CN111999747A - Robust fault detection method for inertial navigation-satellite combined navigation system - Google Patents

Robust fault detection method for inertial navigation-satellite combined navigation system Download PDF

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CN111999747A
CN111999747A CN202010888727.3A CN202010888727A CN111999747A CN 111999747 A CN111999747 A CN 111999747A CN 202010888727 A CN202010888727 A CN 202010888727A CN 111999747 A CN111999747 A CN 111999747A
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navigation system
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fault detection
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CN111999747B (en
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张闯
郭沐壮
郭晨
刘程
鲁峰
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Dalian Maritime University
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    • 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
    • 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
    • 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
    • Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation

Abstract

The invention provides a robust fault detection method of an inertial navigation-satellite combined navigation system. The method comprises the following steps: carrying out optimization estimation on the built strapdown inertial navigation system/global navigation satellite system integrated navigation system by adopting a singular value decomposition-based cubature Kalman filter algorithm; introducing error detection filtering based on a frame into a singular value decomposition-based cubature Kalman filter algorithm, carrying out linearization processing on the constructed integrated navigation system, and calculating a judgment threshold value for fault detection of a sensor when the integrated navigation system is subjected to integrated navigation in the error detection process; comparing the relation between the fault and the judgment threshold, and if the fault is small, improving the state estimation precision of the volume fault detection filter through a self-adaptive algorithm based on the measurement residual error and the scale factor; if the fault is largerThen robust batch processing H is used∞Filtering is performed to ensure the stability at this time. The invention can well ensure the robustness of fault detection.

