CN113984042B - Series combined navigation method applicable to high-dynamic aircraft - Google Patents

Abstract

The present invention provides a serial combination navigation method suitable for highly dynamic aircraft, which adopts a series combination of two working modes: "quick acquisition" and "high-precision combined filtering". In the initial stage of aircraft flight, the uncontrolled navigation under external ballistic constraints During flight, the initial attitude rapid estimator performs carrier initialization based on three input values: the measurement value of the three-axis geomagnetic sensor, the three-axis angular rate output by the angular rate filter, and the body speed in the navigation coordinate system output by the satellite navigation system. The parameters are quickly acquired in the air and the initial flight parameters of the current carrier are output; when the aircraft enters the controlled flight stage, the flight attitude high-precision filter is used to complete the high-precision filter estimation of the flight attitude in real time using the discrete EKF filtering algorithm.

Description

A tandem integrated navigation method suitable for highly dynamic aircraft

Technical Field

The invention relates to the technical field of flight attitude measurement of conventional ammunition, high-speed rotating ammunition, high-dynamic aircraft, etc., and in particular to a tandem combined navigation method suitable for high-dynamic aircraft.

Background Art

After conventional ammunition is fired, the projectile flies by inertia force throughout the whole process under the constraint of external ballistics, and has "high dynamic" characteristics such as high initial velocity, high overload and high-speed rotation. In the intelligent navigation transformation of conventional ammunition, the axial roll angular rate of the projectile is as high as 14000°/s, which makes it difficult for the existing gyroscope to simultaneously meet the requirements of the missile-borne test. In addition, the large overload of the launch is as high as 10000g, which is very likely to cause large measurement errors, short-term failure, or even damage to the airborne sensor. The harsh application environment such as high initial velocity, high overload, and high spin makes it difficult for the existing mature airborne combined navigation system to be directly transplanted and applied to the intelligent transformation of high-dynamic aircraft such as conventional ammunition, and there are problems such as incomplete parameter testing, low measurement accuracy or poor system reliability. Therefore, seeking a navigation system suitable for aircraft with high overload, high spin and high dynamic motion characteristics is one of the key technologies that need to be solved in the intelligent transformation of conventional ammunition.

Summary of the invention

In response to the above problems, this patent proposes a serial combined navigation method suitable for high-dynamic aircraft. In response to the above problems, the present invention adopts a serial combination scheme of two working modes, "fast acquisition" and "high-precision combined filtering", according to the movement characteristics of high-dynamic aircraft. Specifically, in the initial stage of aircraft flight, the initial parameters of the carrier are quickly acquired in the air; then the navigation system switches to the high-precision combined filtering mode and enters the controlled flight stage, finally achieving high-precision estimation of the controlled flight parameters of the high-dynamic aircraft.

Specifically, the method for tandem combined navigation of highly dynamic aircraft includes:

In the initial stage of the flight, the aircraft is in uncontrolled flight under the constraint of external ballistics. The initial attitude fast estimator is used to calculate the measured values of the three-axis geomagnetic sensor. The three-axis angular rate output of the angular rate filter And the carrier speed in the navigation coordinate system output by the satellite navigation system Three input values are used to quickly obtain the initial parameters of the carrier in the air and output the initial flight parameters of the current carrier.

When the aircraft enters the controlled flight phase, the flight attitude high-precision filter is respectively based on the initial flight parameters The measured value H ^b of the three-axis geomagnetic sensor, the three-axis angular rate and the carrier speed Four input values, using discrete EKF filtering algorithm to complete high-precision filtering estimation of flight attitude in real time

The input data of the angular rate filter is the actual measured value of the MEMS gyroscope. and and the measurement value ^Hb of the three-axis geomagnetic sensor; the MEMS gyroscope includes a single-axis gyroscope Gx and a single-axis gyroscope Gz installed on the X-axis and the Z-axis of the carrier coordinate system OXb ^{, Yb,} and ^Zb respectively; the sensitive directions of each axis of the three-axis geomagnetic sensor completely coincide with the carrier coordinate system, and is used to measure the geomagnetic field vector information in the carrier coordinate system; the receiver antenna of the satellite navigation system is installed on the surface of the aircraft shell.

