CN103941273A - Adaptive filtering method of onboard inertia/satellite integrated navigation system and filter - Google Patents
Adaptive filtering method of onboard inertia/satellite integrated navigation system and filter Download PDFInfo
- Publication number
- CN103941273A CN103941273A CN201410129008.8A CN201410129008A CN103941273A CN 103941273 A CN103941273 A CN 103941273A CN 201410129008 A CN201410129008 A CN 201410129008A CN 103941273 A CN103941273 A CN 103941273A
- Authority
- CN
- China
- Prior art keywords
- mrow
- msub
- mfrac
- math
- phi
- 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.)
- Granted
Links
- 238000001914 filtration Methods 0.000 title claims abstract description 60
- 238000000034 method Methods 0.000 title claims abstract description 29
- 230000003044 adaptive effect Effects 0.000 title claims abstract description 24
- 238000005259 measurement Methods 0.000 claims abstract description 107
- 238000004364 calculation method Methods 0.000 claims abstract description 25
- 239000011159 matrix material Substances 0.000 claims description 83
- 230000001133 acceleration Effects 0.000 claims description 6
- 238000005096 rolling process Methods 0.000 claims description 4
- 238000005070 sampling Methods 0.000 claims description 4
- 230000008569 process Effects 0.000 abstract description 3
- 230000008859 change Effects 0.000 abstract description 2
- 230000007547 defect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000004927 fusion Effects 0.000 description 1
- 238000012417 linear regression Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000001629 suppression Effects 0.000 description 1
Classifications
-
- 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
- G01S19/48—Determining position by combining or switching between position solutions derived from the satellite radio beacon positioning system and position solutions derived from a further system
- G01S19/49—Determining position by combining or switching between position solutions derived from the satellite radio beacon positioning system and position solutions derived from a further system whereby the further system is an inertial position system, e.g. loosely-coupled
-
- 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
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/20—Instruments for performing navigational calculations
Landscapes
- Engineering & Computer Science (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- Computer Networks & Wireless Communication (AREA)
- Navigation (AREA)
Abstract
The invention discloses an adaptive Kalman filtering method of an onboard inertia/satellite integrated navigation system and a filter. A state noise variance array and a measurement noise variance array are updated in real time through a state estimation error and a measurement error in the filtering process, and therefore the adaptive change of internal state information and external measurement information of the system is achieved. The method has the advantages of being moderate in calculation amount, high in estimation accuracy and the like so that adaptive adjustment of Kalman filter parameters can be achieved, and therefore the navigation and measurement accuracy of the system is improved.
Description
Technical Field
The invention relates to the technical field of navigation, in particular to a self-adaptive filtering method and a filter of an airborne inertia/satellite combined navigation system.
Background
The INS/GPS (Inertial Navigation System/Global Positioning System, Inertial/satellite integrated Navigation System) is an accurate carrier Navigation and motion parameter measurement System, has the advantages of good reliability, high accuracy, various output parameters and the like, and is widely applied in the field of aviation. A commonly used INS and GPS data fusion algorithm is a Kalman filter, which can achieve optimal estimation of system state given the statistical properties of the system noise.
However, a large number of engineering practices show that in an airborne use environment, an inertial sensor in the INS is susceptible to flight environments such as temperature, electromagnetism and vibration, so that the noise characteristics of the sensor are unknown and changed in real time; in addition, the measurement noise of the GPS is also unknown due to changes in the measurement conditions of the GPS receiver and the antenna. The problems can cause the estimation accuracy of the conventional Kalman filter to be reduced, and even cause filtering divergence in serious cases, so how to effectively and adaptively estimate the statistical characteristics of the state noise and the measurement noise of the system, ensure the estimation accuracy of the filter and have important engineering significance.
In the filtering calculation process of the self-adaptive estimation filtering, the estimation and correction are continuously carried out on the unknown statistical parameters of the model state noise and the measurement noise, and the suppression effect on the divergence of the filter is realized. The application number 201010552746.5 entitled adaptive filtering method based on different measurement characteristics of GPS/INS integrated navigation system discloses an adaptive filtering method for estimating measurement noise covariance by using a dual-system measurement cross-difference sequence, which realizes real-time tracking of GPS noise. The application number 201110385191.4 entitled adaptive filtering method based on observation noise variance matrix estimation discloses a method for dynamically estimating and measuring noise characteristics by utilizing a cross-difference sequence, and meanwhile, a scale factor is constructed to realize adaptive adjustment of filtering gain. However, it can be seen that none of these methods adaptively estimates the system state noise. When the performance of a sensor in a system is changed or fails due to external environment interference, system state noise obviously changes, becomes a main factor influencing filtering precision, and must be processed.
Disclosure of Invention
The invention aims to overcome the defects of the existing filtering method and provides a self-adaptive filtering method and a filter of an airborne inertial/satellite combined navigation system so as to improve the navigation accuracy of an airborne INS/GPS system. For this purpose, the following scheme is adopted.
An adaptive Kalman filtering method for an airborne inertial/satellite integrated navigation system comprises the following steps:
establishing an error model of an inertial navigation system as a state equation of a Kalman filter, taking the position difference and the speed difference between the inertial navigation system and a satellite positioning system as measurement quantities, and establishing a measurement equation of the Kalman filter, wherein the state equation and the measurement equation are respectively as follows:
therein is in the shape ofThe state vector is XkThe measurement vector is ZkThe state matrix is phik,k-1The measurement matrix is Hk，Wk-1、VkThe white noise matrixes are respectively zero-mean state white noise and measurement white noise, and the noise variance matrixes are respectively Qk-1And Rk；
Initializing a state vector X based on system characteristics and initial position, velocity and attitude information of an inertial navigation system0A measurement vector Z0State matrix phik,k-1A measurement matrix HkState estimation error variance matrix P0And measuring the noise variance matrix RkAnd state noise variance matrix Qk；
Carrying out strapdown calculation by utilizing the angular velocity and acceleration information relative to the inertial space measured by the inertial measurement unit to obtain the position, velocity and attitude value of the inertial navigation system at the current moment;
performing Kalman filtering calculation by using navigation data of inertial navigation system and measurement data of satellite positioning system, and estimating value by using filteringCorrecting navigation data of inertial navigation system while updating phik,k-1And Hk；
Calculating the measurement residual vkSum state estimation residual DeltaXkFurther estimate and update the measurement noise variance matrix RkAnd state noise variance matrix QkTo obtain an estimated value
Setting an operation upper limit M, judging whether the filtering frequency reaches, if not, returning to the step of strapdown resolving, otherwise, finishing filtering.
