Satellite transit signal coverage analysis method and related equipment

Abstract

The embodiment of the invention provides a satellite transit signal coverage analysis method and related equipment, and belongs to the technical field of satellites. The method comprises the steps of obtaining two rows of orbit element sets of a target satellite; performing transit analysis of the target time point according to the two rows of orbit element sets to obtain a satellite orbit state of the target satellite at the target time point; performing static geometric coverage analysis according to the satellite orbit state to obtain a first signal coverage area of the target satellite at the target time point; querying a sky visual field graph according to the first signal coverage area, and determining sky visual field information in the first signal coverage area; the sky visible range graph is constructed based on a digital elevation model and field measurement data; and correcting the first signal coverage area according to the sky visual field information to obtain a second signal coverage area. According to the invention, the satellite transit signal coverage analysis precision can be improved.

Description

Technical Field

[0001] This application relates to the field of satellite technology, and in particular to a method and related equipment for analyzing satellite transit signal coverage. Background Technology

[0002] In the field of satellite communication and remote sensing technology, analyzing signal distribution during satellite transit is a crucial technology, directly impacting satellite system performance evaluation, resource optimization, and the reliability and integrity of data acquisition. Currently, the analysis of satellite transit coverage primarily relies on simulation tools based on orbital mechanics and geometric projection. These methods calculate the instantaneous coverage area of ​​the satellite and the transit time window for specific ground targets by inputting acquired or predicted satellite orbital parameters (such as altitude and inclination) and payload performance parameters (such as antenna beamwidth and pointing). However, this method typically treats the ground as an ideal, smooth sphere, failing to adequately consider the impact of complex environmental factors such as actual terrain obstruction and urban building block shielding on signal propagation paths, resulting in low accuracy in satellite transit signal coverage analysis. Summary of the Invention

[0003] The main objective of this application is to propose a satellite transit signal coverage analysis method and related equipment, aiming to improve the accuracy of satellite transit signal coverage analysis.

[0004] To achieve the above objectives, one aspect of this application proposes a satellite transit signal coverage analysis method, comprising the following steps: Obtain the two-line orbital feature set of the target satellite; Based on the two sets of orbital elements, a transit analysis is performed at the target time point to obtain the satellite orbital status of the target satellite at the target time point. Based on the satellite orbital state, a static geometric coverage analysis is performed to obtain the first signal coverage area of ​​the target satellite at the target time point; The sky visibility map is queried based on the first signal coverage area to determine the sky visibility information within the first signal coverage area; wherein, the sky visibility map is constructed based on a digital elevation model and field measurement data; The first signal coverage area is corrected based on the sky visibility information to obtain the second signal coverage area.

[0005] In some embodiments, obtaining the two-line orbital feature set of the target satellite includes the following steps: Determine whether the target time point is a future time point; If the target time point is a future time point, the latest version of the two-line orbit feature set is obtained from the database; the database stores multiple versions of the two-line orbit feature set collected in different historical times in chronological order, and each version of the two-line orbit feature set is set with a corresponding effective time window according to the epoch time. If the target time point is not a future time point, obtain the two rows of track element sets corresponding to the effective time window where the target time point is located.

[0006] In some embodiments, the step of performing transit analysis at the target time point based on the two rows of orbital feature sets to obtain the satellite orbital state of the target satellite at the target time point includes the following steps: The two sets of orbital elements are input into the satellite orbit estimation model to obtain the estimated orbit of the target satellite; Data at the target time point is extracted from the estimated orbit to obtain the satellite orbit state; the satellite orbit state includes the satellite position and satellite velocity.

[0007] In some embodiments, the sky view map is obtained through the following steps: Field measurement data is collected by a multi-sensor device distributed on the ground, including a gimbal equipped with an inertial navigation system and a laser rangefinder; First line-of-sight data at the equipment distribution points is generated based on the aforementioned field measurement data; A sky view map is constructed based on the first line-of-sight data of each device distribution point; The blank areas of the sky view map are supplemented by using second line-of-sight data obtained from analysis based on a digital elevation model.

[0008] In some embodiments, the satellite overpass signal coverage analysis method further includes the following steps: The satellite receiver is used to receive the satellite signal from the target satellite; The peak time of the signal is obtained by extracting features from the satellite signal. The satellite orbit estimation model is corrected based on the peak signal time and the predicted time of the satellite orbit passing the receiver by the satellite orbit estimation model.

[0009] In some embodiments, the step of correcting the satellite orbit estimation model based on the signal peak time and the predicted time of the satellite orbit passing the receiver by the satellite orbit estimation model includes the following steps: If the deviation between the peak time of the signal and the predicted time is greater than a preset deviation, the true position of the satellite is inferred from the decoded data of the satellite signal. The satellite orbit estimation model is corrected based on the actual location.

[0010] To achieve the above objectives, another aspect of this application proposes a satellite transit signal coverage analysis system, comprising: The first module is used to acquire the two-line orbital feature set of the target satellite; The second module is used to perform transit analysis at the target time point based on the two sets of orbital elements to obtain the satellite orbital status of the target satellite at the target time point. The third module is used to perform static geometric coverage analysis based on the satellite orbital state to obtain the first signal coverage area of ​​the target satellite at the target time point; The fourth module is used to query the sky visibility map based on the first signal coverage area to determine the sky visibility information within the first signal coverage area; wherein, the sky visibility map is constructed based on a digital elevation model and field measurement data; The fifth module is used to correct the first signal coverage area based on the sky visibility information to obtain the second signal coverage area.

[0011] To achieve the above objectives, another aspect of this application provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the above-described method.

[0012] To achieve the above objectives, another aspect of the embodiments of this application proposes a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method.

[0013] To achieve the above objectives, another aspect of the embodiments of this application proposes a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0014] The embodiments of this application include at least the following beneficial effects: This application provides a satellite transit signal coverage analysis method, system, electronic device, storage medium, and program product. The scheme first acquires two rows of orbital element sets of the target satellite, performs transit analysis at the target time point based on the two rows of orbital element sets to obtain the satellite orbital state at the target time point, and performs static geometric coverage analysis based on the satellite orbital state to obtain the first signal coverage area of ​​the target satellite at the target time point, thereby initially determining the coverage range during satellite transit. Then, based on the first signal coverage area, a sky visibility map is queried to determine the sky visibility information within the first signal coverage area. This sky visibility map is constructed based on a digital elevation model and field measurement data, and can accurately represent the sky visibility at various locations under obstruction conditions. The first signal coverage area is then corrected based on the sky visibility information to obtain a second signal coverage area considering obstructions, thereby improving the accuracy of satellite transit signal coverage analysis. Attached Figure Description

[0015] Figure 1 This is a flowchart of the satellite transit signal coverage analysis method provided in the embodiments of this application; Figure 2 A schematic diagram illustrating the implementation process of the satellite orbit prediction system provided in this application embodiment; Figure 3 This application provides a schematic diagram illustrating the concept of satellite orbital state reasoning at a predicted time. Figure 4 This application provides a schematic diagram illustrating the implementation process of the sky visibility generation system in an embodiment. Figure 5 This application provides a schematic diagram illustrating the implementation process of a multi-sensor hardware active detection mode in an embodiment. Figure 6 A schematic diagram illustrating the implementation process of the DEM data calculation mode provided in this application embodiment; Figure 7 This application provides a schematic diagram illustrating the implementation process of a satellite signal processing system in an embodiment. Figure 8 This application provides a schematic diagram of the satellite signal processing and model verification and update process in an embodiment. Figure 9 This is a schematic diagram of the hardware structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0016] 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 of this application and are not intended to limit it. In the following description, when referring to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with those of this application; they are merely examples of apparatuses and methods consistent with some aspects of the embodiments of this application as detailed in the appended claims.

