An aircraft altitude measurement method using a three-in-one body combined Kalman filter

Abstract

The application provides a method for mixed height measurement by using an inertial navigation device, a barometer and GPS, three heights are jointly input into a three-in-one Kalman filter to obtain Kalman filter heights between each other, and the output height of the Kalman filter is obtained by averaging; then, the output height of the Kalman filter is used to obtain three height errors by subtracting inertial height, barometric height and GPS height, and then the prior variance data of the Kalman filter is adjusted according to the errors; the observation variance data of the Kalman filter is adjusted according to the integral values of the three height errors; finally, the inertial comprehensive error is formed by comparing the three-in-one height comprehensive signal with the inertial compensation height, then the non-linear transformation is carried out, and the feedback compensation is carried out to the inertial acceleration and inertial velocity signals to carry out the correction compensation integration, and the inertial compensation height signal is formed as the final output of the three-in-one combined Kalman filter height measurement method.

Description

Technical Field

[0001] This invention relates to the field of high-precision altitude measurement for vehicles, moving bodies, and unmanned aerial vehicles. More specifically, it relates to a design method for an aircraft altitude measurement method employing a three-in-one combined Kalman filter. Background Technology

[0002] Accurate altitude measurement plays an undeniably crucial role in ensuring the safety and stability of aircraft airborne control. However, various altitude measurement devices often operate only within a specific range, or their data accuracy is good in one range but poor in another. Therefore, how to deeply integrate data obtained from multiple measuring instruments to leverage their strengths and compensate for their weaknesses is a key issue studied and addressed in hybrid altitude measurement. Based on this background, this invention studies a three-in-one Kalman filter approach. A comprehensive Kalman filter is designed to simultaneously apply Kalman filtering to inertial altitude, barometric altitude, and GPS altitude. The resulting data are then fused to form an altitude error, which is further used to adjust the variance of the Kalman filter, thereby ensuring high accuracy and reliability of the overall altitude measurement data. Furthermore, while inertial altitude exhibits good independence and autonomy, it can diverge over time. Therefore, a two-stage correction and compensation process is applied to the Kalman-filtered combined altitude and the inertial altitude, resulting in excellent altitude measurement accuracy.

[0003] It should be noted that the signals described in the background section above are only used to enhance the understanding of the background of the present invention, and therefore may include signals that do not constitute prior art known to those skilled in the art. Summary of the Invention

[0004] The purpose of this invention is to provide an aircraft altitude measurement method using a three-in-one combined Kalman filter, thereby overcoming, to at least some extent, the problem of difficulty in fusing multiple altitude measurement data due to limitations and defects in related technologies.

[0005] According to one aspect of the present invention, an aircraft altitude measurement method employing a three-in-one combined Kalman filter is provided, comprising the following steps:

[0006] Step S10: Install a SiA200 MEMS accelerometer on the carrier and integrate the data to obtain the inertial velocity and inertial altitude; install a GPS receiver on the carrier and measure the carrier's GPS altitude; install a barometric altimeter on the carrier and measure the carrier's barometric altitude as follows:

[0007] v = ∫adt;

[0008] y j =∫vdt;

[0009] Where a(n) corresponds to the carrier acceleration signal measured by the SiA200 MEMS accelerometer at time t = n*ΔT, abbreviated as a, where ΔT is the output sampling period of the data, dt represents the integral of the time signal, v is the inertial velocity signal, and y j The inertial altitude signal is used; a barometric altimeter is installed on the carrier to measure the carrier's air pressure altitude, which is recorded as y. q This is called the barometric altitude signal. Then, according to the accelerometer's data interval period ΔT, the altitude data is discretized to obtain the data y. q (n), where n = 1, 2, 3…, corresponds to the barometric altitude signal data at time t = n*ΔT; a GPS receiver is installed on the carrier to measure the carrier's GPS altitude, denoted as y. g This is called the GPS altitude signal. Then, according to the data interval ΔT of the accelerometer, the altitude data is discretized to obtain the data y. g (n), where n = 1, 2, 3..., corresponds to the GPS altitude signal data at time t = n*ΔT.

