CN111964675A - Intelligent aircraft navigation method for blackout area - Google Patents
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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
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:
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,is a mathematical platform error angle in a strapdown inertial navigation system,are respectively asxY, z three-axis mathematical platform error angle, vnIs the error in the speed of the carrier,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,respectively the random walk errors of the x, y and z axes of the gyroscope,for random walk driving noise of the gyroscope,respectively random walk driving noise of x, y and z axes of the gyroscope,is the output white noise of the gyroscope,white noise output by the gyroscope on the x, y and z axes respectively,for the random walk error of the accelerometer,respectively random walk errors of the x, y and z axes of the accelerometer,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:
where n denotes the navigation coordinate system (geographical system), b denotes the carrier coordinate system,the angular rate error of the geographic system relative to the inertial system,is the angular velocity of the geographic system relative to the inertial system,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,is the angular rate error of the earth's system relative to the inertial system,is the angular rate error of the geographic system relative to the earth system,is the angular velocity of the earth's system relative to the inertial system,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,
as a preferred embodiment of the present invention, the measurement equation in step 2 is:
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:
the node function for each layer is as follows:
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:
Derivation of the above equation yields:
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:
combining the four formulas, and simplifying the four formulas according to a quaternion operation rule to obtain the following components:
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:
in the formula, q13Is part of the error quaternion vector.
Ignoring the second order small quantity while linearizing it yields:
the state variables are defined as follows:
X=[q1 q2 q3 bx by bz]T
the following can be obtained:
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:
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:
wherein, KkIs a filter gain array;
calculating a filtered value:
wherein,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:
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,is a mathematical platform error angle in a strapdown inertial navigation system,error angle v of three-axis mathematical platform of x, y, z, respectivelynIs the error in the speed of the carrier,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,respectively the random walk errors of the x, y and z axes of the gyroscope,for random walk driving noise of the gyroscope,are gyroscopes x, y,The random walk of the z-axis drives the noise,is the output white noise of the gyroscope,white noise output by the gyroscope on the x, y and z axes respectively,for the random walk error of the accelerometer,respectively random walk errors of the x, y and z axes of the accelerometer,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:
where n denotes the navigation coordinate system (geographical system), b denotes the carrier coordinate system,the angular rate error of the geographic system relative to the inertial system,is the angular velocity of the geographic system relative to the inertial system,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,is the earth's system phaseFor an angular rate error of the inertial system,is the angular rate error of the geographic system relative to the earth system,is the angular velocity of the earth's system relative to the inertial system,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,
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:
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.
The node function for each layer is as follows:
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:
Derivation of the above equation yields:
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:
combining the four formulas, and simplifying the four formulas according to a quaternion operation rule to obtain the following components:
according to the multiplication rule and in the case of small attitude error, the error quaternion Q can be approximated to[33]:
In the formula, q13Is part of the error quaternion vector.
