CN110196068B - Residual vector fault detection and isolation method for polar region centralized filtering integrated navigation system - Google Patents
Residual vector fault detection and isolation method for polar region centralized filtering integrated navigation system Download PDFInfo
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- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
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- 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
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Abstract
The invention provides a residual error vector fault detection and isolation method of a polar region centralized filtering integrated navigation system, which aims at the polar region integrated navigation system of navigation equipment based on a grid inertial navigation system and uses a residual error x 2 Based on the inspection method, designing a residual vector χ by designing the residual vector 2 The fault detection and isolation method realizes the fault detection and isolation of the centralized filter and improves the reliability of the integrated navigation system based on the centralized filter bank in the polar region. Compared with a federal filter, the centralized filter has better filtering precision, the method enables the centralized filter to have fault detection and isolation capability, and the fault tolerance of the integrated navigation system is improved on the premise of not sacrificing the estimation precision of the filter.
Description
Technical Field
The invention relates to a fault detection method for polar region centralized filtering integrated navigation, in particular to a fault detection and isolation method for residual vector of a polar region centralized filtering integrated navigation system.
Background
The high-precision high-reliability navigation technology is one of important preconditions for the operation and safe navigation of a carrier in a polar region, and the navigation reliability is the precondition for ensuring the navigation precision. To achieve higher reliability of integrated navigation, a federal filter capable of fault detection and isolation is often applied to integrated navigation. However, the federal filter is not an optimal filter. Compared with a federal filter, the centralized Kalman filtering with the optimal estimation characteristic can provide higher estimation precision, but the centralized filter cannot detect and isolate faults and is poor in reliability because the traditional state x is 2 Fault detection method or residual x 2 Fault detectionThe method can detect the fault detection of the integrated navigation measurement information, but cannot locate and isolate the fault observation information of the centralized filter. In summary, the existing fault detection and isolation methods cannot isolate the fault observation information with optimal estimation characteristics and filtered in a combined navigation set.
Disclosure of Invention
The invention aims to provide a residual vector fault detection and isolation method for a polar region centralized filtering integrated navigation system, and the reliability of the polar region integrated navigation system is improved.
The purpose of the invention is realized by the following steps: the method comprises the following steps:
the method comprises the following steps: selecting a grid coordinate system as a navigation coordinate system, taking an inertial navigation system as a main navigation system, introducing external observation information, and constructing a polar region integrated navigation system;
step two: grouping the measurement information according to whether the fault condition of the measurement information of the integrated navigation system is independent, wherein Z = [ Z ] 1 z 2 … z n ] T Designing a fault threshold of each measurement information group;
step three: predicting the measurement information at the k moment of the filtering period according to the filter output state estimation at the k-1 moment of the previous filtering period;
step four: according to the measured information
And predictive metrology information
Calculating a measurement information prediction residual r k ；
Step five: calculating the measurement residual variance of each measurement information group;
step six: designing a binary hypothesis, and checking whether the measurement information fails or not by checking a residual mean value:
step seven: designing a fault detection vector function, and calculating a fault detection vector gamma;
step eight: constructing a fault detection residual error flag bit vector H = [ H ] 1 h 2 … h n ]Standard measurement information fault conditions;
step nine: judging the fault condition of the measurement information of the integrated navigation system by using the vector H obtained in the step eight, if no fault exists, carrying out filtering estimation, and repeating the steps from the third step to the eighth step; and if a certain measured information group has a fault, reconstructing a filter, isolating fault observation information, and repeating the steps from the third step to the eighth step.
The invention also includes such structural features:
1. and in the second step, the information fault probability in different measurement information groups is independent, and the information fault conditions in the same measurement information group are related to each other.
2. Taking the ith group of measurement information as an example, predicting the filtering period measurement information:
wherein, the corner mark k represents the time k,
the predicted value at the moment of the ith group of measurement information k,
a measurement matrix phi corresponding to the ith group of measurement information in the measurement model k,k-1 For the state transition matrix at time k,
is the estimated value of the state of the filter at the k-1 moment; and predicting the 1 st to n groups of measurement information to obtain a measurement information prediction vector group.
