Aircraft attitude estimation method based on navigation coordinate system ESKF algorithm and maneuvering acceleration segmented isolation

Abstract

An aircraft attitude estimation method based on a navigation coordinate system ESKF algorithm and maneuvering acceleration segmented isolation comprises the steps that 1, three-axis acceleration information measured by an accelerometer of a general aviation aircraft and output data of a gyroscope are obtained, and maneuvering acceleration segmented isolation is achieved; 2) calculating an attitude angle; 3) calculating an attitude angle error caused by gyroscope noise, and inputting the attitude angle error into the time prediction equation; 4) obtaining a measurement matrix and a measurement equation about attitude angle errors through maneuvering acceleration segmented isolation and calculation of an output result of an attitude angle; and 5) calculating Kalman gain according to an output result of the measurement equation, performing measurement updating, and obtaining an error-compensated attitude angle according to an updated attitude angle error output by measurement updating and an attitude angle output by calculating the attitude angle. According to the method, the attitude error angle is defined on the navigation coordinate system, the accuracy of aircraft attitude estimation can be effectively improved, and large errors caused by the fact that the maneuvering acceleration of a carrier is too large on the horizontal plane of a carrier system are effectively reduced.

Description

Technical Field

[0001] The invention belongs to the technical field of aircraft attitude estimation, and relates to an aircraft attitude estimation method based on an ESKF algorithm of a navigation coordinate system and segmented isolation of maneuvering acceleration. Background Art

[0002] As the main parameter to characterize the flight state, the aircraft attitude is one of the important input quantities of the flight control system and an important part of navigation. The traditional flight control loop usually uses attitude control as the inner loop and speed and position as the outer loop. It continuously corrects and feeds back the flight control situation through the estimation of the aircraft attitude. Therefore, the estimation of the aircraft flight attitude plays a decisive and key role in flight safety. At present, the attitude estimation algorithms used for MEMS sensors mainly include the Mahony complementary filter algorithm, the Madgwick filter algorithm and various variants of the Kalman filter fusion algorithm. The Mahony complementary filter algorithm estimates the attitude by using the cross product error compensation of the accelerometer data and the gravity data, which is mainly used for rotary and fixed-wing drones; the Madgwick filter algorithm uses the attitude quaternion estimated by the accelerometer to perform gradient descent to obtain the error compensation attitude. However, both algorithms do not consider the influence of noise and have problems such as being unable to handle maneuvering acceleration well. In contrast, the Kalman filter algorithm determines the gain by seeking the minimum covariance of the state quantity, thereby achieving the optimal estimation of the attitude. Among many Kalman filter algorithms, the Extended Kalman filter (EKF) has large nonlinear errors when processing nonlinear models; the Unscented Kalman filter algorithm has high complexity and is not suitable for practical engineering. The Error State Kalman filter (ESKF) uses the attitude error angle for filtering, which can not only eliminate the constraint problem caused by the four degrees of freedom of the quaternion, but also has relatively low algorithm complexity. However, in MEMS sensors without geomagnetometers, the yaw angle cannot be corrected. The use of the ESKF algorithm will incorrectly correct the yaw angle, resulting in an increase in the attitude estimation error, and cannot achieve high-precision detection of flight attitude.

[0003] In order to improve the attitude estimation precision and accuracy of general aviation aircraft during flight, the present invention proposes an aircraft attitude estimation method based on navigation coordinate system ESKF algorithm and maneuvering acceleration segment isolation. Summary of the invention

[0004] In view of the shortcomings of the prior art in terms of accuracy of flight attitude estimation, the present invention proposes a method for general aviation aircraft attitude estimation, specifically an aircraft attitude estimation method based on navigation coordinate system ESKF algorithm and maneuvering acceleration segmentation isolation.

