CN112304309B - Method for calculating combined navigation information of hypersonic vehicles based on cardiac array - Google Patents

Abstract

The invention discloses a method for calculating the combined navigation information of a hyper-aircraft based on a cardiac array, which separately obtains system state information output by INS, speed and position information output by GNSS, and attitude information output by CNS; The position information is input into the first local filter for filtering; the system state information and attitude information are input into the second local filter for filtering; the state information of the inertial navigation/satellite integrated navigation subsystem and the state of the inertial navigation/astronomical integrated navigation subsystem are input The information is decomposed into several sub-states; each sub-state is respectively fused with the system predicted value output by the main filter to obtain the final system state information of the HV; the present invention adopts the cardiac array structure based on the Faddeeva algorithm to realize the HV navigation system. real-time.

Description

A method for calculating the integrated navigation information of a highly advanced aircraft based on a cardiac array

technical field

The invention belongs to the technical field of navigation of super aircraft, and in particular relates to a method for calculating integrated navigation information of super aircraft based on a cardiac array.

Background technique

Hypersonic vehicle (HV) refers to an aerospace vehicle with a flight speed greater than 5 times the speed of sound. Due to the space-time characteristics of HV such as extremely wide flight airspace, extremely fast flight speed and extremely strong maneuverability, the huge military and civil value of HV has been widely valued and studied in depth by the world's aerospace powers. The strategic commanding heights of the sky field". However, with the continuous acceleration of the practical process of HV, the speed advantage of HV also puts forward new requirements for the accuracy and real-time performance of the navigation system. High-performance navigation technology suitable for HV will become the focus of research in the field of HV in the future.

At present, HV navigation usually adopts INS/GNSS/CNS constructed by Inertial Navigation System (INS), Global Navigation Satellite System (GNSS) and Celestial Navigation System (CNS). The combined system, which can simultaneously correct the INS speed error, position error and attitude error, not only has high precision but also has strong autonomy, and is considered to be the most ideal integrated navigation scheme for HV. The INS/GNSS/CNS combined system has been exploratively applied in HVs. For example, the hypersonic sharp leading edge flight test SHEFEX-2 conducted in Germany in 2012, the test aircraft used the INS/GNSS/CNS integrated navigation system to verify The availability of the system has been improved; in the follow-up hypersonic repeatable flight experiment ReFEx, its test vehicle will still use this integrated navigation method.

For HV navigation, only by providing high-precision navigation information, can the HV complete the given task on the basis of safe flight. However, there is a contradiction between the real-time performance and accuracy of the navigation information solution. The higher the solution accuracy of the navigation algorithm, the greater the computational complexity and the worse the real-time performance. For the INS/GNSS/CNS combined system suitable for HV, a federated filtering structure is usually used to comprehensively process the heterogeneous navigation information output by INS, GNSS and CNS. However, in the local filter and main filter of federated filtering, a large number of matrix operations related to the system state are involved, resulting in high computational complexity; in particular, due to the high state dimension of the INS/GNSS/CNS combined system, the amount of computation is more It is a sharp increase, which makes it difficult to meet the high real-time requirements of HV for the navigation solution, thus hindering the further improvement of the overall performance of the HV navigation system.

SUMMARY OF THE INVENTION

The purpose of the present invention is to provide a method for calculating the integrated navigation information of a hyper-aircraft based on a cardiac array, so as to solve the problem of poor real-time performance in the process of calculating the navigation information.

The present invention adopts the following technical solutions: a method for calculating the integrated navigation information of a hyper-aircraft based on a cardiac array, comprising the following steps:

Obtain system status information output by INS, speed and position information output by GNSS, and attitude information output by CNS;

Input the system state information and speed and position information into the first local filter for filtering, obtain the state information of the inertial navigation/satellite integrated navigation subsystem and send it to the main filter;

Input the system state information and attitude information into the second local filter for filtering to obtain the state information of the inertial navigation/astronomical integrated navigation subsystem and send it to the main filter;

Decompose the state information of the inertial navigation/satellite integrated navigation subsystem and the state information of the inertial navigation/astronomical integrated navigation subsystem into several sub-states;

Data fusion of each sub-state with the state prediction value output by the main filter is performed to obtain the final system state information of HV;

Wherein, the matrix operations in the first local filter, the second local filter and the main filter are all implemented by a cardiac array.

