite, and completing fault-tolerant processing on the satellite navigation gradual change type historical fault.
The gradual-change fault backtracking fault-tolerant method suitable for the inertial/satellite combined navigation system adopts a hybrid chi-square detection method, can quickly and effectively detect gradual-change fault information of satellite navigation, adopts inertial navigation backtracking and Kalman backtracking algorithms to remove historical fault information in the system, and further improves the reliability of the system.
The method stores the key data of inertial navigation by using a sliding storage mode, effectively saves the storage space, and can quickly output the body navigation information while avoiding the pollution of the historical fault information of the satellite by using a pure inertial navigation tracing method, thereby improving the practicability of the engineering.

Claims (1)

1. A gradual-change fault backtracking fault-tolerant method for an inertia and satellite integrated navigation system is characterized by comprising the following steps:
step 1, an inertial integrated navigation system binds initial navigation parameters and performs initial alignment of the inertial navigation system;
step 2, collecting sensor data, and storing [ t ] in a sliding mannerk-M,tk]The sensor data in the h/T time period mainly includes gyroscope information ωbAccelerometer information fbAnd satellite navigation latitude, longitude, altitude informationWherein, tkIs the kth sampling time, M is the backtracking number, h is the backtracking time, TsB represents a carrier coordinate system for a sampling period;
step 3, according to the satellite navigation position information, data analysis processing is carried out by using a state chi-square and residual chi-square mixed detection method, detection and identification are carried out on the gradual-change type fault, and when the satellite navigation has no gradual-change type fault, the step 4 is carried out; when the satellite navigation is in the gradual change type fault, performing step 5;
step 4, the system is switched into an inertia/satellite integrated navigation mode, time updating and measurement updating are carried out by using a Kalman filtering algorithm, and satellite navigation information of the kth sampling moment is subjected toFusing, and outputting the corrected body position, speed and posture information;
step 5, an attitude matrix from the k sampling moment carrier coordinate system b of the inertial navigation system to the navigation coordinate system nSpeed informationAnd location informationGyroscope information at kth sampling time stored by slidingAnd accelerometer informationPerforming inertial navigation backtracking algorithm to obtain an organism attitude matrix at the k-1 sampling momentSpeed of rotationAnd position
Step 6, performing a Kalman filter backtracking algorithm, and resolving position information by inertial navigation at the k-1 th sampling momentPosition information of satellite navigation using sliding stored k-1 th sampling timeObtaining Kalman backtracking measurement information at the k-1 sampling moment:
step 7, estimating the state of the Kalman filter at the kth sampling momentSum state estimation mean square error PkPerforming time backtracking update, and recurrently predicting the state at the k-1 th sampling moment by one stepSum-state one-step prediction mean square error Pk-1/k：
Wherein,Φk/k-1a state one-step transition matrix of the Kalman filter from the k-1 sampling moment to the k sampling moment is obtained;Γk-1driving a matrix for the system noise at the k-1 th sampling moment; qk-1A system noise matrix at the k-1 sampling moment;
and 8, performing Kalman measurement backtracking update by backtracking measurement information and time backtracking update:
Pk-1＝(I-Kk-1Hk-1)Pk-1/k
wherein, Kk-1Filtering a gain matrix at the k-1 th sampling moment; hk-1A k-1 sampling moment measurement matrix is obtained; rk-1Measuring a noise matrix at the k-1 sampling moment; i is an identity matrix;
step 9, obtaining error estimator according to Kalman backtracking algorithmFeeding back the position information corresponding to the k-1 sampling moment of inertial navigation backtracking calculation to obtain an attitude matrix which is not fused with the gradual change type fault information of the satellite at the k-1 sampling momentSpeed of rotationAnd position