[0017] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0018] Before providing a detailed description of the embodiments of this application, some of the nouns and terms involved in the embodiments of this application will be explained first. The nouns and terms involved in the embodiments of this application are subject to the following interpretations.

[0019] SkyViewFactor (SVF) is a metric used to quantify the visibility of the sky at a given point. Its value is defined as the ratio of the area of ​​the visible sky at that point to the area of ​​the entire sky hemisphere, with a result between 0 and 1. Simply put, if you are standing in an unobstructed open area where the entire sky hemisphere is visible, the SVF value is close to 1; however, if you are in a narrow street or a dense forest where parts of the sky are obstructed by buildings, trees, or other obstacles, the SVF value will decrease, even approaching 0.

[0020] The SGP4 / SDP4 models are standard mathematical models used to predict the position and velocity of satellites or space debris in Earth orbit. The SGP4 model is typically used for low Earth orbit (LEO) objects, while the SDP4 model is suitable for deep space orbit objects (such as geostationary orbit). They can calculate the future position and velocity of satellites based on two-line orbital elements (TLE).

[0021] A Digital Elevation Model (DEM) is a model that uses a dataset of planar coordinates (X, Y) and elevations (Z) of regular grid points to represent the undulating shape of the ground. It is a branch of Digital Terrain Models (DTM) and is mainly used to describe the spatial distribution of regional landforms.

[0022] SDR (Software-Defined Radio) is a radio communication technology whose core idea is to use software to define and implement the functions of wireless communication protocols (such as modulation and demodulation, frequency band selection), rather than relying on hard-wired circuits. This allows devices to be flexibly upgraded through software updates to adapt to different communication standards.

[0023] A PLL (Phase-Locked Loop) is an electronic control system that uses phase synchronization for automatic tracking. It controls a voltage-controlled oscillator (VCO) by comparing the phase difference between the input and output signals and converting this difference into a voltage signal. This allows the frequency and phase of the output signal to accurately track the input signal. It is widely used for signal synchronization, frequency synthesis, and modulation / demodulation.

[0024] TLE (Two-Line Element), also known as a two-line orbital element set in this embodiment, is a standard data format used to describe the orbital parameters of spacecraft (such as satellites and space debris) at a specific time (epoch). It consists of two lines of text, each with 69 characters, containing the key parameters needed to calculate the object's orbit and forming the basis for orbit prediction using models such as SGP4 / SDP4.

[0025] INS (Inertial Navigation System) is an autonomous navigation system that does not rely on external information. It calculates the vehicle's velocity, position, and attitude by measuring the vehicle's own acceleration (achieved by accelerometers) and angular motion (achieved by gyroscopes) and performing calculations such as integration. Its core advantages lie in its stealth and independence, but errors accumulate over time.

[0026] In related technologies, the analysis of satellite transit coverage mainly relies on simulation tools based on orbital mechanics and geometric projection. These methods calculate the instantaneous coverage area of ​​the satellite and the transit time window of specific ground targets by inputting collected or predicted satellite orbital parameters and payload performance parameters. However, this method usually treats the ground as an ideal smooth sphere and does not fully consider the impact of complex environmental factors such as actual terrain obstruction and urban building block shielding on the signal propagation path, resulting in low accuracy of satellite transit signal coverage analysis.

[0027] In view of this, this application provides a satellite transit signal coverage analysis method and related equipment. This method analyzes the satellite orbit state based on two sets of orbital elements, and then analyzes the overall signal coverage area of ​​the satellite transit. Furthermore, it combines the constructed sky view map to analyze the signal coverage area considering obstructions, thereby improving the accuracy of satellite transit signal coverage analysis.

[0028] The satellite transit signal coverage analysis method provided in this application relates to the field of satellite technology. This method can be applied to a terminal, a server, or software running on either a terminal or a server. In some embodiments, the terminal can be a smartphone, tablet, laptop, desktop computer, smart speaker, smartwatch, or vehicle-mounted terminal, but is not limited to these. The server can be configured as an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms. The server can also be a node server in a blockchain network. The software can be an application implementing the satellite transit signal coverage analysis method, but is not limited to the above forms.

[0029] This application can be used in a wide variety of general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics devices, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices. This application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform specific tasks or implement specific abstract data types. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0030] Figure 1 This is an optional flowchart of the satellite transit signal coverage analysis method provided in the embodiments of this application. Figure 1 The method may include, but is not limited to, steps S101 to S105. S101, Obtain the two-line orbital feature set of the target satellite; S102, perform transit analysis at the target time point based on the two rows of orbital element sets to obtain the satellite orbital status of the target satellite at the target time point; S103, Static geometric coverage analysis is performed based on the satellite orbital status to obtain the first signal coverage area of ​​the target satellite at the target time point; S104, Based on the first signal coverage area, query the sky visibility map to determine the sky visibility information within the first signal coverage area; wherein, the sky visibility map is constructed based on the digital elevation model and field measurement data; S105, the first signal coverage area is corrected based on the sky visibility information to obtain the second signal coverage area.

[0031] Steps S101 to S105, as illustrated in this embodiment, analyze the satellite orbital state based on the two-line orbital element set, and then analyze the overall signal coverage area of ​​the satellite transit. Furthermore, by combining the constructed sky view map, the signal coverage area considering obstructions is analyzed, thus improving the accuracy of satellite transit signal coverage analysis. In addition, the sky view map in this embodiment is constructed based on a digital elevation model and field measurement data. Compared to the method of constructing a sky view map using a digital elevation model, it can more accurately represent ground obstruction, further improving the accuracy of satellite transit signal coverage analysis.

[0032] In step S101 of some embodiments, after determining the target satellite to be analyzed, the two-line set of orbital elements actively reported by the satellite can be obtained from the relevant agency system.

[0033] According to some embodiments of this application, step S101 may include, but is not limited to, the following steps: S201, Determine whether the target time point is a future time point; S202, when the target time point is a future time point, retrieve the latest version of the two-line track feature set from the database; the database stores multiple versions of the two-line track feature set collected in different historical times in chronological order, and each version of the two-line track feature set sets a corresponding effective time window according to the epoch time. S203, when the target time point is not a future time point, obtain the set of two rows of track elements corresponding to the effective time window where the target time point is located.