Further, the measurement equations of the first local filter and the second local filter are:

_zi (k)=H _i (k)x(k)+v _i (k)(i=1,2),

Among them, _zi (k) is the measurement vector of the i-th local filter at the k-th time, H _i (k) is the measurement matrix, x(k) is the discrete system state vector, and v _i (k) is Measurement noise, the variance is R _i (k) (i=1,2);

The filtering methods of the first local filter and the second local filter are:

为第k时刻第i个局部滤波器得到的局部状态估计值，

为第k时刻第i个局部滤波器的预测值，

F(k)为第k时刻离散后的状态转移矩阵，

为第(k-1)时刻第i个局部滤波器得到的局部状态估计值，

为

的误差协方差阵，I_n为n阶的单位矩阵，

为第k时刻第i个局部滤波器的预测值的误差协方差阵，

为

的误差协方差矩阵，Q_i(k)为第i个局部滤波器的系统噪声方差，R_i(k)为第k时刻第i个局部滤波器的两侧噪声方差。in,

is the local state estimate obtained by the i-th local filter at the k-th time,

is the predicted value of the i-th local filter at the k-th time,

F(k) is the discrete state transition matrix at time k,

is the local state estimate obtained by the ith local filter at the (k-1)th time,

for

The error covariance matrix of , In is the identity matrix of order _n ,

is the error covariance matrix of the predicted value of the i-th local filter at the k-th time,

for

The error covariance matrix of , Q _i (k) is the system noise variance of the i-th local filter, and R _i (k) is the noise variance of the i-th local filter on both sides at the k-th time.

Further, performing data fusion of each sub-state with the state predicted value output by the main filter includes:

According to each sub-state, the state prediction value output by the main filter is decomposed into sub-state prediction values corresponding to the sub-state;

Fusion of each sub-state and the predicted value of the sub-state to obtain the sub-state fusion solution;

The obtained sub-state fusion solutions are combined to obtain the final system state information of HV.

Further, the sub-state is the attitude error, the velocity position error or the constant value error of the gyro accelerometer.

Further, fusing each sub-state and sub-state predicted values includes:

为第k时刻第j个子状态的融合解，

为

对应的误差协方差阵，。in,

is the fusion solution of the jth substate at the kth time,

for

The corresponding error covariance matrix, .

Further, after combining the obtained sub-state fusion solutions, it includes:

Use the fused state information to reset the state estimates of the main filter and the local filter, and correct the error of the INS

Further, the matrix operations in the first local filter, the second local filter and the main filter are all realized by the cardiac array structure based on the Faddeev algorithm:

Wherein, the cardiac array structure includes a plurality of interconnected processing units;

The Faddeev algorithm includes:

Combine the four input matrices A, B, C and D into a new matrix M,

其中，D+CA^-1B为输出矩阵，矩阵变换利用高斯消元法实现。The following transformation is performed on the matrix M, so that A becomes an upper triangular matrix, -C becomes a zero matrix, and the lower triangular elements of D are no longer eliminated, that is

Among them, D+CA ^-1 B is the output matrix, and the matrix transformation is realized by the Gaussian elimination method.

The beneficial effects of the present invention are as follows: the present invention adopts the cardiac array structure based on the Faddeeva algorithm, which can improve the calculation speed of the matrix operation involved in the federated filtering; The method reduces the dimension of the input state of the main filter, further reduces the computational complexity of the data fusion process of the main filter, and realizes the real-time performance of the HV navigation system.

