CN111964675A - Intelligent aircraft navigation method for blackout area - Google Patents

Intelligent aircraft navigation method for blackout area Download PDF

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CN111964675A
CN111964675A CN202010619609.2A CN202010619609A CN111964675A CN 111964675 A CN111964675 A CN 111964675A CN 202010619609 A CN202010619609 A CN 202010619609A CN 111964675 A CN111964675 A CN 111964675A
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康骏
熊智
王融
张玲
安竞轲
李欣童
李婉玲
曹志国
周帅琳
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Nanjing University of Aeronautics and Astronautics
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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
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Abstract

The invention provides an aircraft intelligent navigation method of a black-barrier area, which mainly comprises the steps of establishing a state equation of an integrated navigation system; combining information provided by satellite navigation and astronomical navigation to construct a measurement equation based on an extended Kalman filter; before the aircraft passes through a black-out region, performing mode training on signals under the condition that the satellite signals work normally by adopting a Mamdani neural network; when the satellite signals cannot be received through the black-out region, a fuzzy system is adopted to extract sensor information knowledge from a navigation information base established by a neural network; and when the filtering period is reached, the satellite navigation information is read, the extended Kalman filtering measurement is updated, and the feedback correction is carried out on the system state quantity. The invention obviously improves the precision and the reliability of the navigation system in the black-obstacle area, solves the problem of high-precision navigation information output under the condition that no satellite signal is available in the black-obstacle area, ensures the effective utilization of the navigation information of the aircraft, and has wide applicability.

Description

Intelligent aircraft navigation method for blackout area
Technical Field
The invention relates to the field of aircraft integrated navigation, in particular to an intelligent navigation method for an aircraft in a black-obstacle area.
Background
At present, all countries in the world research and develop aircrafts which can go to and fro inside and outside the atmosphere for many times, and the aircrafts have the capabilities of high speed, high maneuverability and airspace spanning, so that the aircrafts can realize quick reconnaissance, space station material transportation, accurate combat tasks of fighting against enemies and the like. In the flight process of the aircraft, the precise navigation is usually realized by adopting a combination mode of multiple navigation sensors, which is also a key link for success of the task of the aircraft. When the aircraft returns to the earth at the hypersonic speed, the shock wave generated by the hypersonic speed heats the gas around the aircraft, and a plasma sheath is formed around the aircraft due to the action of viscous flow and the shock wave. The electron density in the plasma sheath is very high, which causes a situation similar to the shielding of the metal shell, and causes the interruption of communication transmission, so that navigation means such as satellite navigation, which needs to realize positioning through signal transmission, cannot be used, namely the phenomenon of 'black barrier'. For a navigation system configured with various navigation sensors, in a black-out area, a global navigation positioning system loses lock due to communication interruption; the environment in the atmosphere can cause interference to an astronomical navigation system, is not beneficial to searching for a starry sky, causes interruption of an astronomical signal, and can normally work due to the autonomy of the inertial navigation system. However, the accuracy thereof is gradually lowered, and high-accuracy navigation information cannot be provided for a long time.
At present, the navigation problem of the blackout section is processed by a method of body configuration, but the method cannot deal with the condition that the aircraft has larger maneuverability. Electrochemical methods have also been used to address the problem of navigation in this segment, but such methods do not provide navigational signals when the aircraft is frequently moving into and out of the atmosphere.
Therefore, a method capable of ensuring that the aircraft has reliable navigation information in the black-barrier section is developed, and the method has important significance for improving the navigation and positioning accuracy of the aircraft and ensuring the flight safety of the aircraft.
Disclosure of Invention
The invention aims to solve the technical problem of a reliable navigation method of an aircraft in a black-obstacle area. Before the aircraft returns to the ground from the outside of the atmosphere and passes through a signal-free black barrier region, a neural network is adopted to learn satellite navigation output signals, and when the satellite signals cannot be received through the black barrier region, a fuzzy system is adopted to extract sensor information knowledge from a navigation information base established by the neural network, so that the optimal fusion with inertial navigation is realized, and the precision and the reliability of the black barrier region navigation system are obviously improved.