[0034] In related technologies, satellite orbit analysis commonly employs two-line orbital data sets published by relevant institutions, combined with models such as SGP4 / SDP4, for orbit calculation. However, a set of two-line orbital data sets only possesses high accuracy within a specific time window centered on its epoch. As time progresses, due to the combined effects of various perturbations such as Earth's non-spherical shape, atmospheric drag, and solar radiation pressure, deviations will appear between the actual orbital state and the prediction results based on older two-line orbital data sets. These deviations increase over time, representing a significant problem of accuracy decay in orbit prediction. Current orbit analysis processes do not consider this issue, typically using the latest single version of the two-line orbital data set for orbit prediction, which introduces significant biases into historical satellite orbit analysis. Furthermore, in continuous prediction missions, it is difficult to ensure seamless switching to the optimal data version, thus failing to meet the requirements for high accuracy and continuity in orbit prediction.

[0035] This embodiment aims to overcome the accuracy degradation caused by time constraints in long-term satellite orbit prediction using a single version of two-line orbital features. It automates and automates the prediction process, reducing manual intervention and ensuring the most accurate orbital feature data is used throughout the prediction period, thus improving the accuracy and continuity of orbit prediction. To this end, it collects and saves two-line orbital feature sets from different epochs and matches the most suitable set based on the target time point for subsequent orbit inference, thereby improving the accuracy and automation of satellite orbit prediction. Please refer to [link / reference]. Figure 2 The schematic diagram of the satellite orbit prediction system implementation process is shown below: S11, multi-version two-line track feature database management.

[0036] Data Acquisition: The system is set to retrieve the latest two-line orbital feature set from authoritative data sources at fixed intervals. The two-line orbital feature set covers the satellite's key orbital parameters at a specific epoch, including orbital inclination, right ascension of the ascending node, eccentricity, argument of perigee, mean perigee, mean motion, and orbital perturbation correction parameters.

[0037] Data storage: Newly acquired feature sets are stored together with historical versions in the constructed database. The database design employs an optimized structure to enable rapid indexing and retrieval of data from different versions, meeting the system's requirements for efficient data management.

[0038] S12, Time Validity Window Division.

[0039] The time validity window is determined based on the epoch timestamp of each two-row track feature set. Specifically, the epoch time of the later version feature set serves as the end point of the validity window for the previous version, achieving seamless and continuous coverage of each window on the timeline. For example, if version A is acquired at time t1 and version B is acquired at time t2, then the validity window for version A is from t1 to t2.

[0040] S13, automatic data version matching and retrieval.

[0041] The system is equipped with an intelligent matching engine. For a given orbit prediction time point (i.e., the target time point), if the time point is a future time point, it selects the latest version of the two-row orbit feature set; if the time point is a past time point, it automatically determines the validity window in which the time point falls, and then accurately retrieves and calls the corresponding version of the two-row orbit feature set from the database. For example, to predict the satellite orbit at time T, the matching engine compares T with the time windows of each window. If T is within the validity window corresponding to version B, it calls the data of version B.

[0042] In step S102 of some embodiments, after obtaining a suitable version of the two-line orbit feature set, a transit analysis of the target time point is performed based on the two-line orbit feature set to obtain the satellite orbit status of the target satellite at the target time point.

[0043] According to some embodiments of this application, step S102 may include, but is not limited to, the following steps: S301, input the two rows of orbit element sets into the satellite orbit estimation model to obtain the estimated orbit of the target satellite; S302, extract data at the target time point from the estimated orbit to obtain the satellite orbital state; the satellite orbital state includes the satellite position and satellite velocity.

[0044] In this embodiment, the satellite orbit estimation model can be either the SGP4 or SDP4 model. For details, please refer to [link / reference needed]. Figure 2 After matching the appropriate version of the two-line track elements, the following operations are performed: S14, Orbit Prediction Calculation.

[0045] The matched two rows of orbit feature version data are retrieved and input into the satellite orbit estimation model (such as SGP4 or SDP4 model) for orbit calculation. Based on the orbit parameters and perturbation correction parameters in the feature set, and combined with the principles of satellite orbit dynamics, the model simulates the satellite's trajectory under various perturbation forces and outputs the satellite orbit state vector at the predicted time.

[0046] For example, assuming orbit prediction is performed on a low Earth orbit (LEO) satellite, using a satellite ground monitoring station in a certain region as an application scenario, please refer to... Figure 3 The reasoning behind the satellite's orbital state at the predicted time (i.e., the target time point) is as follows: Ground monitoring stations are configured to retrieve the latest two-line orbit feature sets from relevant technologies once or twice per hour. Higher synchronization density results in higher TLE (Time Limit Expiration) values ​​for the acquired longitude, and vice versa. For example, if a feature set of version V1 is retrieved at 2:00 AM on January 1, 2024, with an epoch time of 2024-01-01T00:00:00Z; and a feature set of version V2 is retrieved at 2:00 AM on January 2, 2024, with an epoch time of 2024-01-02T00:00:00Z, and so on. These feature sets are stored in a locally built multi-version two-line orbit feature database using a relational database management system (such as MySQL), indexed by timestamps and version numbers for convenient and fast retrieval.

[0047] Based on the epoch time, the time validity window for version V1 is from 2024-01-01T00:00:00Z to 2024-01-02T00:00:00Z; the validity window for version V2 is from 2024-01-02T00:00:00Z to 2024-01-03T00:00:00Z. This ensures that each version's feature set has a clearly defined valid time range and continuous temporal coverage.

[0048] Input the prediction time point, and the system determines the validity window corresponding to that time point. If the window can be determined, the corresponding feature version is selected for orbit prediction calculation and the result is output; if the window cannot be determined, the prediction time point is re-entered and the system continues to determine until a prediction result is obtained. For example, to predict the satellite's orbital state data at 3 PM on January 1, 2024 (15:00:00Z), the matching engine determines that it falls within the validity window of version V1 based on the current time 15:00:00Z. Therefore, it retrieves and calls the two-line orbital feature set of version V1 from the database. The called version V1 feature set is input into the SGP4 orbit prediction model. The SGP4 model calculates the satellite's orbital position and velocity state vectors at that moment based on parameters such as orbital inclination, right ascension of the ascending node, eccentricity, argument of perigee, mean perigee angle, and mean motion from the feature set, combined with the Earth's gravitational field model and atmospheric drag model. Calculations show that the satellite's orbital position at 2024-01-01T15:00:00Z is (X1,Y1,Z1), and its velocity is (Vx1,Vy1,Vz1).