[0005] In order to achieve the above object, the technical solution adopted by the present invention is as follows:

[0006] An aircraft attitude estimation method based on the navigation coordinate system ESKF algorithm and segmented isolation of maneuvering acceleration, the aircraft attitude estimation method is based on the error state Kalman filter algorithm in the navigation coordinate system (ESKF based on Navigation Coordinate System, NCS-ESKF) and the isolation of maneuvering acceleration according to a set segmented threshold. The attitude angle error is defined in the navigation coordinate system. When the reference vector cannot be introduced to correct the heading angle, the aircraft attitude estimation error can be reduced. At the same time, the maneuvering acceleration in the horizontal plane direction of the aircraft is segmented and isolated through the threshold, that is, different maneuvering acceleration thresholds are set in the aircraft's taxiing phase and flight phase respectively, ultimately improving the accuracy of aircraft attitude estimation. The specific method is as follows: Figure 1 As shown. First, obtain the three-axis acceleration information measured by the accelerometer of the general aviation aircraft and the output data of the gyroscope (including three-axis angular velocity information and noise), and implement segmented isolation of maneuvering acceleration according to the set threshold; second, calculate the attitude angle according to the three-axis acceleration information and three-axis angular velocity information obtained in the first step; third, on the basis of completing the second step, calculate the attitude angle error caused by the gyroscope noise according to the gyroscope noise obtained in the first step, and input it into the time prediction equation; fourth, obtain the measurement matrix and the measurement equation about the attitude angle error through the output results of segmented isolation of maneuvering acceleration and calculation of the attitude angle; finally, calculate the Kalman gain according to the output results of the measurement equation and perform measurement update, and then obtain the attitude angle after error compensation according to the updated attitude angle error output by the measurement update and the attitude angle output by the calculated attitude angle. The specific steps are as follows:

[0007] Step 1: First, obtain the measurement data of the accelerometer and gyroscope (three-axis acceleration information, three-axis angular velocity information), and perform segmented isolation of maneuvering acceleration. The details are as follows:

[0008] The 3-axis acceleration information measured by the accelerometer is a x 、a y 、a z , respectively represent the three-axis acceleration information in the three directions of x, y, and z of the carrier coordinate system; the three-axis angular velocity information p, q, and r measured by the gyroscope respectively represent the angular velocity in the three directions of x, y, and z of the carrier coordinate system. The carrier coordinate system is a coordinate system defined on the carrier, that is, the origin is the center of gravity of the carrier, the x-axis points to the right side of the carrier, the y-axis points to the front of the carrier, and the z-axis points to the top of the carrier.

[0009] Since the measurement has a certain error, part of the output data of the gyroscope is noise. Before solving the attitude of the aircraft, the accelerometer must be used to align the current position of the aircraft. Considering that there is only one vector with an absolute direction, gravity, the heading angle of the current aircraft position is considered to be zero degrees. Using the 3-axis acceleration information measured by the accelerometer, the roll angle θ and pitch angle of the aircraft attitude at this time are obtained through acceleration processing. It is calculated as follows:

[0010]

[0011] When the maneuvering acceleration of the carrier is too large in the horizontal plane of the carrier coordinate system, it will cause a large attitude estimation error. The present invention proposes to isolate the maneuvering acceleration in sections according to a set threshold value to improve the accuracy of the aircraft attitude solution. Since the maneuvering acceleration is generally in the horizontal plane of the carrier coordinate system, and the larger acceleration has a smaller impact than the Z axis, it is necessary to isolate the maneuvering acceleration in the horizontal plane direction of the aircraft through a threshold value. In view of the different accelerations of the aircraft in the ground taxiing stage and the flight stage, the present invention sets the maneuvering acceleration threshold in sections to improve the accuracy of the aircraft attitude estimation. The horizontal plane maneuvering acceleration thresholds for the taxiing stage and the flight stage are set to 0.1g and 0.01g, respectively.

[0012] Step 2: Calculate the aircraft attitude angle based on the three-axis acceleration information and the three-axis angular velocity information. The details are as follows:

[0013] The aircraft attitude is solved using quaternions, and its differential equation is as follows:

[0014]

[0015] in, It is the attitude quaternion (q0~q3, q0 represents the pitch angle) from the navigation coordinate system (the east direction is the x-axis, the north direction is the y-axis, and the z-axis points to the sky) to the airborne coordinate system under the Hamilton rule. q1 represents the rolling angle θ, q2 represents the heading angle, and q3 represents the cycle time dt); is the quaternion composed of angular velocity; ⊙ is the quaternion multiplication.