An adaptive kalman filter for an airborne integrated inertial/satellite navigation system, comprising:
the equation establishing unit is used for establishing an error model of the inertial navigation system as a state equation of the Kalman filter, taking the position difference and the speed difference between the inertial navigation system and the satellite positioning system as measurement quantities, and establishing a measurement equation of the Kalman filter, wherein the state equation and the measurement equation are respectively as follows:
wherein the state vector is XkThe measurement vector is ZkThe state matrix is phik,k-1The measurement matrix is Hk，Wk-1、VkThe white noise matrixes are respectively zero-mean state white noise and measurement white noise, and the noise variance matrixes are respectively Qk-1And Rk；
A parameter initialization unit for initializing the state vector X according to the system characteristics and the initial position, velocity and attitude information of the inertial navigation system0A measurement vector Z0State matrix phik,k-1A measurement matrix HkState estimation error variance matrix P0And measuring the noise variance matrix RkAnd state noise variance matrix Qk；
The inertial data solving unit is used for carrying out strapdown resolving by utilizing the angular velocity and acceleration information relative to the inertial space measured by the inertial measurement unit to obtain the position, the velocity and the attitude value of the inertial navigation system at the current moment;
a filter calculation unit for performing Kalman filter calculation by using the navigation data of the inertial navigation system and the measurement data of the satellite positioning system, and using the filter estimation valueCorrecting navigation data of inertial navigation system while updating phik,k-1And Hk；
An estimation update unit for calculating a measurement residual vkSum state estimation residual DeltaXkFurther estimate and update the measurement noise variance matrix RkAnd state noise variance matrix QkTo obtain an estimated value
And the filtering frequency judging unit is used for setting an operation upper limit M, judging whether the filtering frequency reaches or not, returning to the step of strapdown calculation if the filtering frequency does not reach the operation upper limit M, and ending the filtering if the filtering frequency does not reach the operation upper limit M.
Compared with the prior art, the invention has the advantages that:
(1) the invention improves the calibration method of the scale factor error of the flexible IMU (Inertial measurement unit), refines the calibration of the scale factor under small input angular velocity, and establishes the linear regression equation of the scale factor and the input angular velocity according to the discovered 'hyperbolic' rule of the flexible gyroscope.
(2) The present invention improves the scaling factor error compensation method of an IMU. And (3) obtaining an accurate scale factor by repeatedly iterating the established regression equation by utilizing the original data, and further improving the error compensation precision of the IMU on the basis.
Drawings
FIG. 1 is a schematic flow chart of an adaptive filtering method of an airborne inertial/satellite integrated navigation system according to the present invention;
fig. 2 is a schematic structural diagram of an adaptive filter of the airborne inertial satellite/satellite combined navigation system of the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the detailed description and specific examples, while indicating the scope of the invention, are intended for purposes of illustration only and are not intended to limit the scope of the invention.
As shown in fig. 1, the adaptive filtering method of the combined navigation system of airborne inertia/satellite of the present invention comprises the following specific steps:
step s 101: establishing an error model of an Inertial Navigation System (INS) as a state equation of a Kalman filter of an INS/GPS combined system, measuring the position difference and the speed difference of the INS and the GPS as measurement quantities, and establishing a measurement equation of the filter as follows respectively
Wherein the state vector is XkThe measurement vector is ZkThe state matrix is phik,k-1The measurement matrix is Hk，Wk-1、VkThe white noise matrixes are respectively zero-mean state white noise and measurement white noise, and the noise variance matrixes are respectively Qk-1And Rk。
Selecting a system state vector as X ═ phi delta v delta p epsilon +]TWherein the attitude error phi is [ phi ]e φn φu]TVelocity error δ v ═ δ ve δvn δvu]TThe position error δ p is [ δ L δ λ δ h ═ δ L λ δ h]TGyro error ═ epsilone εn εu]TAnd accelerometer error [ tom [ + ]e ▽n ▽u]T. The state equation is then:
Φ(4,2)＝-fu，Φ(4,3)＝fn
Φ(5,1)＝fu，Φ(5,3)＝-fe
Φ(6,1)＝-fn，Φ(6,2)＝fe
wherein,is the error of pitching, rolling and course attitude angle ve、vn、vuIs east, north and sky velocity, Rm、RnThe meridian plane and the unitary plane earth radius, p represents the position, L represents the local latitude, h represents the local height, f represents the local latitudee、fn、fuIs the east, north and sky forces, omegaieIs the rotational angular velocity of the earth.
Selecting the measurement vector as Wherein
Wherein,respectively represents the east, north and sky speeds measured by the inertial navigation system,respectively represents east, north and sky speeds, L, measured by the satellite positioning systemINS、λINS、hINSRespectively representing the latitude, longitude and altitude L measured by the inertial navigation systemGPS、λGPS、hGPSRespectively representing latitude, longitude and altitude measured by a satellite positioning system;
the measurement equation is:
H(1,4)＝1,H(2,5)＝1,H(3,6)＝1
H(4,7)＝Rm,H(5,8)＝RncosL,H(6,9)＝1
step s 102: according to the system characteristics and the initial latitude L, longitude lambda, altitude h and northeast speed v of the INSe、vn、vuAnd postureInitializationState vector X0A measurement vector Z0State matrix phik,k-1A measurement matrix HkState estimation error variance matrix P0Measuring a noise variance matrix R and a state noise variance matrix Q;
step s 103: carrying out strapdown resolving by using angular velocity and acceleration information relative to an inertial space measured by an Inertial Measurement Unit (IMU) to obtain the position, velocity and attitude value of the INS at the current moment;
step s 104: using INS navigation data and GPS measurement data at the k moment to perform Kalman filtering calculation, wherein the filter equation is
Wherein,estimating vector, K, for state one-step predictionkFor filtering the gain matrix, PkEstimating an error covariance matrix, P, for a statek，k-1The covariance matrix is predicted for the state estimation error one step.