[0049] In step S103 of some embodiments, based on the aforementioned satellite orbital state and combined with relevant parameters such as the target satellite's attitude at the target time point, static geometric coverage analysis is performed to obtain the first signal coverage area of ​​the target satellite at the target time point. Specifically, based on dynamic equations and Kalman filtering (such as Extended Kalman Filter EKF), the angular velocity measurement value from the gyroscope is fused with the observed attitude provided by star sensors, etc., to extrapolate the satellite attitude at any given time. Alternatively, a satellite attitude extrapolation model can be constructed using linear regression machine learning algorithms, or a deep learning model such as LSTM (Long Short-Term Memory Network) can be used to analyze historical attitude telemetry data to predict future attitudes. The first signal coverage area refers to the area of ​​the ground that the satellite can detect.

[0050] In step S104 of some embodiments, a sky visibility map is queried based on the first signal coverage area to determine the sky visibility information within the first signal coverage area. The sky visibility information includes line-of-sight data (i.e., line-of-sight conditions at each elevation angle) at various locations within the first signal coverage area. To improve the accuracy of the line-of-sight data, the sky visibility map is constructed based on a digital elevation model and field measurement data.

[0051] According to some embodiments of this application, the sky view map in step S104 can be obtained through, but is not limited to, the following steps: S401 collects field measurement data through a multi-sensor device distributed on the ground, including a gimbal equipped with an inertial navigation system and a laser rangefinder; S402, Generate first line-of-sight data at the equipment distribution points based on actual measurement data; S403, construct a sky view map based on the first line-of-sight data of each device distribution point; S404 uses second line-of-sight data obtained from analysis based on a digital elevation model to fill in the blank areas of the sky view map.

[0052] In calculating the visible sky field (SVF), traditional methods primarily rely on fisheye lens photography, using algorithms such as the Steyn algorithm based on equal angle of incidence to calculate SVF values. However, this method has several limitations. On one hand, lens projection model deviations can lead to inaccurate calculation results; on the other hand, ambient light interference can affect data reliability. Furthermore, this method cannot be dynamically updated in real time, making it difficult to meet the real-time requirements of practical applications. While regular grid-based DEM-based SVF analysis methods, such as the tilt method and skyline algorithm, can analyze terrain occlusion, they require interpolation calculations for each grid point, resulting in significant data redundancy and low computational efficiency. Moreover, they can only reflect static terrain occlusion and cannot detect occlusion by dynamic features (such as trees and moving vehicles), limiting their application in real-world scenarios.

[0053] This embodiment addresses the problems of low accuracy, poor real-time performance, and inability to detect dynamic ground occlusion in traditional sky visibility calculation methods. It provides a method capable of real-time dynamic updating and high-precision sky visibility calculation, and able to detect various types of ground occlusion, thereby constructing a high-precision sky visibility map to meet the needs of sky visibility analysis in different scenarios (such as real-time decision-making and planning stages). Please refer to... Figure 4 The diagram illustrates the implementation process of the sky visibility generation system. The architecture shows that the system consists of a multi-sensor hardware active detection subsystem (i.e., multi-sensor devices) and a DEM data calculation subsystem. In the multi-sensor hardware active detection subsystem, the INS inertial navigation calibration unit calibrates the gimbal PTZ control unit, which in turn controls the laser rangefinder scanning unit to perform scanning, transmitting calibration information, azimuth information, and distance information to the data recording and processing unit. In the DEM data calculation subsystem, the DEM data loading unit provides data to the line-of-sight analysis algorithm unit, which then transmits the analysis results to the visibility result generation unit. Finally, the data generated by both subsystems is transmitted to the data fusion and display unit for fusion and display, as detailed below: On the one hand, please refer to Figure 5 Skyline visibility data is generated using a multi-sensor hardware active detection mode, as follows: Calibration Phase: Geomagnetic / azimuth calibration is performed using the INS inertial navigation system. The INS precisely calibrates the gimbal by sensing the characteristics of the Earth's magnetic and gravitational fields, ensuring that the gimbal's north direction is zeroed, providing an accurate azimuth reference for subsequent scanning measurements.

[0054] Horizontal Scan: The PTZ (Pan-Tilt-Zoom) unit performs a 360° horizontal rotation scan at a set step angle (e.g., 1°). During the rotation, the INS (Inertial Navigation System) provides real-time feedback on the azimuth angle of the PTZ unit, enabling the system to accurately determine the horizontal position of the scan.

[0055] Elevation Angle Detection: At each horizontal azimuth angle, the laser rangefinder performs a vertical scan from 0 to 90 degrees. The laser rangefinder measures distance by emitting a laser beam and receiving the reflected light. A distance timeout determination mechanism is employed: if the laser beam does not receive a reflected signal within a set time (e.g., 100ms), it is determined to be pointing towards an unobstructed area of ​​the sky, and the elevation angle at this time is recorded as the unobstructed critical elevation angle; if a reflected signal is received, the measured distance to the ground object is obtained, yielding the actual field measurement data.

[0056] Data Generation: The system synchronously records GPS coordinates, timestamps, azimuth-elevation matrix, and other information during the scanning process to generate the first line-of-sight data. This includes GPS coordinates determining the geographic location, timestamps recording the measurement time, and the azimuth-elevation matrix reflecting the line-of-sight in each measurement direction, providing comprehensive data support for constructing a sky view map.

[0057] On the other hand, please refer to Figure 6 Sky visibility data was generated using the DEM data calculation model, as follows: Data input: The system loads regular grid DEM or triangular network DEM data. These data represent topographic information in the form of elevation matrices and can be obtained from professional geographic information databases.

[0058] Visibility Analysis: Visibility analysis is performed using the tilt method or the skyline algorithm. Taking the tilt method as an example, with the observation point as the center, the elevation difference between each grid point in the DEM data and the line connecting it to the observation point is calculated. An elevation difference threshold is set; if the elevation difference is lower than the threshold, the grid point is considered to be visible to the observation point and assigned a value of 1; otherwise, it is not visible and assigned a value of 0. To improve computational efficiency, a temporary matrix is ​​introduced to record the occlusion height of the visible surface. When calculating a new grid point, the occlusion height information in the corresponding direction is first queried from the temporary matrix to avoid repeated interpolation calculations.

[0059] Results Generation: Based on the visibility analysis results, a rasterized visibility map is output to intuitively display the distribution of visible and non-visible areas within the region, i.e., the second visibility data; or a ray-shaped visibility map is generated to present the visibility range in different directions at 360° azimuth intervals (such as 1° intervals), providing users with clear visibility information.

[0060] Furthermore, when constructing the sky view map, the sky view map is first constructed based on the accurate sky view data (i.e., the first line of sight data of each device distribution point) generated by the multi-sensor hardware active detection mode. For areas with missing data, sky view data (i.e., second line of sight data) is generated based on the DEM data calculation mode to fill in the missing data, thereby obtaining a complete sky view map.

[0061] For example, using a mountainous area as a scenario, we will perform sky visibility analysis to illustrate the specific implementation methods of the two modes.