[0010] Step S20: Set the initial value of the error covariance signal in the three-in-one combined Kalman filter, set the initial value of the predicted value signal of the filter's error covariance to 0, and solve for the error covariance signal; then set the initial value of the filter's prior variance signal to 0, and solve for the predicted value signal of the error covariance; then set the initial value of the filter's observation variance signal to 0, and solve for the gain signal of the Kalman filter; then, based on the gain signal of the Kalman filter, input the inertial altitude signal and the barometric altitude signal, and solve for the filter's inertial-barometric mixed altitude signal; then, based on the gain signal of the Kalman filter, input the inertial altitude signal and the GPS altitude signal, and solve for the filter's inertial-GPS mixed altitude signal; finally, based on the gain signal of the Kalman filter, input the barometric altitude signal and the GPS altitude signal, and solve for the filter's barometric-GPS mixed altitude signal.

[0011] Step S30: Averaging the inertial pressure mixed altitude signal, the inertial GPS mixed altitude signal, and the pressure-GPS mixed altitude signal to obtain the Kalman-filtered three-in-one altitude composite signal; then, calculating the inertial relative filtering error signal, pressure relative filtering error signal, and GPS relative filtering error signal relative to the Kalman-filtered three-in-one altitude composite signal based on the inertial altitude signal, pressure altitude signal, and GPS altitude signal, respectively; then, performing nonlinear integration on the aforementioned relative filtering error signals to obtain the inertial error nonlinear integral signal, the pressure error nonlinear integral signal, and the GPS error relative integral signal, respectively; then, performing comprehensive feedback on the inertial relative filtering error signal, the pressure relative filtering error signal, and the GPS relative filtering error signal to obtain the filter prior variance signal; finally, performing comprehensive feedback on the inertial error nonlinear integral signal, the pressure error nonlinear integral signal, and the GPS error relative integral signal to obtain the filter observation variance signal; thus completing the variance feedback closed-loop adjustment of the filter output.

[0012] Step S40: Set the initial value of the inertial compensation height to the initial value of the inertial height. Compare the Kalman filter three-in-one height synthesis signal with the inertial compensation height signal to obtain the inertial synthesis error signal. Then, perform two nonlinear transformations to obtain the inertial synthesis error acceleration nonlinear signal and the inertial synthesis error velocity nonlinear signal, respectively. Superimpose the inertial synthesis error acceleration nonlinear signal with the carrier acceleration signal to obtain the acceleration synthesis signal, and then integrate it to obtain the inertial compensation velocity signal. Superimpose the inertial synthesis error velocity nonlinear signal to obtain the velocity synthesis signal, and then integrate it to obtain the inertial compensation height signal. The inertial compensation height signal is then used as the height output signal of the three-in-one combined Kalman filter altimetry.

[0013] In one exemplary embodiment of the present invention, the initial value of the error covariance signal in the three-in-one combined Kalman filter is set, and the initial value of the predicted value signal of the filter's error covariance is set to 0. The error covariance signal is then solved. Next, the initial value of the filter's prior variance signal is set to 0, and the predicted value signal of the error covariance is solved. Then, the initial value of the filter's observation variance signal is set to 0, and the gain signal of the Kalman filter is solved. Then, based on the gain signal of the Kalman filter, an inertial altitude signal and a barometric altitude signal are input, and the inertial-barometric mixed altitude signal of the filter is solved. Then, based on the gain signal of the Kalman filter, an inertial altitude signal and a GPS altitude signal are input, and the inertial-GPS mixed altitude signal of the filter is solved. Finally, based on the gain signal of the Kalman filter, a barometric altitude signal and a GPS altitude signal are input, and the barometric-GPS mixed altitude signal of the filter is solved, including:

[0014]

[0015] pw1 (n)=p w (n-1)+Q w1 ;

[0016] b w (n)=p w1 (n)·[p w1 (n)+R w1 ] 1 ;

[0017] y jq (n)=y j (n)+b w (n)·[y j (n)-y q (n)];

[0018] y jg (n)=y j (n)+b w (n)·[y j (n)-y g (n)];

[0019] y qg (n)=y q (n)+b w (n)·[y q (n)-y g (n)];

[0020] Where p w (n) is the error covariance signal of the Kalman filter, p w (1) is p w (n) The value at the first sampling point is chosen to be 0; p w1 (n) is the predicted value signal of the error covariance, whose initial value is chosen to be 0; Q w1 Let b be the prior variance signal of the filter. w (n) is the gain signal of the Kalman filter, initially set to 0, Q w1 The a priori variance signal of the filter is initially set to 0, and R... w1 The initial value of the observed variance signal for the filter is set to 0; y jq (n) represents the inertial pressure mixing altitude signal; y jg (n) represents the inertial GPS hybrid altitude signal; y qg (n) represents the barometric GPS mixed altitude signal.