Description of drawings

FIG. 1 is a schematic flowchart of a method for calculating the integrated navigation information of an ultra-high aircraft based on a cardiac array according to the present invention;

Fig. 2 is the cardiac array realization structure diagram of Faddeeva algorithm in the embodiment of the present invention;

3 is a comparison diagram of the horizontal position error between the method according to the embodiment of the present invention and the INS/GNSS/CNS integrated navigation method;

FIG. 4 is a comparison diagram of the filtering time consumption and the data fusion time consumption of the method according to the embodiment of the present invention and the INS/GNSS/CNS integrated navigation method.

Detailed ways

The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

The present invention discloses a method for calculating integrated navigation information of a high-tech aircraft based on a cardiac array, as shown in FIG. 1 , including the following steps:

Obtain the system state information output by the INS, the speed and position information output by the GNSS, and the attitude information output by the CNS respectively; input the system state information output by the INS and the speed and position information output by the GNSS into the first local filter for filtering to obtain inertial The state information of the navigation/satellite integrated navigation subsystem is sent to the main filter; the system state information output by the INS and the attitude information output by the CNS are input into the second local filter for filtering, and the state of the inertial navigation/astronomical integrated navigation subsystem is obtained. The information is sent to the main filter; the state information of the inertial navigation/satellite integrated navigation subsystem and the state information of the inertial navigation/astronomical integrated navigation subsystem are decomposed into several sub-states; each sub-state is separately associated with the output of the main filter. The state prediction value is fused to obtain the final system state information of the HV; wherein, the matrix operations in the first local filter, the second local filter and the main filter are all implemented by cardiac arrays.

The invention adopts the cardiac array structure based on the Faddeeva algorithm, which can improve the calculation speed of the matrix operation involved in the federated filtering; at the same time, the main filter of the combined navigation federated filtering is improved, and the state quantity classification processing method is adopted to reduce the main filter. The dimension of the input state further reduces the computational complexity of the main filter data fusion process, thereby improving the real-time performance of the HV navigation system.

In the embodiment of the present invention, the filters are divided into local filters and main filters, and the local filters estimate the state of the navigation subsystem. In this embodiment, two navigation subsystems are included, namely the INS/GNSS navigation subsystem and the INS/CNS navigation subsystem.

First, according to the error equation of the INS navigation system, the state equation of the INS/GNSS/CNS integrated navigation system is established.

Select the east-north-sky geographic coordinate system (g system) as the navigation coordinate system (n system), and record the inertial coordinate system as the i system, the earth coordinate system as the e system, the carrier coordinate system as the b system, and the calculated navigation coordinate system as n' series. The INS attitude error equation and velocity error equation are:

为姿态误差，

为

在计算导航坐标系的投影，

为地球自转角速度，

为高超飞行器自身运动产生的角速度，φ＝(φ_E,φ_N,φ_U)^T为高超飞行器(HV)的姿态角误差，

为

的计算误差，

为姿态变换矩阵，

为陀螺仪量测误差，

为连续速度误差，

为加速度计比力输出，δf^b为加速度计量测误差，

为

在计算导航坐标系的投影，

为

在计算导航坐标系的投影，δvⁿ＝(δv_E,δv_N,δv_U)^T为高超飞行器(HV)的速度误差，

为

的计算误差，

为

的计算误差。in,

is the attitude error,

for

When calculating the projection of the navigation coordinate system,

is the angular velocity of the Earth's rotation,

is the angular velocity generated by the motion of the hyper-vehicle itself, φ=(φ _E , φ _N , φ _U ) ^T is the attitude angle error of the hyper-vehicle (HV),

for

calculation error,

is the attitude transformation matrix,

is the gyroscope measurement error,

is the continuous velocity error,

is the specific force output of the accelerometer, δf ^b is the measurement error of the accelerometer,

for

When calculating the projection of the navigation coordinate system,

for

When calculating the projection of the navigation coordinate system, δv ⁿ =(δv _E ,δv _N ,δv _U ) ^T is the speed error of the hyper-vehicle (HV),

for

calculation error,

for

calculation error.