[0016] Using the first-order Picard algorithm to discretize and solve the differential equation shown in formula (2) we can obtain formula (3) to complete the calculation of the aircraft's attitude angle at this time.

[0017]

[0018] in, Represents the predicted attitude quaternion at time k; It represents the attitude quaternion at time k-1; p, q, r represent the angular rates in the x, y, z directions of the carrier coordinate system respectively; dt represents the sampling time.

[0019] Step 3: Calculate the attitude angle error caused by gyroscope noise and make a time prediction. The details are as follows:

[0020] In the ESKF algorithm, the attitude angle is a nominal state quantity and does not participate in the filtering process. It is usually considered to perform Kalman filtering on the attitude error angle to compensate for the real-time aircraft attitude. The attitude angle error is defined in the navigation coordinate system (the east direction is the x-axis, the north direction is the y-axis, and the z-axis points to the sky), and then the attitude error angle is substituted into the time prediction, and the equation is as follows:

[0021]

[0022] in, and δθ k-1 They represent the predicted attitude error angle at time k and the attitude error angle at time k-1 respectively; is the transposed matrix of the predicted direction cosine matrix at time k-1 for time k; ω is the angular rate random noise.

[0023] Since the attitude error angle is defined in the navigation coordinate system, it can be directly superimposed, but the random noise needs to be transferred to the navigation coordinate system through the direction cosine matrix for processing. In the time prediction stage, the covariance of the attitude error angle also needs to be considered, as shown in formula (5):

[0024]

[0025] in, represents the prediction covariance matrix for time k; P k-1 represents the covariance at time k-1; Q is the noise matrix of the gyroscope, which generally refers to the random walk of the gyroscope angle; DCM k / k-1 It represents the predicted direction cosine matrix at time k-1 for time k. The calculation formula is as follows:

[0026]

[0027] Step 4: Through the segmented isolation of maneuvering acceleration in step 1 and the output results of calculating the attitude angle in step 2, the measurement matrix and the measurement equation for the attitude error angle are obtained. The details are as follows:

[0028] Due to the large drift error of the accelerometer, it is difficult to solve the attitude differential equation shown in formula (2). However, acceleration can provide absolute angle information in static or low dynamic conditions, so the measurement process generally considers using acceleration data for compensation. The equation of the acceleration sensor is as follows:

[0029]

[0030] Among them, Z k represents the output acceleration vector, G represents the gravity vector in the navigation coordinate system, a k is the maneuvering acceleration vector of the carrier.

[0031] In the Error State Kalman Filter (ESKF) algorithm, it is necessary to derive the equation for the attitude error angle of the accelerometer defined in the navigation coordinate system, as shown in formula (8):

[0032]

[0033] in, Express The exponential mapping of Respectively expressed as The components in the x, y, and z directions in the coordinate system are calculated as follows:

[0034]

[0035] Further Linearization is performed, as shown in formula (10)

[0036]

[0037] Where I is the identity matrix, express The cross product matrix of

[0038] Substituting formula (10) into formula (9) and solving the partial derivatives, we can obtain the measurement matrix and the measurement equation for the attitude angle error:

[0039]

[0040] Among them, H k represents the measurement process matrix at time k, |G × Represents the cross product matrix of the gravity vector.

[0041] Step 5: Calculate the Kalman gain and perform measurement updates to finally obtain the aircraft attitude angle after error compensation. The details are as follows:

[0042] 5.1) First, the Kalman gain is calculated for the measurement equation output result shown in formula (11). The calculation method is as follows:

[0043]

[0044] Where R represents the noise of the measurement process, which generally refers to the random walk of the acceleration rate; Kk represents the calculated Kalman gain; Represents the prediction covariance matrix for time k.

[0045] 5.2) Then, substitute the calculated Kalman gain into formula (13) for measurement update to obtain the updated error attitude angle:

[0046]

[0047] 5.3) Finally, the updated error attitude angle is compensated to the aircraft attitude angle, as shown in formula (14), and finally the attitude angle after error compensation is obtained, including the pitch angle Roll angle θ, heading angle and cycle time dt.

