Method for reducing recapture duration of communication-in-moving antenna based on Kalman filtering

Abstract

The invention discloses a method for reducing recapture time of a communication-in-moving antenna based on Kalman filtering, and belongs to the technical field of communication. According to the method, a set of integrated navigation system is constructed by integrating a satellite positioning system and an MEMS inertial measurement unit. On the basis of a traditional satellite positioning / inertial loose combination integrated navigation system, according to the working state of a communication-in-moving antenna, the positioning state of a satellite positioning system and the motion state of a communication-in-moving antenna mounting carrier, different types of course error measurement information are introduced, and the observation vector of a Kalman filter is autonomously expanded; the method effectively estimates the internal error of the integrated navigation system, improves the course keeping precision, reduces the shielding recapture time, also can improve the course keeping precision when the satellite positioning system fails, and is suitable for different types of installation carriers.

Description

Technical Field

[0001] The present invention relates to the field of communication technology, and in particular to a method for reducing the recapture time of a communication-in-motion antenna based on Kalman filtering. Background Art

[0002] With the widespread application of satellite communication technology, it plays an irreplaceable role in global data transmission and communication services. However, in practical applications, satellite antennas are often blocked by factors such as buildings, mountains, clouds, and the structure of the aircraft itself (such as rotors), resulting in signal attenuation or even interruption, seriously affecting the continuity and reliability of communication. Therefore, studying and optimizing satellite antenna blockage recapture technology is of great significance to improving the overall performance of satellite communication systems.

[0003] In the event of an emergency where the satellite signal is blocked, the moving antenna should immediately switch from the tracking mode to the geo-pointing stable working mode and maintain this state until the blockage disappears completely. In this process, the blockage recapture time becomes one of the key indicators for measuring system performance. Traditional solutions include two strategies. First, the moving antenna is equipped with a high-precision inertial navigation system that can continuously maintain a high level of heading and attitude accuracy to ensure that the satellite signal can be quickly recaptured after the obstruction disappears. The recapture time is usually no more than three seconds (for obstructions within twenty minutes). However, this solution faces high cost challenges. The second is to use a medium- and low-precision inertial navigation system combined with a rotation search mechanism to adapt to different blockage durations, but the recapture time under this solution is generally longer, often exceeding ten seconds. Summary of the invention

[0004] In view of this, the present invention proposes a method for reducing the recapture time of a moving antenna based on Kalman filtering. The method constructs a combined navigation system by integrating a satellite positioning system and a MEMS inertial measurement unit. According to the working state of the moving antenna, the positioning state of the satellite positioning system, and the motion state of the carrier on which the moving antenna is installed, the observation vector of the Kalman filter is autonomously expanded to effectively estimate the internal error of the combined navigation system, improve its heading keeping accuracy, and thus reduce the occlusion recapture time.

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

[0006] A method for reducing the recapture time of a moving antenna based on Kalman filtering comprises the following steps:

[0007] (1) Establish a Kalman filter model for the satellite positioning / inertial integrated navigation system including heading error;

[0008] (2) judging the motion state of the mounting carrier of the mobile communication antenna according to the output of the inertial measurement unit; judging the type of the mounting carrier of the mobile communication antenna according to the heading of the satellite positioning system and the integrated navigation system;

[0009] (3) According to the working state of the moving antenna, when the satellite signal is locked, the target heading error is calculated based on the theoretical heading calculated based on the relative position of the satellite and the moving antenna and the current heading calculated based on the heading of the satellite positioning system and the integrated navigation system, and expanded to the measurement vector of the Kalman filter; when the occlusion phenomenon occurs, the target heading error is calculated in combination with the installation carrier type and the motion state, and expanded to the measurement vector of the Kalman filter;

[0010] (4) Perform Kalman filter time update and measurement update to estimate the internal error of the integrated navigation system.

