Vehicle-mounted station satellite dynamic tracking device and method

Abstract

The application relates to the technical field of satellite communication and discloses a kind of vehicle station satellite dynamic tracking device and method, and its technical scheme points are: the theoretical angle of pointing to satellite of calculating antenna, drive antenna to rotate to the angle and carry out coarse alignment, and start frame search when not capturing effective satellite signal;According to real-time vehicle speed, the main and auxiliary tracking modes are adaptively switched, and the servo drive parameters are dynamically adjusted based on the processed tracking deviation to drive the antenna to track the satellite in real time;The tracking angle of the antenna is compensated in real time;When judging star loss, the angle that the antenna should point to currently is predicted, and conical scanning is carried out with the predicted angle as the center to recapture satellite signal.The application adopts adaptive hybrid tracking mode switching, multi-dimensional error real-time compensation and "inertial navigation prediction + conical scanning" fast recovery cooperative mechanism, and realizes high-precision, high-stability, fast-recovery satellite dynamic tracking under complex road conditions and high-speed movement.

Description

Technical Field

[0001] This invention relates to the field of satellite communication technology, and more specifically, to a vehicle-mounted satellite dynamic tracking device and method. Background Technology

[0002] Vehicle-mounted satellite communication systems (also known as "mobile communication") need to continuously and stably track geostationary satellites to maintain the communication link while the vehicle is in motion. Current technologies primarily rely on either single inertial navigation tracking or single-pulse tracking. While inertial navigation, based on sensors such as gyroscopes and accelerometers, offers high autonomy, it suffers from an inherent flaw: zero-bias errors accumulate over time, leading to a decline in long-term tracking accuracy. Single-pulse tracking, which obtains tracking error by comparing the signal strength of multiple beams, offers higher accuracy, but it is prone to tracking interruptions due to signal loss in complex road conditions such as high-speed driving, sharp turns, or passage through tunnels and bridges that cause temporary signal blockage. Furthermore, existing systems generally lack comprehensive compensation mechanisms for mechanical installation errors and vehicle motion coupling errors. Moreover, the reacquisition process after satellite signal loss often involves a large search range and long processing time, making it difficult to simultaneously meet the stringent requirements of high accuracy, high stability, and rapid recovery in complex driving environments such as high speeds and bumpy conditions.

[0003] Therefore, the present invention provides a vehicle-mounted satellite dynamic tracking device and method, which improves the above-mentioned technical problems. Summary of the Invention

[0004] This disclosure aims to address the shortcomings of existing technologies by providing a vehicle-mounted station satellite dynamic tracking device and method. The present invention employs an adaptive hybrid tracking mode switching, multi-dimensional error real-time compensation, and a rapid recovery coordination mechanism of "inertial navigation prediction + conical scanning" to solve the problems of low tracking accuracy, poor stability, and slow satellite loss recovery of existing vehicle-mounted stations under complex road conditions and high-speed movement.

[0005] To achieve the above objectives, the present disclosure proposes the following technical solutions:

[0006] In a first aspect, embodiments of this disclosure propose a vehicle-mounted satellite dynamic tracking device comprising:

[0007] The antenna module is equipped with a single-pulse receiving unit and a beacon signal detection unit. The single-pulse receiving unit is used to receive satellite signals and extract the tracking deviation of the antenna in at least one direction. The beacon signal detection unit is used to detect the strength of the satellite beacon signal in real time.

[0008] The attitude perception module is used to collect real-time pose, motion status and driving speed information of the vehicle-mounted vehicle.

[0009] The signal processing module is communicatively connected to the antenna module and is used to calculate the tracking deviation based on the output of the single-pulse receiving unit, and to filter the tracking deviation and attitude data.

[0010] The servo drive module is used to drive the antenna to rotate according to control commands. Its controller can dynamically adjust the control parameters according to the vehicle's speed and the rate of change of tracking error.

[0011] The main control module is communicatively connected to the attitude perception module, the signal processing module, and the servo drive module, respectively. It is used to adaptively switch the tracking mode according to the driving speed collected by the attitude perception module, and to start the satellite loss recovery process when the beacon signal detection unit determines that the satellite has been lost, and to control the servo drive module to reacquire the signal based on inertial navigation prediction and conical scanning.

[0012] As a preferred embodiment of the present invention, the attitude perception module includes a GPS receiver, an inertial navigation unit, and a vehicle speed sensor. The data from each sensor are output after being preprocessed by synchronization alignment, amplitude limiting filtering, and Kalman filtering.

[0013] In a preferred embodiment of the present invention, the controller of the servo drive module is a fuzzy PID controller, which uses the vehicle speed v and the rate of change of tracking error as the load factors. As input, the fuzzy inference output corrects the PID parameters, and the interpolation parameters in the fuzzy inference process are dynamically corrected based on the angle adjustment error of the encoder feedback, thereby achieving closed-loop adaptive optimization of the control parameters.

