CN113670339A - Integrated navigation system fault detection method based on improved residual error detection method - Google Patents
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Abstract
The application provides a combined navigation system fault detection method based on an improved residual error detection method, which comprises the following steps: three thresholds T are setD1、TD2、TD3And make TD1<TD2<TD2(ii) a Calculating residual error r (k) of a Kalman filter in the combined navigation system and variance A (k) of the residual error, and constructing an original detection function lambda (k) according to r (k) and A (k); using three thresholds TD1、TD2、TD3And the original detection function lambda (k) to obtain a control function mu (lambda)k) (ii) a For the control function mu (lambda)k) Carrying out weighted average processing to obtain an improved fault detection function lambda'k(ii) a And judging the improved fault detection function through a fault detection strategy to detect whether the integrated navigation system has a fault.
Description
Technical Field
The invention belongs to the technical field of integrated navigation systems, and particularly relates to an integrated navigation system fault detection method based on an improved residual error detection method.
Background
In navigation system applications, it has been difficult to meet the requirements of modern navigation for accuracy, continuity, integrity, usability using a single navigation device. For example, inertial navigation can realize autonomous navigation independent of external information, and output various navigation information of position, speed and attitude in real time, but the error of inertial navigation is accumulated continuously with time, resulting in divergence of navigation accuracy. The satellite navigation can output navigation information such as high-precision position, speed and the like, but the satellite navigation signal is easy to receive shielding, deception, interference and the like, so that the navigation information output by the satellite navigation is unstable and even wrong. Different navigation sensors are combined to form a combined navigation system, so that the purposes of making up for deficiencies and complementing advantages can be achieved, and the overall performance of navigation is further improved.
Kalman filtering is a common method for realizing data fusion of different navigation devices, various errors of a navigation system are estimated, and the estimation of an error state is used for correcting the system, so that the purpose of integrated navigation is achieved.
However, as the number of navigation devices participating in the combination increases, the overall failure rate of the combined navigation system also increases. When one of the navigation devices fails, the output data of the navigation device is filtered and fused, so that the fusion result is polluted and fails, and the whole integrated navigation system is affected. Therefore, a navigation device which is in failure needs to be detected in time by using a failure detection algorithm.
Commonly used fault detection algorithm in integrated navigation system has a state χ2Test method and residual X2And (5) a test method. Wherein x is due to residual error2The inspection method has small calculation amount, is convenient for engineering realization and is widely applied. Conventional residual χ2The inspection method can effectively detect the mutation fault, but the method can not effectively detect the gradual change fault because the state of the method can track the measurement value by using Kalman filtering to measure and update. Furthermore, the conventional residual χ2The detection method has poor noise robustness processing effect, and false alarms are easily generated, so that the subsystem which normally works is frequently detected to have faults, and the stability of the system is influenced.
Disclosure of Invention
The technical problems solved by the invention are as follows: the combined navigation system fault detection algorithm based on the improved residual error detection method is provided, and the problems that an existing combined navigation system fault detection algorithm is poor in detection effect, frequent in false alarm, insensitive to slow-changing fault detection and the like are solved.
In order to solve the technical problem, the present application provides a method for detecting a fault of an integrated navigation system based on an improved residual error test method, where the method includes:
three thresholds T are setD1、TD2、TD3And make TD1<TD2<TD2；
Calculating residual error r (k) of a Kalman filter in the combined navigation system and variance A (k) of the residual error, and constructing an original detection function lambda (k) according to r (k) and A (k);
using three thresholds TD1、TD2、TD3And the original detection function lambda (k) to obtain a control function mu (lambda)k)；
For the control function mu (lambda)k) Carrying out weighted average processing to obtain an improved fault detection function lambda'k；
And judging the improved fault detection function through a fault detection strategy to detect whether the integrated navigation system has a fault.
Preferably, three thresholds T are setD1、TD2、TD3The method specifically comprises the following steps:
by inquiring chi square2Distribution table, setting three threshold values TD1、TD2、TD3And make TD1<TD2<TD2。
Preferably, the method includes calculating a residual r (k) of the kalman filter in the integrated navigation system and a variance a (k) of the residual, and constructing an original detection function λ (k) according to r (k) and a (k), and specifically includes:
computing residuals for an integrated navigation system Kalman filter
Calculating the variance A (k) H of the residualkP(k|k-1)HT k+ R (k), constructing original detection function lambda (k) ═ r according to r (k) and A (k)T(k)A(k)-1r (k), wherein ZkIs a measured value，
Is a predicted value of the system measurement, HkIs the measurement matrix, P (k | k-1) is the mean square error matrix of the one-step prediction, and R (k) is the variance matrix of the measurement noise.