3. The residual error r of the measurement information prediction in step four k Comprises the following steps:
4. taking the ith group of measurement information as an example, calculating the measurement residual variance as follows:
wherein, P k/k-1 For one-step prediction of mean square error, R i Measuring the noise variance;
calculating the measurement residual variance of the 1 st to n th measurement information sets
To
5. The sixth step is specifically as follows: if the ith set of measurement information is not faulty, the measurement information prediction residual can be regarded as zero-mean white Gaussian noise (r) following normal distribution i ～N(0,A i ) (ii) a If the measurement information of the integrated navigation system has faults, the residual statistical characteristics are changed, and the average value is not zero; therefore, a binary hypothesis is designed, and whether the measurement information is faulty or not is checked by checking the residual mean value:
no fault occurs:
E{r i }＝0,E{r i r i T }＝A i
a fault occurs:
E{r i }＝μ,E{(r i -μ)(r i -μ) T }＝A i 。
6. and the calculated fault detection vector gamma in the step seven is as follows:
wherein, γ i (i =1,2, \8230;, n) compliance χ 2 And the distribution can reflect the fault condition of the ith group of measurement information.
8. The eighth step specifically comprises: the failure determination criterion is:
wherein h is i Forming fault detection residual error flag bit vector H = [ H = 1 h 2 … h n ]If H is a zero vector, it indicates that the measurement information of the integrated navigation system has no fault, and if H is a non-zero vector, the measurement information group corresponding to the non-zero item has a fault.
Compared with the prior art, the invention has the beneficial effects that: the invention innovatively provides a fault detection method for centralized filtering, which realizes the detection and positioning of the measurement fault of the centralized filter by designing and using a fault detection vector, can detect and isolate the fault of polar region combined navigation measurement information based on the centralized filtering, effectively improves the reliability of a centralized filtering combined navigation system, improves the comprehensive performance of polar region combined navigation, ensures the navigation precision and reliability of polar region carriers, has stable performance and is easy to realize, and therefore, the invention has high engineering application value.
Drawings
Fig. 1 is a basic flow chart of the error suppression method for the grid inertial navigation system according to the present invention;
FIG. 2 is a diagram of the corresponding flag values in the measurement information error and fault detection flag bit vectors;
FIG. 3 is a plot of attitude angle error for a navigation system;
FIG. 4 is a navigation system speed error curve;
FIG. 5 is a navigation system position error curve;
FIG. 6 is an enlarged partial schematic view of a navigation system speed error curve and position error curve.
Detailed Description
The invention is described in further detail below with reference to the drawings and the detailed description.
The invention discloses a polar region centralized filtering combined navigation system residual vector chi 2 Fault detection and isolation method. The method aims at a polar region integrated navigation system of navigation equipment based on a grid inertial navigation systemSystem, by residual x 2 Based on the inspection method, designing a residual vector χ by designing the residual vector 2 The fault detection and isolation method realizes the fault detection and isolation of the centralized filter and improves the reliability of the integrated navigation system based on the centralized filter bank in the polar region. Compared with a federal filter, the centralized filter has better filtering precision, the method enables the centralized filter to have fault detection and isolation capability, and the fault tolerance of the integrated navigation system is improved on the premise of not sacrificing the estimation precision of the filter.
With reference to fig. 1 to 5, the present invention provides an acoustic velocity measurement assisted polar region grid inertial navigation error suppression method, a flow chart of which is shown in fig. 1, and the method mainly includes the following steps:
(1) Selecting a grid coordinate system as a navigation coordinate system, taking an inertial navigation system as a main navigation system, introducing external observation information, and constructing a polar region integrated navigation system;
(2) Grouping the measurement information according to whether the fault condition of the measurement information of the integrated navigation system is independent, wherein Z = [ Z = [ [ Z ] 1 z 2 … z i … z n ] T And designing the fault threshold of each measured information group. The information failure probability in different measurement information groups is independent, and the information failure conditions in the same measurement information group are related to each other.
(3) Taking the ith group of measurement information as an example, the measurement information of the filtering period (k time) is predicted according to the filter output state estimation of the previous filtering period (k-1 time):
wherein, the corner mark k represents the time k,
the predicted value at the moment of the ith group of measurement information k,
is the first in the measurement modelMeasurement matrix corresponding to i groups of measurement information, phi k,k-1 For the state transition matrix at time k,
the filter state estimate at time k-1.
And predicting the 1 st to n groups of measurement information to obtain a measurement information prediction vector group.
(4) According to the measured information
And predictive metrology information
Calculating a measurement information prediction residual r k ：
(5) Taking the ith set of measurement information as an example, the measurement residual variance is calculated as follows:
wherein, P k/k-1 For one-step prediction of mean square error, R i To measure the noise variance.