[0014] As a preferred embodiment of the present invention, the main control module has a built-in tracking mode switching unit configured to: when the real-time vehicle speed is less than or equal to a first preset threshold, adopt a hybrid tracking mode that uses inertial navigation data as the main component and corrects for single-pulse tracking errors; when the real-time vehicle speed is greater than the first preset threshold, switch to a hybrid tracking mode that uses single-pulse tracking as the main component and motion prediction using inertial navigation data.

[0015] As a preferred embodiment of the present invention, the main control module is also used to perform error compensation, specifically: based on the pre-calibrated and stored antenna mechanical installation error angle β, combined with the real-time acquired carrier roll angle α, the target pointing angle of the antenna is compensated in real time through spatial coordinate transformation.

[0016] As a preferred embodiment of the present invention, the satellite loss recovery process includes:

[0017] Based on the last valid tracking data latched at the moment of satellite loss and the angular velocity data output in real time by the inertial navigation unit, the current angle that the antenna should point to is predicted through an attitude recursion algorithm.

[0018] Centered on the predicted angle, the servo drive module is controlled to drive the antenna to perform a conical scan;

[0019] During the scanning process, if the beacon signal detection unit detects that the beacon signal strength has reached or exceeded the preset threshold, it will immediately stop scanning and switch back to the dynamic tracking stage.

[0020] Secondly, this disclosure proposes a method for dynamic satellite tracking of a vehicle-mounted station, comprising the following steps:

[0021] S1. Initial satellite alignment steps: Based on the preset satellite orbit parameters and the initial position of the vehicle, calculate the theoretical satellite alignment angle of the antenna, drive the antenna to rotate to that angle for coarse alignment, and start frame search if no valid satellite signal is captured.

[0022] S2. Dynamic tracking steps: Real-time acquisition of carrier motion data and extraction of tracking deviation, adaptive switching of main and auxiliary tracking modes based on real-time vehicle speed, and dynamic adjustment of servo drive parameters based on the processed tracking deviation to drive the antenna to track the satellite in real time.

[0023] S3. Error Compensation Steps: Based on the pre-calibrated antenna mechanical installation error and combined with the real-time attitude of the carrier, the tracking angle of the antenna is compensated in real time.

[0024] S4. Satellite Loss Recovery Steps: When satellite loss is determined, the current angle that the antenna should be pointing at is predicted based on inertial navigation data, and a conical scan is performed with the predicted angle as the center to reacquire the satellite signal.

[0025] As a preferred technical solution of the present invention, in the dynamic tracking step, the adaptive switching of the main and auxiliary tracking modes specifically means: when the vehicle speed is less than or equal to 30 km / h, a hybrid mode is adopted that mainly uses inertial navigation data and corrects for single-pulse errors; when the vehicle speed is greater than 30 km / h, it is switched to a hybrid mode that mainly uses single-pulse tracking and uses inertial navigation data for motion prediction.

[0026] As a preferred embodiment of the present invention, in the satellite loss recovery step, the mathematical model for predicting the antenna pointing angle is as follows:

[0027] ;

[0028] in, Let be the predicted angle at time t; For the moment of losing stars The antenna pointing angle; ω(τ) is the angular velocity of the carrier around the axis, which is collected in real time by the fiber optic inertial navigation unit; The inertial drift compensation amount for the angle.

[0029] As a preferred technical solution of the present invention, the frame search is centered on the theoretical star angle, and adopts a spiral search path within the preset range of roll and pitch angles, with the step size gradient decreasing until the beacon signal strength is detected to reach or exceed the preset threshold.

[0030] In summary, the present invention has the following beneficial effects:

[0031] Firstly, this invention effectively overcomes the shortcomings of a single tracking method by integrating inertial navigation and single-pulse tracking technology, adaptively switching between primary and secondary tracking modes based on real-time vehicle speed, combining Kalman filtering to reduce data noise, and real-time compensation for mechanical installation errors. This achieves high-precision performance with static tracking accuracy ≤0.1° and dynamic tracking accuracy ≤0.2°.

[0032] Secondly, the fuzzy PID controller used in the servo drive module can dynamically and adaptively adjust the control parameters according to the vehicle's driving speed and the rate of change of tracking error. This makes the system extremely robust when facing complex working conditions such as bumpy roads and high-speed driving (≤120km / h), significantly shortening the signal interruption time (≤1s) and ensuring the continuous and stable communication link.

[0033] Thirdly, this invention designs an efficient satellite loss recovery mechanism of "inertial navigation prediction + conical scanning". After satellite loss, inertial navigation data is first used to perform short-term high-precision prediction to quickly locate the potential signal area. Then, a conical scan is performed within a small area for precise positioning, shortening the recovery time after satellite loss to less than 1 second (for short-term obstruction), which greatly improves the continuity of communication.