[0048]

[0049] Compared with the prior art, the present invention has the following beneficial effects:

[0050] The present invention proposes an aircraft attitude estimation method based on the ESKF algorithm of the navigation coordinate system and the segmented isolation of maneuvering acceleration. The attitude error angle is defined in the navigation coordinate system, so that the yaw angle is not affected by the measurement update before and after the algorithm estimation, and the accuracy of the aircraft attitude estimation is effectively improved; at the same time, when estimating the aircraft attitude, the threshold is set in segments to isolate its maneuvering acceleration in segments, which can effectively reduce the large error caused by the maneuvering acceleration of the carrier being too large on the horizontal plane of the carrier system. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 It is a flow chart of the aircraft attitude estimation method based on the navigation coordinate system ESKF algorithm and maneuvering acceleration segment isolation proposed by the present invention.

[0052] Figure 2 It is a comparison chart of the roll angle, pitch angle and true value of the attitude angle estimation of the present invention and other algorithms; Figure 2 (a) is a comparison diagram of the roll angle calculated by the NCS-ESKF algorithm proposed in the present invention and the actual roll angle; Figure 2 (b) is a comparison diagram of the pitch angle calculated by the NCS-ESKF algorithm proposed in the present invention and the actual pitch angle; Figure 2 (c) is a comparison chart of the roll angle calculated by the ESKF algorithm and the actual roll angle; Figure 2 (d) is a comparison chart of the pitch angle calculated by the ESKF algorithm and the actual pitch angle;

[0053] Figure 2 (e) is a comparison chart of the roll angle calculated by the Mahony algorithm and the actual roll angle; Figure 2 (f) is a comparison chart between the pitch angle calculated by the Mahony algorithm and the actual pitch angle; Figure 2 (g) is a comparison chart of the roll angle calculated by the EKF algorithm and the actual roll angle; Figure 2 (h) is a comparison chart between the pitch angle calculated by the EKF algorithm and the actual pitch angle.

[0054] Figure 3 are the roll angle and pitch angle of attitude angle estimation at different horizontal maneuvering acceleration thresholds; Figure 3 (a) is a comparison diagram of the roll angle calculated by the NCS-ESKF algorithm proposed in the present invention and the actual roll angle when the maneuvering acceleration threshold is set to 0.01g; Figure 3 (b) is a comparison diagram of the pitch angle calculated by the NCS-ESKF algorithm proposed in the present invention and the actual pitch angle when the maneuvering acceleration threshold is set to 0.01g; Figure 3 (c) is a comparison diagram of the roll angle calculated by the NCS-ESKF algorithm proposed in the present invention and the actual roll angle when the maneuvering acceleration threshold is set to 0.1g; Figure 3 (d) is a comparison diagram of the pitch angle calculated by the NCS-ESKF algorithm proposed in the present invention and the actual pitch angle when the maneuvering acceleration threshold is set to 0.1g; Figure 3 (e) is a comparison diagram of the roll angle calculated by the NCS-ESKF algorithm proposed in the present invention and the actual roll angle when the horizontal plane maneuvering acceleration thresholds in the taxiing phase and the flight phase are set to 0.1g and 0.01g respectively; Figure 3 (f) is a comparison diagram of the pitch angle calculated by the NCS-ESKF algorithm proposed in the present invention and the actual pitch angle when the horizontal plane maneuvering acceleration thresholds in the taxiing phase and the flight phase are set to 0.1g and 0.01g respectively. DETAILED DESCRIPTION

[0055] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments.

[0056] In order to verify the aircraft attitude estimation method based on the navigation coordinate system ESKF algorithm and maneuvering acceleration segmented isolation proposed in the present invention, an aircraft attitude estimation system based on the ADIS16507 sensor is designed. The real-time three-axis angular rate and three-axis acceleration data are obtained by using the ADIS16507 sensor, and the data is transmitted to the GD32F407VGT6 controller in real time through the SPI communication protocol for data filtering and algorithm estimation attitude. In order to facilitate data reading and analysis, real-time communication between the system and the PC is also established. We installed the designed aircraft attitude estimation system on the DA42 aircraft of Shanxi Sanjin General Aviation Company for flight experiments, and used the measured data during the flight experiment to estimate the aircraft attitude. A total of two experiments were conducted. The first test time was 4200 seconds, and a set of data was recorded every 0.02 seconds, and a total of 210,000 sets of data were obtained; the second test time was 2570 seconds, and a set of data was recorded every 0.02 seconds, and a total of 128,495 sets of data were obtained. The first experimental data is used to compare the error state Kalman filter algorithm (NCS-ESKF) based on the navigation coordinate system proposed in this paper with other algorithms; the second experimental data is used to verify the effect of the segmented isolation of maneuvering acceleration proposed by the present invention on improving the accuracy of aircraft attitude estimation. The implementation process of the present invention is described in detail below.

