Giant constellation interference analysis method based on CUDA (Compute Unified Device Architecture) parallel computing

Abstract

The invention discloses a giant constellation interference analysis method based on CUDA parallel computing, and relates to the technical field of compatibility analysis between satellite systems. The invention aims to solve the technical problems that the interference scene between giant low-orbit constellations is complex, the interference analysis calculation amount is large, the calculation complexity is high, the parallelism degree of a current algorithm is low, and the utilization rate of calculation resources is low. The technical key points are as follows: CUDA parameter initialization, orbit deduction, visibility calculation, earth station satellite selection, visibility calculation of an interference link and an interfered link, interference index calculation, after calculation of all threads is completed, a calculation result of giant constellation interference analysis is transmitted to a CPU memory from a GPU video memory, and calculation is ended. According to the method disclosed by the invention, the characteristics of independence and parallel calculation of calculation in each sub-process of the compatibility analysis of the giant constellation are utilized, the large-scale multi-thread parallel calculation capability and storage capability of the GPU are fully utilized, the compatibility analysis process is optimized, the calculation efficiency and the utilization rate of calculation resources are improved, and the real-time performance of the compatibility analysis of the giant constellation is improved.

Description

Technical Field

[0001] The present invention belongs to the field of compatibility analysis between satellite systems, and in particular relates to a giant constellation interference analysis method based on CUDA parallel computing in this field. Background Art

[0002] With the rapid deployment of a large number of non-geostationary orbit (NGSO) satellites, the interference problem between giant low-orbit constellations has become increasingly prominent. Effective interference analysis is required before satellite launch to support international coordination with other satellite systems. However, the large scale of low-orbit giant constellations and complex interference scenarios require a large amount of calculation and a long time to deduce, which cannot meet the timeliness of interference analysis. It is imperative to propose a giant constellation interference analysis method that utilizes the independence of each calculation link and the characteristics of parallel computing to optimize the compatible analysis process and improve the computing efficiency and utilization of computing resources.

[0003] Document number CN113193901B discloses a large constellation interference avoidance method, which includes: obtaining visibility forecasts of all satellites in the current forecast period for the selected ground station; determining the first traversal period; obtaining the visibility forecasts of all satellites in the starting traversal time + interval time period, and obtaining the number of satellites whose signals can be received by the selected ground station in the time period, and when the number of satellites exceeds the number allowed by the technical indicators of the selected ground station, selecting to shut down a number of satellites; repeatedly obtaining the satellites that need to be shut down in the next interval time period, and giving an avoidance plan for the first traversal period; repeating the above steps to obtain the visibility forecasts and avoidance plans of all satellites in the remaining forecast periods; summarizing the final traversal plan, and for satellites whose shutdown time is less than the predetermined time, modifying their avoidance strategies to bias. This prior art can efficiently and quickly solve the interference time period set according to the satellite transit forecast, autonomously control the satellite payload power on and off or adjust the side swing angle, and avoid the complex process of manual intervention to solve the interference. However, no solution has been given for how to improve the computational efficiency and utilization of computing resources in giant constellation interference analysis and improve the real-time performance of giant constellation compatibility analysis. Summary of the invention

[0004] The technical problem to be solved by the present invention is to provide a giant constellation interference analysis method based on CUDA parallel computing (CUDA, the full name is Computer Unified Device Architecture, a parallel computing architecture) to address the problems of complex interference scenarios between giant low-orbit constellations, large amount of interference analysis calculations, high computational complexity, low parallelism of current algorithms, and low utilization of computing resources.