Description

Robust fault detection method for inertial navigation-satellite combined navigation system
Technical Field
The invention relates to the technical field of signal processing of shipborne navigation instruments, in particular to a robust fault detection method of an inertial navigation-satellite combined navigation system.
Background
The inertial navigation system is an autonomous navigation system which does not depend on any external information and does not radiate energy to the outside, has the characteristics of good concealment and can work in various complex environments such as air, ground, underwater and the like, and is mainly divided into a platform type inertial navigation system and a strapdown type inertial navigation system. As a core navigation system of various naval vessels and civil ships, the precision and the reliability of the strapdown inertial navigation system directly influence various performance indexes of an equipment carrier of the strapdown inertial navigation system.
The existing ship strap-down inertial navigation system and global navigation satellite system (SINS/GNSS) integrated navigation system utilizes an extended Kalman filter to form a signal estimation process of a specific time window, and a nonlinear signal system is adjusted to be the signal estimation process of a linear signal system. The statistical model is used for effectively identifying and detecting the measured value in the measurement matrix which reflects the track and is formed in the estimation process, the abnormal condition of the measured value is judged and eliminated under the influence of the existence of the linearized residual error in the measurement matrix formed in the effective track estimation process, and the robustness of fault detection is difficult to guarantee in the prior art.
Disclosure of Invention
In light of the above-mentioned technical problems, a robust fault detection method for an inertial navigation-satellite integrated navigation system is provided. The technical means adopted by the invention are as follows:
a robust fault detection method for an inertial navigation-satellite combined navigation system comprises the following steps:
step 1, optimizing and estimating a built strapdown inertial navigation system/global navigation satellite system integrated navigation system by using a singular value decomposition-based cubature Kalman filter algorithm;
step 2, introducing error detection filtering based on a frame into the singular value decomposition-based cubature Kalman filter algorithm, linearizing the constructed strapdown inertial navigation system/global navigation satellite system integrated navigation system, and calculating a judgment threshold value for fault detection of a sensor when the ship inertial navigation-satellite integrated navigation system is integrated in the error detection process;
step 3, comparing the relation between the fault and the judgment threshold, and if the fault is small, improving the state estimation precision of the volume fault detection filter through a self-adaptive algorithm based on the measurement residual error and the scale factor; if the fault is large, robust batch processing H is adopted∞Filtering is performed to ensure the stability at this time.
Further, in the step 1, the combined navigation system of the strapdown inertial navigation system/global navigation satellite system is constructed as follows:
Figure BDA0002656299050000021
fcas an error function of a nonlinear strapdown inertial navigation system, BcIs formed by a strapdown matrix
Figure BDA0002656299050000022
And CkConstituent Unit Module matrix, Bc＝Bcf，DcfAnd DcCoefficients corresponding to system fault f and system noise error v in the observation equation, respectively, and Dc＝Dcf＝[0,I]TV. noise vk∈l2[0,N]And ωk∈[0,N]，l2Representing n-dimensional Euclidean space, fkSubscript k represents step length or time for a fault matrix carried by the system;
kalman filtering is executed by adopting a singular value decomposition-based cubature Kalman filter algorithm, and the specific operation steps are as follows:
step 11, performing time update on the cubature kalman filter based on singular value decomposition, specifically,
Figure BDA0002656299050000023
where k is 1.. n, and χ is a volume sample point, svd represents a singular value decomposition matrix algorithm, which propagates by:
Figure BDA0002656299050000024
wherein f iscFor the system matrix in the system (1), the updated sampling points are used to obtain the system state
Figure BDA0002656299050000025
And its covariance matrix, where QkFor the system noise observation matrix, the detailed operation process is as follows:
Figure BDA0002656299050000026
step 12, updating the process by using a volumetric kalman filter based on singular value decomposition, specifically,
Figure BDA0002656299050000031
the predicted observation vector and associated covariance matrix are updated as follows, where RkTo observe the noise matrix:
Figure BDA0002656299050000032
updated cross covariance matrix
Figure BDA0002656299050000033
Can be obtained in the following way: .
Figure BDA0002656299050000034
Step 13, calculating matrix gain KkUpdated state matrix
Figure BDA0002656299050000035
And the covariance matrix P updated with the following timek|k。
Figure BDA0002656299050000036
Further, in the step 2, the combined navigation system of the strapdown inertial navigation system/global navigation satellite system constructed in the step may be linearized as:
Figure BDA0002656299050000037
Figure BDA0002656299050000038
ekandkis a matrix with zero mean and covariance of the statistical linearization error and state and measurement functions that preserve the nonlinear nature of the normal distribution term, and is calculated as follows.
Figure BDA0002656299050000039
Parameter HkAnd
Figure BDA00026562990500000310
the method specifically comprises the following steps:
Figure BDA00026562990500000311
at this point, the system (8) satisfies the volumetric fault detection filter form as follows, from which the prior probability distribution can be referenced
Figure BDA00026562990500000312
And a weight WkThe selection is as follows:
Figure BDA00026562990500000313
the system (11) can be reconfigured as follows:
Figure BDA00026562990500000314
JNthe threshold value is calculated in the failure detection mode in the following manner.
Figure BDA0002656299050000041
(12) The partial parameters in (3) are specifically updated as follows:
Figure BDA0002656299050000042
further, in step 3, the adaptive part specifically includes:
adaptive RkThe following were used:
Figure BDA0002656299050000043
adaptive QkThe following were used:
Figure BDA0002656299050000044
wherein:
Figure BDA0002656299050000045
further, in step 3, the robustness optimization specifically includes:
Figure BDA0002656299050000046
the gamma is optimized and adjusted as follows:
Figure BDA0002656299050000047