[0021] In one exemplary embodiment of the present invention, the Kalman-filtered three-in-one altitude composite signal is obtained by averaging the inertial barometric altitude signal, the inertial GPS altitude signal, and the barometric GPS altitude signal. Then, the inertial relative filtering error signal, the barometric relative filtering error signal, and the GPS relative filtering error signal relative to the Kalman-filtered three-in-one altitude composite signal are obtained from the inertial altitude signal, the barometric altitude signal, and the GPS altitude signal, respectively. Then, nonlinear integration is performed on the aforementioned relative filtering error signals to obtain the inertial error nonlinear integral signal, the barometric error nonlinear integral signal, and the GPS error relative integral signal, respectively. Finally, the inertial relative filtering error signal, the barometric relative filtering error signal, and the GPS relative filtering error signal are used for comprehensive feedback to obtain the filter's prior variance signal. Finally, the inertial error nonlinear integral signal, the barometric error nonlinear integral signal, and the GPS error relative integral signal are used for comprehensive feedback to obtain the filter's observation variance signal. Thus, the output of the filter is adjusted by the variance feedback closed-loop control as follows:

[0022]

[0023] e1(n)=2y z (n)-y jq (n)-y j (n);

[0024] e2(n)=2y z (n)-y jg (n)-y g (n);

[0025] e3(n)=2y z (n)-y qg (n)-y q (n);

[0026]

[0027]

[0028]

[0029]

[0030]

[0031] Where y z The Kalman filter is used to synthesize the three-in-one high-intensity signal; e1 is the inertial relative filter error signal, e3 is the barometric pressure relative filter error signal, e2 is the GPS relative filter error signal; s1 is the inertial error nonlinear integral signal, s3 is the barometric pressure error nonlinear integral signal, and s2 is the GPS error relative integral signal; ka1 ε1, k a2 k a3 Q w1 k b1 k b2 k b3 Q is a constant parameter. w1 Let R be the prior variance signal of the filter. w1 This is the observed variance signal of the filter.

[0032] In one exemplary embodiment of the present invention, the initial value of the inertial compensation height is set to the initial value of the inertial height. The Kalman filter three-in-one height synthesis signal is compared with the inertial compensation height signal to obtain the inertial synthesis error signal. Then, two nonlinear transformations are performed to obtain the inertial synthesis error acceleration nonlinear signal and the inertial synthesis error velocity nonlinear signal, respectively. The inertial synthesis error acceleration nonlinear signal is superimposed on the carrier acceleration signal to obtain the acceleration synthesis signal, and then integrated to obtain the inertial compensation velocity signal. The inertial synthesis error velocity nonlinear signal is then superimposed to obtain the velocity synthesis signal, and then integrated to obtain the inertial compensation height signal. The inertial compensation height signal is used as the height output signal of the three-in-one combined Kalman filter altimetry.

[0033] e a (n)=y z (n)-y o (n);

[0034]

[0035]

[0036] a d =a+f a (n);

[0037]

[0038] v d =v b +f b (n);

[0039]

[0040] Where ea is the inertial synthesis error signal; l1, l2, l3, l4, and ε2 are constant nonlinear transformation parameters; f a For the inertial synthesis error acceleration nonlinear signal, f b For the inertial synthesis error velocity nonlinear signal, a d For the acceleration composite signal, v d For the velocity composite signal, v bFor inertial compensation velocity signal, h b This is for inertial compensation altitude signal.

[0041] Ultimately, the inertial compensation height signal is used as the height output signal of the three-in-one combined Kalman filter altimetry, which has good accuracy and stability characteristics, while avoiding the disadvantage of the inertial measurement height error accumulating over time.

[0042] Beneficial effects

[0043] This invention provides an aircraft altitude measurement method employing a three-in-one combined Kalman filter, with two main innovations: First, it proposes a three-in-one Kalman filter that can simultaneously process the pairwise Kalman filtering relationships between inertial altitude, barometric altitude, and GPS altitude, thus avoiding the cumbersome design of using three separate Kalman filters for processing. Second, by deeply fusing the three data points to form relative error data, it provides feedback to adjust the prior variance and observation variance data of the three-in-one combined Kalman filter, achieving a dynamic combination of the three Kalman filters. This, combined with inertial altitude, forms a correction feedback compensation, resulting in excellent accuracy and data fusion performance.

[0044] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit the invention. Attached Figure Description

[0045] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention. It is obvious that the drawings described below are merely some embodiments of the invention, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort.