[0062] Multi-sensor hardware active detection mode: (1) During the calibration phase, install the gimbal, INS inertial navigation system, and laser rangefinder. The equipment can be installed on a car, a drone, or miniaturized for portability. After powering on, the INS inertial navigation system performs geomagnetic / azimuth calibration on the gimbal by measuring the direction of the Earth's magnetic and gravitational fields. During the calibration process, the INS inertial navigation system sends calibration commands to the gimbal, and the gimbal fine-tunes its direction according to the commands to ensure that the north direction is accurately zeroed. At this time, the azimuth measurement reference of the gimbal is calibrated.

[0063] (2) Horizontal scanning: The PTZ (PTZ) unit begins a 360° horizontal rotation scan in 1° increments. Each time the unit rotates to a new angle, the INS (Inertial Navigation System) feeds back the current azimuth angle value to the system in real time. For example, when the PTZ unit rotates to a 30° azimuth angle, the INS feeds back this angle information to the system, which records the azimuth angle. The entire scanning process is completed within 360 seconds, rotating 1° per second.

[0064] (3) Elevation detection: At each azimuth angle, the laser rangefinder begins a vertical scan from 0° to 90°. The laser rangefinder emits a laser beam, and the distance timeout threshold is set to 150ms. When the laser beam is pointed at an obstruction such as a hillside, a reflected signal is received within 150ms, and the distance is measured to be 50 meters. At this time, the distance value of 50 meters at the azimuth-elevation angle (30°, 20°) is recorded. When the laser beam is pointed at an unobstructed area in the sky, no reflected signal is received within 150ms. The direction is determined to be the sky direction, and the unobstructed area at the azimuth-elevation angle (30°, 60°) is recorded.

[0065] (4) Data generation: The system synchronously records GPS coordinates (e.g., 30.1234°N, 120.5678°E), timestamp (2024-01-01T10:00:00Z), and measurement data of each azimuth and elevation angle to form a line-of-sight vector dataset (i.e., first line-of-sight data). This dataset is stored in array form, with each element containing azimuth, elevation, GPS coordinates, and timestamp information, for example: [(30°, 20°, 30.1234°N, 120.5678°E, 2024-01-01T10:00:00Z), (30°, 60°, 30.1234°N, 120.5678°E, 2024-01-01T10:00:00Z),…].

[0066] DEM data calculation mode: (1) Data input: Obtain high-precision regular grid DEM data of the mountainous area from a geographic information data provider. The grid resolution is 10 meters and the data format is GeoTIFF. Load the data into the system, and the system parses it into an elevation matrix. Each element in the matrix corresponds to the elevation value of a grid point.

[0067] (2) Visibility analysis: Taking a location as observation point O, and using vehicles such as cars and drones as carriers, visibility analysis is performed using the tilt method, with an elevation difference threshold of 20 meters. For each grid point P in the DEM data, the elevation difference between the line connecting observation point O and grid point P is calculated. For example, if the elevation of observation point O is 100 meters, the elevation of grid point P is 110 meters, and the horizontal distance between the two points is 50 meters, the calculated elevation difference is 10 meters, which is less than the threshold of 20 meters. Therefore, the grid point is considered to be visible to the observation point and assigned a value of 1. At the same time, a temporary matrix is ​​used to record the occlusion height of the visible surface. When calculating adjacent grid points, the occlusion height information in the corresponding direction is first queried in the temporary matrix. If it already exists, it is used directly to avoid duplicate calculations.

[0068] (3) Result generation: Based on the line-of-sight analysis results, a rasterized view map (i.e., second line-of-sight data) is generated. In the view map, the visible area is represented in white, and the non-visible area is represented in black. At the same time, a radial line-of-sight view is generated, which displays the 360° line-of-sight range at azimuth intervals of 5°. For example, in the azimuth direction of 30°, information such as the maximum line-of-sight distance in that direction or the distance of the first obstruction encountered is displayed.

[0069] In step S105 of some embodiments, the first signal coverage area is corrected based on the sky visibility information to obtain a second signal coverage area. The first signal coverage area refers to the ground range that the satellite can detect or cover, but this range does not take into account ground obstruction. Therefore, this embodiment further combines the sky visibility information of the first signal coverage area with geometric analysis methods or mathematical formulas to determine whether each point in the first signal coverage area simultaneously meets the conditions of being covered by the satellite and having an unobstructed sky visibility, thereby obtaining the second signal coverage area. This second signal coverage area can accurately characterize the signal coverage of the target satellite on the ground at the target time point.

[0070] According to some embodiments of this application, the satellite overpass signal coverage analysis method of this application may also include, but is not limited to, the following steps: S501 uses a satellite receiver to receive satellite signals from the target satellite; S502, extracts satellite signal features to obtain signal peak time; S503 corrects the satellite orbit estimation model based on the signal peak time and the predicted time of the satellite orbit to the receiver.

[0071] This embodiment also receives satellite signals from the target satellite via a satellite receiver, and updates the satellite orbit estimation model based on the actual received satellite signals, so that the satellite orbit estimation model can output more accurate prediction results.

[0072] Currently, in satellite signal reception and processing, traditional satellite signal receiving equipment relies on dedicated hardware to demodulate specific frequency bands, resulting in poor flexibility and low compatibility with different satellites. While Software-Defined Radio (SDR) technology software-defined signal processing functions and supports dynamic reconstruction of communication protocols, related solutions still suffer from insufficient accuracy and poor real-time performance in satellite transit trajectory verification and multi-mode signal adaptive decoding. For example, traditional baseband processing equipment uses a fixed hardware architecture, making it unable to adapt to the modulation schemes of different satellites (such as FM / APSK / QPSK), and lacks an active signal acquisition mechanism based on orbit prediction, leading to a high signal loss rate. Furthermore, the lack of a two-way verification mechanism between satellite transit prediction and actual signal acquisition results in significant orbit tracking errors, affecting signal reception stability.

[0073] This embodiment addresses the problem that current satellite signal receiving equipment cannot dynamically adapt to multiple satellite frequency bands and modulation methods. It establishes a two-way verification mechanism between satellite overpass prediction and actual signal acquisition to improve tracking accuracy, enhance decoding reliability and adaptive optimization capabilities under adverse channel conditions, and achieve efficient and stable reception and processing of multiple satellite signals. Please refer to... Figure 7 The diagram illustrates the implementation process of a satellite signal processing system. It shows the components and their connections. The satellite signal receiving antenna receives downlink signals and transmits them to the signal acquisition and preprocessing module for preprocessing. The preprocessed signal then enters the signal decoding and satellite identification module for decoding and satellite identification. The decoded data, along with the input TLE data and geographic location information, enters the transit verification and adaptive optimization module for further processing. Finally, the processed data is output by the data output module. Details are as follows: S21, Signal Acquisition and Preprocessing.