Ignoring the second order small quantity while linearizing it yields:
the state variables are defined as follows:
X=[q1 q2 q3 bx by bz]T
the following can be obtained:
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:
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:
wherein, KkIs a filter gain array;
calculating a filtered value:
wherein,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:
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,is a mathematical platform error angle in a strapdown inertial navigation system,error angle v of three-axis mathematical platform of x, y, z, respectivelynIs the error in the speed of the carrier,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,respectively the random walk errors of the x, y and z axes of the gyroscope,for random walk driving noise of the gyroscope,respectively random walk driving noise of x, y and z axes of the gyroscope,is the output white noise of the gyroscope,white noise output by the gyroscope on the x, y and z axes respectively,for the random walk error of the accelerometer,respectively random walk errors of the x, y and z axes of the accelerometer,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:
where n denotes the navigation coordinate system, i.e. the geographical system, b denotes the carrier coordinate system,the angular rate error of the geographic system relative to the inertial system,is the angular velocity of the geographic system relative to the inertial system,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,is the angular rate error of the earth's system relative to the inertial system,is the angular rate error of the geographic system relative to the earth system,is the angular velocity of the earth's system relative to the inertial system,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,
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:
the node function for each layer is as follows:
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:
in the formula,for estimating quaternion, Q is the error between the true value and the estimated value;
derivation of the above equation yields:
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:
combining the four formulas, and simplifying the four formulas according to a quaternion operation rule to obtain the following components:
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:
in the formula, q13Is the error quaternion vector part;
ignoring the second order small quantity while linearizing it yields:
the state variables are defined as follows:
X=[q1 q2 q3 bx by bz]T
the following can be obtained:
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:
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:
wherein, KkIs a filter gain array;
calculating a filtered value:
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Cited By (3)
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CN112578389A (en) * | 2020-12-08 | 2021-03-30 | 浙江景奕智能控制技术有限公司 | Multi-source fusion ROV real-time path planning method |
CN117109571A (en) * | 2023-10-25 | 2023-11-24 | 北京控制工程研究所 | Navigation error rapid convergence method and device, electronic equipment and storage medium |
CN117719702A (en) * | 2024-02-07 | 2024-03-19 | 长光卫星技术股份有限公司 | Composite attitude control method, equipment and medium for high-precision staring of satellite |
Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US7376507B1 (en) * | 2004-05-27 | 2008-05-20 | Sandia Corporation | Geophysics-based method of locating a stationary earth object |
CN101858748A (en) * | 2010-05-28 | 2010-10-13 | 南京航空航天大学 | Fault-tolerance autonomous navigation method of multi-sensor of high-altitude long-endurance unmanned plane |
CN104034329A (en) * | 2014-06-04 | 2014-09-10 | 南京航空航天大学 | Multi-integrated navigation processing device under launch inertial system and navigation method of multi-integrated navigation processing device |
CN109781099A (en) * | 2019-03-08 | 2019-05-21 | 兰州交通大学 | A kind of navigation methods and systems of adaptive UKF algorithm |
-
2020
- 2020-06-30 CN CN202010619609.2A patent/CN111964675A/en active Pending
Patent Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US7376507B1 (en) * | 2004-05-27 | 2008-05-20 | Sandia Corporation | Geophysics-based method of locating a stationary earth object |
CN101858748A (en) * | 2010-05-28 | 2010-10-13 | 南京航空航天大学 | Fault-tolerance autonomous navigation method of multi-sensor of high-altitude long-endurance unmanned plane |
CN104034329A (en) * | 2014-06-04 | 2014-09-10 | 南京航空航天大学 | Multi-integrated navigation processing device under launch inertial system and navigation method of multi-integrated navigation processing device |
CN109781099A (en) * | 2019-03-08 | 2019-05-21 | 兰州交通大学 | A kind of navigation methods and systems of adaptive UKF algorithm |
Non-Patent Citations (3)
Title |
---|
崔留争 等,: ""神经网络辅助卡尔曼滤波在组合导航中的应用"", 《光学 精密工程》 * |
景羿铭 等,: ""基于极限学习机的黑障区智能导航算法"", 《导航与控制》 * |
熊智 等,: ""发射系下SINS/GPS/CNS组合导航***联邦粒子滤波算法"", 《南京航空航天大学学报》 * |
Cited By (6)
Publication number | Priority date | Publication date | Assignee | Title |
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CN112578389A (en) * | 2020-12-08 | 2021-03-30 | 浙江景奕智能控制技术有限公司 | Multi-source fusion ROV real-time path planning method |
CN112578389B (en) * | 2020-12-08 | 2021-09-24 | 浙江景奕智能控制技术有限公司 | Multi-source fusion ROV real-time path planning method |
CN117109571A (en) * | 2023-10-25 | 2023-11-24 | 北京控制工程研究所 | Navigation error rapid convergence method and device, electronic equipment and storage medium |
CN117109571B (en) * | 2023-10-25 | 2024-01-23 | 北京控制工程研究所 | Navigation error rapid convergence method and device, electronic equipment and storage medium |
CN117719702A (en) * | 2024-02-07 | 2024-03-19 | 长光卫星技术股份有限公司 | Composite attitude control method, equipment and medium for high-precision staring of satellite |
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