Furthermore, the initial flight parameters are calculated by the initial attitude fast estimator The following calculations are also included:

The initial pitch angle θ ₀ of the carrier is estimated by the velocity information. The pitch angle calculation formula is:

In the formula, and They are respectively represented as the initial velocity components of the aircraft in the navigation coordinate system, measured by the satellite navigation system;

Calculate the carrier's yaw angle from magnetic information And the roll angle γ ₀ initial attitude information, the formula is:

In the formula, and Represented as the measurement output values of the three-axis geomagnetic sensor X, Y and Z axis geomagnetic sensor It is the northward geomagnetic component of the geomagnetic field under the navigation system.

Furthermore, the three-axis angular velocity The angular rate filter equation is composed of the state equation and the observation equation, and the Kalman filter algorithm is used to complete the optimal filter estimation of the state variable X(k). Provide accurate aircraft carrier angular rate information for high-precision flight attitude filters

Furthermore, the state equation also includes:

Selecting the angular velocity as the state variable of the system X(k) = [ω _x , ω _y , ω _z ] ^T , the state equation of the system can be expressed as:

X(k)＝Φ _k,k-1 X(k-1)+w(k);

In the formula, the state transfer matrix The measurement noise w(k) = [ _nx , _ny , _nz ] ^T , satisfies the mean E[w(k)] = 0, and the variance E[w(k), ^wT (j)] = Q(k), where Q(k) is the variance matrix of the system noise sequence.

Furthermore, the observation equation also includes:

Select The angular velocity measurements of each axis are used as the observations of the filtering system. Then the observation equation of the angular rate filter can be expressed as:

Z(k)＝H(k)X(k)+v(k);

In the formula, the measurement array v(k) is the measurement noise of the system, satisfying the mean E[v(k)]=0 and the variance E[v(k),v ^T (j)]=R(k), where R(k) is the variance matrix of the measurement noise sequence.

The angular rate filter equation of the angular rate filter further includes the following two updating processes:

Time update process:

And measurement update process:

In the formula, is the state one-step prediction; P _{(k, k-1)} is the one-step prediction mean square error; K _k represents the filter gain; R (k) is the measurement noise matrix; Q (k-1) is the system noise variance matrix at time k-1; I is the unit matrix; P _k is the estimated mean square error; For state estimation.

Wherein, the flight attitude high-precision filter also includes:

Filter state equation: Select the three-dimensional attitude angle of the aircraft As system state variables And according to the inertial navigation principle, the Euler angle differential equation of the aircraft in the northeast sky navigation coordinate system is derived as the state equation of the system, and the formula is:

where f[X(t),t] is a set of nonlinear equations about X(t):

Where w(t) is the system process noise, and its mean is E[w(t)] = 0, and its variance is E[w(t), w ^T (τ)] = Q(t). is the filtered aircraft carrier angular rate information;

And the filter observation equation: Select the geomagnetic sensor measurement output The velocity information obtained by the satellite navigation system is used to calculate the pitch angle (θ _v ) as the observation quantity of the system. The observation equation of the system is:

Z(t)=h[X(t),t]+v(k);

where h[X(t),t] is a set of nonlinear equations about X(t):

Where v(t) is the measurement noise, and satisfies the mean E[v(t)] = 0 and the variance E[v(t),v ^T (τ)] = Q(t).

Furthermore, the discrete EKF filtering algorithm is used to complete the high-precision filtering estimation of the flight attitude in real time. Also includes:

Constructing the aircraft attitude filter model:

And discretize it to get:

Among them, the state transfer matrix Φ _k,k-1 is calculated according to the following formula:

Where Φ _k,k-1 is the Jacobian matrix, It is expressed as the equation system f[X(t),t] for each state variable The first partial derivative of ; One-step prediction for the state;

Among them, the observation matrix H(k) is the Jacobian matrix:

In the formula, It is expressed as the system of equations h[X(t),t] for each state variable The first partial derivative of ; is the one-step prediction of the state; v(k) is the measurement noise.

In addition, the discrete EKF filtering algorithm also includes the following two filtering processes:

Time update process:

P _k,k-1 =Φ _k,k-1 P _k-1 (Φ _k,k-1 ) ^T +Q _k-1 ;

And the measurement update process:

K _k =P _(k,k-1) (H(k)) ^T [H(k)P _k,k-1 (H(k)) ^T +R(k)] ^-1

P _k =(IK _k H(k))P _{k, k-1} (IK _k H(k)) ^T +K _k R(K)(K _k ) ^T ;

Where K _k is the filter gain; Φ _{k, k-1} is the state transition matrix; H(k) is the measurement matrix; R(k) is the measurement noise matrix; Q(k-1) is the system noise variance matrix at time k-1; P _k-1 is the system estimated variance matrix at the previous moment; P _{k, k-1} is the one-step prediction mean square error; P _k is the estimated mean square error; For state estimation.