Calculating the variance of the measurement residual of the 1 st to n th groups of measurement information
To
(6) Taking the ith set of measurement information as an example, if the ith set of measurement information has no fault, the measurement information prediction residual can be regarded as zero-mean Gaussian white noise (r) following normal distribution i ～N(0,A i ) (ii) a If the measured information of the integrated navigation system has faults, the statistical characteristics of the residual errors are changed, and the average value is notAnd (4) zero. Therefore, a binary hypothesis is designed, and whether the measurement information is faulty or not is checked by checking the residual mean value:
no fault occurs: e { r i }＝0,E{r i r i T }＝A i
A failure occurs: e { r i }＝μ,E{(r i -μ)(r i -μ) T }＝A i
(7) Designing a fault detection vector function, and calculating a fault detection vector gamma:
wherein, γ i (i =1,2, \ 8230;, n) obedience χ 2 And the distribution can reflect the fault condition of the ith group of measurement information.
(8) The failure determination criterion is:
h i forming fault detection residual error flag bit vector H = [ H = 1 h 2 … h n ]If H is a zero vector, it indicates that the measurement information of the integrated navigation system has no fault, and if H is a non-zero vector, the measurement information group corresponding to the non-zero item has a fault.
(9) Judging the fault condition of the measurement information of the integrated navigation system by using the vector H obtained in the step 8, if no fault exists, carrying out filtering estimation, and repeating the steps 3 to 8; and if a certain measurement information group has a fault, reconstructing a filter, isolating fault observation information, and repeating the steps 3 to 8.
In order to verify the reasonability and the feasibility of the invention, based on a Visual Studio 2010 design program, a polar region grid inertial navigation/Doppler/ultra-short baseline integrated navigation system based on centralized Kalman filtering is taken as an experimental object to carry out simulation experiment verification, and simulation schemes, conditions and results are as follows:
(1) Simulation time setting
The simulation time length is 4h, and the simulation step length is 0.01s.
(2) Carrier movement arrangement
Initial latitude 75 ° N, initial longitude 126 ° E.
And simulating the working states of the carrier under the condition of a static base, namely wireless movement and angular movement of the carrier.
(3) Error parameter setting
The constant drift of the three gyroscopes is respectively set to be 0.03 degree/h, 0.03 degree/h and 0.03 degree/h; the zero bias of the accelerometer is set to 3 x 10 -5 g; the ranging precision of the ultra-short baseline positioning system is 10 meters, the direction finding precision is 1 degree, and the noise is zero-mean white noise; and adding a constant error into the Doppler output speed during the period that t is more than 1.0h and less than 1.3h, and simulating the output fault of the combined navigation Doppler equipment.
(4) Simulation result
The performance of the designed fault detection and isolation algorithm is simulated according to the simulation conditions, and fig. 2 is a curve of the measured information error and the corresponding flag value in the fault detection flag bit vector. The measurement information is divided into three measurement information groups according to whether the fault occurrence condition is independent, wherein the three measurement information groups are respectively as follows: ultra-short baseline slope measurement information, ultra-short baseline angle measurement information (azimuth angle and pitch angle), and doppler velocity measurement information. As shown in fig. 2, in the whole simulation experiment, the ultra-short baseline measurement information has no fault, so the fault detection flag corresponding to the ultra-short baseline slope measurement information and the angle measurement information is always 0, which indicates that no fault occurs. In the time period of 0h < t < 1.0h, the Doppler measurement information is not in fault, and the fault detection flag bit of the Doppler measurement information is 0, which indicates no fault; in the time period of 1.0h < t < 1.3h, the Doppler velocity is subjected to sudden change fault, the Doppler measurement information fault detection zone bit is 1, and the fault is indicated to occur, the system is reconstructed, and the fault observed quantity is isolated; and in the time period of 1.3h and t and 4.0h, the Doppler measurement information fault is ended and is recovered to be normal again, the Doppler measurement information fault detection flag bit is 0, the fault is removed, the system is reconstructed, and the velocity information is added again for measurement. FIG. 3, FIG. 4, and FIG. 5 are respectively a grid inertial navigation/Doppler/ultra short navigationThe simulation graph comprises a grid inertial navigation algorithm navigation error curve and a grid inertial navigation/Doppler/ultra-short baseline tight combination navigation algorithm error curve; fig. 6 is a partially enlarged view of the combined navigation error at the fault occurrence stage. 3-6, the combined navigation system with the fault detection algorithm is not affected by the measured information fault. The final simulation result shows that the polar region centralized filtering combined navigation system residual vector chi of the invention 2 The fault detection and isolation method can effectively detect the faults of the centralized filter, the centralized filter has high estimation precision, and the method can improve the reliability of the combined navigation system based on the centralized filter.