[0057] The aircraft attitude estimation method adopted in this embodiment is mainly divided into two parts, namely, the ESKF algorithm based on the navigation coordinate system and the segmented isolation of the maneuvering acceleration. Defining the attitude angle error in the navigation coordinate system can reduce the aircraft attitude estimation error. By isolating the maneuvering acceleration segmentally and setting the horizontal maneuvering acceleration in the taxiing phase and the flight phase to different thresholds, the accuracy of the aircraft attitude estimation can be further improved.

[0058] First, obtain the measurement data (three-axis acceleration information, three-axis angular velocity information, noise) of the ADIS16507 sensor, and implement segmented isolation of maneuvering acceleration according to the set threshold; then, calculate the attitude angle according to the three-axis acceleration information and three-axis angular velocity information obtained in the first step; on the basis of completing the second step, calculate the attitude angle error caused by the noise according to the noise in the measurement data of the first step, and input it into the time prediction equation; then, obtain the measurement matrix and the measurement equation about the attitude angle error through the output results of segmented isolation of maneuvering acceleration and calculation of attitude angle; then, calculate the Kalman gain according to the output results of the measurement equation and perform measurement update, and then obtain the attitude angle after error compensation according to the updated attitude angle error output by the measurement update and the attitude angle output by the calculated attitude angle. The specific steps are as follows:

[0059] Step 1: Obtain the three-axis acceleration information (a) of the DA42 aircraft during flight through the designed aircraft attitude estimation system based on the ADIS16507 sensor. x , a y and a z ), three-axis angular velocity information and noise information, and calculate the roll angle θ and pitch angle of the aircraft attitude at this time according to formula (1) It is calculated as follows:

[0060]

[0061] When the maneuvering acceleration of the carrier is too large on the horizontal plane of the carrier system, it will cause a large error. The present invention proposes to isolate the acceleration in sections according to the acceleration threshold, and set the maneuvering acceleration thresholds of the taxiing stage and the flight stage to 0.1g and 0.01g respectively based on experience.

[0062] Step 2: Calculate the aircraft attitude angle based on the three-axis acceleration information and three-axis angular velocity information obtained in the first step, and use formula (2) to perform attitude solution.

[0063]

[0064] in is the attitude quaternion from the navigation coordinate system to the airborne coordinate system, is the quaternion composed of angular velocity, ⊙ is the quaternion multiplication. The first-order Picard algorithm is used to solve formula (2), and the attitude angle of the aircraft at this time is the initialization attitude angle (formula (3)).

[0065]

[0066] in, Represents the predicted attitude quaternion at time k, Represents the attitude quaternion at time k-1, where p, q, r represent the angular rates in the x, y, and z directions of the carrier coordinate system, respectively, and dt represents the sampling time.

[0067] Step 3: Calculate the aircraft attitude angle error caused by the noise obtained in the first step, define the error attitude angle in the navigation coordinate system (east is the x-axis, north is the y-axis, and the z-axis points to the sky), and then substitute the attitude angle error into the time prediction, as shown in formula (4).

