Communication and guide integrated aircraft positioning method and system

Abstract

The invention belongs to the technical field of three-dimensional positioning of aircrafts, and discloses a communication and guide integrated aircraft positioning method and system. The system comprises a satellite communication module, a GNSS receiver, a ground network module, a PPP / INS combined positioning module, an RTK / INS combined positioning module, an inertial measurement unit, a radio altimeter, an air pressure altimeter, a laser altimeter, a position comprehensive judgment module and a track correction module. External signal sources comprise a GEO communication satellite, an LEO communication satellite, a GNSS navigation satellite and a 4G / 5G ground network. The method solves the problem of inaccurate positioning of the aircraft, can obtain a reliable high-precision positioning result, and is used for correcting the flight path of the aircraft.

Description

Technical Field

[0001] The present invention belongs to the technical field of three-dimensional positioning of aircraft, and specifically relates to a communication-navigation fusion aircraft positioning method and system. Background Art

[0002] Currently, whether it is manned or unmanned aircraft, navigation and flight control have relatively high requirements for three-dimensional positioning accuracy. Otherwise, inaccurate or unavailable positioning will lead to position deviations in blind flying and blind landing states, causing accidents.

[0003] Existing technologies, such as the aerial RTK hovering ground target laser measurement device and method, patent application number: CN202211092110.6, primarily utilize RTK to correct positioning accuracy. However, a problem exists: RTK accuracy exhibits nonlinear degradation as the aircraft's flight altitude changes. This is primarily due to the fact that RTK requires access to a ground communication network to receive correction data, and ground communication networks at high altitudes have poor connectivity. Furthermore, the effectiveness of RTK corrections is significantly affected by the distance from the ground reference station network. At higher altitudes, aircraft move further away from the ground reference station network, reducing the correlation of vertical atmospheric delays and making the differential results inaccurate.

[0004] Therefore, it is necessary to design a positioning method that is insensitive to vertical height changes based on the characteristics of the aircraft's large vertical navigation range. This is the core problem that the patent of this invention aims to solve. Summary of the Invention

[0005] The purpose of the present invention is to provide a communication-navigation fusion aircraft positioning method and system, which introduces precise point positioning (PPP) technology that is insensitive to vertical height changes while retaining RTK positioning technology. It is also necessary to add corresponding communication processing, fusion calculation and logical judgment modules to the aircraft positioning system to improve positioning accuracy.

[0006] The technical solution of the present invention is achieved as follows:

[0007] A communication-navigation fusion aircraft positioning method comprises the following steps:

[0008] Step 1: Obtain height information h, positioning result 1 and positioning result 2;

[0009] Step 2: Determine whether the flight altitude h is higher than 3000m. If the flight altitude h is higher than 3000m, proceed to step 3; otherwise, proceed to step 4.

[0010] Step 3: Determine the obstruction status of the communication satellite; if it is not obstructed, use positioning result 1 as the high-precision positioning result; otherwise, use the dead reckoning result of positioning result 1 as the high-precision positioning result;

[0011] Step 4: Determine whether the ground network status is normal; if normal, use positioning result 2 as the high-precision positioning result, otherwise proceed to step 5;

[0012] Step 5: Determine the obstruction status of the communication satellite. If it is not obstructed, use positioning result 1 as the high-precision positioning result. Otherwise, use the dead reckoning result of positioning result 2 as the high-precision positioning result.

[0013] As a further solution of the present invention, the altitude information h comes from a radio altimeter, a barometric altimeter, and a laser altimeter, and is determined based on the availability and confidence of the three. h can be a weighted average of the three. The specific calculation method is as follows:

[0014] The altitude information of the radio altimeter, barometric altimeter and laser altimeter are h0, h1 and h2 respectively, and the confidence levels of the three results are w0, w1 and w2 respectively. The calculation formula of the altitude information h is:

[0015] h=w0×h0+w1×h1+w2×h2;

[0016] Among them, the weight factors w0, w1, and w2 are between 0 and 1, and their sum is 1, that is, w0+w1+w2=1; when a certain height measuring instrument is unavailable, its weight factor is 0.

[0017] As a further embodiment of the present invention:

[0018] The calculation process of positioning result 1 is as follows:

[0019] Obtain the SSR from the communication satellite, including satellite orbit and clock error parameters, and substitute them into the observation equation to eliminate the influence of these errors on single point positioning, thereby obtaining a high-precision position vector R PPP and velocity vector V PPP ;

[0020] Get the acceleration vector from the IMU as a IMU , the angular velocity vector is w IMU ; Calculate the IMU position R through the INS dynamic model IMU and speed state prediction V IMU :

[0021]

[0022]

[0023] in, is the rotation matrix from the carrier coordinate system to the navigation coordinate system, g is the gravitational acceleration, q IMUis the attitude quaternion of the IMU, * represents quaternion multiplication, the superscript “.” on the left side of the above equation represents the first-order differential of the state variable, b a is the accelerometer zero bias, which is assigned to 0 after initialization, b g is the gyroscope zero bias, which is assigned to 0 after initialization; construct the state vector:

[0024]

[0025] in, They represent IMU 3D position error, IMU 3D velocity error, IMU 3D attitude error angle, accelerometer 3D bias error, gyroscope 3D bias error, PPP 3D position error, and PPP 3D velocity error, respectively. The superscript T indicates the transpose of the array.

