CN111896031A - Error compensation method and system for inertia base combined navigation system under black-out condition - Google Patents
Error compensation method and system for inertia base combined navigation system under black-out condition Download PDFInfo
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Abstract
The invention relates to an error compensation method and system for an inertia base combined navigation system under the condition of black obstacles, which comprises the following steps: establishing a system state equation of error compensation based on an error model of the inertial navigation system; acquiring measurement data of the inertia base combined navigation system at each moment before the current moment to serve as an actual measurement data set; determining a system state estimation value at the last moment by adopting a Kalman filtering method according to the actually measured data set and a system state equation of error compensation; when the current moment is the occurrence of the black barrier, determining the system state estimation value of the current moment by adopting a linear minimum variance estimation method according to the system state estimation value of the previous moment; and correcting the error of the inertial navigation system by using the system state estimated value at the current moment. By the method, error online compensation is carried out on the inertial navigation system in the black-obstacle environment, so that the integrated navigation system keeps high navigation positioning precision, and the adaptability of the inertial-based integrated navigation system to the black-obstacle and other complex environments is enhanced.
Description
Technical Field
The invention relates to the technical field of navigation system error compensation, in particular to an error compensation method and system for an inertia base combined navigation system under the condition of black obstacles.
Background
In order to realize high-precision and high-reliability navigation and positioning of various modern aircrafts, the aircrafts are generally provided with navigation and positioning equipment which takes an inertial navigation system (inertial navigation for short) as a main part and takes a satellite navigation system, a radar navigation system, an astronomical navigation system and the like as an auxiliary part, and the navigation and positioning equipment are organically combined through modern information fusion technologies such as Kalman filtering and the like to form an inertial-based combined navigation system, so that the navigation and positioning equipment not only can make up for the deficiencies of each other, but also can realize the performance superior to that of any navigation system, and is the most ideal aircraft navigation mode recognized by various countries in the world at present.
When the aircraft flies at high supersonic speed in the atmosphere, high-temperature plasma is generated after the surface of the aircraft is in violent friction combustion with air, and the high-temperature plasma can shield or interfere electromagnetic waves and electronic instruments, namely, a phenomenon of so-called 'black barrier' is generated. At the moment, modern navigation means such as satellite navigation, astronomical navigation, radar navigation and the like can not work normally, only an inertial navigation system can still work normally, and the inertial navigation system has a prominent problem: the navigation error is continuously accumulated along with the working time, so that the long-time high-precision navigation can not be realized by only the inertial navigation system. In addition, during long-time high-speed navigation, the aircraft is inevitably affected by various external severe natural environments or artificial interference, especially complex electromagnetic interference, and at this time, the satellite navigation system, the radar navigation system and the like are very likely to be completely shielded or interfered and cannot normally work, and the situation can also be regarded as a 'black barrier' phenomenon in a broad sense.
With the continuous and deep research of China on the relevant technology of the hypersonic flight vehicle, the problem of poor navigation accuracy of the combined navigation system caused by the problem of black barrier needs to be solved urgently in the hypersonic flight process of the flight vehicle.
Disclosure of Invention
The invention aims to provide an error compensation method and system for an inertia base combined navigation system under the condition of black obstacles, which can perform error online compensation on the inertia navigation system in the combined navigation system under the complex severe environments such as the black obstacles, so that the combined navigation system can always keep higher navigation positioning accuracy, and the adaptability of the inertia base combined navigation system to the complex severe environments such as the black obstacles is enhanced.
In order to achieve the purpose, the invention provides the following scheme:
an error compensation method for an inertia base combined navigation system under the condition of black fault comprises the following steps:
establishing a system state equation of error compensation based on an error model of the inertial navigation system;
acquiring measurement data of the inertia base combined navigation system at each moment before the current moment to serve as a measured data set; the measured data set comprises measured data of each moment before the current moment; the current moment is the moment when the black barrier appears;
determining a system state estimation value at the last moment by adopting a Kalman filtering method according to the actually measured data set and the system state equation of error compensation;
determining the system state estimation value at the current moment by adopting a linear minimum variance estimation method according to the system state estimation value at the previous moment;
and correcting the error of the inertial navigation system by using the estimated value of the system state at the current moment.