under the assumption that process disturbance and measurement noise norm are bounded, the invention provides a Fault Detection Filter (FDF) considering disturbance sensitivity and robustness at the same time, and the filter adopts a post filter and an observer gain to carry out fault detection to obtain a state estimation value of a suboptimal solution. The state estimation accuracy of the volume FDF is improved by an adaptive algorithm based on the measurement residuals and the scale factors. In order to ensure the stability when the fault is overlarge, the framework adopts robust H∞A volumetric kalman filter. The invention can well ensure the robustness of fault detection.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a flow chart of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The embodiment discloses a robust fault detection method of an inertial navigation-satellite combined navigation system, which comprises the following steps:
step 1, optimizing and estimating a built strapdown inertial navigation system/global navigation satellite system integrated navigation system by using a singular value decomposition-based cubature Kalman filter algorithm;
step 2, introducing error detection filtering based on a frame into the singular value decomposition-based cubature Kalman filter algorithm, linearizing the constructed strapdown inertial navigation system/global navigation satellite system integrated navigation system, and calculating a judgment threshold value for fault detection of a sensor when the ship inertial navigation-satellite integrated navigation system is integrated in the error detection process;
step 3, comparing the relation between the fault and the judgment threshold, and if the fault is small, improving the state estimation precision of the mode volume fault detection filter through an adaptive algorithm based on the measurement residual error and the scale factor; if the fault is large, robust batch processing H is adopted∞Filtering is performed to ensure the stability at this time.
As shown in fig. 1, the specific steps of this embodiment are as follows:
the built strapdown inertial navigation system/global navigation satellite system integrated navigation system (SINS/GNSS) is as follows:
Figure BDA0002656299050000061
fcas an error function of a nonlinear strapdown inertial navigation system, BcIs formed by a strapdown matrix
Figure BDA0002656299050000062
And CkConstituent Unit Module matrix, Bc＝Bcf，DcfAnd DcCoefficients corresponding to system fault f and system noise error v in the observation equation, respectively, and Dc＝Dcf＝[0,I]TV. noise vk∈l2[0,N]And ωk∈[0,N]，l2Representing n-dimensional Euclidean space, fkSubscript k represents step length or time for a fault matrix carried by the system;
if the optimal estimation is needed, the system is a strong nonlinear system considering the characteristics of complex sea environment and ship motion, and a volume Kalman filter (SVD-CKF) algorithm based on singular value decomposition can be adopted to perform Kalman filtering. The specific operation steps are as follows:
step 11, performing time update on the cubature kalman filter based on singular value decomposition, specifically,
Figure BDA0002656299050000063
where k is 1.. n, and χ is a volume sampling point, svd represents a singular value decomposition matrix algorithm for improving the robustness of the algorithm. The above sampling points are propagated by:
Figure BDA0002656299050000064
wherein f iscFor the system matrix in the system (1), the updated sampling points are used to obtain the system state
Figure BDA0002656299050000065
And its covariance matrix, where QkFor the system noise observation matrix, the detailed operation process is as follows:
Figure BDA0002656299050000066
step 12, performing process updating on the volume kalman filter based on singular value decomposition, specifically, updating the sampling points again, wherein the method specifically comprises the following steps:
Figure BDA0002656299050000067
the predicted observation vector and associated covariance matrix are updated as follows, where RkTo observe the noise matrix:
Figure BDA0002656299050000071
updated cross covariance matrix
Figure BDA0002656299050000072
Can be obtained in the following way:
Figure BDA0002656299050000073
step 13, calculating matrix gain KkUpdated state matrix
Figure BDA0002656299050000074
And the covariance matrix P updated with the following timek|k。
Figure BDA0002656299050000075
Will be based on Hi/H∞The error detection filtering of the framework is introduced into the conventional SVD-CKF. The system (1) can be linearized as:
Figure BDA0002656299050000076
results of binding SVD-CKF.
Figure BDA0002656299050000077
ekAndkthe statistical linearization error of the nonlinear property of the normal distribution item is kept, and the state and measurement function has a matrix with zero mean and covariance, and the calculation mode is as follows:
Figure BDA0002656299050000078
parameter HkAnd
Figure BDA0002656299050000079
the method specifically comprises the following steps:
Figure BDA00026562990500000710
at this point, the system (8) satisfies the volumetric fault detection filter form as follows, from which the prior probability distribution can be referenced
Figure BDA00026562990500000711
And a weight WkThe selection is as follows:
Figure BDA00026562990500000712
the system (11) can be reconfigured as follows:
Figure BDA00026562990500000713
JNfor the calculation of the threshold value during fault detection, the updating method is as follows:
Figure BDA00026562990500000714
(12) the partial parameters in (3) are specifically updated as follows:
Figure BDA0002656299050000081
although SVD-CKF is fused to the H-basedi/H∞In the error detection filtering of the frame, the sensor can be detected when the ship SINS/GNSS combined navigation is navigatedAnd (4) a barrier. Although the sensitivity of fault diagnosis and the robustness of error interference can be considered, the method is slightly deficient for the accuracy of state estimation, because the algorithm is based on a method obtained by suboptimal estimation. For this reason, QR adaptive optimization is performed on the method to improve the accuracy of the estimation.
Adaptive RkThe following were used:
Figure BDA0002656299050000082
adaptive QkThe following were used:
Figure BDA0002656299050000083
wherein:
Figure BDA0002656299050000084
self-adaptive optimization is carried out to ensure that SVD-CKF is fused with H-basedi/H∞After error detection filtering of the framework, accurate state estimation can be performed, but the SINS/GNSS generates singular values when there are large faults and errors. If the error is too large, the integrated navigation system will also fail. In order to improve the stability of the algorithm as much as possible, adaptive optimization is abandoned for a system with larger errors or faults, and robust optimization is used instead. And judging the threshold value for adaptive optimization and robust optimization can refer to J in fault detection filteringN,k. At this time, the specific process is as follows:
Figure BDA0002656299050000085
the gamma is optimized and adjusted as follows:
Figure BDA0002656299050000091
finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.