The position error equation of INS is:

和

为HV的东、北向速度；

和

为HV的纬度和高度，δL和δh为纬度误差和经度误差；R_M与R_N为地球子午圈和卯酉圈的主曲率半径；δv_N、δv_E、δv_U分别表示北、东、天方向的速度误差。Among them, δp=(δL,δλ,δh) is the position error of HV,

and

are the east and north speed of HV;

and

are the latitude and altitude of HV, δL and δh are the latitude error and longitude error; R _M and R _N are the principal curvature radii of the earth’s meridian and Mao unitary circles; δv _N , δv _E , δv _U represent north, east, and sky respectively Orientation velocity error.

Usually, the constant drift ε ^b and constant offset ▽ ^b corresponding to the gyroscope and accelerometer can be described by random constants, namely:

Define system states:

x(t)＝[φ,δv ⁿ ,δp,ε ^b ,▽ ^b ] ^T (5)

According to the defined system state, combined with equations (1) to (4), the state equation of the integrated navigation system of the hyper-aircraft can be established:

为系统噪声向量，

和

为姿态变换矩阵，

分别表示加速度计和陀螺仪的噪声。Among them, F(t) is the system state transition matrix,

is the system noise vector,

and

is the attitude transformation matrix,

represent the noise of the accelerometer and gyroscope, respectively.

Second, establish the measurement equation of the INS/GNSS navigation subsystem of the hyper-aircraft (that is, the measurement equation of the first local filter):

Taking the difference between the speed and position output by GNSS and INS as the measurement, the measurement equation of the INS/GNSS subsystem can be expressed as:

v_v(k)和v_p(k)为量测噪声，分别对应于GNSS接收机的速度误差和位置误差，H_v＝[0_3×3,diag(1,1,1),0_3×9]。Among them, z ₁ (k) is the measurement vector of the first local filter at time k, x(k) is the discretized system state, v ₁ (k) is the measurement noise,

v _v (k) and v _p (k) are measurement noises, corresponding to the velocity error and position error of the GNSS receiver, respectively, H _v =[0 _3×3 ,diag(1,1,1),0 _{3× 9} ].

Thirdly, establish the measurement equation of the INS/CNS navigation subsystem of the ultra-high aircraft:

Taking the difference between the attitudes output by the CNS and the INS as the measurement, the measurement equation of the INS/CNS subsystem can be expressed as:

z ₂ (k)=H ₂ (k)x(k)+v ₂ (k) (8)

Among them, z ₂ (k) is the measurement vector of the second local filter at time k, H ₂ (k)=[I _3×3 ,0 _3×12 ]; v ₂ (k) is the measurement noise, corresponding to Due to the measurement error of the star sensor, I _3×3 is a 3*3 order identity matrix.

In the embodiment of the present invention, a federated filtering structure with dimension reduction classification features is designed, as shown in FIG. 1 . In the first layer of the designed federated filtering structure, the Kalman filtering algorithm is used to obtain the local state estimates of the INS/GNSS and INS/CNS navigation subsystems in parallel. details as follows:

By discretizing the state equation (6) of the integrated navigation system of the highly advanced aircraft, the discrete state equation of the system can be obtained as

x(k)=F(k)x(k-1)+w(k) (9)

In the formula, x(k) is the system state, F(k) is the state transition matrix after discretization, w(k) is the system noise, and the variance is Q(k).

The measurement equations of the INS/GNSS and INS/CNS navigation subsystems can be expressed as:

_zi (k)=H _i (k)x(k)+v _i (k)(i=1,2) (10)

为第i个局部滤波器的量测向量；H_i(k)为量测矩阵；v_i(k)为量测噪声，方差为R_i(k)(i＝1,2)。in,

is the measurement vector of the i-th local filter; H _i (k) is the measurement matrix; vi (k) is the measurement noise, and the variance is R _i (k) ( _i =1,2).