[0068]

[0069] in, and δθ k-1 They represent the predicted attitude error angle at time k and the attitude error angle at time k-1 respectively. is the transposed matrix of the predicted direction cosine matrix at time k-1 for time k, and ω is the angular rate random noise. Since the attitude error angle is defined in the navigation coordinate system, it can be directly superimposed, but the random noise needs to be transferred to the navigation coordinate system through the direction cosine matrix for processing. In the time prediction stage, the covariance of the attitude error angle also needs to be considered, as shown in formula (5):

[0070]

[0071] in, represents the prediction covariance matrix for time k, P k-1 represents the covariance at time k-1. Q is the noise matrix of the gyroscope, which generally refers to the random walk of the measured angle. DCM k / k-1 Represents the predicted direction cosine matrix at time k-1 for time k, as shown in formula (6)

[0072]

[0073] Step 4: Obtain the measurement matrix and the measurement equation of the attitude error angle. Because acceleration can provide absolute angle information in static or low dynamic conditions, acceleration data is used to compensate for the attitude angle error during the measurement process. The equation for acceleration is:

[0074]

[0075] Among them, Z k represents the output acceleration vector, G represents the gravity vector in the navigation coordinate system, a k is the maneuvering acceleration vector of the carrier. Formula (7) is transformed into the attitude error angle equation defined in the navigation coordinate system, as shown in formula (8).

[0076]

[0077] Express The exponential mapping of They are The three components of can be calculated by formula (9).

[0078]

[0079] Solve for partial derivatives with respect to the system state You need to first Linearization is performed, that is:

[0080]

[0081] I is the identity matrix, express The cross product matrix of ; Substitute it into formula (9) and solve the partial derivative to obtain the measurement matrix and the measurement equation about the attitude error angle:

[0082]

[0083] Among them, H k represents the measurement process matrix at time k, G × Represents the cross product matrix of the gravity vector.

[0084] Step 5: Obtain the aircraft attitude angle after error compensation. The Kalman gain can be calculated using formula (12).

[0085]

[0086] Where R represents the noise of the measurement process, generally refers to the random walk of the acceleration rate, K k represents the calculated Kalman gain, represents the predicted covariance matrix at time k. Then the calculated Kalman gain is substituted into the measurement update of formula (13) to obtain the updated error attitude angle:

[0087]

[0088] Then the updated error attitude angle is compensated to the aircraft attitude angle, as shown in formula (14), and finally the attitude angle after error compensation is obtained, including the pitch angle Roll angle θ, heading angle and cycle time dt.

[0089]

[0090] After obtaining the attitude angle after error compensation, the attitude estimation results of the method proposed in the present invention (NCS-ESKF algorithm) are compared with those of the ESKF algorithm, the Mahony algorithm and the EKF algorithm based on the same flight data of the first flight experiment. Figure 2 As shown. Figure 2It can be seen that the attitude estimation method proposed in the present invention has a mean absolute error (MAE) of 0.809° and 0.934° for attitude estimation of the roll angle and pitch angle, respectively, which has the best attitude estimation performance compared with other methods. At the same time, based on the comparison of the roll angle and pitch angle of the actual flight attitude of the DA42 aircraft, the root mean square error (RMSE), determination coefficient R2, and mean square error (MSE) of the aircraft attitude estimation are also calculated. The various performance indicators of aircraft attitude estimation are shown in Table 1. It can be seen from Table 1 that the various indicators of the NCS-ESKF algorithm perform well, especially the roll angle, whose RMSE and MSE are 0.478 and 1.287 less than those of the ESKF algorithm; R 2 It is also improved by 0.06, and the degree of fitting to the true value is better. This shows that the NCS-ESKF algorithm proposed in the present invention can indeed improve the accuracy of aircraft attitude estimation.

[0091] Table 1 Comparison of attitude estimation errors of different algorithms

[0092]

[0093] Based on 128,495 sets of data obtained from the data of the second flight experiment, the effects of the segmented isolation of maneuvering acceleration (i.e., segmented setting of maneuvering acceleration threshold) and the fixed acceleration threshold on aircraft attitude estimation proposed in the present invention are compared and verified. In this embodiment, the horizontal plane maneuvering acceleration threshold of the aircraft carrier system is set to 0.1g and 0.01g during the ground rollout phase and the flight phase, respectively; in contrast, the horizontal maneuvering acceleration is a fixed threshold (0.1g or 0.01g) during the entire rollout and flight phases. Based on the NCS-ESKF algorithm, the aircraft attitude estimation effects under three different horizontal maneuvering acceleration conditions are as follows: Figure 3 The corresponding performance indicators are shown in Table 2. Figure 3 It can be clearly seen from Table 2 that, by comparison, the method of setting the segmented threshold in the two stages of taxiing and flying has a smaller attitude error estimated. The MAE of the roll angle and pitch angle are 0.954° and 0.867°, respectively, which are reduced by 0.277° and 0.118°, respectively, compared with the case where the threshold is only set to 0.01g. In addition, the R2 of this method is also closer to 1 than the other two methods, and has a better degree of true value fitting. Therefore, the segmented isolation of maneuvering acceleration proposed in the present invention can effectively improve the estimation accuracy of the aircraft attitude.