Step 10, backtracking to the satellite navigation fault point, and storing the fault point in [ t ]k-M,tk]And (3) carrying out forward tracing resolving on the information of the gyroscope and the accelerometer from the fault point to the kth sampling time in the time period, recurrently obtaining the information of the gyroscope and the accelerometer to the kth sampling time, and outputting the body attitude, the speed and the position information after isolating the historical fault information of the satellite.
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Cited By (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111337025A (en) * 2020-04-28 2020-06-26 中国人民解放军国防科技大学 Positioning and orientating instrument hole positioning method suitable for long-distance horizontal core drilling machine
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Citations (25)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101464935A (en) * 2009-01-09 2009-06-24 哈尔滨工程大学 AUV intelligent fault-tolerance combined navigation simulation system based on network
CN101464152A (en) * 2009-01-09 2009-06-24 哈尔滨工程大学 Adaptive filtering method for SINS/GPS combined navigation system
CN101477488A (en) * 2009-01-16 2009-07-08 哈尔滨工程大学 Key service system oriented system repentance recovery method and system
CN101865693A (en) * 2010-06-03 2010-10-20 天津职业技术师范大学 Multi-sensor combined navigation system for aviation
CN102176031A (en) * 2011-01-06 2011-09-07 中国科学院国家授时中心 System time difference based receiver completeness failure detection method in dual-mode navigation system
RU2012104402A (en) * 2009-07-10 2013-08-20 Сагем Дефенс Секьюрите METHOD FOR DETERMINING CARRIER NAVIGATION PARAMETERS AND HYBRIDIZATION DEVICE RELATED TO THE PANK OF CALMAN FILTERS
CN103434610A (en) * 2013-09-03 2013-12-11 哈尔滨工程大学 Docking positioning guiding method for offshore oil drilling platform
CN104181574A (en) * 2013-05-25 2014-12-03 成都国星通信有限公司 Strapdown inertial navigation system/global navigation satellite system combined based navigation filter system and method
CN104422948A (en) * 2013-09-11 2015-03-18 南京理工大学 Embedded type combined navigation system and method thereof
CN105637437A (en) * 2013-10-11 2016-06-01 斯奈克玛 Monitoring of an aircraft engine to anticipate maintenance operations
CN106289249A (en) * 2015-05-22 2017-01-04 应美盛股份有限公司 For synthesizing the system and method that sensor signal generates
CN106595652A (en) * 2016-11-30 2017-04-26 西北工业大学 Vehicle MCA (motion constraints aided) backtracking type aligning-on-the-move method
CN107894232A (en) * 2017-09-29 2018-04-10 湖南航天机电设备与特种材料研究所 A kind of accurate method for locating speed measurement of GNSS/SINS integrated navigations and system
CN108106635A (en) * 2017-12-15 2018-06-01 中国船舶重工集团公司第七0七研究所 Inertia defends the anti-interference posture course calibration method of long endurance for leading integrated navigation system
CN108291813A (en) * 2015-10-09 2018-07-17 大众汽车有限公司 It is merged by the position data of posture figure
CN108802780A (en) * 2018-03-09 2018-11-13 东南大学 Bias property analysis method between a kind of GPS/BDS differential systems
US20180375939A1 (en) * 2017-06-26 2018-12-27 Veniam, Inc. Systems and methods for self-organized fleets of autonomous vehicles for optimal and adaptive transport and offload of massive amounts of data
CN109163735A (en) * 2018-09-29 2019-01-08 苏州大学 A kind of positive-positive backtracking Initial Alignment Method of swaying base
CN109373999A (en) * 2018-10-23 2019-02-22 哈尔滨工程大学 Combinated navigation method based on failure tolerant Kalman filtering
CN109471146A (en) * 2018-12-04 2019-03-15 北京壹氢科技有限公司 A kind of self-adapted tolerance GPS/INS Combinated navigation method based on LS-SVM
US20190094288A1 (en) * 2017-09-22 2019-03-28 Schweitzer Engineering Laboratories, Inc. Accuracy of event locating on powerlines based on field data
CN109737959A (en) * 2019-03-20 2019-05-10 哈尔滨工程大学 A kind of polar region Multi-source Information Fusion air navigation aid based on federated filter
CN109813342A (en) * 2019-02-28 2019-05-28 北京讯腾智慧科技股份有限公司 A kind of fault detection method and system of inertial navigation-satellite combined guidance system
CN109974697A (en) * 2019-03-21 2019-07-05 中国船舶重工集团公司第七0七研究所 A kind of high-precision mapping method based on inertia system
CN109975851A (en) * 2019-04-04 2019-07-05 河南思维轨道交通技术研究院有限公司 A kind of train line fault point accurate positioning method and system