[0034] Fourth, through multi-dimensional error modeling and compensation mechanisms (including inertial sensor zero bias, mechanical installation errors, etc.), the device can effectively cope with various complex geographical environments and driving conditions, such as mountainous areas and urban canyons. At the same time, the "theoretical calculation coarse alignment + frame search" strategy adopted in the initial satellite alignment phase controls the initial acquisition time to within 3 minutes, improving the convenience of system deployment and use. Attached Figure Description

[0035] Figure 1 A framework diagram of a vehicle-mounted satellite dynamic tracking device provided in an embodiment of the present invention;

[0036] Figure 2 A flowchart of a vehicle-mounted station satellite dynamic tracking method provided in an embodiment of the present invention;

[0037] Figure 3 A schematic diagram illustrating the dynamic tracking mode switching provided in an embodiment of the present invention;

[0038] Figure 4This is a schematic diagram of the path of the frame search algorithm provided in an embodiment of the present invention. Detailed Implementation

[0039] The present application will now be described in detail with reference to specific embodiments. These embodiments will help those skilled in the art to further understand the present application, but do not limit the present application in any way. It should be noted that those skilled in the art can make several modifications and improvements without departing from the concept of the present application. These all fall within the protection scope of the present application.

[0040] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0041] Unless otherwise defined, all technical and scientific terms used in this specification have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the scope of this application. The term "and / or" as used in this specification includes any and all combinations of one or more of the associated listed items.

[0042] Furthermore, the technical features involved in the various embodiments of this application described below can be combined with each other as long as they do not conflict with each other.

[0043] This disclosure aims to address the problems of tracking accuracy being greatly affected by driving conditions, slow recovery after satellite loss, and incomplete error compensation in vehicle-mounted satellite dynamic tracking. Therefore, this disclosure proposes a vehicle-mounted satellite dynamic tracking device and method, employing adaptive hybrid tracking mode switching, multi-dimensional real-time error compensation, and a collaborative mechanism of "inertial navigation prediction + conical scanning" for rapid recovery. This achieves high-precision, high-stability, and rapid recovery satellite dynamic tracking under complex road conditions and high-speed movement.

[0044] Please refer to Figure 1 , Figure 1 A frame diagram of a vehicle-mounted station satellite dynamic tracking device according to an embodiment of the present disclosure is shown.

[0045] Includes: antenna module, attitude sensing module, signal processing module, servo drive module, and main control module;

[0046] Antenna Module: Equipped with a monopulse receiving unit and a beacon signal detection unit. The monopulse receiving unit receives communication signals and beacon signals transmitted by the geostationary satellite. By comparing the signal strength difference of its dual-beam receiving structure, it extracts the tracking deviation of the antenna in the directions of roll angle, elevation angle, and polarization angle. The beacon signal detection unit detects the strength of the geostationary satellite beacon signal in real time, providing a basis for satellite signal acquisition and loss determination.

[0047] Attitude perception module: This module integrates a GPS receiver, a fiber optic inertial navigation unit, and a vehicle speed sensor. The GPS receiver acquires the real-time latitude, longitude, and altitude of the vehicle; the fiber optic inertial navigation unit measures the vehicle's attitude angles (roll, pitch, and heading), angular velocity, and linear acceleration; and the vehicle speed sensor collects the vehicle's real-time speed. After synchronization alignment, amplitude limiting filtering, and Kalman filtering preprocessing, high-precision vehicle pose and motion information is output to the main control module.

[0048] The signal processing module includes a signal strength detection unit, an error extraction unit, and a Kalman filtering unit. The signal strength detection unit converts the beacon signal into an intensity value (dBm) and compares it with a threshold; the error extraction unit calculates the tracking deviation based on the difference in signal strength between the two beams of the single-pulse receiver; and the Kalman filtering unit performs fusion and noise reduction processing on the tracking deviation and attitude data, outputting a stable error estimate.

[0049] Servo drive module: Composed of drive mechanisms for roll, pitch, and polarization angles, and a fuzzy PID controller. The drive mechanisms drive the antenna to rotate according to control commands; the fuzzy PID controller dynamically and adaptively adjusts the proportional, integral, and derivative parameters based on the vehicle's speed and the rate of change of tracking error, achieving precise, fast, and stable control of the antenna pointing.

[0050] Main control module: As the control core, it communicates with the attitude perception module, signal processing module, and servo drive module. Its built-in tracking mode switching unit and satellite loss recovery unit are responsible for coordinating the work of each module, automatically switching tracking strategies according to vehicle speed, and initiating a rapid recovery process in the event of satellite loss.

[0051] For example, the single-pulse receiving unit adopts a dual-beam receiving structure. The angular deviation is calculated by comparing the power of the two beam signals, and the deviation detection accuracy is ≤0.1°. An example formula for calculating the roll angle deviation is as follows:

[0052] ;

[0053] in, For angular deviation, For calibration coefficients, , This represents the power of the dual-beam signal.

[0054] For example, a fuzzy PID controller includes a fuzzy inference unit and a PID parameter adjustment unit. The fuzzy inference unit adjusts the parameters based on the vehicle's speed v and the rate of change of the tracking error. As input, the correction amount of the PID parameters is output through a fuzzy rule table. The PID parameter adjustment unit updates the control parameters in real time accordingly. It combines feedback from the servo motor encoder to perform closed-loop optimization, thereby achieving adaptive adjustment of parameters.

[0055] Specifically, the formula for calculating the quantified correction value is as follows:

[0056] ;

[0057] in, This is the baseline value for the proportionality coefficient (ranging from 5.0 to 6.0). This is the baseline value for the integral coefficient (ranging from 0.5 to 1.0). The base value for the differential coefficient (range 1.0~2.0); Pairs of velocity and error change rate, respectively The correction coefficient function has a value range of 0.8 to 1.2.