Using filtered estimatesCorrecting INS navigation data p ═ (L λ h)T、v＝(ve vn vu)TAndnamely, it is <math>
<mrow>
<msup>
<mi>p</mi>
<mo>′</mo>
</msup>
<mo>=</mo>
<mi>p</mi>
<mo>-</mo>
<mi>δ</mi>
<mover>
<mi>p</mi>
<mo>^</mo>
</mover>
<mo>,</mo>
<msup>
<mi>v</mi>
<mo>′</mo>
</msup>
<mo>=</mo>
<mi>v</mi>
<mo>-</mo>
<mi>δ</mi>
<mover>
<mi>v</mi>
<mo>^</mo>
</mover>
<mo>,</mo>
<msup>
<msubsup>
<mi>C</mi>
<mi>b</mi>
<mi>n</mi>
<mi></mi>
</msubsup>
<mo>′</mo>
</msup>
<mo>=</mo>
<mover>
<mi>φ</mi>
<mo>^</mo>
</mover>
<mo>×</mo>
<msubsup>
<mi>C</mi>
<mi>b</mi>
<mi>n</mi>
</msubsup>
<mo>,</mo>
</mrow>
</math> Wherein
Updating phi simultaneouslyk,k-1And Hk；
Step s 105: calculating the measurement residual vkSum state estimation residual DeltaXkFurther estimate and update the measurement noise variance matrix RkAnd state noise variance matrix Qk；
Step a: setting the length N of a data sampling window, and calculating the measurement residual error v of the current momentk＝Zk-HkΦk,k-1Xk-1Further calculate the covariance matrix of the measurement residuals as
Step b: computing a state estimation residual at a current timeFurther computing a state estimation residual covariance matrix of <math>
<mrow>
<msub>
<mover>
<mi>D</mi>
<mo>^</mo>
</mover>
<mi>k</mi>
</msub>
<mo>=</mo>
<mfrac>
<mn>1</mn>
<mi>N</mi>
</mfrac>
<munderover>
<mi>Σ</mi>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mi>k</mi>
<mo>-</mo>
<mi>N</mi>
<mo>+</mo>
<mn>1</mn>
</mrow>
<mi>k</mi>
</munderover>
<msub>
<mi>ΔX</mi>
<mi>i</mi>
</msub>
<msubsup>
<mi>X</mi>
<mi>i</mi>
<mi>T</mi>
</msubsup>
<mo>;</mo>
</mrow>
</math>
Step c: respectively calculate <math>
<mrow>
<msub>
<mover>
<mi>R</mi>
<mo>^</mo>
</mover>
<mi>k</mi>
</msub>
<mo>=</mo>
<msub>
<mover>
<mi>C</mi>
<mo>^</mo>
</mover>
<mi>k</mi>
</msub>
<mo>-</mo>
<msub>
<mi>H</mi>
<mi>k</mi>
</msub>
<msub>
<mi>P</mi>
<mrow>
<mi>k</mi>
<mo>,</mo>
<mi>k</mi>
<mo>-</mo>
<mn>1</mn>
</mrow>
</msub>
<msubsup>
<mi>H</mi>
<mi>k</mi>
<mi>T</mi>
</msubsup>
<mo>,</mo>
<msub>
<mover>
<mi>Q</mi>
<mo>^</mo>
</mover>
<mi>k</mi>
</msub>
<mo>=</mo>
<msub>
<mover>
<mi>D</mi>
<mo>^</mo>
</mover>
<mi>k</mi>
</msub>
<mo>+</mo>
<msub>
<mi>P</mi>
<mi>k</mi>
</msub>
<mo>-</mo>
<msub>
<mi>Φ</mi>
<mrow>
<mi>k</mi>
<mo>,</mo>
<mi>k</mi>
<mo>-</mo>
<mn>1</mn>
</mrow>
</msub>
<msub>
<mi>P</mi>
<mrow>
<mi>k</mi>
<mo>-</mo>
<mn>1</mn>
</mrow>
</msub>
<msubsup>
<mi>Φ</mi>
<mrow>
<mi>k</mi>
<mo>,</mo>
<mi>k</mi>
<mo>-</mo>
<mn>1</mn>
</mrow>
<mi>T</mi>
</msubsup>
<mo>,</mo>
</mrow>
</math> Thereby realizing Rk、QkUpdating of (1);
step s 106: setting an operation upper limit M, judging whether the filtering times reach, if not, returning to the step s103 to continue filtering calculation, otherwise, ending filtering.
The present invention also provides a filter corresponding to the adaptive filtering method, the structure of which is shown in fig. 2, and the functions of each unit correspond to the steps of the filtering method, and the filter specifically includes:
the equation establishing unit is used for establishing an error model of the inertial navigation system as a state equation of the Kalman filter, taking the position difference and the speed difference between the inertial navigation system and the satellite positioning system as measurement quantities, and establishing a measurement equation of the Kalman filter, wherein the state equation and the measurement equation are respectively as follows:
wherein the state vector is XkThe measurement vector is ZkThe state matrix is phik,k-1The measurement matrix is Hk，Wk-1、VkThe white noise matrixes are respectively zero-mean state white noise and measurement white noise, and the noise variance matrixes are respectively Qk-1And Rk；
A parameter initialization unit for initializing the state vector X according to the system characteristics and the initial position, velocity and attitude information of the inertial navigation system0A measurement vector Z0State matrix phik,k-1A measurement matrix HkState estimation error variance matrix P0And measuring the noise variance matrix RkAnd state noise variance matrix Qk；
The inertial data solving unit is used for carrying out strapdown resolving by utilizing the angular velocity and acceleration information relative to the inertial space measured by the inertial measurement unit to obtain the position, the velocity and the attitude value of the inertial navigation system at the current moment;
a filter calculation unit for performing Kalman filter calculation by using the navigation data of the inertial navigation system and the measurement data of the satellite positioning system, and using the filter estimation valueCorrecting navigation data of inertial navigation system while updating phik,k-1And Hk；
An estimation update unit for calculating a measurement residual vkSum state estimation residual DeltaXkFurther estimate and update the measurement noise variance matrix RkAnd state noise variance matrix QkTo obtain an estimated value
And the filtering frequency judging unit is used for setting an operation upper limit M, judging whether the filtering frequency reaches or not, returning to the step of strapdown calculation if the filtering frequency does not reach the operation upper limit M, and ending the filtering if the filtering frequency does not reach the operation upper limit M.