Patent Citations (25)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101464152A (en) * 2009-01-09 2009-06-24 哈尔滨工程大学 Adaptive filtering method for SINS/GPS combined navigation system
CN101464935A (en) * 2009-01-09 2009-06-24 哈尔滨工程大学 AUV intelligent fault-tolerance combined navigation simulation system based on network
CN101477488A (en) * 2009-01-16 2009-07-08 哈尔滨工程大学 Key service system oriented system repentance recovery method and system
RU2012104402A (en) * 2009-07-10 2013-08-20 Сагем Дефенс Секьюрите METHOD FOR DETERMINING CARRIER NAVIGATION PARAMETERS AND HYBRIDIZATION DEVICE RELATED TO THE PANK OF CALMAN FILTERS
CN101865693A (en) * 2010-06-03 2010-10-20 天津职业技术师范大学 Multi-sensor combined navigation system for aviation
CN102176031A (en) * 2011-01-06 2011-09-07 中国科学院国家授时中心 System time difference based receiver completeness failure detection method in dual-mode navigation system
CN104181574A (en) * 2013-05-25 2014-12-03 成都国星通信有限公司 Strapdown inertial navigation system/global navigation satellite system combined based navigation filter system and method
CN103434610A (en) * 2013-09-03 2013-12-11 哈尔滨工程大学 Docking positioning guiding method for offshore oil drilling platform
CN104422948A (en) * 2013-09-11 2015-03-18 南京理工大学 Embedded type combined navigation system and method thereof
CN105637437A (en) * 2013-10-11 2016-06-01 斯奈克玛 Monitoring of an aircraft engine to anticipate maintenance operations
CN106289249A (en) * 2015-05-22 2017-01-04 应美盛股份有限公司 For synthesizing the system and method that sensor signal generates
CN108291813A (en) * 2015-10-09 2018-07-17 大众汽车有限公司 It is merged by the position data of posture figure
CN106595652A (en) * 2016-11-30 2017-04-26 西北工业大学 Vehicle MCA (motion constraints aided) backtracking type aligning-on-the-move method
US20180375939A1 (en) * 2017-06-26 2018-12-27 Veniam, Inc. Systems and methods for self-organized fleets of autonomous vehicles for optimal and adaptive transport and offload of massive amounts of data
US20190094288A1 (en) * 2017-09-22 2019-03-28 Schweitzer Engineering Laboratories, Inc. Accuracy of event locating on powerlines based on field data
CN107894232A (en) * 2017-09-29 2018-04-10 湖南航天机电设备与特种材料研究所 A kind of accurate method for locating speed measurement of GNSS/SINS integrated navigations and system
CN108106635A (en) * 2017-12-15 2018-06-01 中国船舶重工集团公司第七0七研究所 Inertia defends the anti-interference posture course calibration method of long endurance for leading integrated navigation system
CN108802780A (en) * 2018-03-09 2018-11-13 东南大学 Bias property analysis method between a kind of GPS/BDS differential systems
CN109163735A (en) * 2018-09-29 2019-01-08 苏州大学 A kind of positive-positive backtracking Initial Alignment Method of swaying base
CN109373999A (en) * 2018-10-23 2019-02-22 哈尔滨工程大学 Combinated navigation method based on failure tolerant Kalman filtering
CN109471146A (en) * 2018-12-04 2019-03-15 北京壹氢科技有限公司 A kind of self-adapted tolerance GPS/INS Combinated navigation method based on LS-SVM
CN109813342A (en) * 2019-02-28 2019-05-28 北京讯腾智慧科技股份有限公司 A kind of fault detection method and system of inertial navigation-satellite combined guidance system
CN109737959A (en) * 2019-03-20 2019-05-10 哈尔滨工程大学 A kind of polar region Multi-source Information Fusion air navigation aid based on federated filter
CN109974697A (en) * 2019-03-21 2019-07-05 中国船舶重工集团公司第七0七研究所 A kind of high-precision mapping method based on inertia system
CN109975851A (en) * 2019-04-04 2019-07-05 河南思维轨道交通技术研究院有限公司 A kind of train line fault point accurate positioning method and system