[0094] Table 2 Comparison of attitude estimation errors under different horizontal maneuvering acceleration thresholds

[0095]

[0096] Two flight experiments have proved that the aircraft attitude estimation method based on the navigation coordinate system ESKF algorithm and maneuvering acceleration segment isolation proposed in the present invention can effectively improve the accuracy of aircraft attitude estimation and has good application value.

[0097] Finally, it should be noted that the above examples are only used to express the embodiments of the present invention. It should be understood that the examples are only used to illustrate the present invention and are not used to limit the scope of the present invention. After reading the present invention, it is possible for technicians in this field to make several modifications and improvements without departing from the concept of the present invention. Various equivalent forms of modifications to the present invention all fall within the scope defined by the claims attached to this application and belong to the protection scope of the present invention.

Claims

1. An aircraft attitude estimation method based on navigation coordinate system ESKF algorithm and maneuvering acceleration segment isolation, It is characterized in that The aircraft attitude estimation method comprises: a first step, obtaining three-axis acceleration information measured by an accelerometer of a general aviation aircraft and output data of a gyroscope, wherein the output data includes three-axis angular velocity information and gyroscope noise, and realizing segmented isolation of maneuvering acceleration according to a set threshold value; a second step, calculating the attitude angle according to the three-axis acceleration information and the three-axis angular velocity information obtained in the first step; a third step, calculating the attitude angle error caused by the gyroscope noise according to the gyroscope noise obtained in the first step, and inputting it into a time prediction equation; a fourth step, obtaining a measurement matrix and a measurement equation about the attitude angle error through segmented isolation of maneuvering acceleration and the output result of calculating the attitude angle; a fifth step, calculating the Kalman gain according to the output result of the measurement equation and performing measurement update, and finally obtaining the attitude angle after error compensation according to the updated attitude angle error output by the measurement update and the attitude angle output by the calculated attitude angle.

2. The method for estimating an aircraft attitude based on the navigation coordinate system ESKF algorithm and maneuvering acceleration segmentation isolation according to claim 1, It is characterized in that The first step is as follows: The 3-axis acceleration information measured by the accelerometer is a x 、a y 、a z , respectively represent the three-axis acceleration information in the three directions of x, y, and z of the carrier coordinate system; the three-axis angular velocity information p, q, and r measured by the gyroscope respectively represent the angular velocity in the three directions of x, y, and z of the carrier coordinate system; the carrier coordinate system is a coordinate system defined on the carrier, that is, the origin is the center of gravity of the carrier, the x-axis points to the right side of the carrier, the y-axis points to the front of the carrier, and the z-axis points to the top of the carrier; Before calculating the attitude of the aircraft, the accelerometer is used to initially align the current position of the aircraft, and the heading angle of the current aircraft position is zero degrees; the 3-axis acceleration information measured by the accelerometer is used to obtain the roll angle θ and pitch angle of the aircraft attitude at this time through acceleration processing. Set the horizontal plane maneuvering acceleration thresholds for the taxiing phase and the flight phase, and isolate the horizontal plane maneuvering acceleration in sections according to the set thresholds.

3. The aircraft attitude estimation method based on navigation coordinate system ESKF algorithm and maneuvering acceleration segment isolation according to claim 2, It is characterized in that In the first step described: The roll angle θ and pitch angle of the aircraft attitude The calculation method is as follows: The horizontal plane maneuvering acceleration thresholds in the taxiing phase and the flight phase are set to 0.1g and 0.01g respectively.