As a preferred embodiment, the state equation and the measurement equation established by the equation establishing unit are specifically:
selecting a system state vector as X ═ phi delta v delta p epsilon +]TWherein the attitude error phi is [ phi ]e φn φu]TVelocity error δ v ═ δ ve δvn δvu]TThe position error δ p is [ δ L δ λ δ h ═ δ L λ δ h]TGyro error ═ epsilone εn εu]TAnd accelerometer error [ tom [ + ]e ▽n ▽u]T，
The state equation is then:
Φ(4,2)＝-fu，Φ(4,3)＝fn
Φ(5,1)＝fu，Φ(5,3)＝-fe
Φ(6,1)＝-fn，Φ(6,2)＝fe
wherein,is the error of pitching, rolling and course attitude angle ve、vn、vuIs east, north and sky velocity, Rm、RnThe meridian plane and the unitary plane earth radius, p represents the position, L is the local latitude, lambda is the local longitude, h is the local height, f is the local latitudee、fn、fuIs the east, north and sky forces, omegaieThe rotational angular velocity of the earth;
selecting the measurement vector as Wherein
Wherein,respectively represents the east, north and sky speeds measured by the inertial navigation system,respectively represents east, north and sky speeds, L, measured by the satellite positioning systemINS、λINS、hINSRespectively representing the latitude, longitude and altitude L measured by the inertial navigation systemGPS、λGPS、hGPSRespectively representing satellite positioningLatitude, longitude and altitude measured by the system;
the measurement equation is:
H(1,4)＝1,H(2,5)＝1,H(3,6)＝1
H(4,7)＝Rm,H(5,8)＝RncosL,H(6,9)＝1。
as a preferred embodiment, the filter equation of the filter calculation unit when performing the kalman filter calculation is:
wherein,estimating vector, K, for state one-step predictionkFor filtering the gain matrix, PkEstimating an error covariance matrix, P, for a statek，k-1The covariance matrix is predicted for the state estimation error one step.
As a preferred embodiment, the estimation update unit is used for measuring a noise variance matrix RkAnd state noise variance matrix QkIs estimated value ofThe calculation method comprises the following steps:
setting the length N of a data sampling window, and calculating the measurement residual error v of the current momentk＝Zk-HkΦk,k-1Xk-1Further calculate the covariance matrix of the measurement residuals as
Calculating a state estimate for a current timeResidual error measuring deviceFurther computing a state estimation residual covariance matrix of <math>
<mrow>
<msub>
<mover>
<mi>D</mi>
<mo>^</mo>
</mover>
<mi>k</mi>
</msub>
<mo>=</mo>
<mfrac>
<mn>1</mn>
<mi>N</mi>
</mfrac>
<munderover>
<mi>Σ</mi>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mi>k</mi>
<mo>-</mo>
<mi>N</mi>
<mo>+</mo>
<mn>1</mn>
</mrow>
<mi>k</mi>
</munderover>
<msub>
<mi>ΔX</mi>
<mi>i</mi>
</msub>
<msubsup>
<mi>X</mi>
<mi>i</mi>
<mi>T</mi>
</msubsup>
<mo>;</mo>
</mrow>
</math>
Respectively calculate <math>
<mrow>
<msub>
<mover>
<mi>R</mi>
<mo>^</mo>
</mover>
<mi>k</mi>
</msub>
<mo>=</mo>
<msub>
<mover>
<mi>C</mi>
<mo>^</mo>
</mover>
<mi>k</mi>
</msub>
<mo>-</mo>
<msub>
<mi>H</mi>
<mi>k</mi>
</msub>
<msub>
<mi>P</mi>
<mrow>
<mi>k</mi>
<mo>,</mo>
<mi>k</mi>
<mo>-</mo>
<mn>1</mn>
</mrow>
</msub>
<msubsup>
<mi>H</mi>
<mi>k</mi>
<mi>T</mi>
</msubsup>
<mo>,</mo>
<msub>
<mover>
<mi>Q</mi>
<mo>^</mo>
</mover>
<mi>k</mi>
</msub>
<mo>=</mo>
<msub>
<mover>
<mi>D</mi>
<mo>^</mo>
</mover>
<mi>k</mi>
</msub>
<mo>+</mo>
<msub>
<mi>P</mi>
<mi>k</mi>
</msub>
<mo>-</mo>
<msub>
<mi>Φ</mi>
<mrow>
<mi>k</mi>
<mo>,</mo>
<mi>k</mi>
<mo>-</mo>
<mn>1</mn>
</mrow>
</msub>
<msub>
<mi>P</mi>
<mrow>
<mi>k</mi>
<mo>-</mo>
<mn>1</mn>
</mrow>
</msub>
<msubsup>
<mi>Φ</mi>
<mrow>
<mi>k</mi>
<mo>,</mo>
<mi>k</mi>
<mo>-</mo>
<mn>1</mn>
</mrow>
<mi>T</mi>
</msubsup>
<mo>,</mo>
</mrow>
</math> And further updating the filter parameters.
In summary, the adaptive filtering method and the filter of the present invention are characterized in that during the filtering process, the state noise variance matrix and the measurement noise variance matrix are updated in real time by using the state estimation error and the measurement error, respectively, so as to realize adaptive change of the system internal state information and the external measurement information. The method has the advantages of moderate calculated amount, high estimation precision and the like, can realize the self-adaptive adjustment of the Kalman filter parameters, and improves the navigation and measurement precision of the system.