[0046] Figure 1 This is a flowchart of an aircraft altitude measurement method using a three-in-one combined Kalman filter provided by the present invention;

[0047] Figure 2 This is a physical image of the ACS100 high-precision MEMS accelerometer provided in the embodiments of the present invention;

[0048] Figure 3 This is the acceleration signal curve (unit: meters per second squared) of the carrier of the method provided in the embodiments of the present invention;

[0049] Figure 4 This is the inertial velocity signal curve (unit: meters per second) of the carrier of the method provided in the embodiments of the present invention;

[0050] Figure 5 This is the inertial height signal curve (unit: meters) of the carrier of the method provided in the embodiments of the present invention;

[0051] Figure 6 This is the GPS altitude signal curve (unit: meters) of the carrier of the method provided in the embodiments of the present invention;

[0052] Figure 7 It is the piezoelectric altitude signal (unit: meters) of the carrier of the method provided in the embodiments of the present invention;

[0053] Figure 8 This is the inertial pressure mixing altitude signal curve (unit: meters) of the method provided in the embodiments of the present invention;

[0054] Figure 9 This is the Kalman filter three-in-one highly synthesized signal curve (unit: meters) provided by the embodiment of the present invention;

[0055] Figure 10 It is the inertial compensation altitude signal (unit: meters) provided by the method in the embodiments of the present invention. Detailed Implementation

[0056] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided to make the invention more comprehensive and complete, and to fully convey the concept of the exemplary embodiments to those skilled in the art. The described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a full understanding of embodiments of the invention. However, those skilled in the art will recognize that the technical solutions of the invention may be practiced with one or more of these specific details omitted, or other methods, components, apparatus, steps, etc., may be employed. In other instances, well-known technical solutions are not shown or described in detail to avoid obscuring various aspects of the invention.

[0057] This invention provides a method for hybrid altimetry using an inertial navigation device, a barometer, and GPS. The method involves integrating inertial acceleration twice to obtain the inertial altitude. This inertial altitude is then combined with the barometric altitude measured by the barometer and the GPS altitude measured by the GPS to form a three-in-one Kalman filter. First, the Kalman filter heights between any two altitudes are obtained, and then the three are averaged to obtain the output height of the Kalman filter. Next, the output height of the Kalman filter is used to calculate the differences between the inertial altitude, barometric altitude, GPS altitude, and the combined height between any two altitudes to obtain three altitude errors. The prior variance data of the three-in-one Kalman filter is then adjusted based on these errors. The observation variance data of the three-in-one Kalman filter is adjusted based on the integral values ​​of the three altitude errors. Finally, the combined altitude signal from the Kalman filter is compared with the inertial compensation altitude to form the inertial composite error. This error is then nonlinearly transformed and fed back to the inertial acceleration and inertial velocity signals for correction and compensation integration, ultimately forming the inertial compensation altitude signal, which serves as the final output of the three-in-one combined Kalman filter altimetry method.

[0058] The following will further explain and illustrate, with reference to the accompanying drawings, a method for measuring aircraft altitude using a three-in-one combined Kalman filter according to the present invention. (Reference) Figure 1 As shown, the aircraft altitude measurement method using a three-in-one combined Kalman filter includes the following steps:

[0059] Step S10: Install a SiA200 MEMS accelerometer on the carrier and integrate it to obtain the inertial velocity and inertial height; install a GPS receiver on the carrier and measure the GPS height of the carrier; install a barometric altimeter on the carrier and measure the barometric altimeter height of the carrier.

[0060] Specifically, firstly, a SiA200 MEMS accelerometer is mounted on a carrier, with a physical size of a small 9*9nm package. 2 It is very small, its physical size is as Figure 2 As shown. Power consumption is less than 10mW, and the operating temperature range is -40°C to +125°C. The acceleration range is selectable from 2g to 30g, and the measurement resolution can reach 0.00005g. It is mounted on a carrier, and the acceleration signal in the vertical direction of the carrier is measured, denoted as a(n). Where n = 1, 2, 3…, corresponding to the acceleration signal at time t = n*ΔT, where ΔT is the data output sampling period.

[0061] Secondly, the acceleration measurement data of the carrier is integrated to obtain the inertial velocity signal, denoted as v(n). Then, the velocity signal is further integrated to obtain the inertial height signal, denoted as y. j The integration process for (n) is shown below:

[0062] v = ∫adt;

[0063] y j =∫vdt;

[0064] Where dt represents the integral over the time signal.