Kalman filtering is used to obtain the local state estimates of the INS/GNSS and INS/CNS navigation subsystems, respectively. To remove the correlation between the local filtering solutions, the variance upper bound technique is applied to the system noise variance and the state estimation error covariance, namely

Q _i (k)=β _i ^-1 Q(k) (11)

where β _i (i=1, 2) is the information allocation factor.

Specifically, the filtering methods of the first local filter and the second local filter are:

为第k时可第i个局部滤波器得到的局部状态估计值，

为第k时刻第i个局部滤波器的预测值，

F(k)为第k时刻离散后的状态转移矩阵，

为第(k-1)时刻第i个局部滤波器得到的局部状态估计值，

为

的误差协方差阵，

为第k时刻第i个局部滤波器的预测值的误差协方差阵，

为

的误差协方差矩阵，Q_i(k)为第i个局部滤波器的系统噪声方差，R_i(k)为第k时刻第i个局部滤波器的量测噪声方差。in,

is the local state estimate that can be obtained by the ith local filter at the kth time,

is the predicted value of the i-th local filter at the k-th time,

F(k) is the discrete state transition matrix at time k,

is the local state estimate obtained by the ith local filter at the (k-1)th time,

for

The error covariance matrix of ,

is the error covariance matrix of the predicted value of the i-th local filter at the k-th time,

for

The error covariance matrix of , Q _i (k) is the system noise variance of the ith local filter, and R _i (k) is the measured noise variance of the ith local filter at the kth time.

In the federated filtering structure of the INS/GNSS/CNS integrated navigation system, the Kalman filter algorithm (KF) is used to filter the navigation information of the INS/GNSS subsystem and the INS/CNS subsystem, and the above navigation information is obtained in parallel. The local state estimation of the subsystem; wherein, in the calculation process of the Kalman filter, the cardiac array structure based on the Faddeev algorithm is used to perform the corresponding matrix operation.

In this last step, the local state estimates of the INS/GNSS and INS/CNS subsystems obtained above are respectively input into the main filter of the federated filter, and according to the correlation analysis results between the state components of the navigation subsystem, The system state vector is decomposed into three sub-states; for each sub-state, the state estimates output by each navigation subsystem and the output of the main filter are separately fused to obtain the data fusion results of the estimated values of each sub-state; The state data fusion solution is reorganized to obtain a global estimate of the system state, and the fused state information is used to reset the state estimates of the main filter and local filters, and to correct the error of the INS.

及其误差协方差阵

将得到的局部状态估值与主滤波器输出的时间更新解进行数据融合，即可获得系统状态的全局估计。必须指出的是，在上述卡尔曼滤波过程中，均采用基于Faddeev算法的心动阵列结构处理相关的矩阵运算。After completing the parallel computation of the two local filters, the local state estimate can be obtained

and its error covariance matrix

The global estimate of the system state can be obtained by data fusion of the obtained local state estimate and the time-updated solution output by the main filter. It must be pointed out that in the above Kalman filtering process, the cardiac array structure based on the Faddeev algorithm is used to process the relevant matrix operations.

Because in the INS/GNSS and INS/CNS subsystems, the estimation of the attitude error and the estimation of the velocity and position errors have a very weak interaction. Therefore, as shown in Figure 2, the attitude error and velocity and position errors in the system state vector are first decomposed into two different sub-states. In engineering applications, in order to pursue real-time performance, the constant gyro drift and accelerometer bias are sometimes not included in the state vector of the integrated navigation system. Therefore, the gyro constant drift and the accelerometer zero bias are listed as the third sub-state for the user to choose.

Rewrite the system state vector as

In the formula, x ⁽¹⁾ = (φ _E , φ _N , φ _U ) ^T , x ⁽²⁾ = (δv _E , δv _N , δv _U , δL, δλ, δh) ^T , x ⁽³⁾ = (ε _x ,ε _y ,ε _z ,▽ _x ,▽ _y ,▽ _z ) ^T .