[0058] Specifically, the adaptive adjustment process is as follows: the PID parameter adjustment unit first obtains the output of the fuzzy inference unit. Then, combining the current real-time control parameters, the final control parameters are updated using the following formula:

[0059] ;

[0060] in, These are the PID control parameters at the current moment; after the update, the PID parameter adjustment unit will use the new parameters. The output is sent to the motor control terminal of the servo drive mechanism, while simultaneously acquiring the angle adjustment error fed back from the encoder in real time, and dynamically correcting the correction coefficient function. The interpolation parameters are used to achieve closed-loop adaptive optimization of the control parameters.

[0061] For example, the interpolation parameters of the dynamically adjusted correction coefficient function are as follows:

[0062] Interpolation parameters are used in the fuzzy inference process to realize the input quantity (vehicle speed). Tracking error change rate The key parameter for the smooth mapping between the precise value and the discrete output of the fuzzy rule table is the correction coefficient function. The core components specifically include three categories: input quantization factor, output scaling factor, and interpolation weight coefficient. Their initial setting and dynamic correction logic are as follows:

[0063] Specifically, the types and initial settings of the interpolation parameters are as follows:

[0064] Input quantization factor: including velocity quantization factor And error rate of change quantization factor This is used to convert input quantities within the actual physical domain into the quantization domain of the fuzzy rule table, achieving adaptation from precise values ​​to fuzzy quantities. Among these, the velocity quantization factor... The initial value is set to 0.1 (the quantization domain [-6,6] corresponds to the actual speed range [-60km / h,60km / h]). Error rate of change quantification factor The initial value is set to 10 (the quantization domain [-6, 6] corresponds to the actual error rate of change range). ).

[0065] Output scaling factor: including with corresponding ,and corresponding ,and corresponding This is used to convert the fuzzy values ​​output from the fuzzy rule table into precise values ​​(range 0.8~1.2) of the correction coefficient function. The initial values ​​of all three are uniformly set to 0.2 (the fuzzy output quantization domain [-1,1] corresponds to the correction coefficient function range [0.8,1.2], i.e., the conversion is achieved through "correction coefficient = 1 + 0.2 × fuzzy output value"). ).

[0066] Interpolation weight coefficients: including ω1 and ω2, are used to perform weighted interpolation on the output results of two adjacent rules when the quantized value of the input is between two discrete rule nodes in the fuzzy rule table, ensuring a smooth transition of the correction coefficient function value. The initial values ​​are set to ω1=ω2=0.5, and an equal-weight interpolation strategy is adopted.

[0067] Specifically, the dynamic correction logic for the interpolation parameters is as follows:

[0068] The angle adjustment error ε (i.e., the deviation between the actual antenna pointing angle and the target pointing angle, ε = target angle - actual angle) fed back by the encoder is used as the correction basis. Real-time optimization of interpolation parameters is achieved through closed-loop feedback. The specific correction process is as follows:

[0069] Error assessment: Real-time calculation of the absolute value and trend of angle adjustment error ε. ( If |ε|>0.05° (preset error threshold), the current interpolation parameter is deemed insufficiently adaptable, and parameter correction is initiated; if |ε|≤0.05°, the current interpolation parameter remains unchanged.

[0070] Parameter correction rule: when |ε|>0.05° and When the error increases, increase the quantization factor of the rate of change of error. (Each correction increment is 0.5), increase the output scaling factor. (Each correction increment is 0.02), while adjusting the interpolation weight coefficients ω1=0.6 and ω2=0.4 to improve the sensitivity to error changes; when |ε|>0.05° and When the error decreases, decrease the speed quantization factor. (Each correction reduces the amount by 0.01). Keeping the output scaling factor constant, adjust the interpolation weight coefficients ω1=0.4 and ω2=0.6 to increase the weight of the speed change on the correction coefficient. When |ε|≤0.05° and the settling time≥0.5s, the current interpolation parameters are fixed as the temporary optimal parameters. If the error exceeds the threshold again, the correction is based on these temporary optimal parameters.

[0071] By dynamically adjusting the interpolation parameters as described above, the correction coefficient function can be made more accurate. It can more accurately match different driving speeds of the vehicle (such as low speed, high speed, and bumpy road conditions) and tracking error changes, ensure the adaptability of PID parameter correction values, and ultimately achieve closed-loop adaptive optimization of control parameters.

[0072] For example, to ensure that adaptive adjustment is feasible, a typical example of a fuzzy rule table and a clarification method are provided below:

[0073] Specifically, the fuzzy linguistic variable partitioning is as follows:

[0074] Input linguistic variables: tracking error e, fuzzy subsets are divided into {negative large (NL), negative small (NS), zero (Z), positive small (PS), positive large (PL)}, quantization universe is [-5, 5], corresponding to the actual error range [-0.5°, 0.5°]; error rate of change. The fuzzy subset is divided into {negative large (NL), negative small (NS), zero (Z), positive small (PS), positive large (PL)}, and the quantization domain is [-6,6], corresponding to the actual rate of change range [-0.6° / s, 0.6° / s].