[0074] Parameter settings: Set the corresponding input parameters according to the characteristics of different satellite signals, such as setting the NOAAAPT signal frequency band to 137.5MHz, sampling rate ≥2MS / s, and bandwidth to 40kHz.

[0075] Hardware requirements: Ensure antenna gain ≥ 5dBi, LNA noise figure < 1.5dB, SDR dynamic range ≥ 80dB to ensure high-quality signal reception, reduce noise interference, and improve the dynamic range of signal processing.

[0076] Frequency shift compensation and phase tracking: Pre-compensate for the Doppler frequency shift of satellite signals, such as the typical ±10kHz frequency shift of LEO satellites. Track the carrier phase through a phase-locked loop (PLL) to make the received signal frequency consistent with the expectation, providing a stable signal basis for subsequent demodulation.

[0077] Filtering and Down-Conversion: A square root raised cosine roll-off filter is used to limit the signal spectrum range and reduce sampling timing pulse errors. Digital down-conversion (DDC) is implemented using FPGA to separate multiple satellite signals (such as GPS L1 and BeiDou B1 signals) in parallel, and outputs digital baseband signals (I / Q data streams), signal strength peak timestamps, and initial signal-to-noise ratio estimates.

[0078] S22, Signal Decoding and Satellite Identification.

[0079] Parameter input: Input key parameters such as the modulation type corresponding to the satellite signal (e.g., FM corresponds to NOAAAPT, QPSK corresponds to BeiDou) and frame synchronization header (e.g., BeiDou pilot sequence) to guide the system to correctly process different satellite signals.

[0080] Carrier recovery and error correction: Appropriate carrier recovery methods are employed for different modulation schemes. For example, a Costas ring is used for BPSK signals, and differential coding is used for QPSK signals to avoid phase flipping. Error correction coding mechanisms are employed; for example, BeiDou signals use a combination of BCH and LDPC codes to ensure a bit error rate ≤10%. -8 This improves the accuracy of data transmission.

[0081] Message parsing and modulation identification: Navigation messages are parsed according to a specific structure. For example, BeiDou signals are parsed as follows: superframe (360 seconds) → main frame (30 seconds) → subframe (6 seconds) → word (30 bits). Frame boundaries are confirmed through telemetry words (TLMs). GPS signal subframes are parsed to obtain TOW (Time Week Count), ephemeris parameters (Kepler orbital elements), etc. The modulation scheme is confirmed to be consistent with expectations by checking the constellation diagram shape. Finally, telemetry data (images, orbital parameters, etc.), satellite identifiers (matched with NORAD numbers through ephemeris), and a bit error rate report are output.

[0082] According to some embodiments of this application, step S503 may include, but is not limited to, the following steps: S601: When the deviation between the peak time of the signal and the predicted time is greater than the preset deviation, the true position of the satellite is inferred from the decoded data of the satellite signal. S602, corrects the satellite orbit estimation model based on the actual location.

[0083] In this embodiment, please continue to refer to Figure 7 After receiving and decoding the satellite signal, the satellite orbit estimation model is corrected using relevant data, as follows: S23, Transit Verification and Adaptive Optimization Parameter input: Input TLE data, geographical location (latitude and longitude), signal-to-noise ratio threshold (e.g., 10dB) and other parameters to provide a basis for transit verification and adaptive adjustment.

[0084] Execution conditions: Ensure that the SGP4 model accuracy error is <1km, the time synchronization error is <1 second, the electric rotator stepping accuracy is ≤0.1°, and the Doppler frequency shift is tracked in real time to ensure the high accuracy and stability of the system operation.

[0085] Closed-loop verification and adaptive adjustment: Compare the peak signal time with the predicted time. If the deviation is >10 seconds, feed the deviation data back to the satellite orbit estimation model, and use the decoded data to infer the satellite position for correction. When channel conditions change, such as Doppler shift >5kHz or SNR <10dB, switch to a simplified HARQ mechanism (cancel retransmission, increase FEC redundancy), optimize the communication link, and output a transit verification deviation report (time deviation <10 seconds, azimuth deviation <1°), orbit model correction parameters, and adaptive switching log.

[0086] Please refer to Figure 8 This document describes the satellite signal processing and model verification and update process. It outlines the satellite signal processing flow, starting with setting the receiving parameters. After receiving the satellite signal, pre-compensated Doppler shift, filtering, and digital down-conversion to a digital baseband signal are performed sequentially. Next, signal decoding and satellite identification are conducted, inputting the modulation type and frame synchronization header information. The process involves carrier recovery, error correction decoding, and parsing the navigation message to obtain satellite data. Following this, in the transit verification and adaptive optimization phase, the predicted trajectory is compared with the actual signal's spatiotemporal characteristics. If the deviation exceeds the acceptable range, the orbit model is corrected; if the deviation is within the acceptable range, the channel quality is assessed based on the signal-to-noise ratio (SNR). If the SNR is low, the coding scheme and retransmission mechanism are adjusted; if the SNR is high, the data is directly output. Finally, the processed satellite data is output, and the process ends. The following is a detailed explanation using the reception of NOAA meteorological satellite and BeiDou navigation satellite signals as examples: The signal acquisition and preprocessing process of NOAA weather satellites is as follows: Parameter settings: For NOAA satellite signals, set the center frequency to 137.5MHz, the sampling rate to 2.5MS / s, and the bandwidth to 40kHz.

[0087] Hardware requirements: An antenna with a gain of 6dBi is selected, the LNA noise figure is 1.2dB, and the dynamic range of the SDR device is 85dB.

[0088] Pre-compensation and tracking: Based on the orbital information of NOAA satellites, Doppler shift is pre-compensated, with a typical value of ±8kHz. The carrier phase is tracked through a phase-locked loop (PLL) to ensure stable received signal frequency.

[0089] Filtering and Down-Conversion: A square root raised cosine roll-off filter with a roll-off factor of 0.5 is used to limit the signal spectrum. Digital down-conversion is implemented using an FPGA to convert the received high-frequency signal into a digital baseband signal (I / Q data stream). Simultaneously, the peak signal strength timestamp is recorded as 2024-01-01T14:30:00Z, and the initial signal-to-noise ratio estimate is 15dB.

[0090] The signal decoding and satellite identification process of NOAA weather satellites is as follows: Parameter input: It is known that NOAA satellite signals use FM modulation and the frame synchronization header is a specific frequency sequence.

[0091] Carrier recovery and error correction decoding: A demodulation algorithm suitable for FM signals is used to recover the carrier. Since NOAA satellite image data transmission has relatively low requirements for bit error rate, simple error detection codes are used for error correction to ensure the bit error rate remains within acceptable limits.

[0092] Navigation message analysis and modulation identification: The received signal is analyzed to extract meteorological image data. By analyzing the signal's spectral characteristics, the modulation mode is confirmed to be FM, consistent with expectations. Finally, cloud image data captured by NOAA satellites is obtained.