[0005] The present invention adopts the following technical solution:

[0006] A giant constellation interference analysis method based on CUDA parallel computing, the improvement of which is that it includes the following steps:

[0007] Step 1, CUDA parameter initialization:

[0008] Import the required orbital parameters, constellation parameters, and earth station location parameters into the device-side GPU, and set the thread blocks and number of threads according to the number of satellites;

[0009] Orbital parameters refer to the six orbital parameters: semi-major axis, eccentricity, right ascension of ascending node, orbital inclination, argument of perigee, and argument of true perigee;

[0010] Constellation parameters: number of orbits, number of satellites in each orbit;

[0011] Earth station location parameters: longitude and latitude;

[0012] Step 2, orbit deduction:

[0013] Use the J2 track model to write and run kernel functions 0 Deducing satellite trajectories and calculating the satellite's location at every moment;

[0014] Step 3, visibility calculation:

[0015] Using the known earth station position and the satellite position obtained in step 1, calculate whether the satellite is visible to the earth station. Write and run the kernel function according to the following visibility calculation process: 1 :

[0016] Calculate the distance D between the satellite and the earth station:

[0017]

[0018] In the above formula, x 1 ,y 1 、z 1 is the satellite’s position vector, x 2 ,y 2 、z 2 is the position vector of the earth station;

[0019] The distance R from the satellite to the center of the earth 1 for: The distance R from the earth station to the center of the earth 2 for: The distance D from the satellite and the earth station to the horizon i :

[0020]

[0021] In the above formula, R eis the radius of the Earth;

[0022] When the distance D between the satellite and the earth station is less than D 1 +D 2 When , the satellite and the earth station are visible;

[0023] Step 4, Earth station satellite selection:

[0024] The earth station writes and runs the kernel function according to the satellite selection strategy 2 , select one of the visible satellites obtained in step 3 to establish a link;

[0025] Step 5: Calculate the visibility of the interfering link and the interfered link:

[0026] After both the earth station of the interfering system and the earth station of the interfered system complete satellite selection and link establishment in step 4, the visibility calculation method in step 3 is used to analyze whether the transmitter of the interfering system and the receiver of the interfered system are visible;

[0027] If it is not visible, the deduction time is T 1 Change to T 2 , and return to step 1 to start calculating T 2 Interference situation at the moment; if visible, jump to step 6 to calculate the interference index;

[0028] Step 6, interference index calculation:

[0029] Writing kernel functions 3 Used to calculate interference indicators;

[0030] Step 7: When all threads have completed the calculation, the calculation results of the giant constellation interference analysis are transferred from the GPU memory to the CPU memory, and the calculation is completed.

[0031] Furthermore, in step 4, the satellite selection strategy includes the longest tracking time, the maximum communication angle, the shortest communication distance and the maximum separation angle.

[0032] Further, in step 6, the interference indicators calculated by the interference indicator include C / N, C / I, I / N, C / (I+N), △T / T, PFD, and EPFD. C / N is the signal-to-noise ratio, C / I is the signal-to-interference ratio, I / N is the interference-to-noise ratio, C / (I+N) is the signal-to-interference-to-noise ratio, △T / T is the noise temperature change rate, PFD is the power flux density, and EPFD is the equivalent power flux density.

[0033] The beneficial effects of the present invention are:

[0034] The method disclosed in the present invention utilizes the independence of calculations in each sub-process of giant constellation compatibility analysis and the characteristics of parallel calculation, fully utilizes the large-scale multi-threaded parallel calculation capability and storage capability of GPU, optimizes the compatibility analysis process, performs parallel calculation on the GPU for the compatibility analysis process that can be calculated in parallel, improves the calculation efficiency and the utilization rate of the calculation resources, reduces the compatibility analysis time, and improves the real-time performance of the giant constellation compatibility analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 It is a schematic diagram of compatibility analysis process optimization.

[0036] Figure 2 This is a schematic diagram of the optimized compatibility analysis process.

[0037] Figure 3 It is a flowchart based on CUDA parallel computing. DETAILED DESCRIPTION

[0038] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0039] like Figure 1 As shown in the figure, the giant constellation compatibility analysis is executed in the order of orbit deduction, visibility calculation, earth station selection, visibility calculation of interfering links and interfered links, and interference index calculation. In each sub-process, there are a large number of independent and parallel computational processes, and these sub-processes can be further accelerated by GPU. Taking orbit deduction as an example, at each moment, orbit deductions equal to the number of satellites are required to obtain the satellite positions at the current moment. The steps of the giant low-orbit constellation compatibility analysis are decomposed into multiple parallel blocks for sequential execution, and the calculation process of the satellite in each module is treated as a separate thread. The CPU side calls the kernel function, and the kernel function (kernel) is run on the GPU side. The optimized compatibility analysis process is shown in the figure below. Figure 2 shown.