[0075] Output language variable: scaling factor correction value The fuzzy subset is divided into {negative large (NL), negative small (NS), zero (Z), positive small (PS), positive large (PL)}, and the quantization universe is [-1,1], corresponding to the actual correction value range [-0.5,0.5].

[0076] Specifically, a typical example of a fuzzy rule representation ( As shown in Table 1:

[0077] Table 1. Examples of typical fuzzy rule representations

[0078] ;

[0079] The rules in Table 1 explain, for example, "If e is NL and ec is NL, then..." "PL" indicates that when the tracking error is significantly negative and the rate of change of error is also significantly negative, the proportional coefficient needs to be increased substantially to quickly correct the error.

[0080] Please refer to Figure 2 , Figure 2 A flowchart of a vehicle-mounted station satellite dynamic tracking method according to an embodiment of this disclosure is shown. The overall process mainly includes the following four steps:

[0081] S1. Initial Alignment Phase: The main control module calculates the theoretical alignment angles (roll, pitch, and polarization angles) of the antenna based on preset satellite orbit parameters and the initial position of the vehicle-mounted platform (obtained by the attitude sensing module). The servo drive module then rotates the antenna to this theoretical angle to complete coarse alignment. Subsequently, the signal processing module detects the beacon signal strength. If it reaches or exceeds a preset threshold, the dynamic tracking phase begins; otherwise, a frame search algorithm (using the theoretical point as the center, performing a spiral search within a certain angle range) is initiated until the satellite signal is acquired.

[0082] For example, the specific steps of the frame search algorithm are as follows: with the theoretical star alignment angle as the center, a search box is established within the range of roll angle ±5° and pitch angle ±5°. A spiral search path is adopted, with the step size decreasing from 0.5° to 0.1° until the beacon signal is detected.

[0083] S2, Dynamic Tracking Phase: The attitude perception module collects carrier motion data in real time, and the signal processing module extracts and filters tracking errors in real time. The main control module switches the tracking mode according to the real-time vehicle speed: when the vehicle speed is ≤30 km / h, a hybrid mode using inertial navigation data as the main source and single-pulse error correction is adopted; when the vehicle speed is >30 km / h, it switches to a hybrid mode using single-pulse tracking as the main source and inertial navigation data for motion prediction. The fuzzy PID controller dynamically adjusts the servo drive parameters according to the processed error, driving the antenna to accurately track the satellite in real time.

[0084] For example, the calculation formula for Kalman filter noise reduction is as follows:

[0085] State prediction equation: ;

[0086] Error covariance prediction equation: ;

[0087] Kalman gain equation: ;

[0088] State update equation: ;

[0089] Error covariance update equation: ;

[0090] in, The state vector includes roll tracking error, pitch tracking error, and polarization tracking error. Predict the state at time k. Let A be the state after the update at time k; A is the state transition matrix (based on the first-order kinematic model of the tracking error, with values ​​[1, T; 0, 1], where T is the sampling period of 0.01s). The control input matrix is ​​defined as follows: u is the control input quantity (tracking error rate of change); P is the error covariance matrix; Q is the process noise covariance matrix (defined as diag([1e-6, 1e-6])); R is the observation noise covariance matrix (defined as diag([5e-6, 5e-6])). H is the Kalman gain; H is the observation matrix (with values ​​[1, 0; 0, 1]). is the observation value at time k (the original tracking error output by the error extraction unit); I is the identity matrix; the main control module switches the tracking mode according to the vehicle's driving speed: when the driving speed is ≤30km / h, a hybrid tracking mode with inertial navigation as the main mode and single pulse correction is adopted; when the driving speed is >30km / h, it switches to a hybrid tracking mode with single pulse as the main mode and inertial navigation prediction; the fuzzy PID controller dynamically adjusts the servo drive parameters according to the tracking error after noise reduction, driving the antenna module to correct the pointing in real time.

[0091] S3, Error Compensation Stage: The main control module compensates for the tracking angle by spatial coordinate transformation based on the mechanical installation error of the antenna mount and the real-time attitude of the carrier. The installation error angle β is obtained and stored through the "static multi-attitude calibration method" before leaving the factory, and is used for subsequent real-time compensation calculations to eliminate fixed installation deviations.

[0092] An example angle compensation formula: ;

[0093] in, The target angle after compensation. To measure the angle, The roll angle, This is the initial installation error angle. The calibration was obtained using the following method: The "static multi-attitude calibration method" was employed, with the following steps: First, the vehicle-mounted carrier was placed on a level surface to ensure stable vehicle posture. The roll and pitch angles of the carrier were collected using the attitude sensing module (both should be ≤0.01° to ensure a horizontal baseline posture). Second, the servo drive module was controlled to rotate the antenna module to three preset calibration postures (Posture 1: Roll angle 0°, Pitch angle 30°, Polarization angle 0°; Posture 2: Roll angle 90°, Pitch angle 45°, Polarization angle 45°; Posture 3: Roll angle 180°, Pitch angle 60°, Polarization angle 90°). Third, the actual pointing angle of the antenna in each calibration posture was measured using a high-precision laser positioning instrument and compared with the theoretical pointing angle to obtain three sets of angle deviation values. The fourth step involves fitting the three sets of deviation values ​​using the least squares method to obtain the optimal estimate of the initial installation error angle β, as shown in the formula. ,in, The average angle under three calibration attitudes is taken as close to 0. In the fifth step, the β value obtained from the calibration is configured and sent to the main control module through the web API and stored in the main control module's storage unit (i.e., database) as a fixed parameter for subsequent error compensation. This calibration process is completed before the device leaves the factory. If the antenna module is disassembled or reassembled in the future, the calibration needs to be performed again.