Preferably, three thresholds T are usedD1、TD2、TD3And the original detection function lambda (k) to obtain a control function mu (lambda)k) The method specifically comprises the following steps:
three thresholds TD1、TD2、TD3And the original detection function λ (k) into the following equation:
obtaining a control function mu (lambda)k)。
Preferably, for the control function μ (λ)k) Carrying out weighted average processing to obtain an improved fault detection function lambda'kThe method specifically comprises the following steps:
the original fault detection function λ (k) is normalized and then the control function μ (λ)k) Weighted average is carried out to obtain an improved fault detection function lambda'k。
Preferably, the method for judging the improved fault detection function through the fault detection strategy to detect whether the integrated navigation system has a fault specifically includes:
if the integrated navigation system is in a fault state, setting a disarm threshold value epsilon, and judging the magnitude relation between an improved fault detection function lambda' (k) and epsilon;
if lambda '(k) < epsilon, the warning state is removed, the improved fault detection function lambda' (k) is cleared, and the fault state of the juxtaposed integrated navigation system is a fault;
if lambda' (k) is more than or equal to epsilon, the warning state is not removed, and the fault state of the integrated navigation system is maintained to be fault.
Preferably, if the integrated navigation system is not in a fault state, judging the magnitude relation between the improved fault detection functions λ' (k) and 1;
if lambda' (k) <1, setting the fault state of the integrated navigation system as no fault;
if lambda '(k) is more than or equal to 1, the improved fault detection function lambda' (k) is set to 1, and the fault state of the juxtaposed combination navigation system is a fault.
Preferably, the original fault detection function λ (k) is normalized and then the control function μ (λ;) is normalizedk) Weighted average is carried out to obtain an improved fault detection function lambda'kThe method specifically comprises the following steps:
using formulas
The original fault detection function λ (k) is normalized and then the control function μ (λ)k) Weighted average is carried out to obtain an improved fault detection function lambda'kWherein α is a weighting coefficient.
Finally, the algorithm adopted by the invention can automatically adjust the detection time according to the fault size, and automatically shorten the detection time for sudden fault so as to reduce the detection delay; the detection time is automatically prolonged for the gradual fault so as to utilize more information to improve the detection accuracy. The algorithm can effectively improve the accuracy and effectiveness of fault detection of the integrated navigation system, is easy to realize, and has strong engineering practical value.
Drawings
Fig. 1 is a schematic flow chart of a fault detection algorithm of an integrated navigation system based on an improved residual error detection method provided by the invention.
Detailed Description
Example one
The technical scheme of the invention is as follows: an integrated navigation system fault detection algorithm based on an improved residual error detection method is characterized by comprising the following steps:
the method comprises the following steps: by inquiring chi square2Distribution table, setting three threshold values TD1、TD2、TD3And make TD1<TD2<TD2；
Step two: computing combinationResidual error of navigation system Kalman filter
Calculating the variance A (k) H of the residualkP(k|k-1)HT k+ R (k), constructing original detection function lambda (k) ═ r according to r (k) and A (k)T(k)A(k)-1r (k), wherein ZkIs a value of a measurement of the amount of,
is a predicted value of the system measurement, HkIs a measurement matrix, P (k | k-1) is a mean square error matrix of one-step prediction, R (k) is a variance matrix of measurement noise;
step three: using three thresholds TD1、TD2、TD3And the original detection function lambda (k) to obtain a control function mu (lambda)k)：
Step four: for the control function mu (lambda)k) Carrying out weighted average processing to obtain an improved fault detection function lambda'k
Step five: judging the improved fault detection function through a fault detection strategy, and detecting whether the integrated navigation system has a fault:
a: judging whether the integrated navigation system is in a fault state at present;
a 1: if the integrated navigation system is already in a fault state, a disarm threshold epsilon is set. The threshold value may be set on its own, depending on the severity of the disarm. Judging the magnitude relation between the improved fault detection function lambda' (k) and epsilon;
a 1.1: if lambda '(k) < epsilon, the warning state is removed, the improved fault detection function lambda' (k) is cleared, and the fault state of the combined navigation system is set to be faulty;
a 1.2: if lambda' (k) is more than or equal to epsilon, the warning state is not removed, and the fault state of the integrated navigation system is maintained to be faulty;
a 2: if the integrated navigation system is not in a fault state, judging the magnitude relation between the improved fault detection function lambda' (k) and 1;
a 2.1: if lambda' (k) <1, setting the fault state of the integrated navigation system as no fault;
a 2.2: if lambda '(k) ≧ 1, the improved fault detection function lambda' (k) is set to 1 and the collocated navigation system fault status is faulty.