Optionally, the correcting the error of the inertial navigation system by using the estimated value of the system state at the current time further includes:
and updating the current time to the last time, updating the system state estimation value of the current time to the system state estimation value of the last time, and returning to the step of determining the system state estimation value of the current time by adopting a linear minimum variance estimation method according to the system state estimation value of the last time.
Optionally, the determining the system state estimation value at the current time by using a linear minimum variance estimation method according to the system state estimation value at the previous time specifically includes:
according to the formula
Determining a system state estimation value at the current moment;
wherein,
is the estimated value of the system state at the kth moment, namely the estimated value of the system state at the current moment,
is the system state estimate at time k-1, i.e., the last time, phik,k-1Is the state transition matrix from the k-1 th time to the k-th time.
Alternatively, the formula
The acquisition process specifically comprises:
discretizing the system state equation of the error compensation to obtain a discretized error compensation system state equation;
estimating the system state at the kth moment by adopting a linear minimum variance estimation method according to the actually measured data set to obtain a linear minimum variance estimation equation of the system state at the kth moment;
substituting the discrete error compensation system state equation into the linear minimum variance estimation equation of the system state at the kth moment to obtain a system state estimation formula
An error compensation system of an inertia-based integrated navigation system under the condition of black fault comprises:
the system state equation building module for error compensation is used for building a system state equation for error compensation based on an error model of the inertial navigation system;
the measured data set acquisition module is used for acquiring measured data of the inertia base combined navigation system at each moment before the current moment as a measured data set; the measured data set comprises measured data of each moment before the current moment; the current moment is the moment when the black barrier appears;
the last-time system state estimation value determining module is used for determining the last-time system state estimation value by adopting a Kalman filtering method according to the measured data set and the error compensation system state equation;
a current time system state estimation value determining module, configured to determine a current time system state estimation value by using a linear minimum variance estimation method according to the previous time system state estimation value;
and the error correction module is used for correcting the error of the inertial navigation system by using the system state estimation value at the current moment.
Optionally, the error compensation system for an inertia-based integrated navigation system under the black fault condition further includes:
and the state updating module is used for updating the current time to the last time, updating the system state estimation value of the current time to the system state estimation value of the last time, and returning to the system state estimation value determining module at the current time.
Optionally, the module for determining the estimated value of the system state at the current time specifically includes:
a current time system state estimation value determination unit for determining the current time system state estimation value according to a formula
Determining a system state estimation value at the current moment;
wherein,
is the estimated value of the system state at the kth moment, namely the estimated value of the system state at the current moment,
is the system state estimate at time k-1, i.e., the last time, phik,k-1Is the state transition matrix from the k-1 th time to the k-th time.
Alternatively, the formula
The acquisition process specifically comprises:
the discretization unit is used for discretizing the system state equation of the error compensation to obtain a discretized error compensation system state equation;
a linear minimum variance estimation equation obtaining unit of the system state at the kth moment, configured to estimate the system state at the kth moment by using a linear minimum variance estimation method according to the actually measured data set, so as to obtain a linear minimum variance estimation equation of the system state at the kth moment;
a system state estimation equation determining unit for substituting the discrete error compensation system state equation into the linear minimum variance estimation equation of the system state at the kth moment to obtain a system state estimation formula
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
the invention provides an error compensation method and system for an inertia base combined navigation system under the condition of black obstacles, wherein the method comprises the following steps: establishing a system state equation of error compensation based on an error model of the inertial navigation system; acquiring measurement data of the inertia base combined navigation system at each moment before the current moment to serve as an actual measurement data set; determining a system state estimation value at the last moment by adopting a Kalman filtering method according to the actually measured data set and a system state equation of error compensation; when the current moment is the occurrence of the black barrier, determining the system state estimation value of the current moment by adopting a linear minimum variance estimation method according to the system state estimation value of the previous moment; and correcting the error of the inertial navigation system by using the system state estimated value at the current moment. By the method, the error online compensation can be performed on the inertial navigation system in the combined navigation system under the complex severe environments such as the black obstacle, so that the combined navigation system can always keep higher navigation positioning precision, and the adaptability of the inertial-based combined navigation system to the complex severe environments such as the black obstacle is enhanced.