[0040] Embodiment 1: This embodiment discloses a giant constellation interference analysis method based on CUDA parallel computing, such as Figure 3 As shown, the following steps are included:

[0041] Step 1, CUDA parameter initialization:

[0042] Import the required orbital parameters, constellation parameters, and earth station location parameters into the device-side GPU, set the thread blocks and number of threads according to the number of satellites, make each thread as active as possible, and make full use of computing resources;

[0043] Step 2, orbit deduction:

[0044] Use the J2 track model to write and run kernel functions 0 Deducing satellite trajectories and calculating the satellite's location at every moment;

[0045] Step 3, visibility calculation:

[0046] Using the known earth station position and the satellite position obtained in step 1, calculate whether the satellite is visible to the earth station. Write and run the kernel function according to the following visibility calculation process: 1 :

[0047] Calculate the distance D between the satellite and the earth station:

[0048]

[0049] In the above formula, x 1 ,y 1 、z 1 is the satellite’s position vector, x 2 ,y 2 、z 2 is the position vector of the earth station;

[0050] The distance R from the satellite to the center of the earth 1 for: The distance R from the earth station to the center of the earth 2 for: The distance D from the satellite and the earth station to the horizon i :

[0051]

[0052] In the above formula, R e is the radius of the Earth;

[0053] When the distance D between the satellite and the earth station is less than D 1 +D 2 When D < D 1 +D 2 , visible between satellite and earth station;

[0054] Step 4, Earth station satellite selection:

[0055] The earth station writes and runs the kernel function according to the satellite selection strategy 2 , from the visible satellites obtained in step 3, select one to establish a link; the satellite selection strategy includes the longest tracking time, the maximum communication belief angle, the shortest communication distance and the maximum separation angle;

[0056] Step 5: Calculate the visibility of the interfering link and the interfered link:

[0057] If in a scenario system a interferes with system b, then system a is the interfering system and system b is the interfered system.

[0058] After both the earth station of the interfering system and the earth station of the interfered system complete satellite selection and link establishment in step 4, the visibility calculation method in step 3 is used to analyze whether the transmitter of the interfering system and the receiver of the interfered system are visible;

[0059] If it is not visible, the deduction time is T 1 Change to T 2 , and return to step 1 to start calculating T 2 Interference situation at the moment; if visible, jump to step 6 to calculate the interference index;

[0060] Step 6, interference index calculation:

[0061] According to the requirements, kernel function kernel3 is written to calculate the interference indicators specified by ITU, such as C / N, C / I, I / N, C / (I+N), △T / T, PFD, EPFD, etc.

[0062] Step 7: When all threads have completed the calculation, the calculation results of the giant constellation interference analysis are transferred from the GPU memory to the CPU memory, and the calculation is completed.

[0063] Next, we select Starlink and Oneweb as interference simulation objects to analyze the downlink interference of Starlink to Oneweb. The simulation period is 1 day and the simulation step is 1 second. The orbital parameters of Oneweb are shown in Table 1.

[0064] Table 1: Oneweb constellation orbit parameters

[0065]

[0066] The system parameters of the Oneweb constellation are shown in Table 2 below.

[0067] Table 2: Oneweb constellation system parameters

[0068] parameter Numeric Satellite transmitting antenna peak gain / dBi 28 Satellite transmitting antenna half-power beam / (°) 4.9 Ground station receiving antenna peak gain / dBi 38 Ground station receiving antenna half-power beam / (°) 1.6 Satellite transmission power / dBW 3 Communication frequency / GHz 17.8 Communication bandwidth / MHz 250 System noise temperature / K 300 Ground station location / (°E,°N) (120,40)

[0069] The orbital parameters of the Starlink constellation are shown in Table 3 below.

[0070] Table 3: Starlink constellation orbit parameters

[0071]

[0072] The system parameters of the Starlink constellation are shown in Table 4 below.