The above-mentioned embodiments only express several embodiments of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the present invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (8)
1. An adaptive Kalman filtering method for an airborne inertial/satellite integrated navigation system is characterized by comprising the following steps:
establishing an error model of an inertial navigation system as a state equation of a Kalman filter, taking the position difference and the speed difference between the inertial navigation system and a satellite positioning system as measurement quantities, and establishing a measurement equation of the Kalman filter, wherein the state equation and the measurement equation are respectively as follows:
wherein the state vector is XkThe measurement vector is ZkThe state matrix is phik,k-1The measurement matrix is Hk，Wk-1、VkThe white noise matrixes are respectively zero-mean state white noise and measurement white noise, and the noise variance matrixes are respectively Qk-1And RkK represents the k filtering operation;
initializing a state vector X based on system characteristics and initial position, velocity and attitude information of an inertial navigation system0A measurement vector Z0State matrix phik,k-1A measurement matrix HkState estimation error variance matrix P0And measuring the noise variance matrix RkAnd state noise variance matrix Qk；
Carrying out strapdown calculation by utilizing the angular velocity and acceleration information relative to the inertial space measured by the inertial measurement unit to obtain the position, velocity and attitude value of the inertial navigation system at the current moment;
performing Kalman filtering calculation by using navigation data of inertial navigation system and measurement data of satellite positioning system, and estimating value by using filteringCorrecting navigation data of inertial navigation system while updating phik,k-1And Hk；
Calculating the measurement residual vkSum state estimation residual DeltaXkFurther estimate and update the measurement noise variance matrix RkAnd state noise variance matrix QkTo obtain an estimated value
Setting an operation upper limit M, judging whether the filtering frequency reaches, if not, returning to the step of strapdown resolving, otherwise, finishing filtering.
2. The adaptive Kalman filtering method of an airborne inertial/satellite integrated navigation system according to claim 1, wherein the state equation and the measurement equation are specifically:
selecting a system state vector as X ═ phi delta v delta p epsilon +]TWherein the attitude error phi is [ phi ]e φn φu]TVelocity error δ v ═ δ ve δvn δvu]TThe position error δ p is [ δ L δ λ δ h ═ δ L λ δ h]TGyro error ═ epsilone εn εu]TAnd accelerometer error [ tom [ + ]e ▽n ▽u]T，
The state equation is then:
Φ(4，2)＝-fu，Φ(4，3)＝fn
Φ(5,1)＝fu，Φ(5,3)＝-fe
Φ(6,1)＝-fn，Φ(6,2)＝fe
wherein,is the error of pitching, rolling and course attitude angle ve、vn、vuIs east, north and sky velocity, Rm、RnThe meridian plane and the unitary plane earth radius, p represents the position, L is the local latitude, lambda is the local longitude, h is the local height, f is the local latitudee、fn、fuIs the east, north and sky forces, omegaieThe rotational angular velocity of the earth;
selecting the measurement vector as Wherein
Wherein,respectively represents the east, north and sky speeds measured by the inertial navigation system,respectively represents east, north and sky speeds, L, measured by the satellite positioning systemINS、λINS、hINSRespectively representing the latitude, longitude and altitude L measured by the inertial navigation systemGPS、λGPS、hGPSRespectively representing latitude, longitude and altitude measured by a satellite positioning system;
the measurement equation is:
H(1,4)＝1,H(2,5)＝1,H(3,6)＝1
H(4,7)＝Rm,H(5,8)＝RncosL,H(6,9)＝1。
3. the adaptive kalman filtering method of the combined airborne inertia/satellite navigation system according to claim 1 or 2, wherein the filter equation in performing the kalman filtering calculation is:
wherein,estimating vector, K, for state one-step predictionkFor filtering the gain matrix, PkEstimating an error covariance matrix, P, for a statek，k-1The covariance matrix is predicted for the state estimation error one step.
4. The method according to claim 1 or 2The adaptive Kalman filtering method of the airborne inertia/satellite integrated navigation system is characterized in that a noise variance matrix R is measuredkAnd state noise variance matrix QkIs estimated value ofThe calculation method comprises the following steps:
setting the length N of a data sampling window, and calculating the measurement residual error v of the current momentk＝Zk-HkΦk,k-1Xk-1Further calculate the covariance matrix of the measurement residuals as
Computing a state estimation residual at a current timeFurther computing a state estimation residual covariance matrix of <math>
<mrow>
<msub>
<mover>
<mi>D</mi>
<mo>^</mo>
</mover>
<mi>k</mi>
</msub>
<mo>=</mo>
<mfrac>
<mn>1</mn>
<mi>N</mi>
</mfrac>
<munderover>
<mi>Σ</mi>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mi>k</mi>
<mo>-</mo>
<mi>N</mi>
<mo>+</mo>
<mn>1</mn>
</mrow>
<mi>k</mi>
</munderover>
<msub>
<mi>ΔX</mi>
<mi>i</mi>
</msub>
<msubsup>
<mi>X</mi>
<mi>i</mi>
<mi>T</mi>
</msubsup>
<mo>;</mo>
</mrow>
</math>
Respectively calculate <math>
<mrow>
<msub>
<mover>
<mi>R</mi>
<mo>^</mo>
</mover>
<mi>k</mi>
</msub>
<mo>=</mo>
<msub>
<mover>
<mi>C</mi>
<mo>^</mo>
</mover>
<mi>k</mi>
</msub>
<mo>-</mo>
<msub>
<mi>H</mi>
<mi>k</mi>
</msub>
<msub>
<mi>P</mi>
<mrow>
<mi>k</mi>
<mo>,</mo>
<mi>k</mi>
<mo>-</mo>
<mn>1</mn>
</mrow>
</msub>
<msubsup>
<mi>H</mi>
<mi>k</mi>
<mi>T</mi>
</msubsup>
<mo>,</mo>
<msub>
<mover>
<mi>Q</mi>
<mo>^</mo>
</mover>
<mi>k</mi>
</msub>
<mo>=</mo>
<msub>
<mover>
<mi>D</mi>
<mo>^</mo>
</mover>
<mi>k</mi>
</msub>
<mo>+</mo>
<msub>
<mi>P</mi>
<mi>k</mi>
</msub>
<mo>-</mo>
<msub>
<mi>Φ</mi>
<mrow>
<mi>k</mi>
<mo>,</mo>
<mi>k</mi>
<mo>-</mo>
<mn>1</mn>
</mrow>
</msub>
<msub>
<mi>P</mi>
<mrow>
<mi>k</mi>
<mo>-</mo>
<mn>1</mn>
</mrow>
</msub>
<msubsup>
<mi>Φ</mi>
<mrow>
<mi>k</mi>
<mo>,</mo>
<mi>k</mi>
<mo>-</mo>
<mn>1</mn>
</mrow>
<mi>T</mi>
</msubsup>
<mo>,</mo>
</mrow>
</math> And further updating the filter parameters.