In order to achieve the above object, the following technical solutions are adopted in the present invention to solve the above problems:
an aircraft intelligent navigation method of a blackout area comprises the following steps:
step 1: establishing a state equation of the integrated navigation system, and expanding the error of the inertial sensor into system state variables, wherein the system state variables comprise a random walk process of a gyroscope, a random walk driving white noise, a white noise of a gyroscope, a random walk process of an accelerometer and a random walk driving white noise;
step 2: combining position information provided by satellite navigation and attitude information provided by astronomical navigation to construct a measurement equation based on an extended Kalman filter;
and step 3: before the aircraft enters a black-barrier region, a fuzzy neural network of a Mamdani model is utilized, so that an aircraft navigation system can establish a knowledge base of output information of a satellite navigation sensor through network learning before entering the black-barrier region, and further establish a self-adaptive network;
and 4, step 4: calibrating the attitude of the aircraft by utilizing astronomical navigation at a sampling moment before the aircraft enters a black-obstacle area;
and 5: after entering a black barrier area, extracting and expressing the information knowledge of the satellite navigation sensor in the established satellite navigation output information knowledge base by using a fuzzy system, and realizing the reliable output of navigation signals;
step 6: when a filtering period is reached, reading satellite navigation information output by the fuzzy system, performing extended Kalman filtering measurement updating, and performing feedback correction on system state quantity by adopting extended Kalman filtering to realize effective correction on the combined navigation system;
as a preferred embodiment of the present invention, the state vector of the system in step 1 is:
Figure BDA0002562586730000021
wherein X is a 15-dimensional state vector consisting of attitude error, velocity error, position error and IMU random walk, W is system white noise, X isaTo amplify the state vector of the system noise,
Figure BDA0002562586730000024
is a mathematical platform error angle in a strapdown inertial navigation system,
Figure BDA0002562586730000022
are respectively asxY, z three-axis mathematical platform error angle, vnIs the error in the speed of the carrier,
Figure BDA0002562586730000023
for the velocity error of the three axes in northeast, pnThe position error of the carrier is L, lambda and h are respectively longitude, latitude and altitude error, omegabIs the random walk error of the gyroscope,
Figure BDA0002562586730000031
respectively the random walk errors of the x, y and z axes of the gyroscope,
Figure BDA0002562586730000032
for random walk driving noise of the gyroscope,
Figure BDA0002562586730000033
respectively random walk driving noise of x, y and z axes of the gyroscope,
Figure BDA0002562586730000034
is the output white noise of the gyroscope,
Figure BDA0002562586730000035
white noise output by the gyroscope on the x, y and z axes respectively,
Figure BDA0002562586730000036
for the random walk error of the accelerometer,
Figure BDA0002562586730000037
respectively random walk errors of the x, y and z axes of the accelerometer,
Figure BDA0002562586730000038
random walk driving noise for the accelerometer x, y, z axes respectively. The superscript n denotes the geographical hierarchy and the superscript b denotes the carrier hierarchy.
The state equation of the system is as follows:
Figure BDA0002562586730000039
where n denotes the navigation coordinate system (geographical system), b denotes the carrier coordinate system,
Figure BDA00025625867300000310
the angular rate error of the geographic system relative to the inertial system,
Figure BDA00025625867300000311
is the angular velocity of the geographic system relative to the inertial system,
Figure BDA00025625867300000312
representing a coordinate transformation matrix from a carrier system to a geographical system, fnIn order to output specific force of the accelerometer under geographic conditions,
Figure BDA00025625867300000313
is the angular rate error of the earth's system relative to the inertial system,
Figure BDA00025625867300000314
is the angular rate error of the geographic system relative to the earth system,
Figure BDA00025625867300000315
is the angular velocity of the earth's system relative to the inertial system,
Figure BDA00025625867300000316
angular velocity, g, of the geographic system relative to the Earth's systemn' is gravity acceleration error, RmRadius of a unit of fourth quarternIs the radius of the meridian circle,
Figure BDA00025625867300000317
as a preferred embodiment of the present invention, the measurement equation in step 2 is:
Figure BDA00025625867300000318
in the formula, Hp(t)3×18＝[03×3 03×3 diag[Rn RecosL 1]03×9]3×18，NGNSS(t) is measurement noise.
As a preferred scheme of the present invention, the specific process in step 3 is:
Figure BDA0002562586730000041
Figure BDA0002562586730000042
the node function for each layer is as follows:
a first layer:
Figure BDA0002562586730000043
a second layer:
Figure BDA0002562586730000044
and a third layer:
Figure BDA0002562586730000045
a fourth layer:
Figure BDA0002562586730000046
Figure BDA0002562586730000047
and a fifth layer:
Figure BDA0002562586730000048
on the basis, a learning algorithm with adjusted parameters is further obtained by utilizing an error back propagation algorithm.