[0011] Furthermore, the specific method of step (2) is:

[0012] (201) Convert the heading H of the integrated navigation system to the heading H of the carrier on which the moving antenna is installed C :

[0013]

[0014] H C =arctan2(D4,D5)

[0015] Where angle is the azimuth axis angle of the moving antenna. is the attitude matrix, D1~D9 correspond to the 9 elements in the matrix D;

[0016] (202) According to the following formula, the motion state of the mounting carrier of the mobile communication antenna is determined by the output of the integrated navigation system:

[0017]

[0018] Where f x 、f y and f z is the acceleration measurement value output by the integrated navigation system, N is the number of continuous sampling times not less than 100; when Δ<Δ TH When the motion state is determined to be static, otherwise it is non-static; where Δ TH is the static judgment threshold;

[0019] (203) According to the following formula, the heading output by the satellite positioning system and H C Determine the type of mounting carrier for the mobile communication antenna based on the relationship between:

[0020]

[0021] Where M is the number of consecutive sampling times not less than 10, and N* represents a set of positive integers; when σ<σ TH and μ<μ TH When , the installation carrier is judged to be vehicle-mounted, otherwise it is not vehicle-mounted; σ TH and μ TH are the standard deviation and mean threshold of heading difference respectively.

[0022] Furthermore, in step (3), when an occlusion occurs, the target heading error is calculated in combination with the installation carrier type and the motion state, and expanded to the measurement vector of the Kalman filter; the specific process is:

[0023] (301) When an occlusion occurs, if the satellite positioning system output information is valid, z will be H =heading-H C Extension to the measurement vector of the Kalman filter;

[0024] ①The installation carrier is vehicle-mounted;

[0025] ②The installation carrier is not on-board and the movement state is static;

[0026] (302) If the satellite positioning system output information is invalid and the motion state is static, z H =H' k -H k Extension to the measurement vector of the Kalman filter;

[0027]

[0028] H' k =arctan2(D 4,k ,D 5,k )

[0029] Where angle k and angle k-1 are the azimuth axis angles at time k and k-1 respectively, is the attitude matrix at time k-1, D 1, k~D 9,k Corresponding matrix D k The 9 elements in H k is the heading output of the integrated navigation system at time k.

[0030] The beneficial effects of adopting the above technical solution are:

[0031] 1. The present invention introduces different types of heading error measurement information based on the traditional satellite positioning / inertial combined navigation system, thereby improving the heading keeping accuracy without increasing the hardware cost.

[0032] 2. The present invention utilizes the internal state information of the moving communication antenna to realize automatic identification of the type of the installation carrier and the movement state of the installation carrier, thereby increasing the universality for different installation carriers.

[0033] 3. The present invention can be used in moving antennas of different carrier types. When the satellite positioning system fails, the heading keeping accuracy can be improved, thereby reducing the occlusion recapture time. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 It is a block diagram of the composition and data flow of the integrated navigation system in an embodiment of the present invention; DETAILED DESCRIPTION

[0035] The technical solution of the present invention is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0036] The present invention proposes a method for reducing the recapture time of a moving antenna based on Kalman filtering. The method constructs a combined navigation system by integrating a satellite positioning system and a MEMS inertial measurement unit. According to the working state of the moving antenna, the positioning state of the satellite positioning system, and the motion state of the carrier on which the moving antenna is installed, the observation vector of the Kalman filter is autonomously expanded to effectively estimate the internal error of the combined navigation system, improve its heading keeping accuracy, and thus reduce the occlusion recapture time. Specifically, the following steps are included (see Figure 1 ):

[0037] (1) Establish a Kalman filter model for the satellite positioning / inertial integrated navigation system that includes heading errors. The specific steps are as follows:

[0038] (101) Establish the Kalman filter model of the satellite positioning / inertial combined navigation system, the state vector is a 15-dimensional vector containing misalignment angle, velocity, position and zero bias of the inertial measurement unit sensor. P0 and Q0 are the state vector variance matrix and noise variance matrix of the Kalman filter model respectively. The state transfer equation is:

[0039] x k =Φ k,k-1 x k-1 +Γ k-1 W k-1 (13)

[0040] In the formula, is the misalignment angle error, [δv E δv N δv U ] T is the velocity error in the northeast direction, [δλδLδh] T are the latitude, longitude and altitude errors, [ε xε y ε z ] T is the zero bias of the three axes of the gyroscope, To add the zero offset of the three axes, x k ,x k-1 are the state vectors at time k and k-1 respectively, Φ k,k-1 is the discretized state transfer matrix, Γ k-1 is the system noise driving array, W k-1 is the noise vector of the state, and the state vector variance matrix and noise variance matrix of the Kalman filter model are set. According to the state vector selected in this embodiment, Φ k,k-1 It is expressed as:

[0041]

[0042] Where:

[0043]

[0044] M ap =M1+M2,

[0045] M vp =(v n ×)(2M1+M2)+M3,

[0046]

[0047] t is the filtering period, R Mh , R Nh are the curvature radii of the meridian and the meridian respectively, L is the latitude, h is the altitude, v is the n =[v E v N v U ] T are the speeds in the east, north and sky directions respectively, g e is the earth's gravitational acceleration, β, β1, β2, β3 are constant coefficients, ω ie is the angular velocity of the Earth’s rotation, ω en is the angular velocity relative to the Earth, f sf is the accelerometer output. The superscript “n” indicates the projection into the navigation system.