[0026] The observation update is Z = [R PPP -R IMU , V PPP -V IMU ] T , the superscript T represents the transpose of the array, and the observation equation is Z = HX + v, where H is the observation matrix and v is the observation noise;

[0027] Next, the Kalman filter is used for state estimation and update:

[0028]

[0029] in, is the predicted state vector, is the predicted state covariance matrix, Q k is the process noise covariance matrix, F k is the state transfer matrix at time k, the superscript T represents the transpose of the matrix, P k is the error covariance matrix at time k;

[0030] S k represents the observation noise covariance matrix, H k is the observation matrix at time k, and the superscript T represents the transpose of the matrix. Then the Kalman gain is:

[0031]

[0032] The state update formula is:

[0033]

[0034]

[0035] Where I is the unit diagonal matrix;

[0036] Feedback the error estimated by the Kalman filter to obtain the positioning result 1:

[0037] R1=R IMU +δR IMU ;

[0038] Otherwise, the dead reckoning result of positioning result 1 is used as the high-precision positioning result. The dead reckoning result of positioning result 1 at time T is obtained from positioning result 1 at the most recent time (t0) when it is not blocked, according to the simple IMU integration operation:

[0039]

[0040] As a further embodiment of the present invention:

[0041] The calculation process of positioning result 2 is as follows:

[0042] Obtain OSR from the ground network, including satellite orbit and clock parameters, and substitute them into the observation equation to eliminate the influence of these errors on single point positioning, thereby obtaining a high-precision position vector R RTK and velocity vector V RTK ;

[0043] Get the acceleration vector from the IMU as a IMU , the angular velocity vector is w IMU ; Calculate the IMU position R through the INS dynamic model IMU and speed state prediction V IMU :

[0044]

[0045] in, is the rotation matrix from the carrier coordinate system to the navigation coordinate system, g is the gravitational acceleration, q IMU is the attitude quaternion of the IMU, * represents quaternion multiplication, the superscript “.” on the left side of the above equation represents the first-order differential of the state variable, b a is the accelerometer zero bias, which is assigned to 0 after initialization, b g is the gyroscope zero bias, which is assigned to 0 after initialization; construct the state vector:

[0046]

[0047] in, They represent IMU 3D position error, IMU 3D velocity error, IMU 3D attitude error angle, accelerometer 3D bias error, gyroscope 3D bias error, RTK 3D position error, and RTK 3D velocity error, respectively. The superscript T indicates the transpose of the array.

[0048] The observation update is Z = [RRTK -R IMU , V RTK -V IMU ] T , the superscript T represents the transpose of the array, and the observation equation is Z = HX + v, where H is the observation matrix and v is the observation noise;

[0049] Next, the Kalman filter is used for state estimation and update:

[0050]

[0051] in, is the predicted state vector, is the predicted state covariance matrix, Q k is the process noise covariance matrix, F k is the state transfer matrix at time k, the superscript T represents the transpose of the matrix, P k is the error covariance matrix at time k;

[0052] S k represents the observation noise covariance matrix, H k is the observation matrix at time k, and the superscript T represents the transpose of the matrix. Then the Kalman gain is:

[0053]

[0054] The state update formula is:

[0055]

[0056] Where I is the unit diagonal matrix;

[0057] Feedback the error estimated by the Kalman filter to obtain positioning result 2:

[0058] R2=R IMU +δR IMU ;

[0059] Otherwise, the dead reckoning result of positioning result 2 is used as the high-precision positioning result. The dead reckoning result of positioning result 2 at time T is obtained from positioning result 2 at the most recent time (t0) when the system is not blocked, according to the simple IMU integration operation:

[0060]

[0061] As a further solution of the present invention: in step 3 and step 5, when the communication satellite is blocked, the dead reckoning results of positioning results 1 and 2 are respectively selected.

[0062] As a further solution of the present invention: the prerequisite for outputting positioning result 1 is that the communication satellite is not blocked, and the prerequisite for outputting positioning result 2 is that the ground network status is normal. When both conditions are met, above 3000m, the accuracy of positioning result 1 is better than that of positioning result 2, and below 3000m, the accuracy of positioning result 2 is better than that of positioning result 1.

[0063] A communication and navigation integrated aircraft positioning system, comprising: a satellite communication module, a GNSS receiver, a ground network module, a PPP / INS combined positioning module, a RTK / INS combined positioning module, an IMU, a radio altimeter, a barometric altimeter, a laser altimeter, a position comprehensive judgment module, and a track correction module; its external signal sources include GEO communication satellites, LEO communication satellites, GNSS navigation satellites, and 4G / 5G ground networks;

[0064] The satellite communication module is used to receive PPP correction data from GEO communication satellites and LEO communication satellites, and to determine the obstruction status of satellite communications;

[0065] The GNSS receiver is used to receive L-band radio frequency signals from GNSS navigation satellites;

[0066] The ground network module is used to receive RTK correction data from the 4G / 5G ground network and determine the network status of the ground communication;

[0067] The radio altimeter, barometric altimeter and laser altimeter are used to collect the real-time flight altitude of the aircraft and send it to the position comprehensive judgment module for use;

[0068] The IMU includes a gyroscope and an accelerometer. The gyroscope outputs three-axis heading angular velocity, roll angular velocity, and pitch angular velocity, and the accelerometer outputs three-axis (X, Y, Z) acceleration information.