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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 embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
Fig. 1 is a flowchart of an error compensation method for an inertia based integrated navigation system under a black-out condition according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of an error compensation system of an inertia-based integrated navigation system under a black-out condition according to an embodiment of the present invention.
Detailed Description
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 only a part of the embodiments of the present invention, and not all of the embodiments. 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 invention aims to provide an error compensation method and system for an inertia base combined navigation system under the condition of black obstacles, which can perform error online compensation on the inertia navigation system in the combined navigation system under the complex severe environments such as the black obstacles, so that the combined navigation system can always keep higher navigation positioning accuracy, and the adaptability of the inertia base combined navigation system to the complex severe environments such as the black obstacles is enhanced.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
Fig. 1 is a flowchart of an error compensation method for an inertia based integrated navigation system under a black-out condition according to an embodiment of the present invention, as shown in fig. 1, the error compensation method for an inertia based integrated navigation system under a black-out condition according to the present invention includes:
s101, establishing a system state equation of error compensation based on an error model of the inertial navigation system.
Specifically, the fact that the error compensation of the inertial-based integrated navigation system is to compensate the error of the inertial navigation system is considered, so that the error of the inertial navigation system needs to be selected as a system state based on an error model of the inertial navigation system, and a system state equation for compensating the system error is established. The inertial navigation system in the embodiment of the invention selects a strapdown inertial navigation system.
Errors of the strapdown inertial navigation system mainly include errors of inertial devices, system errors caused by the errors of the inertial devices, and the like, so that the errors need to be analyzed and modeled respectively, and a corresponding error equation is established. Wherein, the error equation of the inertia device is as follows:
wherein,biis a random constant drift of the gyroscope i,
is composed ofbiThe first order differential of the first order of the,
for a random constant error of the accelerometer i,
is composed of
Where i ═ x, y, z denotes gyroscopes or accelerometers mounted along the x, y, z axes of the carrier.
Due to the fact that the inertial device has errors, certain errors exist in the strapdown inertial navigation system and navigation parameters output by the strapdown inertial navigation system, specifically including a mathematical platform attitude error, a speed error and a position error, and the specific error equation is in the form as described below.
The mathematical platform attitude error equation of the strapdown inertial navigation system is as follows:
wherein phi isE、φN、φURespectively representing attitude errors of a mathematical platform of the strapdown inertial navigation system along east, north and sky directions;
is phiEThe first order differential of the first order of the,
is phiNThe first order differential of the first order of the,
is phiUFirst order differentiation of; v. ofE、vN、vUEast, north and sky directions of strapdown inertial navigation systemA speed error; l, lambda and h are respectively latitude, longitude and altitude errors of the strapdown inertial navigation system; w is agiWhite noise, w, for the measurement of gyroscope iaiWhite noise for the accelerometer i; omegaieThe rotational angular velocity of the earth, and R is the radius of the earth; v. ofE、vN、vUThe east, north and sky speeds of the carrier respectively; l, lambda and h are respectively the latitude, longitude and altitude of the carrier position; t isij(i, j ═ 1,2,3) is an attitude matrix
Row i and column j.
The velocity error equation of the strapdown inertial navigation system is as follows:
wherein f isE、fN、fURespectively, the accelerometer outputs equivalent specific forces along the east, north and sky directions.
The position error equation of the strapdown inertial navigation system is as follows:
thereby, the mathematics of the strapdown inertial navigation system is converted according to the error model of the strapdown inertial navigation systemPlatform attitude error, velocity error, position error, gyroscope error and accelerometer error are as system state, include: attitude error phi of mathematical platform along east, north and sky directionsE,φN,φUVelocity error v in east, north and sky directionsE,vN,vUConstant drift of the gyroscope in the weft, warp, height errors L, lambda, h, x, y, z axesbx,by,bzConstant offset of accelerometer in x, y, z axes
I.e. the system state X of the error compensation is:
obviously, in combination with the above system state X, the various error equations of the previous strapdown inertial navigation system can be written in the form of:
the above equation is the system state equation for error compensation. Wherein, F is recorded as a system state matrix; g is recorded as a system noise driving array; w is noted as system white noise. Obviously, the system state equation describes the relationship between the system input and the system state, namely the relationship between various motion parameters of the carrier and various errors of the strapdown inertial navigation system.