5. An adaptive kalman filter for an airborne integrated inertial/satellite navigation system, comprising:
the equation establishing unit is used for establishing an error model of the inertial navigation system as a state equation of the Kalman filter, taking the position difference and the speed difference between the inertial navigation system and the satellite positioning system as measurement quantities, and establishing a measurement equation of the Kalman filter, wherein the state equation and the measurement equation are respectively as follows:
wherein the state vector is XkThe measurement vector is ZkThe state matrix is phik,k-1The measurement matrix is Hk，Wk-1、VkThe white noise matrixes are respectively zero-mean state white noise and measurement white noise, and the noise variance matrixes are respectively Qk-1And Rk；
A parameter initialization unit for initializing the state vector X according to the system characteristics and the initial position, velocity and attitude information of the inertial navigation system0A measurement vector Z0State matrix phik,k-1A measurement matrix HkState estimation error variance matrix P0And measuring the noise variance matrix RkAnd state noise variance matrix Qk；
The inertial data solving unit is used for carrying out strapdown resolving by utilizing the angular velocity and acceleration information relative to the inertial space measured by the inertial measurement unit to obtain the position, the velocity and the attitude value of the inertial navigation system at the current moment;
a filter calculation unit for performing card processing by using the navigation data of the inertial navigation system and the measurement data of the satellite positioning systemKalman filtering calculations using filtered estimatesCorrecting navigation data of inertial navigation system while updating phik,k-1And Hk；
An estimation update unit for calculating a measurement residual vkSum state estimation residual DeltaXkFurther estimate and update the measurement noise variance matrix RkAnd state noise variance matrix QkTo obtain an estimated value
And the filtering frequency judging unit is used for setting an operation upper limit M, judging whether the filtering frequency reaches or not, returning to the step of strapdown calculation if the filtering frequency does not reach the operation upper limit M, and ending the filtering if the filtering frequency does not reach the operation upper limit M.
6. The adaptive kalman filter of the integrated airborne inertial/satellite navigation system according to claim 5, wherein the equation of state and the measurement equation established by the equation establishing unit are specifically:
selecting a system state vector as X ═ phi delta v delta p epsilon +]TWherein the attitude error phi is [ phi ]e φn φu]TVelocity error δ v ═ δ ve δvn δvu]TThe position error δ p is [ δ L δ λ δ h ═ δ L λ δ h]TGyro error ═ epsilone εn εu]TAnd accelerometer error [ tom [ + ]e ▽n ▽u]T，
The state equation is then:
Φ(4,2)＝-fu，Φ(4,3)＝fn
Φ(5,1)＝fu，Φ(5,3)＝-fe
Φ(6,1)＝-fn，Φ(6,2)＝fe
wherein,is the error of pitching, rolling and course attitude angle ve、vn、vuIs east, north and sky velocity, Rm、RnThe meridian plane and the unitary plane earth radius, p represents the position, L is the local latitude, lambda is the local longitude, h is the local height, f is the local latitudee、fn、fuIs the east, north and sky forces, omegaieThe rotational angular velocity of the earth;
selecting the measurement vector as Wherein
Wherein,respectively represents the east, north and sky speeds measured by the inertial navigation system,respectively represents east, north and sky speeds, L, measured by the satellite positioning systemINS、λINS、hINSRespectively representing the latitude, longitude and altitude L measured by the inertial navigation systemGPS、λGPS、hGPSRespectively representing latitude, longitude and altitude measured by a satellite positioning system;
the measurement equation is:
H(1,4)＝1,H(2,5)＝1,H(3,6)＝1
H(4,7)＝Rm,H(5,8)＝RncosL,H(6,9)＝1。
7. the adaptive kalman filter of the integrated airborne inertial/satellite navigation system according to claim 5 or 6, wherein the filter equation of the filter calculation unit in performing the kalman filter calculation is:
wherein,estimating vector, K, for state one-step predictionkFor filtering the gain matrix, PkEstimating an error covariance matrix, P, for a statek，k-1The covariance matrix is predicted for the state estimation error one step.