[0069] The PPP / INS combined positioning module combines the SSR from the satellite communication module, the GNSS observations from the GNSS receiver, and the 6-axis information from the IMU to calculate a positioning result that is insensitive to vertical height changes1;

[0070] The RTK / INS combined positioning module combines the OSR from the ground network module, the GNSS observations from the GNSS receiver, and the 6-axis information from the IMU to calculate the positioning result 2;

[0071] The positioning results of the two, together with the obstruction status from the satellite communication module, the network status from the ground network module and the altitude information of the three altimeters, are calculated in the position comprehensive judgment module to output a reliable high-precision positioning result, which is used to support the track correction module to correct the flight trajectory of the aircraft.

[0072] As a further solution of the present invention: the gyroscope adopts laser, optical fiber or MEMS.

[0073] As a further embodiment of the present invention:

[0074] The calculation process of positioning result 1 is as follows:

[0075] Obtain the SSR from the satellite communication module, including satellite orbit and clock error parameters, and substitute them into the observation equation to eliminate the influence of these errors on single point positioning, thereby obtaining a high-precision position vector R PPP and velocity vector V PPP ;

[0076] Get the acceleration vector from the IMU as a IMU , the angular velocity vector is w IMU ; Calculate the IMU position R through the INS dynamic model IMU and speed state prediction V IMU :

[0077]

[0078] in, is the rotation matrix from the carrier coordinate system to the navigation coordinate system, g is the gravitational acceleration, q IMU is the attitude quaternion of the IMU, * represents quaternion multiplication, the superscript “.” on the left side of the above equation represents the first-order differential of the state variable, b a is the accelerometer zero bias, which is assigned to 0 after initialization, b g is the gyroscope zero bias, which is assigned to 0 after initialization; construct the state vector:

[0079]

[0080] in, They represent IMU 3D position error, IMU 3D velocity error, IMU 3D attitude error angle, accelerometer 3D bias error, gyroscope 3D bias error, PPP 3D position error, and PPP 3D velocity error, respectively. The superscript T indicates the transpose of the array.

[0081] The observation update is Z = [R PPP -R IMU , V PPP -V IMU ] T , the superscript T represents the transpose of the array, and the observation equation is z = HX + v, where H is the observation matrix and v is the observation noise;

[0082] Next, the Kalman filter is used for state estimation and update:

[0083]

[0084] in, is the predicted state vector, is the predicted state covariance matrix, Q k is the process noise covariance matrix, F k is the state transfer matrix at time k, the superscript T represents the transpose of the matrix, P k is the error covariance matrix at time k;

[0085] S k represents the observation noise covariance matrix, H k is the observation matrix at time k, and the superscript T represents the transpose of the matrix. Then the Kalman gain is:

[0086]

[0087] The state update formula is:

[0088]

[0089] Where I is the unit diagonal matrix;

[0090] Feedback the error estimated by the Kalman filter to obtain the positioning result 1:

[0091] R1=R IMU +δR IMU ;

[0092] Otherwise, the dead reckoning result of positioning result 1 is used as the high-precision positioning result. The dead reckoning result of positioning result 1 at time T comes from the positioning result 1 at the most recent time (t0) when it is not blocked, and is obtained by the simple IMU integration operation inside the PPP / INS combined positioning module:

[0093]

[0094] As a further embodiment of the present invention:

[0095] The calculation process of positioning result 2 is as follows:

[0096] Obtain OSR from the ground network module, including satellite orbit and clock error parameters, and substitute them into the observation equation to eliminate the influence of these errors on single point positioning, thereby obtaining a high-precision position vector R RTK and velocity vector V RTK ;

[0097] Get the acceleration vector from the IMU as a IMU , the angular velocity vector is w IMU ; Calculate the IMU position R through the INS dynamic model IMUand speed state prediction V IMU :

[0098]

[0099] in, is the rotation matrix from the carrier coordinate system to the navigation coordinate system, g is the gravitational acceleration, q IMU is the attitude quaternion of the IMU, * represents quaternion multiplication, the superscript “.” on the left side of the above equation represents the first-order differential of the state variable, b a is the accelerometer zero bias, which is assigned to 0 after initialization, b g is the gyroscope zero bias, which is assigned to 0 after initialization; construct the state vector:

[0100]

[0101] in, They represent IMU 3D position error, IMU 3D velocity error, IMU 3D attitude error angle, accelerometer 3D bias error, gyroscope 3D bias error, RTK 3D position error, and RTK 3D velocity error, respectively. The superscript T indicates the transpose of the array.