Example two
In order to make the fault detection algorithm of the integrated navigation system based on the improved residual error detection method more clear, the invention is further described in detail with reference to the accompanying drawings.
The method comprises the following steps: by querying χ2The distribution table sets three threshold values TD1、TD2、TD3And make TD1<TD2<TD2(ii) a Wherein, χ2The distributed freedom degree n is the dimension of observed quantity in the Kalman filter of the integrated navigation system, the false alarm rate alpha is set according to the severity of fault detection, and generally T isD2The alpha of (a) is 5%.
Step two: and calculating the residual error of the Kalman filter in the combined navigation system and the variance of the residual error, and constructing an original detection function. The method comprises the following steps:
a. the integrated navigation system realizes data fusion by using a Kalman filter, and the state equation and the measurement equation are as follows:
b. the recursion value of the system state at the Kth moment can be obtained by the state equation
Is calculated by the formula
c. Obtaining the predicted value measured by the system at the K moment
The calculation formula is as follows:
d. calculating the residual error r (k) of the Kalman filter, wherein the calculation formula is as follows:
wherein Z iskIs a value of a measurement of the amount of,
is the predicted value of the system measurement;
e. the residual error of the kalman filter is a zero-mean white noise process, and therefore the variance a (k) of the residual error is calculated by the formula a (k) HkP(k|k-1)HT k+ R (k), wherein HkIs a measurement matrix, P (k | k-1) is a mean square error matrix of one-step prediction, R (k) is a variance matrix of measurement noise;
f. according to the theory of binary hypothesis, the original detection function λ (k) can be constructed by the formula of λ (k) ═ rT(k)A(k)-1r (k). λ (k) is λ obeying a degree of freedom n2Distribution, i.e. λ (k) - χ2(n) n is the observed quantity ZkDimension (d) of (a).
Step three: deriving a control function mu (lambda) using a threshold and an original detection functionk) The calculation formula is as follows:
step four: for the control function mu (lambda)k) Carrying out weighted average processing to obtain an improved fault detection function lambda'k. The method comprises the following steps:
to original fault detection boxThe number λ (k) is normalized and then the control function μ (λ)k) Weighted average is carried out to obtain an improved fault detection function lambda'kThe calculation formula is as follows:
wherein, alpha is a weighting coefficient, alpha is more than or equal to 1 and is approximately equal to 1, and the alpha determines the proportion of the current check value in the fault detection function.
Step five: and judging the improved fault detection function through a fault detection strategy to detect whether the integrated navigation system has a fault. The method comprises the following steps:
a: judging whether the integrated navigation system is in a fault state at present;
a 1: if the integrated navigation system is already in a fault state, a disarm threshold epsilon is set. The threshold value may be set on its own, depending on the severity of the disarm. Judging the magnitude relation between the improved fault detection function lambda' (k) and epsilon;
a 1.1: if lambda '(k) < epsilon, the warning state is removed, the improved fault detection function lambda' (k) is cleared, and the fault state of the combined navigation system is set to be faulty;
a 1.2: if lambda' (k) is more than or equal to epsilon, the warning state is not removed, and the fault state of the integrated navigation system is maintained to be faulty;
a 2: if the integrated navigation system is not in a fault state, judging the magnitude relation between the improved fault detection function lambda' (k) and 1;
a 2.1: if lambda' (k) <1, setting the fault state of the integrated navigation system as no fault;
a 2.2: if lambda '(k) ≧ 1, the improved fault detection function lambda' (k) is set to 1 and the collocated navigation system fault status is faulty.
Finally, the invention relates to a fault detection algorithm of a combined navigation system, in particular to a fault detection algorithm of a combined navigation system based on an improved residual error detection method. The algorithm firstly sets three threshold values, then calculates the residual error of a Kalman filter in the integrated navigation system and the variance of the residual error, and then constructs an original fault detection function. Further, a control function is obtained by using a threshold value and an original fault detection function. Subsequently, the control function is subjected to weighted average processing to obtain an improved fault detection function. And finally, judging the improved fault detection function through a fault detection strategy so as to detect whether the integrated navigation system has a fault. The algorithm can automatically adjust the detection time according to the size of the fault, and automatically shorten the detection time for sudden fault so as to reduce the detection delay; the detection time is automatically prolonged for the gradual fault so as to utilize more information to improve the detection accuracy. The algorithm adopted by the invention can effectively improve the accuracy and effectiveness of fault detection of the integrated navigation system, is easy to realize and has strong engineering practical value.