[0065] Next, install a GPS receiver on the carrier and measure the carrier's GPS altitude, denoted as y. g Then, according to the data interval ΔT of the accelerometer, the height data is discretized to obtain the data y. g (n), where n = 1, 2, 3..., corresponds to the radio altitude measurement data at time t = n * ΔT, where ΔT is the output sampling period of the data.

[0066] Finally, a barometric altimeter was installed on the carrier to measure the carrier's air pressure altitude, which was recorded as y. q This is called the barometric altitude signal. Then, according to the accelerometer's data interval period ΔT, the altitude data is discretized to obtain the data y. q (n), where n = 1, 2, 3..., corresponds to the barometric altitude signal data at time t = n*ΔT.

[0067] Step S20: Set the initial value of the error covariance signal in the three-in-one combined Kalman filter, set the initial value of the predicted value signal of the filter's error covariance to 0, and solve for the error covariance signal; then set the initial value of the filter's prior variance signal to 0, and solve for the predicted value signal of the error covariance; then set the initial value of the filter's observation variance signal to 0, and solve for the gain signal of the Kalman filter; then, based on the gain signal of the Kalman filter, input the inertial altitude signal and the barometric altitude signal, and solve for the filter's inertial-barometric mixed altitude signal; then, based on the gain signal of the Kalman filter, input the inertial altitude signal and the GPS altitude signal, and solve for the filter's inertial-GPS mixed altitude signal; finally, based on the gain signal of the Kalman filter, input the barometric altitude signal and the GPS altitude signal, and solve for the filter's barometric-GPS mixed altitude signal.

[0068] Specifically, it can be broken down into the following six steps. Step 1: Set the initial value of the error covariance signal in the three-in-one combined Kalman filter, and set the initial value of the predicted value of the filter's error covariance signal to 0. Solve for the error covariance signal as follows:

[0069]

[0070] Where p w (n) is the error covariance signal of the Kalman filter, p w (1) is p w (n) The value at the first sampling point is chosen to be 0; p w1(n) represents the predicted value of the error covariance, initially set to 0. Further calculations are detailed below; b w (n) is the gain signal of the Kalman filter, which is initially set to 0. Its detailed solution is described below.

[0071] The second step involves setting the initial a priori variance signal of the filter to 0, and then solving for the predicted value of the error covariance signal as follows:

[0072] p w1 (n)=p w (n-1)+Q w1 ;

[0073] Q w1 The a priori variance signal of the filter is initially set to 0. Further calculations will follow in the next step. w1 (n) represents the predicted value signal of the error covariance.

[0074] The third step is to set the initial value of the filter observation variance signal to 0, and then solve for the gain signal of the Kalman filter as follows:

[0075] b w (n)=p w1 (n)·[p w1 (n)+R w1 ] -1 ;

[0076] Where R w1 The initial value of the observed variance signal for the filter is set to 0; subsequent calculations will be discussed in the next step, b. w (n) represents the gain signal of the Kalman filter.

[0077] Fourth step: Based on the gain signal of the Kalman filter, input the inertial altitude signal and the barometric altitude signal, and solve for the mixed inertial and barometric altitude signal of the filter as follows:

[0078] y jq (n)=y j (n)+b w (n)·[y j (n)-y q (n)];

[0079] Where y jq (n) represents the inertial pressure mixing altitude signal.

[0080] Fifth, based on the gain signal of the Kalman filter, input the inertial altitude signal and the GPS altitude signal, and solve for the filter's inertial-GPS hybrid altitude signal as follows:

[0081] y jg (n)=y j (n)+bw (n)·[y j (n)-y g (n)];

[0082] Where y jg (n) represents the inertial GPS hybrid altitude signal.

[0083] Step 6: Based on the gain signal of the Kalman filter, input the barometric altitude signal and the GPS altitude signal, and solve for the combined barometric and GPS altitude signal of the filter as follows:

[0084] y qg (n)=y q (n)+b w (n)·[y q (n)-y g (n)];

[0085] Where y qg (n) represents the barometric GPS mixed altitude signal.

[0086] Step S30: Averaging the inertial pressure mixed altitude signal, the inertial GPS mixed altitude signal, and the pressure-GPS mixed altitude signal to obtain the Kalman-filtered three-in-one altitude composite signal; then, calculating the inertial relative filtering error signal, pressure relative filtering error signal, and GPS relative filtering error signal relative to the Kalman-filtered three-in-one altitude composite signal based on the inertial altitude signal, pressure altitude signal, and GPS altitude signal, respectively; then, performing nonlinear integration on the aforementioned relative filtering error signals to obtain the inertial error nonlinear integral signal, the pressure error nonlinear integral signal, and the GPS error relative integral signal, respectively; then, performing comprehensive feedback on the inertial relative filtering error signal, the pressure relative filtering error signal, and the GPS relative filtering error signal to obtain the filter prior variance signal; finally, performing comprehensive feedback on the inertial error nonlinear integral signal, the pressure error nonlinear integral signal, and the GPS error relative integral signal to obtain the filter observation variance signal; thus completing the variance feedback closed-loop adjustment of the filter output.