[0102] The observation update is Z = [R RTK -R IMU , V RTK -V IMU ] T , the superscript T represents the transpose of the array, and the observation equation is z = HX + v, where H is the observation matrix and v is the observation noise;

[0103] Next, the Kalman filter is used for state estimation and update:

[0104]

[0105] in, is the predicted state vector, is the predicted state covariance matrix, Q k is the process noise covariance matrix, F k is the state transfer matrix at time k, the superscript T represents the transpose of the matrix, P k is the error covariance matrix at time k;

[0106] S k represents the observation noise covariance matrix, H k is the observation matrix at time k, and the superscript T represents the transpose of the matrix. Then the Kalman gain is:

[0107]

[0108] The state update formula is:

[0109]

[0110] Where I is the unit diagonal matrix;

[0111] Feedback the error estimated by the Kalman filter to obtain positioning result 2:

[0112] R2=R IMU +δR IMU ;

[0113] Otherwise, the dead reckoning result of positioning result 2 is used as the high-precision positioning result. The dead reckoning result of positioning result 2 at time T comes from positioning result 2 at the most recent time (t0) when it is not blocked, and is obtained by the simple IMU integration operation inside the RTK / INS combined positioning module:

[0114]

[0115] The beneficial effects of this application are:

[0116] 1. This application avoids the influence of the vertical height change of the aircraft when using RTK correction results alone, especially when flying at high altitude, the positioning accuracy is more guaranteed.

[0117] 2. This application avoids the problem of slow error convergence speed faced by simply using PPP correction results, and effectively shortens the initialization time of high-precision positioning.

[0118] 3. When GNSS may not be accurate, this application introduces triple redundant altitude measurement information - radio altitude, pressure altitude and laser altitude, to ensure the accuracy of altitude range judgment, and on this basis ensure the reliability and confidence of the high-precision positioning results output by the position comprehensive judgment module.

[0119] 4. This application simultaneously meets the multiple requirements of positioning result accuracy, high availability and high continuity within the range of conventional aircraft flight altitude (0 to 12,450 meters).

[0120] The present application is described in further detail below with reference to the accompanying drawings of the embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0121] Figure 1 A complete architecture diagram and data flow diagram of a communication-navigation fusion aircraft positioning system;

[0122] Figure 2 The figure is a schematic diagram of the steps of a communication-navigation fusion aircraft positioning method. DETAILED DESCRIPTION

[0123] In order to make the purpose, technical solutions and advantages of the implementation of the present invention clearer, the technical solutions in the embodiments of the present invention will be described in more detail below in conjunction with the drawings in the embodiments of the present invention. In the drawings, the same or similar reference numerals throughout represent the same or similar elements or elements with the same or similar functions. The described embodiments are part of the embodiments of the present invention, not all of the embodiments. The embodiments described below with reference to the drawings are exemplary and are intended to be used to explain the present invention, and should not be understood as limiting the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention. Figure 1-2 The embodiments of the present invention are described in detail.

[0124] See attached Figure 1 The present invention provides an aircraft positioning system that integrates communication and navigation. It introduces the precise point positioning (PPP) technology that is insensitive to vertical height changes while retaining the RTK positioning technology, and needs to add corresponding communication processing, fusion calculation and logic judgment modules to the aircraft positioning system. The aircraft onboard equipment includes a satellite communication module, a GNSS receiver, a ground network module, a PPP / INS combined positioning module, an RTK / INS combined positioning module, an inertial measurement unit (IMU), a radio altimeter (RA), a barometric altimeter, a laser altimeter, a position comprehensive judgment module and a track correction module. Its external signal sources include GEO communication satellites, LEO communication satellites, GNSS navigation satellites, and 4G / 5G ground networks.

[0125] The satellite communication module is used to receive PPP correction data (SSR) from GEO communication satellites and LEO communication satellites, and to determine the obstruction status of satellite communications. The GNSS receiver is used to receive L-band radio frequency signals from GNSS navigation satellites, and the ground network module is used to receive RTK correction data (OSR) from the 4G / 5G ground network and to determine the network status of ground communications. The radio altimeter, barometric altimeter, and laser altimeter are used to collect the real-time flight altitude of the aircraft for use by the position comprehensive judgment module. The inertial measurement unit (IMU) includes a gyroscope and an accelerometer. The gyroscope can be laser, fiber optic, or MEMS, and outputs three-axis heading angular velocity, roll angular velocity, and pitch angular velocity. The accelerometer outputs acceleration information in three axes (X, Y, Z), for a total of six axes. The PPP / INS combined positioning module combines the SSR from the satellite communication module, the GNSS observations from the GNSS receiver, and the six-axis information from the IMU to calculate a positioning result 1 that is insensitive to vertical height changes. The RTK / INS combined positioning module combines the OSR from the ground network module, the GNSS observations from the GNSS receiver, and the six-axis information from the IMU to calculate a positioning result 2. The positioning results of the two, together with the occlusion status from the satellite communication module, the network status from the ground network module, and the high-speed information from the three altimeters, are calculated in the position comprehensive judgment module to output a reliable high-precision positioning result. This result is used to support the track correction module to correct the aircraft's flight trajectory.

[0126] See attached Figure 1-2 The present invention provides a communication-guidance fusion aircraft positioning method, which includes the following steps:

[0127] Step 1: The height information h obtained by the position comprehensive judgment module comes from the attached Figure 1 The radio altimeter h0 (result confidence is w0), the barometric altimeter h1 (result confidence is w1) and the laser altimeter h2 (result confidence is w2) are determined according to the availability and confidence of the three. h can be a weighted average of the three.