As a preferred scheme of the present invention, the specific process in step 4 is:
in order to effectively utilize high-precision astronomical attitude determination information, an error model of the navigation system under the geocentric inertial coordinate system is deduced at the stage. And if the quaternion of the real attitude of the aircraft relative to the earth center inertial coordinate system is Q, then:
Figure BDA0002562586730000049
in the formula,
Figure BDA00025625867300000410
to estimate the quaternion, Q is the error between the true and estimated values.
Derivation of the above equation yields:
Figure BDA00025625867300000411
according to the quaternion kinematic differential equation, the quaternion differential equation of the obtained real attitude of the carrier and the differential equation of the estimated quaternion are respectively as follows:
Figure BDA00025625867300000412
Figure BDA0002562586730000051
combining the four formulas, and simplifying the four formulas according to a quaternion operation rule to obtain the following components:
Figure BDA0002562586730000052
according to the multiplication rule and under the condition that the attitude error is a small quantity, the error quaternion Q can be approximated as:
Figure BDA0002562586730000053
in the formula, q13Is part of the error quaternion vector.
Ignoring the second order small quantity while linearizing it yields:
Figure BDA0002562586730000054
the state variables are defined as follows:
X＝[q1 q2 q3 bx by bz]T
the following can be obtained:
Figure BDA0002562586730000055
correspondingly, f (t) is the system state coefficient matrix, g (t) is the system noise matrix, and w (t) is the system noise.
As a preferred scheme of the present invention, the specific process in step 5 is:
and (3) identifying the state of the step by using a network algorithm in the step 3, and extracting relevant knowledge through a knowledge base to output.
As a preferred embodiment of the present invention, the specific process in step 6 is:
and (5) inputting the satellite navigation signals in the knowledge base obtained in the step (5) and the satellite signals in the alternative step (1) to complete the combination of the black barrier navigation system.
Calculating a theoretical variance matrix and a covariance matrix output by a system:
Figure BDA0002562586730000061
Figure BDA0002562586730000062
wherein, P(XZ)k/k-1One-step prediction covariance matrix, P, for system output(ZZ)k/k-1Is a theoretical square of variance matrix, R, of the system outputkTo measure a noise matrix;
calculating a filter gain array:
Figure BDA0002562586730000063
wherein, KkIs a filter gain array;
calculating a filtered value:
Figure BDA0002562586730000064
Figure BDA0002562586730000065
wherein,
Figure BDA0002562586730000066
for system state estimation, Pk/kEstimating a mean square error matrix, Z, for a system statekIs an infinite distance measurement value;
the system state estimated value obtained according to the formula comprises attitude, position and speed errors, and the navigation parameter calculated by the inertial navigation system subtracts the system navigation error value to obtain a combined navigation corrected value.
The invention has the following beneficial effects:
(1) the method of the invention utilizes the Mamdani neural network to carry out mode training on the signals under the condition that the satellite signals work normally, and solves the problem of high-precision navigation information output under the condition that no satellite signals are available in a black-out area.
(2) The method of the invention constructs the nonlinear measurement equation by using the position information obtained by satellite measurement, ensures the effective utilization of the aircraft navigation information and has wide applicability.
Drawings
FIG. 1 is an overall framework diagram of the black-barrier aircraft navigation algorithm based on knowledge base learning.
FIG. 2 is a block diagram of the MISO fuzzy system based on the Mandani model of the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention, and are not to be construed as limiting the present invention.