In summary, the present invention provides a method for serial combined navigation of high-dynamic aircraft, which adopts a serial combination of two working modes, namely, "fast acquisition" and "high-precision combined filtering". In the initial stage of the aircraft flight, in uncontrolled flight under the constraint of external ballistics, the initial parameters of the carrier are quickly acquired in the air by an initial attitude fast estimator according to the three input values of the measurement value of the three-axis geomagnetic sensor, the three-axis angular velocity output by the angular velocity filter, and the carrier velocity in the navigation coordinate system output by the satellite navigation system, and the initial flight parameters of the current carrier are output; when the aircraft enters the controlled flight stage, the high-precision flight attitude filter is used to complete the high-precision filtering estimation of the flight attitude in real time by adopting the discrete EKF filtering algorithm.

BRIEF DESCRIPTION OF THE DRAWINGS

Figure 1 is a schematic diagram of the flight attitude series combination filter structure.

Figure 2 is a schematic diagram of the navigation sensor installation.

DETAILED DESCRIPTION

In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

As shown in FIG1 , the present invention proposes a method for tandem integrated navigation of highly dynamic aircraft, and the specific process is as follows:

In the initial stage of the flight, the aircraft is in uncontrolled flight under the constraint of external ballistics. The initial attitude fast estimator is used to calculate the measured values of the three-axis geomagnetic sensor. The three-axis angular rate output of the angular rate filter And the carrier speed in the navigation coordinate system output by the satellite navigation system Three input values are used to quickly obtain the initial parameters of the carrier in the air and output the initial flight parameters of the current carrier.

When the aircraft enters the controlled flight phase, the flight attitude high-precision filter is respectively based on the initial flight parameters The measured value H ^b of the three-axis geomagnetic sensor, the three-axis angular rate and the carrier speed Four input values, using discrete EKF filtering algorithm to complete high-precision filtering estimation of flight attitude in real time

The strapdown navigation sensor onboard the high-dynamic aircraft of the present invention mainly comprises a three-axis geomagnetic sensor, a gyroscope and a satellite navigation receiver. The measurement installation method of each sensor is shown in FIG2 .

The input data of the angular rate filter is the actual measured value of the MEMS gyroscope. and and the measurement value H ^b of the three-axis geomagnetic sensor; the MEMS gyroscope includes a single-axis gyroscope Gx and a single-axis gyroscope Gz respectively installed on the X-axis and the Z-axis of the carrier coordinate system O-XbYbZb; the sensitive directions of each axis of the three-axis geomagnetic sensor completely coincide with the carrier coordinate system, and are used to measure the geomagnetic field vector information in the carrier coordinate system; the receiver antenna of the satellite navigation system is installed on the surface of the aircraft shell.

The flight attitude series combined filter used in the present invention is mainly composed of three completely different modules: angular rate filter, initial attitude fast estimator and flight attitude high-precision filter. Specifically:

Fast initial pose estimator

The present invention selects the Northeastern celestial coordinate system (ENU) as the navigation coordinate system (n system). After the high-dynamic aircraft is launched, when the onboard satellite receiver works normally, the real-time velocity information of the aircraft can be measured. Considering that the angle of attack of the aircraft is very small during uncontrolled flight, the initial pitch angle θ ₀ of the carrier can be estimated through the velocity information, and the pitch angle solution formula is:

Formula 1:

In the above formula (1), and They are respectively represented as the initial velocity components of the aircraft in the navigation coordinate system, which are measured by the satellite navigation system, and they are respectively represented as the initial velocity components of the aircraft in the navigation coordinate system, which are measured by the satellite navigation system.

The initial pitch angle of the aircraft can be calculated by formula (1). Therefore, when the pitch angle θ ₀ is calculated by formula (1), the yaw angle of the carrier can be calculated by the magnetic measurement information. and the initial attitude information of the rolling angle γ ₀ , the magnetic attitude solution formula is:

Formula 2:

Formula 3:

In the above formulas (2) and (3), and Represented as the measurement output values of the three-axis geomagnetic sensor X, Y and Z axis geomagnetic sensor It is the northward geomagnetic component of the geomagnetic field under the navigation system.

Therefore, by combining the above formulas (1)-(3), the three-dimensional initial attitude parameters of the aircraft during uncontrolled flight can be quickly calculated: yaw angle The pitch angle θ ₀ and the roll angle γ ₀ provide relatively accurate initial filtering parameters for the series-connected two-stage flight attitude high-precision filter.