Therefore, the state estimates obtained by each local filter can be expressed as

的误差协方差阵为remember

The error covariance matrix of is

为对应于局部状态估值

的误差协方差阵。In the formula,

is the estimation corresponding to the local state

The error covariance matrix of .

及其误差协方差阵

被分解为三个子状态，即

和

类似的，主滤波器的时间更新解(即预测值)

及其误差协方差阵

也可被分解为

和

At this point, the local state estimation

and its error covariance matrix

is decomposed into three sub-states, namely

and

Similarly, the time-updated solution of the main filter (i.e. the predicted value)

and its error covariance matrix

can also be decomposed into

and

After decomposing the system state vector, data fusion of the local state estimates output by the INS/GNSS and INS/CNS navigation subsystems and the sub-local state estimates output by the main filter time update solution can be used to obtain the local state estimates. Then, the global estimation of the state of the INS/GNSS/CNS integrated navigation system can be obtained.

The above fusion of each sub-state and sub-state predicted value includes:

为第k时刻第j个子状态的融合解，

为

对应的误差协方差阵。in,

is the fusion solution of the jth substate at the kth time,

for

The corresponding error covariance matrix.

和

进行重组，便可获得系统状态的全局估计

即：On the basis of the above, the fusion solution of the sub-state estimation

and

reorganization to obtain a global estimate of the state of the system

which is:

的误差协方差阵可表示为because

The error covariance matrix of can be expressed as

as well as

可容易求得。Substitute (19) into (22),

easily obtainable.

Finally, the system state estimates of the output of the local filter and the main filter are reset by the global state estimate:

At the same time, the error of the INS is corrected.

In the above data fusion process, the cardiac array structure is also used to improve the calculation efficiency of the corresponding matrix operation. The cardiac array implementation structure of the Faddeeva algorithm designed in the embodiment of the present invention can improve the operation efficiency of the matrix operation involved in the navigation solution.

The cardiac array implementation structure of Faddeeva algorithm is designed to provide a powerful tool for improving the efficiency of matrix operations involved in navigation solutions.

Faddeev's algorithm implements matrix operations by combining the four input matrices into a new matrix and then performing Gaussian elimination on the matrix. Its algorithm is as follows

Suppose A, B, C, D are four matrices, of which A is a non-singular matrix. Write the matrix M as:

The Faddeev algorithm performs the following transformation on the matrix M, so that A becomes an upper triangular matrix, -C becomes a zero matrix, and the lower triangular elements of D are no longer eliminated, that is

Among them, D+CA ^-1 B is the output matrix, and the matrix transformation can be realized by the Gaussian elimination method. Thus, by properly selecting A, B, C and D, the Faddeev algorithm can be used to solve matrix operations such as addition (subtraction), multiplication, and inversion. For example: if D is a 0 matrix, and B and C are unit matrices, then D+CA ^-1 B=A ^-1 , which realizes the matrix inversion operation.

In the process of Gaussian elimination for the matrix M, the cardiac array structure is used for calculation, so as to improve the calculation speed of the algorithm. The cardiac array is composed of a group of simple and repeated processing elements (Processing element, PE), the input and output data are connected with the boundary PE, the boundary PE and the inner PE, forming a very large-scale integrated circuit for calculation. The biggest advantage of the cardiac array is to improve the calculation speed. For example, the calculation speed of one PE in the traditional structure is 5 million times/s. If the cardiac array is composed of n PEs with the same clock frequency, the calculation speed is 5 million × n million times/s. . Calculation of matrices, especially matrix multiplication, is particularly suitable for the calculation of cardiac arrays. Based on such characteristics, an implementation scheme of the cardiac array shown in Figure 1 is designed to improve the calculation speed of the Faddeev algorithm.

Fig. 2 is a cardiac array implementation structure of the Faddeeva algorithm designed by the present invention, which includes p interconnected PE units. Take the matrix multiplication operation as an example to illustrate: assuming that the matrix A is of order n×p, the matrix B is of order p×m, and the matrix A and B are multiplied to obtain a matrix C of order n×m. The number of multiplication operations involved in the above matrix multiplication is n×p×m, and the number of addition operations is n×p×m. Since the designed cardiac array implementation unit includes p PEs, each PE unit needs to process only n×m multiplication operations and n×m addition operations, so the matrix multiplication operation efficiency is improved by p times.