Claims (5)

1. A robust fault detection method for an inertial navigation-satellite combined navigation system is characterized by comprising the following steps:
step 1, optimizing and estimating a built strapdown inertial navigation system/global navigation satellite system integrated navigation system by using a singular value decomposition-based cubature Kalman filter algorithm;
step 2, introducing error detection filtering based on a frame into the singular value decomposition-based cubature Kalman filter algorithm, linearizing the constructed strapdown inertial navigation system/global navigation satellite system integrated navigation system, and calculating a judgment threshold value for fault detection of a sensor when the ship inertial navigation-satellite integrated navigation system is integrated in the error detection process;
step 3, comparing the relation between the fault and the judgment threshold, and if the fault is small, improving the state estimation precision of the volume fault detection filter through a self-adaptive algorithm based on the measurement residual error and the scale factor; if the fault is large, robust batch processing H is adopted∞Filtering is performed to ensure the stability at this time.
2. The robust fault detection method for the inertial navigation-satellite combined navigation system according to claim 1, wherein in the step 1, the strapdown inertial navigation system/global navigation satellite system combined navigation system is constructed by:
Figure FDA0002656299040000011
fcas an error function of a nonlinear strapdown inertial navigation system, BcIs formed by a strapdown matrix
Figure FDA0002656299040000012
And CkConstituent Unit Module matrix, Bc＝Bcf，DcfAnd DcCoefficients corresponding to system fault f and system noise error v in the observation equation, respectively, and Dc＝Dcf＝[0,I]TV. noise vk∈l2[0,N]And ωk∈[0,N]，l2Representing n-dimensional Euclidean space, fkSubscript k represents step length or time for a fault matrix carried by the system;
kalman filtering is executed by adopting a singular value decomposition-based cubature Kalman filter algorithm, and the specific operation steps are as follows:
step 11, performing time update on the cubature kalman filter based on singular value decomposition, specifically,
Figure FDA0002656299040000013
where k is 1.. n, and χ is a volume sample point, svd represents a singular value decomposition matrix algorithm, which propagates by:
Figure FDA0002656299040000021
wherein f iscFor the system matrix in the system (1), the updated sampling points are used to obtain the system state
Figure FDA0002656299040000022
And its covariance matrix, where QkFor the system noise observation matrix, the detailed operation process is as follows:
Figure FDA0002656299040000023
step 12, performing process updating on the cubature Kalman filter based on singular value decomposition, which comprises the following steps:
Figure FDA0002656299040000024
the predicted observation vector and associated covariance matrix are updated as follows, where RkTo observe the noise matrix:
Figure FDA0002656299040000025
updated cross covariance matrix
Figure FDA0002656299040000026
Can be obtained in the following way:
Figure FDA0002656299040000027
step 13, calculating matrix gain KkUpdated state matrix
Figure FDA0002656299040000028
And the covariance matrix P updated with the following timek|k。
Figure FDA0002656299040000029
3. The robust fault detection method for an inertial navigation-satellite combined navigation system according to claim 2, wherein in the step 2, the constructed strapdown inertial navigation system/global navigation satellite system combined navigation system is linearized into:
Figure FDA00026562990400000210
Figure FDA00026562990400000211
ekandkthe statistical linearization error of the nonlinear property of the normal distribution item is kept, and the state and measurement function has a matrix with zero mean and covariance, and the calculation mode is as follows:
Figure FDA0002656299040000031
parameter HkAnd
Figure FDA0002656299040000032
the method specifically comprises the following steps:
Figure FDA0002656299040000033
at this point, the system (8) satisfies the volumetric fault detection filter form as follows, from which the prior probability distribution can be referenced
Figure FDA0002656299040000034
And a weight WkThe selection is as follows:
Figure FDA0002656299040000035
the system (11) can be reconfigured as follows:
Figure FDA0002656299040000036
JNto enter intoThe calculation mode of the threshold value during the line fault detection is updated as follows:
Figure FDA0002656299040000037
(12) the partial parameters in (3) are specifically updated as follows:
Figure FDA0002656299040000038
4. the method for robust fault detection in an inertial navigation-satellite combined navigation system according to claim 3, wherein in the step 3, the adaptive part specifically comprises:
adaptive RkThe following were used:
Figure FDA0002656299040000039
adaptive QkThe following were used:
Figure FDA00026562990400000310
wherein:
Figure FDA00026562990400000311
5. the method for robust fault detection in an inertial navigation-satellite combined navigation system according to claim 3, wherein in the step 3, the robustness optimization specifically includes:
Figure FDA0002656299040000041
the gamma is optimized and adjusted as follows:
Figure FDA0002656299040000042
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