1: establishing a state equation of the integrated navigation system, and expanding the error of the inertial sensor into system state variables, wherein the system state variables comprise a random walk process of a gyroscope, a random walk driving white noise, a white noise of a gyroscope, a random walk process of an accelerometer and a random walk driving white noise;
firstly, defining the state vector of the system as:
Figure BDA0002562586730000071
wherein X is a 15-dimensional state vector consisting of attitude error, velocity error, position error and IMU random walk, W is system white noise, X isaTo amplify the state vector of the system noise,
Figure BDA0002562586730000072
is a mathematical platform error angle in a strapdown inertial navigation system,
Figure BDA0002562586730000073
error angle v of three-axis mathematical platform of x, y, z, respectivelynIs the error in the speed of the carrier,
Figure BDA0002562586730000074
for the velocity error of the three axes in northeast, pnThe position error of the carrier is L, lambda and h are respectively longitude, latitude and altitude error, omegabIs the random walk error of the gyroscope,
Figure BDA0002562586730000075
respectively the random walk errors of the x, y and z axes of the gyroscope,
Figure BDA0002562586730000076
for random walk driving noise of the gyroscope,
Figure BDA0002562586730000077
are gyroscopes x, y,The random walk of the z-axis drives the noise,
Figure BDA0002562586730000078
is the output white noise of the gyroscope,
Figure BDA0002562586730000079
white noise output by the gyroscope on the x, y and z axes respectively,
Figure BDA00025625867300000710
for the random walk error of the accelerometer,
Figure BDA00025625867300000711
respectively random walk errors of the x, y and z axes of the accelerometer,
Figure BDA00025625867300000712
random walk driving noise for the accelerometer x, y, z axes respectively. The superscript n denotes the geographical hierarchy and the superscript b denotes the carrier hierarchy. The state equation of the system is as follows:
Figure BDA0002562586730000081
where n denotes the navigation coordinate system (geographical system), b denotes the carrier coordinate system,
Figure BDA0002562586730000082
the angular rate error of the geographic system relative to the inertial system,
Figure BDA0002562586730000083
is the angular velocity of the geographic system relative to the inertial system,
Figure BDA0002562586730000084
representing a coordinate transformation matrix from a carrier system to a geographical system, fnIn order to output specific force of the accelerometer under geographic conditions,
Figure BDA0002562586730000085
is the earth's system phaseFor an angular rate error of the inertial system,
Figure BDA0002562586730000086
is the angular rate error of the geographic system relative to the earth system,
Figure BDA0002562586730000087
is the angular velocity of the earth's system relative to the inertial system,
Figure BDA0002562586730000088
is the angular velocity of the geographic system relative to the earth system,
gn′as error of gravitational acceleration, RmRadius of a unit of fourth quarternIs the radius of the meridian circle,
Figure BDA0002562586730000089
2: and (3) combining position information provided by satellite navigation and attitude information provided by astronomical navigation to construct a measurement equation based on the extended Kalman filter:
Figure BDA00025625867300000810
in the formula, Hp(t)3×18＝[03×3 03×3 diag[Rn RecosL 1]03×9]3×18，NGNSS(t) is measurement noise.
3: before the aircraft enters a black-barrier region, a fuzzy neural network of the Mamdani model is utilized, so that the aircraft navigation system can establish a knowledge base of the output information of the satellite navigation sensor through network learning before entering the black-barrier region, and further establish a self-adaptive network.
Figure BDA00025625867300000811
Figure BDA0002562586730000091
The node function for each layer is as follows:
a first layer:
Figure BDA0002562586730000092
a second layer:
Figure BDA0002562586730000093
and a third layer:
Figure BDA0002562586730000094
a fourth layer:
Figure BDA0002562586730000095
Figure BDA0002562586730000096
and a fifth layer:
Figure BDA0002562586730000097
on the basis, a learning algorithm with adjusted parameters is further obtained by utilizing an error back propagation algorithm.
4: calibrating the attitude of the aircraft by utilizing astronomical navigation at a sampling moment before the aircraft enters a black-obstacle area;
in order to effectively utilize high-precision astronomical attitude determination information, an error model of the navigation system under the geocentric inertial coordinate system is deduced at the stage. And if the quaternion of the real attitude of the aircraft relative to the earth center inertial coordinate system is Q, then:
Figure BDA0002562586730000098
in the formula,
Figure BDA0002562586730000099
to estimate the quaternion, Q is the error between the true and estimated values.
Derivation of the above equation yields:
Figure BDA00025625867300000910
according to the quaternion kinematic differential equation, the quaternion differential equation of the obtained real attitude of the carrier and the differential equation of the estimated quaternion are respectively as follows:
Figure BDA00025625867300000911
Figure BDA00025625867300000912
combining the four formulas, and simplifying the four formulas according to a quaternion operation rule to obtain the following components:
Figure BDA00025625867300000913
according to the multiplication rule and in the case of small attitude error, the error quaternion Q can be approximated to[33]：
Figure BDA0002562586730000101
In the formula, q13Is part of the error quaternion vector.
Ignoring the second order small quantity while linearizing it yields:
Figure BDA0002562586730000102
the state variables are defined as follows:
X＝[q1 q2 q3 bx by bz]T
the following can be obtained:
Figure BDA0002562586730000103
correspondingly, f (t) is the system state coefficient matrix, g (t) is the system noise matrix, and w (t) is the system noise.
6: and when the filtering period is reached, reading satellite navigation information output by the fuzzy system, performing extended Kalman filtering measurement updating, and performing feedback correction on the system state quantity by adopting extended Kalman filtering to realize effective correction on the combined navigation system.