[0093] The process of acquiring and preprocessing BeiDou navigation satellite signals is as follows: Parameter settings: For BeiDou satellite B1 band signals, set the center frequency to 1561.098MHz, the sampling rate to 4MS / s, and the bandwidth to 20MHz.

[0094] Hardware requirements: Replace with an antenna with a gain of 8dBi, LNA noise figure of 1.0dB, and SDR device dynamic range of 90dB.

[0095] Pre-compensation and tracking: The Doppler frequency shift of the BeiDou satellite signal is pre-compensated, and its value is dynamically calculated based on the satellite orbit and ground station position. The carrier phase is tracked through a phase-locked loop to ensure stable signal reception.

[0096] Filtering and Down-Conversion: A square root raised cosine roll-off filter is used to limit the spectrum, with a roll-off factor of 0.35. Digital down-conversion is implemented using FPGA to separate the BeiDou B1 signal and output a digital baseband signal (I / Q data stream). The peak signal strength timestamp is recorded as 2024-01-01T14:35:00Z, and the initial signal-to-noise ratio estimate is 18dB.

[0097] The process of signal decoding and satellite identification for BeiDou navigation satellites is as follows: Parameter input: Input the QPSK modulation method used by BeiDou satellites and the specific frame synchronization header pilot sequence.

[0098] Carrier recovery and error correction decoding: Differential coding is used to avoid phase flipping of the QPSK signal, and a combination of BCH code and LDPC code is used for error correction decoding to ensure a bit error rate ≤10. -8 .

[0099] Navigation message parsing and modulation identification: Following the BeiDou signal frame structure, the system is parsed step-by-step from superframes to subframes to extract navigation message information, including satellite orbit parameters and time information. The modulation scheme is confirmed as QPSK by analyzing the constellation diagram shape, which matches expectations. Finally, navigation and positioning data for the BeiDou satellites are obtained.

[0100] The process of transit verification and model adaptive optimization based on BeiDou satellites is as follows: Parameter input: Input the TLE data of the Beidou satellite and the geographical location of the ground station (30°N, 120°E), and set the signal-to-noise ratio threshold to 10dB.

[0101] Execution conditions: Ensure that the accuracy error of the SGP4 model is controlled within 0.8km, the time synchronization error is less than 0.5 seconds, the stepping accuracy of the electric rotator is 0.08°, and the Doppler frequency shift is tracked in real time.

[0102] Closed-loop verification and adaptive adjustment: During signal reception, the predicted satellite transit time is compared with the actual signal peak time. If the actual signal peak time is 12 seconds later than the predicted time (deviation > 10 seconds), this deviation data is fed back to the orbit model. The actual satellite position is then inferred using the decoded satellite orbit parameters to correct the orbit model. When the signal-to-noise ratio drops to 8dB (<10dB), the LDPC coding gain mechanism is activated, and a simplified HARQ mechanism is switched to increase FEC redundancy and reduce retransmissions to optimize the communication link. The transit verification deviation report (time deviation 12 seconds, azimuth deviation 0.6°), orbit model correction parameters (such as eccentricity adjustment value), and adaptive switching logs (LDPC coding activation, HARQ mechanism trigger time, etc.) are recorded.

[0103] According to some embodiments of this application, the embodiments of this application have at least one of the following beneficial effects: (a) Satellite orbit prediction.

[0104] 1. Significantly improves prediction accuracy. Traditional methods rely on a single set of two-row orbital features for orbit calculation. Over time, the prediction accuracy decays due to orbital perturbations. This application constructs a multi-version two-row orbital feature database and defines a time validity window for each version. Based on the target time point, it automatically matches and calls the feature version within the corresponding window for orbit prediction. This ensures that at any prediction time, the most suitable orbital feature data for that time can be used, effectively reducing the cumulative error of long-term orbital extrapolation.

[0105] 2. The system achieves fully automated and efficient operation. The entire data version management, matching, and calculation process requires no manual intervention. It can automatically complete a series of operations from data acquisition, validity window division, data version matching to trajectory prediction calculation. This not only improves the efficiency of trajectory prediction operations but also avoids errors that may occur during manual operation, enhancing the reliability and stability of predictions.

[0106] 3. Excellent compatibility and scalability: The selection of the two-line orbit feature version in this embodiment can be embedded as an optimization module into the existing orbit prediction process, seamlessly integrating with mainstream orbit prediction models such as SGP4 / SDP4. This means that the technology of this invention can be applied to improve prediction accuracy without large-scale modifications to existing satellite orbit prediction systems. Furthermore, the method of this embodiment can adapt to two-line orbit feature data sources with different update frequencies. Whether it's a real-time data source with a high update frequency or a data source with relatively infrequent updates, it can effectively manage data and predict orbits. This scalability has broad applicability in different satellite monitoring application scenarios.

[0107] (ii) Sky Visibility Field (SVF) generation.

[0108] 1. Real-time dynamic updates and high-precision measurements: The multi-sensor hardware active detection mode features real-time dynamic updates, enabling it to detect changes in ground features within seconds, thus improving the accuracy of ground obstruction representation. This mode employs a laser ranging-timeout determination mechanism and millisecond-level timing-triggered synchronization, achieving millimeter-level detection accuracy with an INS azimuth error of less than 0.1°. Compared to traditional fisheye lens photography, the hardware detection mode of this application embodiment significantly improves accuracy, enabling more accurate acquisition of sky visibility information.

[0109] 2. Highly efficient and adaptable large-scale analysis: The DEM data calculation mode is suitable for large-scale preliminary screening, such as urban planning and radar base station site selection in the early planning stages. Through adaptive grid division, the DEM grid resolution is dynamically adjusted according to the terrain complexity, improving calculation efficiency while ensuring calculation accuracy, and enabling rapid processing of data over large areas.

[0110] 3. Dual-mode complementarity enhances reliability: the multi-sensor hardware active detection mode and the DEM data calculation mode complement each other. Dynamic ground feature information detected by the hardware mode can be used to verify and calibrate dynamic occlusion conditions not covered in the DEM data, and the hardware data can also calibrate DEM elevation errors. Meanwhile, the large-scale terrain occlusion information provided by the DEM data calculation mode can provide reference and supplementation for the hardware mode measurements. The combination of these two modes improves the reliability of full-domain visibility analysis, making the generated sky visibility results more accurate and comprehensive, meeting the needs of different application scenarios for sky visibility analysis.

[0111] (III) Satellite signal processing.

[0112] 1. Multi-satellite compatibility and reduced cost: The software-defined radio-based system achieves multi-satellite compatibility, enabling parallel processing of multiple satellite signals such as GPS, BeiDou, and NOAA through software configuration. Compared to traditional satellite signal receiving equipment that relies on dedicated hardware, the embodiments of this application reduce the need for multiple dedicated hardware components, thereby lowering the cost of satellite signal reception.