[0128] h=w0×h0+w1×h1+w2×h2;

[0129] Among them, the weight factors w0, w1, and w2 are between 0 and 1, and their sum is 1, that is, w0+w1+w2=1;

[0130] When an altimeter is unavailable, its weight factor is 0.

[0131] Step 2: Determine whether the flight altitude is higher than 3000m. If it is higher than 3000m, proceed to step 3; otherwise, proceed to step 4.

[0132] Step 3: Determine the obstruction status of the communication satellite. If it is not obstructed, the positioning result 1 is used as the high-precision positioning result. The calculation process of the PPP / INS combined positioning module to obtain the positioning result 1 is as follows:

[0133] Obtain the SSR from the satellite communication module, including satellite orbit and clock error parameters, and substitute them into the observation equation to eliminate the influence of these errors on single point positioning, thereby obtaining a high-precision position vector R PPP and velocity vector V PPP ;

[0134] Get the acceleration vector from the IMU as a IMU , the angular velocity vector is w IMU ; Calculate the IMU position R through the INS dynamic model IMU and speed state prediction V IMU :

[0135]

[0136] in, is the rotation matrix from the carrier coordinate system to the navigation coordinate system, g is the gravitational acceleration, q IMU is the attitude quaternion of the IMU, * represents quaternion multiplication, the superscript “.” on the left side of the above equation represents the first-order differential of the state variable (that is, the first-order derivative of the state variable with respect to time), b a is the accelerometer zero bias, which is assigned to 0 after initialization, b g is the gyroscope zero bias, which is assigned to 0 after initialization; construct the state vector:

[0137]

[0138] in, They represent IMU 3D position error, IMU 3D velocity error, IMU 3D attitude error angle, accelerometer 3D bias error, gyroscope 3D bias error, PPP 3D position error, and PPP 3D velocity error, respectively. The superscript T indicates the transpose of the array.

[0139] The observation update is Z = [R PPP -R IMU , V PPP -V IMU ] T , the superscript T represents the transpose of the array, and the observation equation is z = HX + v, where H is the observation matrix and v is the observation noise;

[0140] Next, the Kalman filter is used for state estimation and update:

[0141]

[0142] in, is the predicted state vector, is the predicted state covariance matrix, Q k is the process noise covariance matrix, F k is the state transfer matrix at time k (the matrix used to transfer the state at the previous moment to the current moment), the superscript T represents the transpose of the matrix, P k is the error covariance matrix at time k (describing the uncertainty of state estimation and reflecting the statistical characteristics of the error between the estimated value and the true value);

[0143] S k represents the observation noise covariance matrix, H k is the observation matrix at time k (mapping the system's true state space into the observation space, indicating how to obtain the observation value through the state), and the superscript T represents the transpose of the matrix. Then the Kalman gain is:

[0144]

[0145] The state update formula is:

[0146]

[0147] Where I is the unit diagonal matrix;

[0148] Feedback the error estimated by the Kalman filter to obtain the positioning result 1:

[0149] R1=R IMU +δR IMU ;

[0150] Otherwise, the dead reckoning result of positioning result 1 is used as the high-precision positioning result. The dead reckoning result of positioning result 1 at time T comes from the positioning result 1 at the most recent time (t0) when it is not blocked, and is obtained by the simple IMU integration operation inside the PPP / INS combined positioning module:

[0151]

[0152] Step 4: Determine whether the ground network status is normal. If normal, use positioning result 2 as the high-precision positioning result. The calculation process of positioning result 2 obtained by the RTK / INS combined positioning module is as follows:

[0153] Obtain OSR from the ground network module, including satellite orbit and clock error parameters, and substitute them into the observation equation to eliminate the influence of these errors on single point positioning, thereby obtaining a high-precision position vector R RTK and velocity vector V RTK ;

[0154] Get the acceleration vector from the IMU as aIMU , the angular velocity vector is w IMU ; Calculate the IMU position R through the INS dynamic model IMU and speed state prediction V IMU :

[0155]

[0156] in, is the rotation matrix from the carrier coordinate system to the navigation coordinate system, g is the gravitational acceleration, q IMU is the attitude quaternion of the IMU, * represents quaternion multiplication, the superscript “.” on the left side of the above equation represents the first-order differential of the state variable (that is, the first-order derivative of the state variable with respect to time), b a is the accelerometer zero bias, which is assigned to 0 after initialization, b g is the gyroscope zero bias, which is assigned to 0 after initialization; construct the state vector:

[0157]

[0158] in, They represent IMU 3D position error, IMU 3D velocity error, IMU 3D attitude error angle, accelerometer 3D bias error, gyroscope 3D bias error, RTK 3D position error, and RTK 3D velocity error, respectively. The superscript T indicates the transpose of the array.