[0048] (102) Based on the Kalman filter 6-dimensional measurement vector z0 of the satellite positioning / inertial integrated navigation system, the heading error measurement information z is added H , the measurement equation is:

[0049]

[0050] Where Z is the 7-dimensional measurement vector containing position error, velocity error and heading error, V is the measurement noise term, and its noise variance matrix is ​​represented by R matrix. H matrix is ​​the measurement equation coefficient matrix, which is in the following form:

[0051]

[0052] Where H0 is the velocity and position measurement equation coefficient matrix corresponding to the Kalman filter model of the satellite positioning / inertial combined navigation system, and H' 1×15 For expansion items, is the attitude array, C1~C9 corresponds to The 9 elements in . The noise variance matrix R is expressed as follows:

[0053]

[0054] in are the variances of the eastward velocity, northward velocity and celestial velocity of the satellite positioning observation information, are the variances of latitude, longitude and altitude of satellite positioning observation information, is the variance of the heading error observation information.

[0055] (2) judging the motion state of the mounting carrier of the moving communication antenna according to the output of the inertial measurement unit; judging the type of the mounting carrier of the moving communication antenna according to the heading of the satellite positioning system and the integrated navigation system; the specific steps include:

[0056] (201) Convert the heading H of the integrated navigation system to the heading H of the carrier on which the moving antenna is installed C :

[0057]

[0058] H C =arctan2(D4,D5) (20)

[0059] Where angle is the azimuth axis angle of the moving antenna, is the attitude matrix, D1~D9 correspond to the 9 elements in the matrix D;

[0060] (202) According to the following formula, the motion state of the mounting carrier of the mobile communication antenna is determined by the output of the integrated navigation system:

[0061]

[0062] Where f x 、f y and f zis the acceleration measurement value output by the integrated navigation system, and N is the number of consecutive sampling times not less than 100. TH When , the motion state is determined to be static, otherwise it is non-static. TH is the static judgment threshold, generally taken as Δ TH <0.01;

[0063] (203) According to the following formula, the heading output by the satellite positioning system and H C Determine the type of mounting carrier for the mobile communication antenna based on the relationship between:

[0064]

[0065] Where M is the number of consecutive sampling times not less than 10. When σ<σ TH and μ<μ TH When , the installation carrier is determined to be vehicle-mounted, otherwise it is not vehicle-mounted. TH and μ TH are the standard deviation and mean threshold of heading error, generally σ TH <0.05,μ TH <0.2.

[0066] (3) According to the working state of the moving antenna, when the satellite signal is locked, the target heading error is calculated based on the theoretical heading calculated by the relative position of the satellite and the moving antenna and the current heading calculated by the heading of the integrated navigation system, and expanded to the measurement vector of the Kalman filter; when the occlusion phenomenon occurs, the target heading error is calculated in combination with the installation carrier type and the motion state, and expanded to the measurement vector of the Kalman filter; the specific steps include:

[0067] (301) According to the working state of the moving antenna, when the satellite signal is locked, the target heading error z is calculated based on the theoretical heading calculated based on the relative position of the satellite and the moving antenna and the current heading calculated based on the heading of the integrated navigation system. H =H sat -H, and expanded to the measurement vector of the Kalman filter. sat is the theoretical heading calculated based on the relative position of the satellite and the moving antenna, and H is the current heading calculated by the heading of the integrated navigation system;

[0068] (302) When an occlusion occurs, if the satellite positioning system output information is valid, z will be H =heading-H C Expanded to the measurement vector of the Kalman filter:

[0069] ①The installation carrier is vehicle-mounted;

[0070] ②The installation carrier is not on-board and the movement state is static;

[0071] (303) If the satellite positioning system output information is invalid and the motion state is static, z H =H' k -H k Extension to the measurement vector of the Kalman filter;

[0072]

[0073] H' k =arctan2(D 4,k ,D 5,k ) (twenty four)

[0074] Where angle k 、angle k-1 are the azimuth axis angles at time k and k-1 respectively, is the attitude matrix at time k-1, D 1,k ~D 9,k Corresponding matrix D k The 9 elements in H k is the heading output of the integrated navigation system at time k.