[0094] S4. Satellite Loss Recovery Phase: When the beacon signal strength is below the threshold and persists for a certain period of time (e.g., ≥1 second), it is determined to be a satellite loss. The main control module immediately activates the satellite loss recovery unit: First, based on the angular velocity data output by the fiber optic inertial navigation unit, the current angle the antenna should be pointing at is predicted using an attitude recursion algorithm. Then, using this predicted angle as the center, the antenna is controlled to execute a conical scanning algorithm (scanning a circular trajectory within a small angle range). Once the beacon signal is detected again during the scanning process, the scanning is immediately stopped, and the system quickly switches back to the dynamic tracking phase.

[0095] For example, the specific process of the mathematical model and algorithm flow for predicting antenna pointing is as follows:

[0096] Taking the roll angle as an example, and the moment of losing a star. Based on the last effective antenna pointing angle and roll angle θ0, and combined with the real-time angular velocity data output by the fiber optic inertial navigation unit, an attitude recursion algorithm is used to predict the antenna pointing. The mathematical model and process are as follows:

[0097] Specifically, the mathematical model (attitude recursion formula) is as follows:

[0098] ;

[0099] in, When t is respectively (t> The predicted angle; ω(τ) is the angular velocity of the carrier around the axis, which is collected in real time by the fiber optic inertial navigation unit; The inertial drift compensation for the angle is calculated based on the zero-bias estimation result of the Kalman filter, and the value is taken as the zero-bias error value of the inertial navigation unit, ≤0.05° / h; the integration step size is consistent with the inertial navigation data update frequency, which is 0.01s, to achieve continuous attitude recursion and ensure prediction accuracy.

[0100] Specifically, the algorithm flow is as follows:

[0101] The first step is to lock the last valid tracking data the moment the satellite is lost, including... The angular information of the antenna's position at any given time, the carrier information (including velocity, acceleration, and angular velocity), and the inertial zero-bias error value output by the Kalman filter;

[0102] The second step is that the fiber optic inertial navigation unit outputs the carrier angular velocity and the updated attitude angle in real time at a frequency of 100Hz. The main control module calls the above attitude recursion formula to calculate the antenna prediction pointing angle at time t.

[0103] The third step is to use the predicted pointing angle as the initial scanning center of the subsequent conical scanning algorithm, and control the servo drive module to drive the antenna to quickly point to the predicted position, laying the foundation for rapid signal reacquisition.

[0104] For example, the parameters and steps of the conical scan algorithm are as follows:

[0105] Specifically, the core parameters are: the scanning center is the antenna pointing angle predicted by the inertial navigation system, the scanning range is the roll angle ±1° and the pitch angle ±1°, the scanning step size is 0.05°, the scanning speed is 10° / s, and the signal sampling frequency is consistent with that of the beacon signal detection unit (100Hz).

[0106] Specifically, the algorithm steps are as follows:

[0107] The first step is for the main control module to generate a conical scan trajectory command based on the pointing angle predicted by the inertial navigation system (the trajectory is a perfect circle centered on the prediction point, which is achieved through the coordinated periodic changes of the roll and pitch angles).

[0108] The second step is that after receiving the command, the servo drive module drives the roll and pitch drive mechanisms to work together to drive the antenna to perform a conical scan along a preset trajectory. At the same time, the beacon signal detection unit collects the beacon signal strength in real time.

[0109] The third step involves the signal processing module performing real-time analysis of the collected signal strength data. If a signal strength of ≥-100dBm (preset threshold) is detected, it is determined that a satellite signal has been captured, and the scanning is stopped immediately. If no signal is captured, the scanning continues until the preset maximum scanning range (±2° around the prediction point). If no signal is captured, the inertial navigation prediction and scanning process is re-executed.

[0110] Specifically, its core functions are as follows:

[0111] First, the search range is narrowed by using the pointing angle predicted by the inertial navigation system as the center for local scanning, avoiding blind searching across the entire range and significantly shortening the signal reacquisition time. Second, the signal acquisition success rate is improved by using the periodic trajectory of conical scanning to effectively cover signal offset areas caused by inertial navigation prediction deviations and sudden changes in carrier attitude, ensuring that beacon signals can still be quickly acquired even when there are slight deviations in prediction accuracy. Third, dynamic tracking is quickly integrated; after signal acquisition, the antenna pointing can be quickly corrected based on the peak signal strength points obtained during the scanning process, switching back to the dynamic tracking phase to ensure communication continuity.