Angular Rate Filter

Since the onboard gyroscope measures the output ( and ) and the roll rate calculated from magnetic information There are measurement errors, and the error model can be expressed as:

Formula 4:

In the above formula (4), are the measured values of the angular velocity of each axis, ω _x , ω _y , ω _z are the ideal angular velocity of each axis, and n _x , _ny , _nz are the measurement noise of the angular velocity of each axis.

The present invention selects angular velocity as the state variable of the system X(k)=[ω _x ,ω _y ,ω _z ] ^T , then the state equation of the system can be expressed as:

Formula 5:

X(k)＝Φ _k,k-1 X(k-1)+w(k);

In the above formula (5), the state transfer matrix The measurement noise w(k) = [ _nx , _ny , _nz ] ^T , satisfies the mean E[w(k)] = 0 and the variance E[w(k), ^wT (j)] = Q(k), where Q(k) is the variance matrix of the system noise sequence.

Select The angular velocity measurements of each axis are used as the observations of the filtering system. Then the observation equation of the angular rate filter can be expressed as:

Formula 6:

Z(k)＝H(k)X(k)+v(k);

In the above formula (6), the measurement array v(k) is the measurement noise of the system, satisfying the mean E[v(k)]=0 and the variance E[v(k),v ^T (j)]=R(k), where R(k) is the variance matrix of the measurement noise sequence.

Therefore, the present invention uses the state equation (5) and the observation equation (6) to form an angular rate filter equation, and uses the Kalman filter algorithm to complete the optimal filtering estimation of the state variable X(k): Provide accurate aircraft carrier angular rate information for high-precision flight attitude filters

The filtering algorithm of the angular rate filter of the present invention includes the following two updating processes:

(1) Time update process, formula 7:

(2) Measurement update process, formula 8:

In the above formulas (7) and (8), is the state one-step prediction; P _{(k, k-1)} is the one-step prediction mean square error; K _k represents the filter gain; R (k) is the measurement noise matrix; Q (k-1) is the system noise variance matrix at time k-1; I is the unit matrix; P _k is the estimated mean square error; For state estimation.

Flight attitude high precision filter

1. Filter state equation

According to the inertial navigation principle, the Euler angle differential equation of the aircraft in the northeast sky navigation coordinate system can be derived as formula 9:

In the above formula (9), is the carrier attitude change rate, is the filtered aircraft carrier angular rate information.

Select the 3D attitude angle of the aircraft As system state variables If the above formula (9) is selected as the state equation of the system, it can be simplified into the general form formula 10:

In the above formula (10), f[X(t),t] is a nonlinear equation system about X(t).

w(t) is the system process noise, and satisfies the mean E[w(t)] = 0 and the variance E[w(t), w ^T (τ)] = Q(t).

2. Filter observation equation

According to the principle of magnetic measurement, the measurement output of the geomagnetic sensor can be expressed as formula 11:

In the above formula (11), is the geomagnetic field vector in the northeast sky navigation coordinate system; is the posture transformation matrix.

As mentioned above, the pitch angle can be calculated using the velocity information measured by the satellite navigation system, formula 12:

In the above formula, is the velocity of the carrier in the navigation coordinate system.

Select the geomagnetic sensor measurement output The velocity information obtained by the satellite navigation system is used to calculate the pitch angle (θ _v ) as the observation quantity of the system. The magnetic measurement formula (11) and the pitch angle calculation formula (12) are selected as the observation equations of the system, which can be simplified into the general form of formula 13:

Z(t)＝h[X(t),t]+v(t);

In equation (13), v(t) is the measurement noise, and satisfies the mean E[v(t)] = 0 and the variance E[v(t), v ^T (τ)] = Q(t). h[X(t), t] is a set of nonlinear equations about X(t):

3. EKF filtering algorithm

The combined filter state equation (10) and filter observation equation (13) form the aircraft attitude filter model, which is a nonlinear continuous system and can be simplified in general form as formula 14:

The discrete EKF algorithm is used for state estimation. EKF is a filtering method based on the linearization of Taylor series expansion. First, the filter model is linearized, and then the formula (15) is discretized to obtain formula 15:

In the above formula (15), the state transfer matrix Φ _k,k-1 is calculated according to the following formula 16:

Φ _k,k-1 is the Jacobian matrix, It is expressed as the equation system f[X(t),t] for each state variable The first partial derivative of ;

One-step prediction for the state.