[0159] The observation update is Z = [R RTK -R IMU , V RTK -V IMU ] T , the superscript T represents the transpose of the array, and the observation equation is z = HX + v, where H is the observation matrix and v is the observation noise;

[0160] Next, the Kalman filter is used for state estimation and update:

[0161]

[0162] in, is the predicted state vector, is the predicted state covariance matrix, Q k is the process noise covariance matrix, F k is the state transfer matrix at time k, the superscript T represents the transpose of the matrix, P k is the error covariance matrix at time k;

[0163] S k represents the observation noise covariance matrix, H kis the observation matrix at time k, and the superscript T represents the transpose of the matrix. Then the Kalman gain is:

[0164]

[0165] The state update formula is:

[0166]

[0167] Where I is the unit diagonal matrix;

[0168] Feedback the error estimated by the Kalman filter to obtain positioning result 2:

[0169] R2=R IMU +δR IMU .

[0170] If the ground communication network is abnormal, go to step five.

[0171] Step 5: Determine the obstruction status of the communication satellite. If it is not obstructed, use positioning result 1 as the high-precision positioning result. Otherwise, use the dead reckoning result of positioning result 2 as the high-precision positioning result. The dead reckoning result of positioning result 2 at time T is derived from positioning result 2 at the most recent time (t0) when it is not obstructed, and is obtained by the simple IMU integral operation inside the RTK / INS combined positioning module:

[0172]

[0173] Further explain the difference between positioning result 1 and positioning result 2, and why the dead reckoning results of positioning results 1 and 2 are respectively selected when the communication satellite is blocked in steps 3 and 5: the prerequisite for outputting positioning result 1 is that the communication satellite is not blocked, and the prerequisite for outputting positioning result 2 is that the ground network status is normal. When both conditions are met, above 3000m, the accuracy of positioning result 1 is better than that of positioning result 2, and below 3000m, the accuracy of positioning result 2 is better than that of positioning result 1.

[0174] Therefore, at an altitude of more than 3000m, the embodiment actually performs step 1 - step 2 - step 3;

[0175] At low altitudes below 3000 m, the embodiment actually executes step 1 - step 3 - step 4 - step 5.

[0176] Typically, the obstruction state of a communication satellite is similar to that of a GNSS navigation satellite. Once a GNSS navigation satellite is obstructed, the PPP / INS and RTK / INS combined positioning modules cannot acquire GNSS observations and must instead use the IMU for dead-reckoning integration. Based on this, the present invention incorporates a judgment condition design, assuming that when a communication satellite is obstructed, Positioning Result 1 and Positioning Result 2 are the dead-reckoning results.

[0177] So far, the purpose of the present invention has been accomplished.

[0178] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A communication-navigation fusion aircraft positioning method, characterized in that: The following steps are involved: Step 1: Obtain height information h, positioning result 1 and positioning result 2; Step 2: Determine whether the flight altitude h is higher than 3000m. If the flight altitude h is higher than 3000m, proceed to step 3; otherwise, proceed to step 4. Step 3: Determine the obstruction status of the communication satellite; if it is not obstructed, take positioning result 1 as the high-precision positioning result; Otherwise, the dead reckoning result of positioning result 1 is used as the high-precision positioning result; Step 4: Determine whether the ground network status is normal; If normal, take positioning result 2 as the high-precision positioning result, otherwise go to step 5; Step 5: Determine the obstruction status of the communication satellite. If it is not obstructed, use positioning result 1 as the high-precision positioning result. Otherwise, use the dead reckoning result of positioning result 2 as the high-precision positioning result.

2. The aircraft positioning method based on communication and navigation fusion according to claim 1, characterized in that: The altitude information h comes from the radio altimeter, barometric altimeter, and laser altimeter, and is determined based on the availability and confidence of the three. h can be a weighted average of the three. The specific calculation method is as follows: The altitude information of the radio altimeter, barometric altimeter and laser altimeter are h0, h1 and h2 respectively, and the confidence levels of the three results are w0, w1 and w2 respectively. The calculation formula of the altitude information h is: h=w0×h0+w1×h1+w2×h2; Among them, the weight factors w0, w1, and w2 are between 0 and 1, and their sum is 1, that is, w0+w1+w2=1; when a certain height measuring instrument is unavailable, its weight factor is 0.

3. The aircraft positioning method based on communication and navigation fusion according to claim 2, characterized in that: The calculation process of positioning result 1 is as follows: Obtain the SSR from the communication satellite, including satellite orbit and clock error parameters, and substitute them into the observation equation to eliminate the influence of these errors on single point positioning, thereby obtaining a high-precision position vector R PPP and velocity vector V PPP ; Get the acceleration vector from the IMU as a IMU , the angular velocity vector is w IMU ; Calculate the IMU position R through the INS dynamic model IMU and speed state prediction V IMU : in, is the rotation matrix from the carrier coordinate system to the navigation coordinate system, g is the gravitational acceleration, q IMU is the attitude quaternion of the IMU, * represents quaternion multiplication, and the superscript ". " on the left side of the above equation represents the first-order differential of the state variable, b a is the accelerometer zero bias, which is assigned to 0 after initialization, b g is the gyroscope zero bias, which is assigned to 0 after initialization; construct the state vector: in, They represent IMU 3D position error, IMU 3D velocity error, IMU 3D attitude error angle, accelerometer 3D bias error, gyroscope 3D bias error, PPP 3D position error, and PPP 3D velocity error, respectively. The superscript T indicates the transpose of the array. The observation update is Z = [R PPP -R IMU , V PPP -V IMU ] T , the superscript T represents the transpose of the array, and the observation equation is Z = HX + v, where H is the observation matrix and v is the observation noise; Next, the Kalman filter is used for state estimation and update: in, is the predicted state vector, is the predicted state covariance matrix, Q k is the process noise covariance matrix, F k is the state transfer matrix at time k, the superscript T represents the transpose of the matrix, P k is the error covariance matrix at time k; S k represents the observation noise covariance matrix, H k is the observation matrix at time k, and the superscript T represents the transpose of the matrix. Then the Kalman gain is: The state update formula is: Where I is the unit diagonal matrix; Feedback the error estimated by the Kalman filter to obtain the positioning result 1: R1=R IMU +δR IMU ; Otherwise, the dead reckoning result of positioning result 1 is used as the high-precision positioning result. The dead reckoning result of positioning result 1 at time T is obtained from positioning result 1 at the most recent time (t0) when it is not blocked, according to the simple IMU integration operation:

4. The aircraft positioning method based on communication and navigation fusion according to claim 3, characterized in that: The calculation process of positioning result 2 is as follows: Obtain OSR from the ground network, including satellite orbit and clock parameters, and substitute them into the observation equation to eliminate the influence of these errors on single point positioning, thereby obtaining a high-precision position vector R RTK and velocity vector V RTK ; Get the acceleration vector from the IMU as a IMU , the angular velocity vector is w IMU ; Calculate the IMU position R through the INS dynamic model IMU and speed state prediction V IMU : in, is the rotation matrix from the carrier coordinate system to the navigation coordinate system, g is the gravitational acceleration, q IMU is the attitude quaternion of the IMU, * indicates quaternion multiplication, and the superscript ". " on the left side of the above equation indicates the first-order differential of the state variable, b a is the accelerometer zero bias, which is assigned to 0 after initialization, b g is the gyroscope zero bias, which is assigned to 0 after initialization; construct the state vector: in, They represent IMU 3D position error, IMU 3D velocity error, IMU 3D attitude error angle, accelerometer 3D bias error, gyroscope 3D bias error, RTK 3D position error, and RTK 3D velocity error, respectively. The superscript T indicates the transpose of the array. The observation update is Z = [R RTK -R IMU , V RTK -V IMU ] T , the superscript T represents the transpose of the array, and the observation equation is Z = HX + v, where H is the observation matrix and v is the observation noise; Next, the Kalman filter is used for state estimation and update: in, is the predicted state vector, is the predicted state covariance matrix, Q k is the process noise covariance matrix, F k is the state transfer matrix at time k, the superscript T represents the transpose of the matrix, P k is the error covariance matrix at time k; S k represents the observation noise covariance matrix, H k is the observation matrix at time k, and the superscript T represents the transpose of the matrix. Then the Kalman gain is: The state update formula is: Where I is the unit diagonal matrix; Feedback the error estimated by the Kalman filter to obtain positioning result 2: R2=R IMU +δR IMU ; Otherwise, the dead reckoning result of positioning result 2 is used as the high-precision positioning result. The dead reckoning result of positioning result 2 at time T is obtained from positioning result 2 at the most recent time (t0) when it is not blocked, according to the simple IMU integration operation:

5. The aircraft positioning method based on communication and navigation fusion according to claim 4, characterized in that: In steps 3 and 5, when the communication satellite is blocked, the dead reckoning results of positioning results 1 and 2 are respectively selected.

6. The aircraft positioning method based on communication and navigation fusion according to claim 5, characterized in that: The prerequisite for outputting positioning result 1 is that the communication satellite is not blocked, and the prerequisite for outputting positioning result 2 is that the ground network status is normal. When both conditions are met, the accuracy of positioning result 1 is better than that of positioning result 2 above 3000m, and the accuracy of positioning result 2 is better than that of positioning result 1 below 3000m.

7. A communication and navigation fusion aircraft positioning system, characterized in that: It includes satellite communication module, GNSS receiver, ground network module, PPP / INS combined positioning module, RTK / INS combined positioning module, IMU, radio altimeter, barometric altimeter, laser altimeter, position comprehensive judgment module and track correction module; its external signal sources include GEO communication satellite, LEO communication satellite, GNSS navigation satellite, and 4G / 5G ground network; The satellite communication module is used to receive PPP correction data from GEO communication satellites and LEO communication satellites, and to determine the obstruction status of satellite communications; The GNSS receiver is used to receive L-band radio frequency signals from GNSS navigation satellites; The ground network module is used to receive RTK correction data from the 4G / 5G ground network and determine the network status of the ground communication; The radio altimeter, barometric altimeter and laser altimeter are used to collect the real-time flight altitude of the aircraft and send it to the position comprehensive judgment module for use; The IMU includes a gyroscope and an accelerometer. The gyroscope outputs three-axis heading angular velocity, roll angular velocity, and pitch angular velocity, and the accelerometer outputs three-axis (X, Y, Z) acceleration information. The PPP / INS combined positioning module combines the SSR from the satellite communication module, the GNSS observations from the GNSS receiver, and the 6-axis information from the IMU to calculate a positioning result that is insensitive to vertical height changes1; The RTK / INS combined positioning module combines the OSR from the ground network module, the GNSS observations from the GNSS receiver, and the 6-axis information from the IMU to calculate the positioning result 2; The positioning results of the two, together with the obstruction status from the satellite communication module, the network status from the ground network module and the altitude information of the three altimeters, are calculated in the position comprehensive judgment module to output a reliable high-precision positioning result, which is used to support the track correction module to correct the flight trajectory of the aircraft.