8. The adaptive Kalman filter of the integrated inertial/satellite navigation system on board an aircraft according to claim 5 or 6, wherein the estimation update unit is used for measuring noiseAcoustic variance matrix RkAnd state noise variance matrix QkIs estimated value ofThe calculation method comprises the following steps:
setting the length N of a data sampling window, and calculating the measurement residual error v of the current momentk＝Zk-HkΦk,k-1Xk-1Further calculate the covariance matrix of the measurement residuals as
Computing a state estimation residual at a current timeFurther computing a state estimation residual covariance matrix of <math>
<mrow>
<msub>
<mover>
<mi>D</mi>
<mo>^</mo>
</mover>
<mi>k</mi>
</msub>
<mo>=</mo>
<mfrac>
<mn>1</mn>
<mi>N</mi>
</mfrac>
<munderover>
<mi>Σ</mi>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mi>k</mi>
<mo>-</mo>
<mi>N</mi>
<mo>+</mo>
<mn>1</mn>
</mrow>
<mi>k</mi>
</munderover>
<msub>
<mi>ΔX</mi>
<mi>i</mi>
</msub>
<msubsup>
<mi>X</mi>
<mi>i</mi>
<mi>T</mi>
</msubsup>
<mo>;</mo>
</mrow>
</math>
Respectively calculate <math>
<mrow>
<msub>
<mover>
<mi>R</mi>
<mo>^</mo>
</mover>
<mi>k</mi>
</msub>
<mo>=</mo>
<msub>
<mover>
<mi>C</mi>
<mo>^</mo>
</mover>
<mi>k</mi>
</msub>
<mo>-</mo>
<msub>
<mi>H</mi>
<mi>k</mi>
</msub>
<msub>
<mi>P</mi>
<mrow>
<mi>k</mi>
<mo>,</mo>
<mi>k</mi>
<mo>-</mo>
<mn>1</mn>
</mrow>
</msub>
<msubsup>
<mi>H</mi>
<mi>k</mi>
<mi>T</mi>
</msubsup>
<mo>,</mo>
<msub>
<mover>
<mi>Q</mi>
<mo>^</mo>
</mover>
<mi>k</mi>
</msub>
<mo>=</mo>
<msub>
<mover>
<mi>D</mi>
<mo>^</mo>
</mover>
<mi>k</mi>
</msub>
<mo>+</mo>
<msub>
<mi>P</mi>
<mi>k</mi>
</msub>
<mo>-</mo>
<msub>
<mi>Φ</mi>
<mrow>
<mi>k</mi>
<mo>,</mo>
<mi>k</mi>
<mo>-</mo>
<mn>1</mn>
</mrow>
</msub>
<msub>
<mi>P</mi>
<mrow>
<mi>k</mi>
<mo>-</mo>
<mn>1</mn>
</mrow>
</msub>
<msubsup>
<mi>Φ</mi>
<mrow>
<mi>k</mi>
<mo>,</mo>
<mi>k</mi>
<mo>-</mo>
<mn>1</mn>
</mrow>
<mi>T</mi>
</msubsup>
<mo>,</mo>
</mrow>
</math> And further updating the filter parameters.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410129008.8A CN103941273B (en) | 2014-03-31 | 2014-03-31 | Adaptive filtering method of onboard inertia/satellite integrated navigation system and filter |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410129008.8A CN103941273B (en) | 2014-03-31 | 2014-03-31 | Adaptive filtering method of onboard inertia/satellite integrated navigation system and filter |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN103941273A true CN103941273A (en) | 2014-07-23 |
| CN103941273B CN103941273B (en) | 2017-05-24 |
Family
ID=51189019
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201410129008.8A Active CN103941273B (en) | 2014-03-31 | 2014-03-31 | Adaptive filtering method of onboard inertia/satellite integrated navigation system and filter |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN103941273B (en) |
Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104457446A (en) * | 2014-11-28 | 2015-03-25 | 北京航天控制仪器研究所 | Aerial self-alignment method of spinning guided cartridge |
| CN107621264A (en) * | 2017-09-30 | 2018-01-23 | 华南理工大学 | The method for adaptive kalman filtering of vehicle-mounted micro- inertia/satellite combined guidance system |
| CN108628165A (en) * | 2018-05-08 | 2018-10-09 | 中国人民解放军战略支援部队航天工程大学 | Rotary inertia time-varying spacecraft is against optimal Adaptive Attitude Tracking control method |
| CN108759838A (en) * | 2018-05-23 | 2018-11-06 | 安徽科技学院 | Mobile robot multiple sensor information amalgamation method based on order Kalman filter |
| CN112556721A (en) * | 2019-09-26 | 2021-03-26 | 中国科学院微电子研究所 | Method and system for calibrating random error of navigation device filter |
| CN113063429A (en) * | 2021-03-18 | 2021-07-02 | 苏州华米导航科技有限公司 | Self-adaptive vehicle-mounted combined navigation positioning method |
| CN113155156A (en) * | 2021-04-27 | 2021-07-23 | 北京信息科技大学 | Method and device for determining running information, storage medium and electronic device |
| CN113670337A (en) * | 2021-09-03 | 2021-11-19 | 东南大学 | Method for detecting slow-changing fault of GNSS/INS combined navigation satellite |
| CN113959433A (en) * | 2021-09-16 | 2022-01-21 | 南方电网深圳数字电网研究院有限公司 | Combined navigation method and device |
| US11280618B2 (en) * | 2018-09-13 | 2022-03-22 | Ixblue | Positioning system, and associated method for positioning |
| CN114396939A (en) * | 2021-12-07 | 2022-04-26 | 华中光电技术研究所（中国船舶重工集团公司第七一七研究所） | Vector gravity attitude error measurement method and device based on integrated navigation |
Citations (6)
| 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 |
| US20100097268A1 (en) * | 2008-10-21 | 2010-04-22 | Texas Instruments Incorporated | Tightly-coupled gnss/imu integration filter having calibration features |
| CN102096086A (en) * | 2010-11-22 | 2011-06-15 | 北京航空航天大学 | Self-adaptive filtering method based on different measuring characteristics of GPS (Global Positioning System)/INS (Inertial Navigation System) integrated navigation system |
| CN102508278A (en) * | 2011-11-28 | 2012-06-20 | 北京航空航天大学 | Adaptive filtering method based on observation noise covariance matrix estimation |
| CN102819830A (en) * | 2012-08-15 | 2012-12-12 | 北京交通大学 | New point spread function estimation method based on Kallman filtering |
| CN103389506A (en) * | 2013-07-24 | 2013-11-13 | 哈尔滨工程大学 | Adaptive filtering method for strapdown inertia/Beidou satellite integrated navigation system |
-
2014
- 2014-03-31 CN CN201410129008.8A patent/CN103941273B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20100097268A1 (en) * | 2008-10-21 | 2010-04-22 | Texas Instruments Incorporated | Tightly-coupled gnss/imu integration filter having calibration features |
| CN101464152A (en) * | 2009-01-09 | 2009-06-24 | 哈尔滨工程大学 | Adaptive filtering method for SINS/GPS combined navigation system |
| CN102096086A (en) * | 2010-11-22 | 2011-06-15 | 北京航空航天大学 | Self-adaptive filtering method based on different measuring characteristics of GPS (Global Positioning System)/INS (Inertial Navigation System) integrated navigation system |
| CN102508278A (en) * | 2011-11-28 | 2012-06-20 | 北京航空航天大学 | Adaptive filtering method based on observation noise covariance matrix estimation |