[0075] (4) Perform Kalman filter time update and measurement update to estimate the internal error of the integrated navigation system and make corrections to improve its heading keeping accuracy.

[0076] (401) The time update process of Kalman filtering is as follows:

[0077]

[0078] In the formula, Φ k is the state transfer matrix, x k 、x k+1 represents the state vector at time k and time k+1 respectively, Q k Represents the state noise matrix at time k, P k , P k+1 Represent the state vector variance matrix at time k and time k+1 respectively;

[0079] (402) The measurement update process of the Kalman filter is as follows:

[0080]

[0081] In the formula, K k represents the gain matrix at time k, H k Represents the measurement equation coefficient matrix at time k, R k represents the measurement noise matrix, I represents the identity matrix, and Z kis the measurement information matrix at time k;

[0082] (403) After the measurement update, the internal error of the integrated navigation system is estimated and corrected, thereby improving its heading keeping accuracy and thus reducing the recapture time.

[0083] In summary, the above are only preferred embodiments of the present invention and are not intended to limit the protection scope of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for reducing the recapture time of a moving antenna based on Kalman filtering, characterized in that: The following steps are involved: (1) Establish a Kalman filter model for the satellite positioning / inertial integrated navigation system including heading error; (2) judging the motion state of the mounting carrier of the mobile communication antenna according to the output of the inertial measurement unit; judging the type of the mounting carrier of the mobile communication antenna according to the heading of the satellite positioning system and the integrated navigation system; (3) According to the working state of the moving antenna, when the satellite signal is locked, the target heading error is calculated based on the theoretical heading calculated based on the relative position of the satellite and the moving antenna and the current heading calculated based on the heading of the satellite positioning system and the integrated navigation system, and expanded to the measurement vector of the Kalman filter; when the occlusion phenomenon occurs, the target heading error is calculated in combination with the installation carrier type and the motion state, and expanded to the measurement vector of the Kalman filter; (4) Perform Kalman filter time update and measurement update to estimate the internal error of the integrated navigation system.

2. The method for reducing the recapture time of a moving antenna based on Kalman filtering according to claim 1, characterized in that: The specific method of step (2) is as follows: (201) Convert the heading H of the integrated navigation system to the heading H of the carrier on which the moving antenna is installed C : H C =arctan2(D4,D5) Where angle is the azimuth axis angle of the moving antenna. is the attitude matrix, D1~D9 correspond to the 9 elements in the matrix D; (202) According to the following formula, the motion state of the mounting carrier of the mobile communication antenna is determined by the output of the integrated navigation system: Where f x 、f y and f z is the acceleration measurement value output by the integrated navigation system, N is the number of continuous sampling times not less than 100; when Δ<Δ TH When the motion state is determined to be static, otherwise it is non-static; where Δ TH is the static judgment threshold; (203) According to the following formula, the heading output by the satellite positioning system and H C Determine the type of mounting carrier for the mobile communication antenna based on the relationship between: Where M is the number of consecutive sampling times not less than 10, and N* represents a set of positive integers; when σ<σ TH and μ<μ TH When , the installation carrier is judged to be vehicle-mounted, otherwise it is not vehicle-mounted; σ TH and μ TH are the standard deviation and mean threshold of heading difference respectively.

3. The method for reducing the recapture time of a moving antenna based on Kalman filtering according to claim 2, characterized in that: In step (3), when an occlusion occurs, the target heading error is calculated in combination with the installation carrier type and the motion state, and expanded to the measurement vector of the Kalman filter; the specific process is: (301) When an occlusion occurs, if the satellite positioning system output information is valid, z will be H =heading-H C Extension to the measurement vector of the Kalman filter; ①The installation carrier is vehicle-mounted; ②The installation carrier is not on-board and the movement state is static; (302) If the satellite positioning system output information is invalid and the motion state is static, z H =H' k -H k Extension to the measurement vector of the Kalman filter; H' k =arctan2(D 4,k ,D 5,k ) Where angle k and angle k-1 are the azimuth axis angles at time k and k-1 respectively, is the attitude matrix at time k-1, D 1,k ~D 9,k Corresponding matrix D k The 9 elements in H k is the heading output of the integrated navigation system at time k.

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