And (5) inputting the satellite navigation signals in the knowledge base obtained in the step (5) and the satellite signals in the alternative step (1) to complete the combination of the black barrier navigation system.
Calculating a theoretical variance matrix and a covariance matrix output by a system:
Figure BDA0002562586730000104
Figure BDA0002562586730000105
wherein, P(XZ)k/k-1One-step prediction covariance matrix, P, for system output(ZZ)k/k-1Is a theoretical square of variance matrix, R, of the system outputkTo measure a noise matrix;
calculating a filter gain array:
Figure BDA0002562586730000111
wherein, KkIs a filter gain array;
calculating a filtered value:
Figure BDA0002562586730000112
Figure BDA0002562586730000113
wherein,
Figure BDA0002562586730000114
for system state estimation, Pk/kEstimating a mean square error matrix, Z, for a system statekIs an infinite distance measurement value;
the system state estimated value obtained according to the formula comprises attitude, position and speed errors, and the navigation parameter calculated by the inertial navigation system subtracts the system navigation error value to obtain a combined navigation corrected value.
The above embodiments are only for illustrating the technical idea of the present invention, and the protection scope of the present invention is not limited thereby, and any modifications made on the basis of the technical scheme according to the technical idea of the present invention fall within the protection scope of the present invention.

Claims (7)

1. An aircraft intelligent navigation method of a blackout area is characterized by comprising the following steps:
step 1: establishing a state equation of the integrated navigation system, and expanding the error of the inertial sensor into system state variables, wherein the system state variables comprise a random walk process of a gyroscope, a random walk driving white noise, a white noise of a gyroscope, a random walk process of an accelerometer and a random walk driving white noise;
step 2: combining position information provided by satellite navigation and attitude information provided by astronomical navigation to construct a measurement equation based on an extended Kalman filter;
and step 3: before the aircraft enters a black-barrier region, a fuzzy neural network of a Mamdani model is utilized, so that an aircraft navigation system can establish a knowledge base of output information of a satellite navigation sensor through network learning before entering the black-barrier region, and further establish a self-adaptive network;
and 4, step 4: calibrating the attitude of the aircraft by utilizing astronomical navigation at a sampling moment before the aircraft enters a black-obstacle area;
and 5: after entering a black barrier area, extracting and expressing the information knowledge of the satellite navigation sensor in the established satellite navigation output information knowledge base by using a fuzzy system, and realizing the reliable output of navigation signals;
step 6: and when the filtering period is reached, reading satellite navigation information output by the fuzzy system, performing extended Kalman filtering measurement updating, and performing feedback correction on the system state quantity by adopting extended Kalman filtering to realize effective correction on the combined navigation system.
2. The method for intelligently navigating the aircraft in the blackcurrant area according to claim 1, wherein the state vector of the system in the step 1 is:
Figure FDA0002562586720000011
wherein X is a 15-dimensional state vector consisting of attitude error, velocity error, position error and IMU random walk, W is system white noise, X isaTo amplify the state vector of the system noise,
Figure FDA0002562586720000014
is a mathematical platform error angle in a strapdown inertial navigation system,
Figure FDA0002562586720000012
error angle v of three-axis mathematical platform of x, y, z, respectivelynIs the error in the speed of the carrier,
Figure FDA0002562586720000013
for the velocity error of the three axes in northeast, pnThe position error of the carrier is L, lambda and h are respectively longitude, latitude and altitude error, omegabIs the random walk error of the gyroscope,
Figure FDA0002562586720000021
respectively the random walk errors of the x, y and z axes of the gyroscope,
Figure FDA0002562586720000022
for random walk driving noise of the gyroscope,
Figure FDA0002562586720000023
respectively random walk driving noise of x, y and z axes of the gyroscope,
Figure FDA0002562586720000024
is the output white noise of the gyroscope,
Figure FDA0002562586720000025
white noise output by the gyroscope on the x, y and z axes respectively,
Figure FDA0002562586720000026
for the random walk error of the accelerometer,
Figure FDA0002562586720000027
respectively random walk errors of the x, y and z axes of the accelerometer,
Figure FDA0002562586720000028
random walk driving noise of an accelerometer x, y and z axes respectively; the superscript n represents the geographical system, and the superscript b represents the carrier system;
the state equation of the system is as follows:
Figure FDA0002562586720000029
where n denotes the navigation coordinate system, i.e. the geographical system, b denotes the carrier coordinate system,
Figure FDA00025625867200000210
the angular rate error of the geographic system relative to the inertial system,
Figure FDA00025625867200000211
is the angular velocity of the geographic system relative to the inertial system,
Figure FDA00025625867200000212
representing a coordinate transformation matrix from a carrier system to a geographical system, fnIn order to output specific force of the accelerometer under geographic conditions,
Figure FDA00025625867200000213
is the angular rate error of the earth's system relative to the inertial system,
Figure FDA00025625867200000214
is the angular rate error of the geographic system relative to the earth system,
Figure FDA00025625867200000215
is the angular velocity of the earth's system relative to the inertial system,
Figure FDA00025625867200000216
angular velocity, g, of the geographic system relative to the Earth's systemn′As error of gravitational acceleration, RmRadius of a unit of fourth quarternIs the radius of the meridian circle,
Figure FDA00025625867200000217
3. the method according to claim 1, wherein the measurement equation in step 2 is:
Figure FDA0002562586720000031
in the formula, Hp(t)3×18＝[03×3 03×3 diag[Rn RecosL 1] 03×9]3×18，NGNSS(t) is measurement noise.