In the above formula (15), the observation matrix H(k) is the Jacobian matrix,

It is expressed as the system of equations h[X(t),t] for each state variable The first-order partial derivative of

is the one-step prediction of the state; v(k) is the measurement noise.

Therefore, the filtering algorithm based on discrete EKF includes the following two filtering processes:

(1) Time update process, using formula 17:

P _k,k-1 =Φ _k,k-1 P _k-1 (Φ _k,k-1 ) ^T +Q _k-1 ;

(2) Measurement update process, using formula 18:

K _k =P _(k,k-1) (H(k)) ^T [H(k)P _k,k-1 (H(k)) ^T +R(k)] ^-1 ;

P _k =(IK _k H(k))P _k,k-1 (IK _k H(k)) ^T +K _k R(k)(K _k ) ^T ;

In the above filter equations (17)-(18), K _k is the filter gain; Φ _k,k-1 is the state transition matrix; H(k) is the measurement matrix; R(k) is the measurement noise matrix; Q(k-1) is the system noise variance matrix at time k-1; P _k-1 is the system estimated variance matrix at the previous time; P _k,k-1 is the one-step prediction mean square error; P _k is the estimated mean square error; For state estimation.

It can be seen that the initial attitude parameters provided by the initial attitude fast estimator Through the above-mentioned series combination scheme of the present invention, the problems and algorithm requirements faced by the airborne navigation system at different stages are different. In the uncontrolled flight stage when the initial parameters are unknown, the requirement for the navigation algorithm is to quickly estimate the initial parameters, and the estimation accuracy is second; while in the subsequent controlled flight stage, the initial parameters are known (with low accuracy), but the airborne navigation system shows strong nonlinearity and has large solution errors. The requirement for the navigation algorithm at this stage is to improve the accuracy of the filter estimation and take into account the real-time problem.

The above-mentioned embodiments only express several implementation methods of the present invention, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present invention. It should be pointed out that, for a person skilled in the art, several variations and improvements can be made without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the attached claims.

Claims (7)