[0112] In one specific embodiment of this application, the device of the present invention can be implemented by the following hardware and software in concert:

[0113] Antenna module: Employs a 0.8-meter aperture, dual-axis (tilt and pitch) strapdown antenna structure. The monopulse receiver operates in the Ku band (e.g., receiving 12.25-12.75 GHz, transmitting 14.00-14.50 GHz). The beacon signal detection unit has a sensitivity of no less than -120 dBm.

[0114] Attitude perception module: The GPS receiver uses a high-precision module with a positioning accuracy of ≤1 meter. The fiber optic inertial navigation unit can be an IMU-3000 model, with an attitude angle measurement range of ±180°, an angular velocity range of ±500° / s, and an attitude angle measurement accuracy better than 0.05° / h. The vehicle speed sensor uses a Hall effect sensor with an accuracy of ±0.5 km / h. All sensor data is transmitted via a CAN bus.

[0115] Signal processing module: The core processor can be an FPGA (such as XC7K325T) to realize signal strength detection, rapid error extraction and Kalman filtering algorithm.

[0116] Servo drive module: The drive mechanisms for all three axes use DC servo motors, requiring a speed ≥100° / s and an acceleration ≥800° / s², and are equipped with high-precision encoders. The fuzzy PID control algorithm runs in the servo controller.

[0117] Main control module: Employs a high-performance embedded processor (such as ARM Cortex-A9) and runs the Linux operating system. It communicates with the signal processing module via Ethernet and controls the servo drive module via an RS485 bus, coordinating the entire system process.

[0118] In a specific embodiment of this application, taking a specific application scenario as an example, the implementation steps of this method are illustrated as follows:

[0119] Assuming the target being tracked is the “Zhongxing-16” geostationary satellite (orbital position 110.5°E), the vehicle-mounted station is initially located in Beijing (approximately 39.9°N, 116.4°E).

[0120] Initial alignment: The main control module calculates the theoretical alignment angle (e.g., roll angle 185°, pitch angle 45°) based on the satellite orbital position and initial location. The attitude sensing module acquires initial attitude data, and the servo drive module drives the antenna to rotate to this angle. The signal processing module detects that the beacon signal strength is -90 dBm (preset threshold -100 dBm), which meets the condition, and the system directly enters the dynamic tracking phase.

[0121] Dynamic Tracking: The vehicle travels at a speed of 60 km / h. The attitude perception module reports the vehicle's attitude changes in real time (e.g., roll angle fluctuation ±3°). The signal processing module calculates the roll angle error of 0.15° and the pitch angle error of 0.12° from the single-pulse signal, which are reduced to 0.08° and 0.06° respectively after Kalman filtering. Since the vehicle speed is >30 km / h, the main control module activates a tracking mode of "single-pulse as the main method and inertial navigation prediction as the auxiliary method". The fuzzy PID controller outputs optimized control parameters (e.g., based on the current error and vehicle speed) according to the current error and vehicle speed. This drives the servo motor to quickly correct the antenna's orientation.

[0122] Error compensation: The main control module calls the pre-calibrated and stored installation error angle β, and combines it with the real-time acquired carrier roll angle α, and uses the spatial coordinate transformation formula to perform real-time compensation for the antenna command angle, eliminating the system error caused by mechanical installation deviation.

[0123] Satellite Loss Recovery: As the vehicle enters the tunnel, the beacon signal strength drops to -125 dBm for 1.2 seconds, indicating a satellite loss. The main control module immediately locks the attitude and angular velocity data from the instant before the loss and predicts the current antenna pointing angle using an attitude recursion algorithm. Subsequently, the control antenna performs a conical scan within ±1° (0.05° step) centered on this predicted point. 1.8 seconds later, as the vehicle exits the tunnel, the signal strength recovers to -95 dBm during the scan. The system immediately stops scanning, quickly corrects the pointing using the signal peaks captured during the scan, and seamlessly switches back to dynamic tracking mode.

[0124] For example, to verify the effectiveness of the present invention, multi-condition tests were conducted, and the results are shown in Table 2:

[0125] Table 2. Experimental Data Verification

[0126] ;

[0127] As can be seen from the test data in Table 2, the present invention can maintain high tracking accuracy under different driving conditions, and has short satellite connection and satellite loss recovery time, which meets the actual needs of vehicle-mounted satellite communication.

[0128] The above are merely preferred embodiments of the present invention. The scope of protection of the present invention is not limited to the above embodiments. All technical solutions falling within the scope of the present invention's concept are within the scope of protection of the present invention. It should be noted that for those skilled in the art, any improvements and modifications made without departing from the principle of the present invention should also be considered within the scope of protection of the present invention.