Non-Patent Citations (8)

* Cited by examiner, † Cited by third party
Title
SHENG BAO等: "Aerodynamic model/INS/GPS failure-tolerant navigation method for multirotor UAVs based on federated Kalman Filter", 《2017 CHINESE AUTOMATION CONGRESS (CAC)》, 1 January 2018 (2018-01-01), pages 1121 - 1125 *
YIDING SUN等: "In-Motion Attitude and Position Alignment for Odometer-Aided SINS Based on Backtracking Scheme", 《IEEE ACCESS》, 1 January 2019 (2019-01-01), pages 20211 - 20224 *
ZHAO LONG: "Study on fault-tolerant SINS/GPS integrated navigation system", 《IEEE INTERNATIONAL CONFERENCE MECHATRONICS AND AUTOMATION, 2005》, 8 May 2006 (2006-05-08), pages 1 - 4 *
丁宏升: "一种组合导航系统的故障检测方法仿真研究", 《航空计算技术》 *
丁宏升: "一种组合导航系统的故障检测方法仿真研究", 《航空计算技术》, vol. 48, no. 1, 25 January 2018 (2018-01-25), pages 79 - 81 *
宋丽杰: "GPS/INS紧组合导航系统的故障诊断方法研究", 《中国优秀博硕士学位论文全文数据库(硕士) 信息科技辑》, 15 March 2018 (2018-03-15), pages 136 - 1554 *
成研等: "单轴旋转捷联惯导系统回溯式对准方法", 《航空精密制造技术》, vol. 54, no. 1, 15 February 2018 (2018-02-15), pages 16 - 20 *
车宁宇: "联邦滤波算法在INS/GPS/CNS组合导航系统中应用的研究", 《中国优秀博硕士学位论文全文数据库(硕士) 信息科技辑》, 15 June 2018 (2018-06-15), pages 136 - 965 *

Cited By (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111337025A (en) * 2020-04-28 2020-06-26 中国人民解放军国防科技大学 Positioning and orientating instrument hole positioning method suitable for long-distance horizontal core drilling machine
CN111999747A (en) * 2020-08-28 2020-11-27 大连海事大学 Robust fault detection method for inertial navigation-satellite combined navigation system
CN111999747B (en) * 2020-08-28 2023-06-20 大连海事大学 Robust fault detection method for inertial navigation-satellite integrated navigation system
CN112179347A (en) * 2020-09-18 2021-01-05 西北工业大学 Combined navigation method based on spectrum red shift error observation equation
CN113514064A (en) * 2021-04-23 2021-10-19 南京航空航天大学 Robust factor graph multi-source fault-tolerant navigation method
CN113514064B (en) * 2021-04-23 2024-01-30 南京航空航天大学 Multi-source fault-tolerant navigation method for robust factor graph
CN113432604A (en) * 2021-06-29 2021-09-24 广东工业大学 IMU/GPS combined navigation method capable of sensitively detecting fault
CN113432604B (en) * 2021-06-29 2023-05-19 广东工业大学 IMU/GPS integrated navigation method capable of sensitively detecting faults
CN115727846A (en) * 2023-01-09 2023-03-03 中国人民解放军国防科技大学 Multithreading backtracking type integrated navigation method for strapdown inertial navigation and satellite navigation
CN116736358A (en) * 2023-08-09 2023-09-12 北京理工大学 Long baseline carrier phase differential positioning method suitable for satellite navigation
CN116736358B (en) * 2023-08-09 2023-11-03 北京理工大学 Long baseline carrier phase differential positioning method suitable for satellite navigation

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