[0087] Specifically, it can be broken down into the following four steps. The first step is to average the inertial barometric altitude signal, the inertial GPS altitude signal, and the barometric GPS altitude signal to obtain the Kalman-filtered three-in-one altitude composite signal as follows;

[0088]

[0089] Where y z This is a highly integrated signal combining Kalman filtering, three-in-one filtering, and so on.

[0090] The second step involves calculating the relative inertial, barometric, and GPS altitude signals relative to the Kalman-filtered three-in-one altitude composite signal, as follows:

[0091] e1(n)=2y z (n)-y jq (n)-y j (n);

[0092] e2(n)=2y z (n)-y jg (n)-y g (n);

[0093] e3(n)=2y z (n)-y qg (n)-y q (n);

[0094] Where e1 is the inertial relative filtering error signal, e3 is the air pressure relative filtering error signal, and e2 is the GPS relative filtering error signal.

[0095] The third step involves performing nonlinear integration using the aforementioned relative filtered error signal to obtain the following nonlinear integral signals: inertial error, barometric pressure error, and GPS error:

[0096]

[0097]

[0098]

[0099] Where s1 is the inertial error nonlinear integral signal, s3 is the barometric pressure error nonlinear integral signal, s2 is the GPS error relative integral signal, and ε1 is a constant parameter.

[0100] The fourth step involves comprehensively feeding back the inertial relative filter error signal, the barometric pressure relative filter error signal, and the GPS relative filter error signal to obtain the filter's prior variance signal; and comprehensively feeding back the inertial error nonlinear integral signal, the barometric pressure error nonlinear integral signal, and the GPS error relative integral signal to obtain the filter's observation variance signal, as follows:

[0101]

[0102]

[0103] Where k a1 k a2 Q w1 kb1 k b2 k b3 Q is a constant parameter. w1 Let R be the prior variance signal of the filter. w1 This is the observed variance signal of the filter.

[0104] Step S40: Set the initial value of the inertial compensation height to the initial value of the inertial height. Compare the Kalman filter three-in-one height synthesis signal with the inertial compensation height signal to obtain the inertial synthesis error signal. Then, perform two nonlinear transformations to obtain the inertial synthesis error acceleration nonlinear signal and the inertial synthesis error velocity nonlinear signal, respectively. Superimpose the inertial synthesis error acceleration nonlinear signal with the carrier acceleration signal to obtain the acceleration synthesis signal, and then integrate it to obtain the inertial compensation velocity signal. Superimpose the inertial synthesis error velocity nonlinear signal to obtain the velocity synthesis signal, and then integrate it to obtain the inertial compensation height signal. The inertial compensation height signal is then used as the height output signal of the three-in-one combined Kalman filter altimetry.

[0105] Specifically, this can be broken down into the following four steps. Step 1: Set the initial value of the inertial compensation height to the initial value of the inertial height. Compare the Kalman-filtered three-in-one height synthesis signal with the inertial compensation height signal to obtain the inertial synthesis error signal as follows:

[0106] e a (n)=y z (n)-y o (n);

[0107] Where ea is the inertial synthesis error signal.

[0108] The second step involves performing two nonlinear transformations on the inertial synthesis error signal to obtain the inertial synthesis error acceleration nonlinear signal and the inertial synthesis error velocity nonlinear signal, as follows:

[0109]

[0110]

[0111] Where l1, l2, l3, l4, and ε are constant nonlinear transformation parameters; f a For the inertial synthesis error acceleration nonlinear signal, f b The velocity nonlinear signal represents the inertial synthesis error.

[0112] The third step involves superimposing the nonlinear acceleration signal of the inertial synthesis error onto the carrier acceleration signal to obtain a comprehensive acceleration signal, which is then integrated to obtain the inertial compensation velocity signal as follows:

[0113] a d =a+fa (n);

[0114]

[0115] Where ad is the composite acceleration signal, v b This is the inertial compensation velocity signal.