| CN102819830A (en) * | 2012-08-15 | 2012-12-12 | 北京交通大学 | New point spread function estimation method based on Kallman filtering |
| CN103389506A (en) * | 2013-07-24 | 2013-11-13 | 哈尔滨工程大学 | Adaptive filtering method for strapdown inertia/Beidou satellite integrated navigation system |
Non-Patent Citations (1)
| Title |
|---|
| 徐婷婷: ""VTS系统船舶跟踪和预测的新技术研究"", 《中国博士学位论文全文数据库工程科技||辑》 * |
Cited By (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104457446B (en) * | 2014-11-28 | 2016-02-10 | 北京航天控制仪器研究所 | A kind of aerial Alignment Method of the guided cartridge that spins |
| CN104457446A (en) * | 2014-11-28 | 2015-03-25 | 北京航天控制仪器研究所 | Aerial self-alignment method of spinning guided cartridge |
| CN107621264A (en) * | 2017-09-30 | 2018-01-23 | 华南理工大学 | The method for adaptive kalman filtering of vehicle-mounted micro- inertia/satellite combined guidance system |
| CN107621264B (en) * | 2017-09-30 | 2021-01-19 | 华南理工大学 | Self-adaptive Kalman filtering method of vehicle-mounted micro-inertia/satellite integrated navigation system |
| CN108628165A (en) * | 2018-05-08 | 2018-10-09 | 中国人民解放军战略支援部队航天工程大学 | Rotary inertia time-varying spacecraft is against optimal Adaptive Attitude Tracking control method |
| CN108759838A (en) * | 2018-05-23 | 2018-11-06 | 安徽科技学院 | Mobile robot multiple sensor information amalgamation method based on order Kalman filter |
| US11280618B2 (en) * | 2018-09-13 | 2022-03-22 | Ixblue | Positioning system, and associated method for positioning |
| CN112556721A (en) * | 2019-09-26 | 2021-03-26 | 中国科学院微电子研究所 | Method and system for calibrating random error of navigation device filter |
| CN112556721B (en) * | 2019-09-26 | 2022-10-28 | 中国科学院微电子研究所 | Method and system for calibrating random error of navigation device filter |
| CN113063429A (en) * | 2021-03-18 | 2021-07-02 | 苏州华米导航科技有限公司 | Self-adaptive vehicle-mounted combined navigation positioning method |
| CN113063429B (en) * | 2021-03-18 | 2023-10-24 | 苏州华米导航科技有限公司 | Self-adaptive vehicle-mounted integrated navigation positioning method |
| CN113155156A (en) * | 2021-04-27 | 2021-07-23 | 北京信息科技大学 | Method and device for determining running information, storage medium and electronic device |
| CN113670337A (en) * | 2021-09-03 | 2021-11-19 | 东南大学 | Method for detecting slow-changing fault of GNSS/INS combined navigation satellite |
| CN113959433A (en) * | 2021-09-16 | 2022-01-21 | 南方电网深圳数字电网研究院有限公司 | Combined navigation method and device |
| CN113959433B (en) * | 2021-09-16 | 2023-12-08 | 南方电网数字平台科技(广东)有限公司 | Combined navigation method and device |
| CN114396939A (en) * | 2021-12-07 | 2022-04-26 | 华中光电技术研究所（中国船舶重工集团公司第七一七研究所） | Vector gravity attitude error measurement method and device based on integrated navigation |
Also Published As
| Publication number | Publication date |
|---|---|
| CN103941273B (en) | 2017-05-24 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN103941273B (en) | Adaptive filtering method of onboard inertia/satellite integrated navigation system and filter | |
| CN106990426B (en) | Navigation method and navigation device | |
| CN104655152B (en) | A kind of real-time Transfer Alignments of airborne distributed POS based on federated filter | |
| CN109931955B (en) | Initial alignment method of strap-down inertial navigation system based on state-dependent lie group filtering | |
| CN105091907B (en) | DVL orientation alignment error method of estimation in SINS/DVL combinations | |
| Stančić et al. | The integration of strap-down INS and GPS based on adaptive error damping | |
| CN102252677A (en) | Time series analysis-based variable proportion self-adaptive federal filtering method | |
| CN103744098A (en) | Ship's inertial navigation system (SINS)/Doppler velocity log (DVL)/global positioning system (GPS)-based autonomous underwater vehicle (AUV) combined navigation system | |
| CN104697526A (en) | Strapdown inertial navitation system and control method for agricultural machines | |
| Xue et al. | In-motion alignment algorithm for vehicle carried SINS based on odometer aiding | |
| CN103941274B (en) | Navigation method and terminal | |
| CN103900565A (en) | Method for obtaining inertial navigation system attitude based on DGPS (differential global positioning system) | |
| CN106441291B (en) | A kind of integrated navigation system and air navigation aid based on strong tracking SDRE filtering | |
| de La Parra et al. | Low cost navigation system for UAV's | |
| CN104913781A (en) | Unequal interval federated filter method based on dynamic information distribution | |
| CN108931791A (en) | Defend used tight integration clock deviation update the system and method | |
| EP3388787B1 (en) | Polar region operating attitude and heading reference system | |
| CN102538788B (en) | Low-cost damping navigation method based on state estimation and prediction | |
| CN105806363A (en) | Alignment method of an underwater large misalignment angle based on SINS (Strapdown Inertial Navigation System)/DVL (Doppler Velocity Log) of SRQKF (Square-root Quadrature Kalman Filter) | |
| US20200293067A1 (en) | State estimation | |
| CN112902956A (en) | Course initial value acquisition method for handheld GNSS/MEMS-INS receiver, electronic equipment and storage medium | |
| JP5022747B2 (en) | Mobile body posture and orientation detection device | |
| CN111795708B (en) | Self-adaptive initial alignment method of land inertial navigation system under base shaking condition | |
| Emami et al. | A low complexity integrated navigation system for underwater vehicles | |
| CN105606093B (en) | Inertial navigation method and device based on gravity real-Time Compensation |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| CP01 | Change in the name or title of a patent holder | ||
| CP01 | Change in the name or title of a patent holder |
Address after: 510080 water Donggang 8, Dongfeng East Road, Yuexiu District, Guangzhou, Guangdong. Co-patentee after: Beihang University Patentee after: ELECTRIC POWER RESEARCH INSTITUTE, GUANGDONG POWER GRID CO., LTD. Address before: 510080 water Donggang 8, Dongfeng East Road, Yuexiu District, Guangzhou, Guangdong. Co-patentee before: Beihang University Patentee before: Electrical Power Research Institute of Guangdong Power Grid Corporation |