4. The method for estimating an aircraft attitude based on the navigation coordinate system ESKF algorithm and maneuvering acceleration segmentation isolation according to claim 2, It is characterized in that The second step is as follows: The aircraft attitude is solved using quaternions, and its differential equation is as follows: in, is the attitude quaternion from the navigation coordinate system to the airborne coordinate system under the Hamiltonian rule, where q 0 Indicates the pitch angle q 1 Indicates the roll angle θ, q 2 represents the heading angle, q 3 represents the cycle time dt); is the quaternion composed of angular velocity; ⊙ is the quaternion multiplication; The first-order Picard algorithm is used to discretize and solve the differential equation shown in formula (2), and formula (3) is obtained to complete the calculation of the aircraft's attitude angle at this time; in, Represents the predicted attitude quaternion at time k; It represents the attitude quaternion at time k-1; p, q, r represent the angular rates in the x, y, z directions of the carrier coordinate system respectively; dt represents the sampling time.

5. The method for estimating an aircraft attitude based on the navigation coordinate system ESKF algorithm and maneuvering acceleration segmentation isolation according to claim 4, It is characterized in that The third step is as follows: First, define the attitude angle error in the navigation coordinate system, where the east direction is the x-axis, the north direction is the y-axis, and the z-axis points to the sky. Then substitute the attitude error angle into the time prediction, and the equation is as follows: in, and δθ k-1 They represent the predicted attitude error angle at time k and the attitude error angle at time k-1 respectively; is the transposed matrix of the predicted direction cosine matrix at time k-1 for time k; ω is the angular rate random noise; The attitude error angle is directly superimposed, and the random noise is transferred to the navigation coordinate system through the direction cosine matrix for processing; the covariance of the attitude error angle also needs to be considered in the time prediction stage, as shown in formula (5): in, represents the prediction covariance matrix for time k; P k-1 represents the covariance at time k-1; Q is the noise matrix of the gyroscope, which refers to the random walk of the gyroscope angle; DCM k / k-1 Represents the predicted direction cosine matrix of time k-1 for time k.

6. The method for estimating an aircraft attitude based on the navigation coordinate system ESKF algorithm and maneuvering acceleration segment isolation according to claim 5, It is characterized in that In the third step, the direction cosine matrix DCM is predicted k / k-1 The calculation formula is as follows:

7. The method for estimating an aircraft attitude based on the navigation coordinate system ESKF algorithm and maneuvering acceleration segment isolation according to claim 5, It is characterized in that The fourth step is as follows: The measurement process considers using acceleration data for compensation. The equation of the acceleration sensor is as follows: Among them, Z k represents the output acceleration vector, G represents the gravity vector in the navigation coordinate system, a k is the maneuvering acceleration vector of the carrier; In the error state Kalman filter algorithm, it is necessary to derive the equation of the attitude error angle of the accelerometer defined in the navigation coordinate system, as shown in formula (8): in, Express The exponential mapping of Respectively expressed as The components in the x, y, and z directions in the coordinate system are calculated as follows: right Linearization is performed, as shown in formula (10) Where I is the identity matrix, express The cross product matrix of Substitute formula (10) into formula (9) and solve the partial derivatives to obtain the measurement matrix and the measurement equation for the attitude angle error: Among them, H k represents the measurement process matrix at time k, |G × Represents the cross product matrix of the gravity vector.

8. The method for estimating an aircraft attitude based on the navigation coordinate system ESKF algorithm and maneuvering acceleration segment isolation according to claim 7, It is characterized in that The fifth step is as follows: 5.1) First, the Kalman gain is calculated for the output result of the measurement equation shown in formula (11) to obtain the Kalman gain; 5.2) Then, substitute the calculated Kalman gain into formula (13) for measurement update to obtain the updated error attitude angle: 5.3) Finally, the updated error attitude angle is compensated to the aircraft attitude angle, as shown in formula (14), and finally the attitude angle after error compensation is obtained, including the pitch angle Roll angle θ, heading angle and cycle time dt; 9. The method for estimating an aircraft attitude based on the navigation coordinate system ESKF algorithm and segmented isolation of maneuvering acceleration according to claim 8, It is characterized in that In the fifth step 5.1), the formula for calculating the Kalman gain is as follows: Where R represents the noise of the measurement process, which refers to the random walk of the acceleration rate; K k represents the calculated Kalman gain; Represents the prediction covariance matrix for time k.

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