[0116] The fourth step involves superimposing the inertial compensation velocity signal with the inertial synthesis error velocity nonlinear signal to obtain the velocity synthesis signal. This synthesis signal is then integrated to obtain the inertial compensation altitude signal, as follows:

[0117] v d =v b +f b (n);

[0118]

[0119] Where vd is the combined velocity signal, h b This is for inertial compensation altitude signal.

[0120] Case Implementation and Computer Simulation Results Analysis

[0121] In step S10, ΔT = 0.001 is set, and a SiA200 MEMS accelerometer is installed on the carrier to measure the acceleration signal of the carrier. Figure 3 As shown, the inertial velocity signal of the carrier is obtained by integration. Figure 4 As shown, the inertial altitude signal of the carrier is obtained by further integration. Figure 5 As shown, the GPS altitude of the carrier was obtained using GPS measurements. Figure 6 As shown, the pneumatic height of the carrier is measured using a barometric altimeter. Figure 7 As shown. In step S20, the inertial pressure mixing altitude signal is obtained as follows: Figure 8 As shown. In step S30, k is selected. a1 =0.3, ε1=0.5, k a2 =0.3, k a3 =0.4, k b1 =0.5, k b2 =0.3, k b3 =0.2, resulting in a highly synthesized Kalman filter signal, such as... Figure 9 As shown. In step S40, ε2 = 0.3, l1 = -0.2, l2 = -0.1, l3 = -0.05, and l4 = -0.03 are selected to obtain the inertial compensation altitude signal as shown. Figure 10 As shown.

[0122] Depend on Figure 3 It can be seen that random errors exist in acceleration measurements. These random errors accumulate over time, from... Figure 4 It can be seen that this will cause errors in velocity, and after integration, it will cause errors in position, such as... Figure 5 As shown. However, in the short term, the accuracy of inertial altitude is relatively high, while Figure 6 GPS altitude and Figure 7 The accuracy of barometric altitude measurement is worse than that of inertial altitude measurement, but the errors between GPS altitude and barometric altitude are relatively stable and do not increase over time. Inertial altitude, however, becomes less accurate than both GPS and barometric altitude over time. Figure 8 It can be seen that the accuracy of the mixing altitude is improved compared to that of the barometric altitude; Figure 9 It can be seen that the accuracy of the Kalman filter-integrated altitude signal is significantly improved; the accuracy of the inertial compensation altitude signal obtained using this data will further increase slightly, such as... Figure 10 As shown, this demonstrates that the three-in-one combined height measurement scheme proposed in this invention is effective and has high engineering practical value.

[0123] Other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of such inventions. This application is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not specified herein. The specification and embodiments are to be considered exemplary only, and the true scope and spirit of the invention are indicated by the claims.