1. A method for tandem integrated navigation of highly dynamic aircraft, characterized by comprising: In the initial stage of the flight, the aircraft is in uncontrolled flight under the constraint of external ballistics. The initial attitude fast estimator is used to calculate the measured values of the three-axis geomagnetic sensor. The three-axis angular rate output of the angular rate filter And the carrier speed in the navigation coordinate system output by the satellite navigation system Three input values are used to quickly obtain the initial parameters of the carrier in the air and output the initial flight parameters of the current carrier. When the aircraft enters the controlled flight phase, the flight attitude high-precision filter is respectively based on the initial flight parameters The measured value H ^b of the three-axis geomagnetic sensor, the three-axis angular rate and the carrier speed Four input values, using discrete EKF filtering algorithm to complete high-precision filtering estimation of flight attitude in real time The input data of the angular rate filter is the actual measured value of the MEMS gyroscope and and the measured value H ^b of the three-axis geomagnetic sensor; the MEMS gyroscope includes a single-axis gyroscope Gx and a single-axis gyroscope Gz respectively installed on the X-axis and the Z-axis of the carrier coordinate system OX ^b Y ^b Z ^b ; the sensitive directions of each axis of the three-axis geomagnetic sensor completely coincide with the carrier coordinate system, and are used to measure the geomagnetic field vector information in the carrier coordinate system; the receiver antenna of the satellite navigation system is installed on the surface of the aircraft shell; The initial flight parameters are calculated by the initial attitude fast estimator The following calculations are also included: The initial pitch angle _θ0 and yaw angle of the carrier are estimated using velocity information The pitch angle calculation formula is: In the formula, and They are respectively represented as the initial velocity components of the aircraft in the navigation coordinate system, measured by the satellite navigation system; The initial attitude information of the carrier's roll angle γ ₀ is calculated from the magnetic measurement information. The formula is: In the formula, and They are respectively represented as the measurement output values of the three-axis geomagnetic sensor X, Y and Z axes; is the geomagnetic field component under the navigation system; The flight attitude high-precision filter also includes: Filter state equation: Select the three-dimensional attitude angle of the aircraft As system state variables And according to the inertial navigation principle, the Euler angle differential equation of the aircraft in the northeast sky navigation coordinate system is derived as the state equation of the system, and the formula is: where f[X(t),t] is a set of nonlinear equations about X(t): Where w(t) is the system process noise, and satisfies the mean E[w(t)] = 0 and the variance E[w(t), w ^T (τ)] = Q(t); is the filtered aircraft carrier angular rate information; And the filter observation equation: Select the geomagnetic sensor measurement output The velocity information obtained by the satellite navigation system is used to calculate the pitch angle (θ _v ) as the observation quantity of the system. The observation equation of the system is: Z(t)＝h[X(t),t]+v(t); where h[X(t), t] is a set of nonlinear equations about X(t): Where v(t) is the measurement noise, and satisfies the mean E[v(t)] = 0 and the variance E[v(t),v ^T (τ)] = Q(t); It is the output value measured by each axis of the three-axis geomagnetic sensor. 2. A method for tandem integrated navigation of a highly dynamic aircraft according to claim 1, characterized in that the three-axis angular velocity The angular rate filter equation is composed of the state equation and the observation equation, and the Kalman filter algorithm is used to complete the optimal filter estimation of the state variable X(k). Provide accurate aircraft carrier angular rate information for high-precision flight attitude filters 3. The method for tandem integrated navigation of a highly dynamic aircraft according to claim 2, wherein the state equation further comprises: Selecting the angular rate as the state variable of the system X(k) = [ω _x , ω _y , ω _z ] ^T , the state equation of the system is expressed as: X(k)＝Φ _k,k-1 X(k-1)+w(k); In the formula, the state transfer matrix The measurement noise w(k) = [ _nx , _ny , _nz ] ^T , satisfies the mean E[w(k)] = 0, and the variance E[w(k), ^wT (j)] = Q(k), where Q(k) is the variance matrix of the system noise sequence. 4. The method for tandem integrated navigation of a highly dynamic aircraft according to claim 2, wherein the observation equation further comprises: Select The angular velocity measurements of each axis are used as the observations of the filtering system. Then the observation equation of the angular rate filter can be expressed as: Z(k)＝H(k)X(k)+v(k); In the formula, the measurement array v(k) is the measurement noise of the system, satisfying the mean E[v(k)]=0 and the variance E[v(k),v ^T (j)]=R(k), where R(k) is the variance matrix of the measurement noise sequence. 5. The method for tandem integrated navigation of a highly dynamic aircraft according to claim 2, wherein the angular velocity filter equation of the angular velocity filter further comprises the following two updating processes: Time update process: And measurement update process: In the formula, is the state one-step prediction; P _{(k, k-1)} is the one-step prediction mean square error; K _k represents the filter gain; R (k) is the measurement noise matrix; Q (k-1) is the system noise variance matrix at time k-1; I is the unit matrix; P _k is the estimated mean square error; is the state estimation; Φ _k,k-1 is the state transfer matrix; Z(k) is the observation quantity of the system; H(k) is the observation matrix of the system; P _k-1 is the prediction mean square error. 6. The method for tandem integrated navigation of a highly dynamic aircraft according to claim 1, characterized in that the discrete EKF filtering algorithm is used to perform high-precision filtering estimation of flight attitude in real time. Also includes: Constructing the aircraft attitude filter model: And discretize it to get: Where Z(k) is the system observation; H(k) is the observation matrix; V(k) is the measurement noise; Among them, the state transfer matrix Φ _k,k-1 is calculated according to the following formula: Where Φ _k,k-1 is the Jacobian matrix, It is expressed as the equation system f[X(t),t] for each state variable The first partial derivative of ; One-step prediction for the state; Among them, the observation matrix H(k) is the Jacobian matrix: In the formula, It is expressed as the system of equations h[X(t),t] for each state variable The first partial derivative of ; is the one-step prediction of the state; v(k) is the measurement noise. 7. The method for tandem integrated navigation of a highly dynamic aircraft according to claim 6, wherein the discrete EKF filtering algorithm further comprises the following two filtering processes: Time update process: P _{k, k-1} = Φ _{k, k-1} P _k-1 (Φ _{k, k-1} ) ^T +Q _k-1 ; And the measurement update process: K _k =P _k,k-1 (H(k)) ^T [H(k)P _k,k-1 (H(k)) ^T +R(k)] ^-1 ; P _k =(IK _k H(k))P _{k, k-1} (IK _k H(k)) ^T +K _k R(k)(K _k ) ^T ; Where K _k is the filter gain; Φ _{k, k-1} is the state transition matrix; H(k) is the measurement matrix; R(k) is the measurement noise matrix; Q(k-1) is the system noise variance matrix at time k-1; P _k-1 is the system estimated variance matrix at the previous moment; P _{k, k-1} is the one-step prediction mean square error; P _k is the estimated mean square error; is the state estimation; is the one-step prediction of the state; I is the identity matrix.