Let the above-mentioned continuous system state equation be discretized into the following form:
Xk＝Φk,k-1Xk-1+k-1Wk-1
wherein, Xk-1、XkThe system states at the k-1 and k-th time, phik,k-1Is the state transition matrix from time k-1 to time k,k-1for discrete system noise-driven arrays, Wk-1A noise sequence is excited for a discrete system.
S102, acquiring measurement data of the inertia base combined navigation system at each moment before the current moment to serve as a measured data set; the measured data set comprises measured data of each moment before the current moment; the current moment is the moment when the black barrier appears.
And S103, determining the system state estimation value at the previous moment by adopting Kalman filtering according to the actually measured data set and the system state equation of error compensation.
Considering that the output of each subsystem of the integrated navigation system is normal before the black-fault phenomenon occurs, namely the measurement of the integrated navigation system is known at the moment, and only the strapdown inertial navigation system can normally output navigation parameters after the black-fault phenomenon occurs, the error of the inertial navigation system during the black-fault period is estimated online in real time by using the known measurement data of the integrated navigation system before the black-fault occurs and the real-time output information of the inertial navigation system during the black-fault period and adopting a linear minimum variance estimation method.
And S104, determining the system state estimation value at the current moment by adopting a linear minimum variance estimation method according to the system state estimation value at the previous moment. In particular, according to the formula
Determining a system state estimation value at the current moment; wherein,
is the estimated value of the system state at the kth moment, namely the estimated value of the system state at the current moment,
is the system state estimate at time k-1, i.e., the last time, phik,k-1Is the state transition matrix from the k-1 th time to the k-th time.
Formula (II)
The acquisition process specifically comprises:
step 401, discretizing the system state equation of the error compensation,obtaining a discrete error compensation system state equation Xk＝Φk,k-1Xk-1+k-1Wk-1。
Step 402, estimating the system state at the kth moment by adopting a linear minimum variance estimation method according to the actually measured data set to obtain a linear minimum variance estimation equation of the system state at the kth moment;
step 403, substituting the discrete error compensation system state equation into the linear minimum variance estimation equation of the system state at the kth moment to obtain a system state estimation formula
Specifically, suppose that a black-out condition occurs at the k-th time, that is, the inertial-based integrated navigation filter at the k-th time has no measurement data, and the measurement data at the k-1 previous times exist. Then, based on the measured data Z of k-1 previous time points1、Z2…Zk-1For the system state X at the current k momentkMaking linear minimum variance estimates, i.e.
Here, E*[·]Representing a linear minimum variance estimate.
Substituting the discretized form of the previous system state equation into the above equation and estimating the linear property according to the linear minimum variance can obtain:
and W is known from the discretization form of the state equation of the systemk-1To X onlykHas an influence on Z1、Z2…Zk-1Not correlation, then E*[Wk-1/Z1,Z2…Zk-1]＝E*[Wk-1]Is equal to 0, thus having
Because the combined navigation system has the black fault condition at the kth moment and the measured data of the combined navigation system at the previous k-1 moments exist, the measured data Z at the previous k-1 moments can be obtained1、Z2…Zk-1Performing Kalman filtering calculation of normal integrated navigation to obtain the system state estimation value at the k-1 th moment
Since Kalman filtering is also a linear minimum variance estimation, it is obtained by Kalman filtering calculation
Can be considered as being based on the measurement data Z of k-1 previous time points1、Z2…Zk-1For the system state at the k-1 th time
Linear minimum variance estimation is
Thus, the system state X at the k-th time can be obtained from the above relationskIs estimated by the formula
And S105, correcting the error of the inertial navigation system by using the system state estimated value at the current moment.