Claims

1. A method for measuring aircraft altitude using a three-in-one combined Kalman filter, characterized in that, Includes the following steps: Step S10: Install a SiA200 MEMS accelerometer on the carrier and integrate the data to obtain the inertial velocity and inertial altitude; install a GPS receiver on the carrier and measure the carrier's GPS altitude; install a barometric altimeter on the carrier and measure the carrier's barometric altitude as follows: v = ∫adt; and j =∫vdt; Where a(n) corresponds to the carrier acceleration signal measured by the SiA200 MEMS accelerometer at time t = n*ΔT, abbreviated as a, where ΔT is the output sampling period of the data, dt represents the integral of the time signal, v is the inertial velocity signal, and y j The inertial altitude signal is used; a barometric altimeter is installed on the carrier to measure the carrier's air pressure altitude, which is recorded as y. q This is called the barometric altitude signal. Then, according to the accelerometer's data interval period ΔT, the altitude data is discretized to obtain the data y. q (n), where n = 1, 2, 3…, corresponds to the barometric altitude signal data at time t = n*ΔT; a GPS receiver is installed on the carrier to measure the carrier's GPS altitude, denoted as y. g This is called the GPS altitude signal. Then, according to the data interval ΔT of the accelerometer, the altitude data is discretized to obtain the data y. g (n), where n = 1, 2, 3..., corresponds to the GPS altitude signal data at time t = n*ΔT; Step S20: Set the initial value of the error covariance signal in the three-in-one combined Kalman filter, set the initial value of the predicted value signal of the filter's error covariance to 0, and solve for the error covariance signal; then set the initial value of the filter's prior variance signal to 0 and solve for the predicted value signal of the error covariance; then set the initial value of the filter's observation variance signal to 0 and solve for the gain signal of the Kalman filter; then, based on the gain signal of the Kalman filter, input the inertial altitude signal and the barometric altitude signal, and solve for the filter's inertial-barometric mixed altitude signal; then, based on the gain signal of the Kalman filter, input the inertial altitude signal and the GPS altitude signal, and solve for the filter's inertial-GPS mixed altitude signal; finally, based on the gain signal of the Kalman filter, input the barometric altitude signal and the GPS altitude signal, and solve for the filter's barometric-GPS mixed altitude signal as follows: p w1 (n)=p w (n-1)+Q w1 ; b w (n)=p w1 (n)·[p w1 (n)+R w1 ] -1 ; and jq (n)=y j (n)+b w (n)·[and j (n)-y q (n)]; and jg (n)=y j (n)+b w (n)·[and j (n)-y g (n)]; and qg (n)=y q (n)+b w (n)·[and q (n)-y g (n)]; Where p w (n) is the error covariance signal of the Kalman filter, p w (1) is p w (n) The value at the first sampling point is chosen to be 0; p w1 (n) represents the predicted value of the error covariance, with its initial value set to 0; b w (n) is the gain signal of the Kalman filter, initially set to 0, Q w1 The a priori variance signal of the filter is initially set to 0, and R... w1 The initial value of the observed variance signal for the filter is set to 0; y jq (n) represents the inertial pressure mixing altitude signal; y jg (n) represents the inertial GPS hybrid altitude signal; y qg (n) represents the barometric GPS mixed altitude signal; Step S30: Averaging the inertial pressure mixed altitude signal, the inertial GPS mixed altitude signal, and the pressure-GPS mixed altitude signal to obtain the Kalman-filtered three-in-one altitude composite signal; then, calculating the inertial relative filtering error signal, pressure relative filtering error signal, and GPS relative filtering error signal relative to the Kalman-filtered three-in-one altitude composite signal from the inertial altitude signal, pressure altitude signal, and GPS altitude signal respectively; then, performing nonlinear integration on the aforementioned relative filtering error signals to obtain the inertial error nonlinear integral signal, the pressure error nonlinear integral signal, and the GPS error relative integral signal respectively; then, performing comprehensive feedback on the inertial relative filtering error signal, the pressure relative filtering error signal, and the GPS relative filtering error signal to obtain the filter prior variance signal; finally, performing comprehensive feedback on the inertial error nonlinear integral signal, the pressure error nonlinear integral signal, and the GPS error relative integral signal to obtain the filter observation variance signal; thus completing the variance feedback closed-loop adjustment of the filter output as follows: e1(n)=2y z (n)-y jq (n)-y j (n); e2(n)=2y z (who jg (who g (n): e3(n)=2y z (n)-y qg (n)-y q (n); Where y z The Kalman filter is used to synthesize the three-in-one high-intensity signal; e1 is the inertial relative filter error signal, e3 is the barometric pressure relative filter error signal, e2 is the GPS relative filter error signal; s1 is the inertial error nonlinear integral signal, s3 is the barometric pressure error nonlinear integral signal, and s2 is the GPS error relative integral signal; k a1 ε1, k a2 k a3 Q w1 k b1 k b2 k b3 Q is a constant parameter. w1 Let R be the prior variance signal of the filter. w1 The variance signal observed by the filter; Step S40: Set the initial value of the inertial compensation height to the initial value of the inertial height. Compare the Kalman filter three-in-one height synthesis signal with the inertial compensation height signal to obtain the inertial synthesis error signal. Then, perform two nonlinear transformations to obtain the inertial synthesis error acceleration nonlinear signal and the inertial synthesis error velocity nonlinear signal, respectively. Superimpose the inertial synthesis error acceleration nonlinear signal with the carrier acceleration signal to obtain the acceleration synthesis signal, and then integrate it to obtain the inertial compensation velocity signal. Superimpose the inertial synthesis error velocity nonlinear signal to obtain the velocity synthesis signal, and then integrate it to obtain the inertial compensation height signal. The inertial compensation height signal is then used as the height output signal of the three-in-one combined Kalman filter altimetry as follows: and a (n)=y z (n)-y o (n); a d =a+f a (n); v d =v b +f b (n); Where e a ε is the inertial synthesis error signal; l1, l2, l3, l4, and ε2 are constant nonlinear transformation parameters; f a For the inertial synthesis error acceleration nonlinear signal, f b For the inertial synthesis error velocity nonlinear signal, a d For the acceleration composite signal, v d For the velocity composite signal, v b For inertial compensation velocity signal, h b This is for inertial compensation altitude signal.

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