Validation example:

陀螺常值漂移及白噪声为0.1°/h和

陀螺仪和加速度计的数据更新率为100Hz；GNSS水平定位精度为5m,高度误差为8m,速度误差为0.05m/s，数据更新率为20Hz；CNS的姿态误差均方根为5″，数据更新率为10Hz。INS/GNSS和INS/CNS导航子系统的滤波周期分别为0.05s和0.1s,主滤波器的数据融合周期为0.1s，仿真时间为1000s。Taking the INS/GNSS/CNS integrated navigation system as an example, the performance of the proposed method of the present invention is evaluated by computer simulation. In the simulation experiment, the initial position of the ultra-high vehicle is set to 106.022° east longitude, 34.246° north latitude, and the altitude is 45km; the initial velocities in the east, north and sky directions are 150m/s, 2100m/s and 0m/s; the accelerometer is zero Partial and white noise are 10 ^-3 g and

The gyro constant drift and white noise are 0.1°/h and

The data update rate of the gyroscope and accelerometer is 100Hz; the GNSS horizontal positioning accuracy is 5m, the height error is 8m, the velocity error is 0.05m/s, and the data update rate is 20Hz; the CNS attitude error root mean square is 5″, the data The update rate is 10Hz. The filtering periods of the INS/GNSS and INS/CNS navigation subsystems are 0.05s and 0.1s, respectively, the data fusion period of the main filter is 0.1s, and the simulation time is 1000s.

In the simulation experiment, a cardiac array structure with two PEs is used to deal with the matrix operations involved in the process of solving the navigation information.

The horizontal position error of the INS/GNSS/CNS integrated navigation system of the hyper-aircraft is defined as:

In the formula, L and δL are the latitude and latitude estimation errors of the hyper-aerocraft, and δλ is the longitude estimation error of the hyper-aerocraft.

Figure 3 shows the horizontal position error of the hyper-aircraft calculated according to the federated filtering and the proposed navigation information solution method. It can be seen that the positioning accuracy of the method of the present invention is similar to that of federated filtering, and there is almost no loss of accuracy. The main reasons are: 1) the cardiac array implementation structure designed in the present invention does not have precision loss when performing matrix operations; 2) the federated filtering structure with dimension reduction and classification features designed by the present invention has no effect on the system in the main filter. The state is decomposed and reorganized. However, in the INS/GNSS and INS/CNS subsystems, the interaction between attitude error estimation and velocity and position error estimation is very weak, and the above decomposition and reorganization process accuracy loss is small.

Figure 4 shows the computational time-consuming of the navigation solution using federated federated filtering and the proposed navigation information solution method. Figure 4(a) is the filter solution of the navigation subsystem (INS/GNSS and INS/CNS system). Figure 4(b) is a comparison chart of the time-consuming calculation of the data fusion process of the main filter. It can be seen that since the cardiac array structure is used to perform the relevant matrix operations, and the dimension of the input state of the main filter is reduced according to the state quantity classification processing method, the proposed navigation information calculation method is used in the filtering process and the data fusion process. Compared with the federated filtering, the calculation time is reduced by 54.7% and 78.5% respectively, which significantly improves the computational efficiency of the federated filtering and improves the real-time performance of the INS/GNSS/CNS integrated navigation system of the high-level aircraft.

The invention takes the INS/GNSS/CNS combined system as the object, and proposes a fast solution method for HV integrated navigation information, so as to solve the contradiction between the accuracy and real-time performance in the HV navigation solution process. A cardiac array structure of the Faddeeva algorithm to improve the calculation speed of the matrix operation involved in the federated filtering; at the same time, the main filter of the combined navigation federated filtering is improved, and the state quantity classification processing method is adopted to reduce the input state of the main filter. dimension, which further reduces the computational complexity of the data fusion process of the main filter; the invention can effectively improve the real-time performance of the HV navigation system.