After S105, further comprising:
and updating the current time to the last time, updating the system state estimation value of the current time to the system state estimation value of the last time, and returning to the step S104. Similarly, the estimated value of the system state at the k-th moment is further determined
For the system at the k +1 th timeState of the System Xk+1The system state X at the k +1 th moment can be obtained by linear minimum variance estimationk+1Is estimated by the formula
By analogy, the estimated value of the system state at the k +2 th moment can be obtained by calculation
Thus, a recursion calculation formula of system state estimation under severe environments such as blackout and the like can be obtained as
Therefore, under severe environments such as a black barrier, the system state X (namely, the error of the strapdown inertial navigation system) can be estimated online in real time by using the recursive calculation formula of the system state estimation until the black barrier is finished.
The error compensation method of the inertia base combined navigation system under the black-out condition provided by the invention fully utilizes the propagation characteristic of the system error, establishes a system state equation, combines with the previously known external measurement data, carries out linear minimum variance estimation on the system state, namely the inertial navigation system error under the condition that the external measurement data is not updated for a short time, realizes the continuous updating calculation of the system state, further realizes the continuous correction on the error of the inertial navigation system, effectively solves the problems that the navigation systems such as satellites, radars and the like can not work normally under the condition that the black-out condition occurs, the error of the inertial navigation system diverges rapidly along with time, the rapid decline of the precision of the whole combined navigation system can not meet the navigation requirement with higher precision, and obviously enhances the adaptability of the inertia base combined navigation system to various complex and severe environments. The method has the advantages of ingenious conception, easy realization of engineering, low cost and strong practicability, and is very suitable for the application fields of aerospace, aviation, weaponry and the like.
The invention also provides an error compensation system of the inertia-based integrated navigation system under the condition of black fault, as shown in fig. 2, the system comprises:
and the error compensation system state equation building module 1 is used for building an error compensation system state equation based on an error model of the inertial navigation system.
The measured data set acquisition module 2 is used for acquiring measured data of the inertia base combined navigation system at each moment before the current moment as a measured data set; the measured data set includes measured data at each time prior to the current time. The current moment is the moment when the black barrier appears;
and the last-time system state estimation value determining module 3 is used for determining the last-time system state estimation value by adopting a Kalman filtering method according to the actually measured data set and the error compensation system state equation.
And the current-time system state estimation value determining module 4 is used for determining the current-time system state estimation value by adopting a linear minimum variance estimation method according to the previous-time system state estimation value.
And the error correction module 5 is used for correcting the error of the inertial navigation system by using the system state estimation value at the current moment.
Preferably, the error compensation system for the inertia-based integrated navigation system under the black fault condition further includes:
and the state updating module is used for updating the current time to the last time, updating the system state estimation value of the current time to the system state estimation value of the last time, and returning to the current time system state estimation value determining module 4.
Preferably, the module for determining the estimated value of the system state at the current time specifically includes:
a current time system state estimation value determination unit for determining the current time system state estimation value according to a formula
And determining the system state estimation value at the current moment.
Wherein,
is the estimated value of the system state at the kth moment, namely the estimated value of the system state at the current moment,
is the system state estimate at time k-1, i.e., the last time, phik,k-1Is the state transition matrix from the k-1 th time to the k-th time.
Preferably, the formula
The acquisition process specifically comprises:
the discretization unit is used for discretizing the system state equation of the error compensation to obtain a discretized error compensation system state equation;
a linear minimum variance estimation equation obtaining unit of the system state at the kth moment, configured to estimate the system state at the kth moment by using a linear minimum variance estimation method according to the actually measured data set, so as to obtain a linear minimum variance estimation equation of the system state at the kth moment;
a system state estimation equation determining unit for substituting the discrete error compensation system state equation into the linear minimum variance estimation equation of the system state at the kth moment to obtain a system state estimation formula
The system state estimation value of the current moment in the embodiment of the invention
The mean square error matrix of (a) is solved,
has an error of
Then
Discretizing a previous system state equationSubstituting the formula into the formula and finishing to obtain the final product
Thus, the system state estimation at the k-th time
Of mean square error matrix of
Due to Wk-1Influencing only XkWithout affecting Xk-1To do so
Thereby Wk-1And
is not related, and is due to E [ W ]k-1]The system state estimation under the severe environment such as black barrier can be obtained by deduction by using the formula as 0
Of mean square error matrix of
By the above mean square error matrix PkFor previous system state estimates
And (5) judging the quality degree.