[0113] 2. High-precision tracking and improved stability: The two-way verification module controls the azimuth error of the transit prediction within ±0.5° by comparing the spatiotemporal characteristics of the predicted trajectory with those of the actual signal, which greatly improves the accuracy of the accumulation trajectory analysis.

[0114] 3. Enhanced anti-interference capability: Under adverse channel conditions, such as a signal-to-noise ratio below 10dB, the decoding success rate is improved through LDPC coding gain. The satellite signal processing system of this application embodiment has adaptive optimization capability, which can dynamically adjust the coding scheme and retransmission mechanism according to channel quality, significantly enhancing anti-interference capability and improving signal transmission reliability.

[0115] This application also proposes a satellite transit signal coverage analysis system, including: The first module is used to acquire the two-line orbital feature set of the target satellite; The second module is used to perform transit analysis at the target time point based on the two sets of orbital elements, and to obtain the satellite orbital status of the target satellite at the target time point. The third module is used to perform static geometric coverage analysis based on the satellite orbital status to obtain the first signal coverage area of ​​the target satellite at the target time point. The fourth module is used to query the sky visibility map based on the first signal coverage area to determine the sky visibility information within the first signal coverage area; wherein, the sky visibility map is constructed based on the digital elevation model and field measurement data; The fifth module is used to correct the first signal coverage area based on the sky visibility information to obtain the second signal coverage area.

[0116] It is understood that the methods described in the above method embodiments are applicable to this system embodiment. The specific functions implemented in this system embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0117] This application also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the above-described method. This electronic device can be any smart terminal, including tablet computers, in-vehicle computers, etc.

[0118] It is understood that the content of the above method embodiments is applicable to this device embodiment. The specific functions implemented by this device embodiment are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0119] Please see Figure 9 , Figure 9 The hardware structure of an electronic device according to another embodiment is illustrated. The electronic device includes: The processor 901 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application. The memory 902 can be implemented as a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 902 can store the operating system and other applications. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 902 and is called and executed by the processor 901. The input / output interface 903 is used to implement information input and output; The communication interface 904 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.). Bus 905 transmits information between various components of the device (e.g., processor 901, memory 902, input / output interface 903, and communication interface 904); The processor 901, memory 902, input / output interface 903, and communication interface 904 are connected to each other within the device via bus 905.

[0120] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method.

[0121] It is understood that the content of the above method embodiments is applicable to this storage medium embodiment. The specific functions implemented in this storage medium embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0122] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0123] It is understood that the content of the above method embodiments is applicable to the embodiments of this program product. The specific functions implemented by the embodiments of this program product are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0124] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0125] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

[0126] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of this application, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0127] The system embodiments described above are merely illustrative. The modules described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0128] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0129] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or modules is not necessarily limited to those steps or modules explicitly listed, but may include other steps or modules not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0130] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0131] In the embodiments provided in this application, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for instance, the division of modules described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0132] The modules described above as separate components may or may not be physically separate. The components shown as modules may or may not be physical modules; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0133] Furthermore, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module. The integrated modules described above can be implemented in hardware or as software functional modules.

[0134] If the integrated modules are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes multiple instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0135] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.

Claims

1. A method of satellite overflight signal coverage analysis, characterized in that, The method comprises the following steps: acquiring a two-line element set of a target satellite; performing transit analysis at a target time point based on the two-line element set to obtain a satellite orbit state of the target satellite at the target time point; performing static geometric coverage analysis based on the satellite orbit state to obtain a first signal coverage area of the target satellite at the target time point; querying a sky visibility diagram based on the first signal coverage area to determine sky visibility information in the first signal coverage area; wherein the sky visibility diagram is constructed based on a digital elevation model and field measurement data; correcting the first signal coverage area based on the sky visibility information to obtain a second signal coverage area.

2. The method of claim 1, wherein, The acquiring of the two-line element set of the target satellite comprises the following steps: determining whether the target time point is a future time point; in the case that the target time point is a future time point, acquiring a latest version of the two-line element set from a database; the database stores a plurality of versions of the two-line element set collected at different times in chronological order, and each version of the two-line element set sets a corresponding valid time window according to an epoch time; in the case that the target time point is not a future time point, acquiring the two-line element set corresponding to the valid time window in which the target time point is located.

3. The method of claim 2, wherein, The transit analysis at the target time point based on the two-line element set to obtain the satellite orbit state of the target satellite at the target time point comprises the following steps: inputting the two-line element set into a satellite orbit calculation model to obtain a calculated orbit of the target satellite; extracting data at the target time point from the calculated orbit to obtain the satellite orbit state; the satellite orbit state comprises a satellite position and a satellite speed.

4. The method of claim 1, wherein, The sky visibility diagram is obtained through the following steps: collecting field measurement data through a plurality of sensor devices distributed on the ground; the plurality of sensor devices comprise a gimbal carrying an inertial navigation system and a laser range finder; generating first line-of-sight data at device distribution points based on the field measurement data; constructing a sky visibility diagram based on the first line-of-sight data of each device distribution point; complementing blank areas of the sky visibility diagram with second line-of-sight data obtained based on digital elevation model analysis.

5. The method of claim 3, wherein, The satellite transit signal coverage analysis method further comprises the following steps: receiving a satellite signal of the target satellite using a satellite receiver; extracting a signal peak time from the satellite signal; correcting the satellite orbit calculation model based on the signal peak time and a predicted time of passing through the satellite receiver.

6. The method of claim 5, wherein, The correction of the satellite orbit calculation model based on the signal peak time and the predicted time of passing through the satellite receiver comprises the following steps: in the case that a deviation between the signal peak time and the predicted time is greater than a preset deviation, inversely deducing a real position of the satellite based on decoding data of the satellite signal; correcting the satellite orbit calculation model based on the real position.

7. A satellite overflight signal coverage analysis system characterized by, The method comprises: a first module for acquiring a two-line element set of a target satellite; The second module is configured to perform transit analysis at a target time point according to the two sets of orbital elements, to obtain a satellite orbit state of the target satellite at the target time point. The third module is configured to perform static geometric coverage analysis according to the satellite orbit state, to obtain a first signal coverage area of the target satellite at the target time point. The fourth module is configured to query a sky visibility diagram according to the first signal coverage area, to determine sky visibility information in the first signal coverage area; wherein the sky visibility diagram is constructed based on digital elevation model and field measurement data. The fifth module is configured to correct the first signal coverage area according to the sky visibility information, to obtain a second signal coverage area.

8. An electronic device, comprising: The electronic device comprises a memory and a processor, the memory stores a computer program, and the processor implements the method in any one of claims 1 to 6 when executing the computer program.

9. A computer-readable storage medium storing a computer program, the computer program comprising instructions that, when executed by a computer, cause the computer to perform the method of any one of claims 1 to 8. The computer program is executed by the processor to implement the method in any one of claims 1 to 6.

10. A computer program product comprising a computer program, characterized in that, The computer program is executed by the processor to implement the method in any one of claims 1 to 6.

Images