Claims

1. A vehicle-mounted satellite dynamic tracking device, characterized in that, include: The antenna module is equipped with a single-pulse receiving unit and a beacon signal detection unit. The single-pulse receiving unit is used to receive satellite signals and extract the tracking deviation of the antenna in at least one direction. The beacon signal detection unit is used to detect the strength of the satellite beacon signal in real time. The attitude perception module is used to collect real-time pose, motion status and driving speed information of the vehicle-mounted vehicle. The signal processing module is communicatively connected to the antenna module and is used to calculate the tracking deviation based on the output of the single-pulse receiving unit, and to filter the tracking deviation and attitude data. The servo drive module is used to drive the antenna to rotate according to control commands. Its controller can dynamically adjust the control parameters according to the vehicle's speed and the rate of change of tracking error. The main control module is communicatively connected to the attitude perception module, the signal processing module, and the servo drive module, respectively. It is used to adaptively switch the tracking mode according to the driving speed collected by the attitude perception module, and to start the satellite loss recovery process when the beacon signal detection unit determines that the satellite has been lost, and to control the servo drive module to reacquire the signal based on inertial navigation prediction and conical scanning. The main control module has a built-in tracking mode switching unit, configured as follows: when the real-time vehicle speed is less than or equal to a first preset threshold, a hybrid tracking mode is adopted, which mainly uses inertial navigation data and corrects for single-pulse tracking errors; when the real-time vehicle speed is greater than the first preset threshold, it switches to a hybrid tracking mode that mainly uses single-pulse tracking and uses inertial navigation data for motion prediction. The satellite recovery process includes: Based on the last valid tracking data latched at the moment of satellite loss and the angular velocity data output in real time by the inertial navigation unit, the current angle that the antenna should point to is predicted through an attitude recursion algorithm. Centered on the predicted angle, the servo drive module is controlled to drive the antenna to perform a conical scan; During the scanning process, if the beacon signal detection unit detects that the beacon signal strength has reached or exceeded the preset threshold, it will immediately stop scanning and switch back to the dynamic tracking stage.

2. The vehicle-mounted satellite dynamic tracking device according to claim 1, characterized in that, The attitude perception module includes a GPS receiver, an inertial navigation unit, and a vehicle speed sensor. The data from each sensor are output after being preprocessed by synchronization alignment, amplitude limiting filtering, and Kalman filtering.

3. The vehicle-mounted satellite dynamic tracking device according to claim 1, characterized in that, The controller of the servo drive module is a fuzzy PID controller, which measures the vehicle's speed v and the rate of change of tracking error. As input, the fuzzy inference output corrects the PID parameters, and the interpolation parameters in the fuzzy inference process are dynamically corrected based on the angle adjustment error of the encoder feedback, thereby achieving closed-loop adaptive optimization of the control parameters.

4. The vehicle-mounted satellite dynamic tracking device according to claim 1, characterized in that, The main control module is also used to perform error compensation, specifically: based on the pre-calibrated and stored antenna mechanical installation error angle β, combined with the real-time acquired carrier roll angle α, the target pointing angle of the antenna is compensated in real time through spatial coordinate transformation.

5. A method for dynamic satellite tracking of a vehicle-mounted station, applied to the vehicle-mounted station satellite dynamic tracking device according to any one of claims 1-4, characterized in that, Includes the following steps: S1. Initial satellite alignment steps: Based on the preset satellite orbit parameters and the initial position of the vehicle, calculate the theoretical satellite alignment angle of the antenna, drive the antenna to rotate to that angle for coarse alignment, and start frame search if no valid satellite signal is captured. S2. Dynamic tracking steps: Real-time acquisition of carrier motion data and extraction of tracking deviation, adaptive switching of main and auxiliary tracking modes based on real-time vehicle speed, and dynamic adjustment of servo drive parameters based on the processed tracking deviation to drive the antenna to track the satellite in real time. S3. Error Compensation Steps: Based on the pre-calibrated antenna mechanical installation error and combined with the real-time attitude of the carrier, the tracking angle of the antenna is compensated in real time. S4. Satellite Loss Recovery Steps: When satellite loss is determined, the current angle that the antenna should be pointing at is predicted based on inertial navigation data, and a conical scan is performed with the predicted angle as the center to reacquire the satellite signal.

6. The method for dynamic satellite tracking of a vehicle-mounted station according to claim 5, characterized in that, In the dynamic tracking step, the adaptive switching of primary and secondary tracking modes specifically involves: when the vehicle speed is less than or equal to 30 km / h, a hybrid mode is adopted that primarily uses inertial navigation data and corrects for single-pulse errors; when the vehicle speed is greater than 30 km / h, the mode is switched to a hybrid mode that primarily uses single-pulse tracking and performs motion prediction using inertial navigation data.

7. The method for dynamic satellite tracking of a vehicle-mounted station according to claim 5, characterized in that, In the satellite loss recovery step, the mathematical model for predicting the antenna pointing angle is: ； in, Let be the predicted angle at time t; For the moment of losing stars The antenna pointing angle; ω(τ) is the angular velocity of the carrier around the axis, which is collected in real time by the fiber optic inertial navigation unit; The inertial drift compensation amount for the angle.

8. The method for dynamic satellite tracking of a vehicle-mounted station according to claim 5, characterized in that, The frame search is centered on the theoretical star alignment angle and adopts a spiral search path within the preset range of roll and pitch angles, with the step size gradient decreasing until the beacon signal strength is detected to reach or exceed the preset threshold.

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