4. The intelligent navigation method for the aircraft in the blackcurrant area according to claim 1, wherein the specific process of the step 3 is as follows:
Figure FDA0002562586720000032
Figure FDA0002562586720000033
the node function for each layer is as follows:
a first layer:
Figure FDA0002562586720000034
a second layer:
Figure FDA0002562586720000035
and a third layer:
Figure FDA0002562586720000036
a fourth layer:
Figure FDA0002562586720000037
Figure FDA0002562586720000038
and a fifth layer:
Figure FDA0002562586720000039
on the basis, a learning algorithm with adjusted parameters is further obtained by utilizing an error back propagation algorithm.
5. The intelligent navigation method for the aircraft in the blackcurrant area according to claim 1, wherein the specific process of the step 4 is as follows:
in order to effectively utilize high-precision astronomical attitude determination information, a navigation system error model under a geocentric inertial coordinate system is deduced at the stage; and if the quaternion of the real attitude of the aircraft relative to the earth center inertial coordinate system is Q, then:
Figure FDA00025625867200000310
in the formula,
Figure FDA00025625867200000311
for estimating quaternion, Q is the error between the true value and the estimated value;
derivation of the above equation yields:
Figure FDA00025625867200000312
according to the quaternion kinematic differential equation, the quaternion differential equation of the obtained real attitude of the carrier and the differential equation of the estimated quaternion are respectively as follows:
Figure FDA0002562586720000041
Figure FDA0002562586720000042
combining the four formulas, and simplifying the four formulas according to a quaternion operation rule to obtain the following components:
Figure FDA0002562586720000043
according to the multiplication rule and under the condition that the attitude error is a small quantity, the error quaternion Q can be approximated as:
Figure FDA0002562586720000044
in the formula, q13Is the error quaternion vector part;
ignoring the second order small quantity while linearizing it yields:
Figure FDA0002562586720000045
the state variables are defined as follows:
X＝[q1 q2 q3 bx by bz]T
the following can be obtained:
Figure FDA0002562586720000046
correspondingly, f (t) is the system state coefficient matrix, g (t) is the system noise matrix, and w (t) is the system noise.
6. The intelligent navigation method for the aircraft in the blackcurrant area according to claim 1, wherein the specific process of the step 5 is as follows:
and (3) identifying the state of the step by using a network algorithm in the step 3, and extracting relevant knowledge through a knowledge base to output.
7. The intelligent navigation method for the aircraft in the blackcurrant area according to claim 1, wherein the specific process of the step 6 is as follows:
completing the combination of the black barrier section navigation system by using the satellite navigation signals in the knowledge base obtained in the step 5 and the input of the satellite signals in the alternative step 1;
calculating a theoretical variance matrix and a covariance matrix output by a system:
Figure FDA0002562586720000051
Figure FDA0002562586720000052
wherein, P(XZ)k/k-1One-step prediction covariance matrix, P, for system output(ZZ)k/k-1Is a theoretical square of variance matrix, R, of the system outputkTo measure a noise matrix;
calculating a filter gain array:
Figure FDA0002562586720000053
wherein, KkIs a filter gain array;
calculating a filtered value:
Figure FDA0002562586720000054
Figure FDA0002562586720000055
wherein,
Figure FDA0002562586720000056
for system state estimation, Pk/kEstimating a mean square error matrix, Z, for a system statekIs an infinite distance measurement value.
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