Combining the above analyses, the following analysis results were obtained: polar region centralized filtering combined navigation system residual vector x 2 The fault detection and isolation method can ensure the navigation performance and reliability of the navigation system during long-term navigation in the polar region. Therefore, the invention can more comprehensively improve the navigation performance and meet the application requirements of the navigation system on reliability and precision in long-time working in a polar region.
Claims (3)
1. A method for detecting and isolating residual vector faults of a polar region centralized filtering integrated navigation system is characterized by comprising the following steps: the method comprises the following steps:
the method comprises the following steps: selecting a grid coordinate system as a navigation coordinate system, taking an inertial navigation system as a main navigation system, introducing external observation information, and constructing a polar region integrated navigation system;
step two: grouping the measurement information according to whether the fault condition of the measurement information of the integrated navigation system is independent, wherein Z = [ Z ] 1 z 2 …z n ] T Designing a fault threshold of each measurement information group;
step three: predicting the measurement information at the k moment of the filtering period according to the filter output state estimation at the k-1 moment of the previous filtering period; taking the ith group of measurement information as an example, the filter period measurement information is predicted:
wherein, the corner mark k represents the time k,
the predicted value at the moment of the ith group of measurement information k,
a measurement matrix phi corresponding to the ith group of measurement information in the measurement model k,k-1 For the state transition matrix at time k,
is the estimated value of the state of the filter at the k-1 moment; predicting the 1 st to n groups of measurement information to obtain a measurement information prediction vector group;
step four: according to the measured information
And predictive metrology information
Calculating a measurement information prediction residual r k ；
Step five: calculating the measurement residual variance of each measurement information group; taking the ith set of measurement information as an example, the measurement residual variance is calculated as:
wherein, P k/k-1 For one-step prediction of mean square error, R i Measuring a noise variance;
calculating the variance of the measurement residual of the 1 st to n th groups of measurement information
To
Step six: designing a binary hypothesis, and checking whether the measurement information is failed or not by checking a residual mean value: if the ith set of measurement information is not faulty, the measurement information prediction residual can be regarded as zero-mean white Gaussian noise (r) following normal distribution i ～N(0,A i ) (ii) a If the measurement information of the integrated navigation system has faults, the residual statistical characteristics are changed, and the average value is not zero; therefore, a binary hypothesis is designed, and whether the measurement information is faulty or not is checked by checking the residual mean value:
no fault occurs:
E{r i }＝0,E{r i r i T }＝A i
a failure occurs:
E{r i }＝μ,E{(r i -μ)(r i -μ) T }＝A i ；
step seven: designing a fault detection vector function, and calculating a fault detection vector gamma; calculating a fault detection vector Γ as:
wherein, theta i (i =1,2, \ 8230;, n) obedience χ 2 Distribution, which can reflect the fault condition of the ith group of measurement information group;
step eight: constructing fault detection residual error flag bit vector H = [ H ] 1 h 2 …h n ]Measuring the information fault condition in a standard way; the failure judgment criterion is as follows:
wherein h is i Constituting fault detection residual error flag bit vector H = [ H = 1 h 2 …h n ]If H is zero vector, it indicates the integrated navigation system measurement messageIf H is a non-zero vector, the measurement information group corresponding to the non-zero item has a fault;
step nine: judging the fault condition of the measurement information of the integrated navigation system by using the vector H obtained in the step eight, if no fault exists, carrying out filtering estimation, and repeating the steps from the third step to the eighth step; and if a certain measurement information group has a fault, carrying out filter reconstruction, isolating fault observation information, and repeating the steps from three to eight.
2. The method of claim 1, wherein the fault detection and isolation method comprises the steps of: in the second step, the information fault probabilities in different measurement information sets are independent, and the information fault conditions in the same measurement information set are correlated.
3. The polar region centralized filtering combined navigation system residual vector fault detection and isolation method according to claim 2, wherein: the residual error r of the measurement information prediction in step four k Comprises the following steps:
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