Claims (8)
1. An integrated navigation system fault detection method based on an improved residual error detection method is characterized by comprising the following steps:
three thresholds T are setD1、TD2、TD3And make TD1<TD2<TD2；
Calculating residual error r (k) of a Kalman filter in the combined navigation system and variance A (k) of the residual error, and constructing an original detection function lambda (k) according to r (k) and A (k);
using three thresholds TD1、TD2、TD3And the original detection function lambda (k) to obtain a control function mu (lambda)k)；
For the control function mu (lambda)k) Carrying out weighted average processing to obtain an improved fault detection function lambda'k；
And judging the improved fault detection function through a fault detection strategy to detect whether the integrated navigation system has a fault.
2. The method as claimed in claim 1, wherein the three thresholds T are setD1、TD2、TD3The method specifically comprises the following steps:
by inquiring cardχ2Distribution table, setting three threshold values TD1、TD2、TD3And make TD1<TD2<TD2。
3. The method for detecting the fault of the integrated navigation system based on the improved residual error inspection method according to claim 1, wherein a residual error r (k) of the kalman filter in the integrated navigation system and a variance a (k) of the residual error are calculated, and an original detection function λ (k) is constructed according to r (k) and a (k), and specifically comprises:
computing residuals for an integrated navigation system Kalman filter
Calculating the variance A (k) H of the residualkP(k|k-1)HT k+ R (k), constructing original detection function lambda (k) ═ r according to r (k) and A (k)T(k)A(k)-1r (k) wherein ZkIs a value of a measurement of the amount of,
is a predicted value of the system measurement, HkIs the measurement matrix, P (k | k-1) is the mean square error matrix of the one-step prediction, and R (k) is the variance matrix of the measurement noise.
4. The method as claimed in claim 1, wherein three thresholds T are used for detecting the malfunction of the integrated navigation system based on the improved residual error test methodD1、TD2、TD3And the original detection function lambda (k) to obtain a control function mu (lambda)k) The method specifically comprises the following steps:
three thresholds TD1、TD2、TD3And the original detection function λ (k) into the following equation:
obtaining a control function mu (lambda)k)。
5. The integrated navigation system fault detection method based on the improved residual error detection method as claimed in claim 1, wherein the control function μ (λ) is obtainedk) Carrying out weighted average processing to obtain an improved fault detection function lambda'kThe method specifically comprises the following steps:
the original fault detection function λ (k) is normalized and then the control function μ (λ)k) Weighted average is carried out to obtain an improved fault detection function lambda'k。
6. The method as claimed in claim 1, wherein the step of evaluating the improved fault detection function by the fault detection strategy to detect whether the integrated navigation system fails specifically comprises:
if the integrated navigation system is in a fault state, setting a disarm threshold value epsilon, and judging the magnitude relation between an improved fault detection function lambda' (k) and epsilon;
if lambda '(k) < epsilon, the warning state is removed, the improved fault detection function lambda' (k) is cleared, and the fault state of the juxtaposed integrated navigation system is a fault;
if lambda' (k) is more than or equal to epsilon, the warning state is not removed, and the fault state of the integrated navigation system is maintained to be fault.
7. The method of claim 1, wherein the combined navigation system fault detection method based on the improved residual error test method,
if the integrated navigation system is not in a fault state, judging the magnitude relation between the improved fault detection function lambda' (k) and 1;
if lambda' (k) <1, setting the fault state of the integrated navigation system as no fault;
if lambda '(k) is more than or equal to 1, the improved fault detection function lambda' (k) is set to 1, and the fault state of the juxtaposed combination navigation system is a fault.
8. A substrate according to claim 5The combined navigation system fault detection method for improving the residual error detection method is characterized in that the original fault detection function lambda (k) is subjected to normalization processing, and then the control function mu (lambda (k)) is subjected to normalization processingk) Weighted average is carried out to obtain an improved fault detection function lambda'kThe method specifically comprises the following steps:
using formulas
The original fault detection function λ (k) is normalized and then the control function μ (λ)k) Weighted average is carried out to obtain an improved fault detection function lambda'kWherein α is a weighting coefficient.
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