The real-time estimation value of the inertial navigation system error is proved to meet the unbiased requirement through mathematical derivation, and then the online estimation and correction compensation of the inertial navigation system error can be realized by utilizing the estimation value.
Before the black fault condition appears at the kth moment, all the parts of the integrated navigation system are normal, at the moment, the integrated navigation system carries out conventional Kalman filtering, and the unbiased cognition of the Kalman filtering is that
The recursive formula of the system state estimation obtained by combining the above derivation is as follows:
thus, using the two equations above, one can obtain:
the same can be obtained
By analogy, the recursion calculation formula of the system state estimation is obtained
Is the system state XkUnbiased estimation of (d).
Therefore, when the inertia base combined navigation system faces the complex and severe environment such as the black obstacle, no navigation equipment such as a satellite navigation system, a radar navigation system and the like outputs, but the strapdown inertial navigation system can still normally output, at the moment, the error compensation method of the inertia base combined navigation system under the condition of the black obstacle can calculate the estimated value of the error of the inertial navigation system at the current moment on line in real time, the error of the strapdown inertial navigation system can be corrected and compensated by using the estimated value, and the output of the strapdown inertial navigation system after being corrected and compensated is used as the output of the whole combined navigation system, so that the inertia base combined navigation system can still keep higher navigation precision under the complex and severe environment such as the black obstacle.
The invention has the advantages that:
1. the error compensation method and the error compensation system for the inertia base combined navigation system under the condition of the black obstacle provide a technical method which is feasible in engineering and obvious in effect for solving the problem of maintaining the combined navigation precision under the complicated severe environments such as the black obstacle, and the method has the advantages of high precision, ingenious conception, low cost, easiness in implementation of the engineering and the like.
2. The method not only can be suitable for solving the problem of 'black barrier' caused by high-temperature plasma generated by high-supersonic-speed flight of an aircraft in the atmosphere, but also can be further popularized and applied to generalized 'black barrier' occasions in which satellite navigation, radar navigation and the like cannot work normally due to various complex electromagnetic interferences, effectively ensures the navigation and positioning accuracy of the inertia-based combined navigation system in complex severe environments such as the black barrier and the like, and obviously enhances the adaptability of the system to severe environments.
3. According to the method, the error model of the inertial navigation system is utilized, the inertial navigation system error is selected as the state, and the system state equation of error compensation is established, so that the propagation characteristic of the inertial navigation system error is disclosed, and a necessary model foundation is laid for the subsequent online estimation of the inertial navigation system error.
4. According to the method, the known measurement information of the integrated navigation system before the black barrier appears and the real-time output information of the inertial navigation system during the black barrier appears are fully utilized, and the error of the inertial navigation system during the black barrier appears is estimated on line in real time by adopting the linear minimum variance estimation, so that not only is the estimation result of the error of the inertial navigation system obtained in real time, but also the estimation result is estimated by utilizing a mean square error calculation formula, and therefore the system error compensation is possible to realize.
5. The theoretical derivation proves that the estimated value of the system state of the inertial navigation system error at the current moment meets the unbiased requirement, so that the error compensation technology provided by the invention is fully demonstrated.
6. The invention does not change the original structure of the inertia-based integrated navigation system, does not need to be equipped with additional auxiliary equipment, only needs to embed the corresponding compensation method into the original integrated navigation computer, can solve the problem of maintaining the precision of the integrated navigation system under the complicated and severe environments such as black obstacles and the like by using the technology provided by the invention, and has the advantages of easy realization of engineering, low cost and good practicability.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. For the system disclosed by the embodiment, the description is relatively simple because the system corresponds to the method disclosed by the embodiment, and the relevant points can be referred to the method part for description.
The principles and embodiments of the present invention have been described herein using specific examples, which are provided only to help understand the method and the core concept of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In view of the above, the present disclosure should not be construed as limiting the invention.