8. The communication-navigation fusion aircraft positioning system according to claim 7, characterized in that: Gyroscopes are made of laser, fiber optic or MEMS.

9. The communication-navigation fusion aircraft positioning system according to claim 8, characterized in that: The calculation process of positioning result 1 is as follows: Obtain the SSR from the satellite communication module, including satellite orbit and clock error parameters, and substitute them into the observation equation to eliminate the influence of these errors on single point positioning, thereby obtaining a high-precision position vector R PPP and velocity vector V PPP ; Get the acceleration vector from the IMU as a IMU , the angular velocity vector is w IMU ; Calculate the IMU position R through the INS dynamic model IMU and speed state prediction V IMU : in, is the rotation matrix from the carrier coordinate system to the navigation coordinate system, g is the gravitational acceleration, q IMU is the attitude quaternion of the IMU, * represents quaternion multiplication, and the superscript ". " on the left side of the above equation represents the first-order differential of the state variable, b a is the accelerometer zero bias, which is assigned to 0 after initialization, b g is the gyroscope zero bias, which is assigned to 0 after initialization; construct the state vector: in, They represent IMU 3D position error, IMU 3D velocity error, IMU 3D attitude error angle, accelerometer 3D bias error, gyroscope 3D bias error, PPP 3D position error, and PPP 3D velocity error, respectively. The superscript T indicates the transpose of the array. The observation update is Z = [R PPP -R IMU , V PPP -V IMU ] T , the superscript T represents the transpose of the array, and the observation equation is z = HX + v, where H is the observation matrix and v is the observation noise; Next, the Kalman filter is used for state estimation and update: in, is the predicted state vector, is the predicted state covariance matrix, Q k is the process noise covariance matrix, F k is the state transfer matrix at time k, the superscript T represents the transpose of the matrix, P k is the error covariance matrix at time k; S k represents the observation noise covariance matrix, H k is the observation matrix at time k, and the superscript T represents the transpose of the matrix. Then the Kalman gain is: The state update formula is: Where I is the unit diagonal matrix; Feedback the error estimated by the Kalman filter to obtain the positioning result 1: R1=R IMU +δR IMU ; Otherwise, the dead reckoning result of positioning result 1 is used as the high-precision positioning result. The dead reckoning result of positioning result 1 at time T comes from the positioning result 1 at the most recent time (t0) when it is not blocked, and is obtained by the simple IMU integration operation inside the PPP / INS combined positioning module:

10. The communication-navigation fusion aircraft positioning system according to claim 9, characterized in that: The calculation process of positioning result 2 is as follows: Obtain OSR from the ground network module, including satellite orbit and clock error parameters, and substitute them into the observation equation to eliminate the influence of these errors on single point positioning, thereby obtaining a high-precision position vector R RTK and velocity vector V RTK ; Get the acceleration vector from the IMU as a IMU , the angular velocity vector is w IMU ; Calculate the IMU position R through the INS dynamic model IMU and speed state prediction V IMU : in, is the rotation matrix from the carrier coordinate system to the navigation coordinate system, g is the gravitational acceleration, q IMU is the attitude quaternion of the IMU, * indicates quaternion multiplication, and the superscript ". " on the left side of the above equation indicates the first-order differential of the state variable, b a is the accelerometer zero bias, which is assigned to 0 after initialization, b g is the gyroscope zero bias, which is assigned to 0 after initialization; construct the state vector: in, They represent IMU 3D position error, IMU 3D velocity error, IMU 3D attitude error angle, accelerometer 3D bias error, gyroscope 3D bias error, RTK 3D position error, and RTK 3D velocity error, respectively. The superscript T indicates the transpose of the array. The observation update is Z = [R RTK -R IMU , V RTK -V IMU ] T , the superscript T represents the transpose of the array, and the observation equation is z = HX + v, where H is the observation matrix and v is the observation noise; Next, the Kalman filter is used for state estimation and update: in, is the predicted state vector, is the predicted state covariance matrix, Q k is the process noise covariance matrix, F k is the state transfer matrix at time k, the superscript T represents the transpose of the matrix, P k is the error covariance matrix at time k; S k represents the observation noise covariance matrix, H k is the observation matrix at time k, and the superscript T represents the transpose of the matrix. Then the Kalman gain is: The state update formula is: Where I is the unit diagonal matrix; Feedback the error estimated by the Kalman filter to obtain positioning result 2: R2=R IMU +δR IMU ; Otherwise, the dead reckoning result of positioning result 2 is used as the high-precision positioning result. The dead reckoning result of positioning result 2 at time T comes from positioning result 2 at the most recent time (t0) when it is not blocked, and is obtained by the simple IMU integration operation inside the RTK / INS combined positioning module:

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