Claims (6)

1. A method for solving combined navigation information of a hypersonic vehicle based on a cardiac array is characterized by comprising the following steps:

respectively acquiring system state information output by an INS, speed and position information output by a GNSS (global navigation satellite system) and attitude information output by a CNS (central nervous system);

inputting the system state information and the speed and position information into a first local filter for filtering to obtain state information of the inertial navigation/satellite combined navigation subsystem and sending the state information to a main filter;

inputting the system state information and the attitude information into a second local filter for filtering to obtain state information of the inertial navigation/astronomical combined navigation subsystem and sending the state information to a main filter;

decomposing the state information of the inertial navigation/satellite integrated navigation subsystem and the state information of the inertial navigation/astronomical integrated navigation subsystem into a plurality of sub-states;

performing data fusion on each substate and a state predicted value output by a main filter to obtain final system state information of the HV;

matrix operation in the first local filter, the second local filter and the main filter is realized by adopting a cardiac array structure based on a Faddeev algorithm;

wherein the cardiac array structure comprises a plurality of interconnected processing units; the input and output data are connected with the boundary processing unit, and the boundary processing unit is connected with the internal processing unit;

the Faddeev algorithm includes:

combining four input matrixes of A, B, C and D into a new matrix M,

the matrix M is transformed such that A becomes an upper triangular matrix, -C becomes a zero matrix and the lower triangular elements of D are not eliminated, i.e.

Wherein, D + CA ^-1 B is an output matrix, and matrix transformation is realized by a Gaussian elimination method.

2. The integrated hyper-navigation information solution method based on the cardiac array as set forth in claim 1, wherein the measurement equations of the first local filter and the second local filter are:

z _i (k)＝H _i (k)x(k)+v _i (k)，i＝1,2，

wherein z is _i (k) Is the measurement vector of the ith local filter at the kth time, H _i (k) For the measurement matrix, x (k) is the discrete system state vector, v _i (k) To measure noise;

the filtering method of the first local filter and the second local filter comprises the following steps:

wherein,

the local state estimate obtained for the ith local filter at time k,

the predicted value of the ith local filter at the kth time,

f (k) is a state transition matrix after the k-th moment dispersion,

the local state estimate obtained for the ith local filter at time (k-1),

is composed of

Error covariance matrix of (I) _n Is an identity matrix of order n,

is an error covariance matrix of the predicted values of the ith local filter at time k,

is composed of

Error covariance matrix of (2), Q _i (k) System noise variance, R, for the ith local filter _i (k) The measured noise variance of the ith local filter at time k.

3. The method for solving the combined navigation information of the hyper-aircraft based on the cardiac array as claimed in claim 2, wherein the data fusion of each sub-state with the state prediction value output by the main filter comprises:

decomposing the state prediction value output by the main filter into sub-state prediction values corresponding to the sub-states according to each sub-state;

fusing each sub-state with the sub-state prediction value to obtain a sub-state fusion solution;

and combining the obtained sub-state fusion solutions to obtain the final system state information of the HV.

4. The method for solving the combined navigation information of the hypersonic aircraft based on the cardiac array as claimed in claim 3, wherein the sub-state is an attitude error, a speed position error or a constant error of a gyro accelerometer.

5. The cardiac array-based combined hyper-aircraft navigation information solution method according to claim 4, wherein fusing each of the sub-states and the sub-state prediction values comprises:

wherein,

for the fusion solution for the jth sub-state at time k,

is composed of

Corresponding error covariance matrix.

6. The method for solving the combined navigation information of the hyper-aircraft based on the cardiac array as claimed in claim 5, wherein the combination of the obtained sub-state fusion solutions comprises:

and resetting the state estimation values of the main filter and the local filter by using the fused state information, and correcting the error of the INS.

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