Claims (8)
1. An error compensation method for an inertia-based combined navigation system under the condition of black fault is characterized by comprising the following steps:
establishing a system state equation of error compensation based on an error model of the inertial navigation system;
acquiring measurement data of the inertia base combined navigation system at each moment before the current moment to serve as a measured data set; the measured data set comprises measured data of each moment before the current moment; the current moment is the moment when the black barrier appears;
determining a system state estimation value at the last moment by adopting a Kalman filtering method according to the actually measured data set and the system state equation of error compensation;
determining the system state estimation value at the current moment by adopting a linear minimum variance estimation method according to the system state estimation value at the previous moment;
and correcting the error of the inertial navigation system by using the estimated value of the system state at the current moment.
2. The method for compensating the error of the inertia based integrated navigation system under the black-out condition as claimed in claim 1, wherein the correcting the error of the inertia navigation system using the estimated value of the system state at the current time further comprises:
and updating the current time to the last time, updating the system state estimation value of the current time to the system state estimation value of the last time, and returning to the step of determining the system state estimation value of the current time by adopting a linear minimum variance estimation method according to the system state estimation value of the last time.
3. The method for compensating the error of the inertia based integrated navigation system under the black-out condition according to claim 1, wherein the determining the estimated value of the system state at the current time by using a linear minimum variance estimation method according to the estimated value of the system state at the previous time specifically comprises:
according to the formula
Determining a system state estimation value at the current moment;
wherein,
is the estimated value of the system state at the kth moment, namely the estimated value of the system state at the current moment,
is the system state estimate at time k-1, i.e., the last time, phik,k-1Is the state transition matrix from the k-1 th time to the k-th time.
4. The method of claim 3, wherein the formula is a formula for compensating the error of the inertia-based integrated navigation system under the black barrier condition
The acquisition process specifically comprises:
discretizing the system state equation of the error compensation to obtain a discretized error compensation system state equation;
estimating the system state at the kth moment by adopting a linear minimum variance estimation method according to the actually measured data set to obtain a linear minimum variance estimation equation of the system state at the kth moment;
substituting the discrete error compensation system state equation into the linear minimum variance estimation equation of the system state at the kth moment to obtain a system state estimation formula
5. An error compensation system of an inertia-based integrated navigation system under the condition of black fault is characterized by comprising:
the system state equation building module for error compensation is used for building a system state equation for error compensation based on an error model of the inertial navigation system;
the measured data set acquisition module is used for acquiring measured data of the inertia base combined navigation system at each moment before the current moment as a measured data set; the measured data set comprises measured data of each moment before the current moment; the current moment is the moment when the black barrier appears;
the last-time system state estimation value determining module is used for determining the last-time system state estimation value by adopting a Kalman filtering method according to the measured data set and the error compensation system state equation;
a current time system state estimation value determining module, configured to determine a current time system state estimation value by using a linear minimum variance estimation method according to the previous time system state estimation value;
and the error correction module is used for correcting the error of the inertial navigation system by using the system state estimation value at the current moment.
6. The system for compensating for errors in an inertia based combined navigation system under black fault of claim 5, further comprising:
and the state updating module is used for updating the current time to the last time, updating the system state estimation value of the current time to the system state estimation value of the last time, and returning to the system state estimation value determining module at the current time.
7. The system for compensating error of an inertia based integrated navigation system under the black-out condition of claim 5, wherein the module for determining the estimated value of the system state at the current time specifically comprises:
a current time system state estimation value determination unit for determining the current time system state estimation value according to a formula
Determining a system state estimation value at the current moment;
wherein,
is the estimated value of the system state at the kth moment, namely the estimated value of the system state at the current moment,
is the system state estimate at time k-1, i.e., the last time, phik,k-1Is the state transition matrix from the k-1 th time to the k-th time.
8. The system of claim 7, wherein the formula is a system for compensating for errors in an inertial based integrated navigation system under black barrier
The acquisition process specifically comprises:
the discretization unit is used for discretizing the system state equation of the error compensation to obtain a discretized error compensation system state equation;
a linear minimum variance estimation equation obtaining unit of the system state at the kth moment, configured to estimate the system state at the kth moment by using a linear minimum variance estimation method according to the actually measured data set, so as to obtain a linear minimum variance estimation equation of the system state at the kth moment;
a system state estimation equation determining unit for substituting the discrete error compensation system state equation into the linear minimum variance estimation equation of the system state at the kth moment to obtain a system state estimation formula
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