[0073] Table 4: Starlink constellation system parameters

[0074]

[0075] Step 1: CUDA parameter initialization, copy the required orbit parameters, constellation parameters and earth station position parameters to the device (GPU) video memory. To improve the overall computing efficiency and avoid unnecessary data copying between the main memory and the video memory, the required data is copied to the GPU video memory at one time. The orbit deduction, visibility calculation, earth station selection, interference link and interfered link visibility calculation and interference index calculation are all performed in the GPU.

[0076] Step 2: Use the J2 track model to write and run the kernel function 0 The satellite trajectory is deduced to calculate the satellite position at each moment. In this scenario, the number of satellites is 1969+11908=13877. Set the block and thread. The maximum number of threads, i.e. the total number of times required for orbit deduction, is: 13877 (satellites) × 24 × 60 × 60 (times);

[0077] Step 3: Use the known earth station position and the satellite position obtained in step 1 to calculate whether the satellite is visible to the earth station. Write and run the kernel function according to the following visibility calculation process: 1 :

[0078] Calculate the distance D between the satellite and the earth station:

[0079]

[0080] In the above formula, x 1 ,y 1 、z 1 is the satellite’s position vector, x 2 ,y 2 、z 2 is the position vector of the earth station;

[0081] The distance R from the satellite to the center of the earth 1 for: The distance R from the earth station to the center of the earth 2 for: The distance D from the satellite and the earth station to the horizon i :

[0082]

[0083] In the above formula, R e is the radius of the Earth;

[0084] When the distance D between the satellite and the earth station is less than D1 +D 2 When D < D 1 +D 2 , visible between satellite and earth station;

[0085] Step 4: Earth station satellite selection: The earth station writes and runs the kernel function according to the satellite selection strategy 2 , select one satellite from the visible satellites obtained in step 3 to establish a link. Specific satellite selection strategies include: longest tracking time, maximum communication angle, shortest communication distance, maximum separation angle, etc.;

[0086] Step 5: Calculate the visibility of the interfering link and the interfered link: After both the earth station of the interfering system and the earth station of the interfered system complete the satellite selection and link establishment in step 4, use the visibility calculation method in step 3 to analyze whether the transmitter of the interfering system and the receiver of the interfered system are visible.

[0087] If it is not visible, the deduction time is T 1 Change to T 2 , and return to step 1 to start calculating T 2 Interference situation at the moment; if visible, jump to step 6 to calculate the interference index;

[0088] Step 6: Interference index calculation: write kernel function according to requirements 3 Calculate the interference indicators specified by ITU, such as C / N, C / I, I / N, C / (I+N), △T / T, PFD, EPFD, etc.

[0089] Step 7: When all threads have completed the calculation, the calculation results of the giant constellation interference analysis are transferred from the GPU memory to the CPU memory, and the calculation is completed.

[0090] It has been verified that the method proposed in the present invention solves the technical problem proposed in the present invention. The method of the present invention has been verified through practical application of the technical effect and practicality claimed by the present invention.

[0091] The method of the present invention has been verified through simulation experiments and practical applications, and the technical effects claimed by the present invention have been verified.

[0092] The algorithm (method) proposed in the present invention is the underlying technical core of the present invention, and various products can be derived based on the algorithm.

[0093] Based on the algorithm (method) proposed in the present invention, a giant constellation interference analysis system based on CUDA parallel computing is developed using a programming language. The system has program modules corresponding to the steps of the above technical solution, and executes the steps of the above giant constellation interference analysis method based on CUDA parallel computing during operation.

[0094] The computer program of the developed system (software) is stored on a computer-readable storage medium, and the computer program is configured to implement the steps of the above-mentioned giant constellation interference analysis method based on CUDA parallel computing when called by a processor. That is, the present invention is materialized on a carrier to become a computer program product.

[0095] A giant constellation interference analysis method device based on CUDA parallel computing, the device comprising at least one processor and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the above-mentioned giant constellation interference analysis method based on CUDA parallel computing to realize analysis of giant constellation interference.

[0096] Various implementations of the systems and techniques described herein can be realized in digital electronic circuit systems, integrated circuit systems, dedicated ASICs (application specific integrated circuits), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0097] The computer programs (also referred to as programs, software, software applications, or codes) of the present invention include machine instructions for programmable processors, and these computer programs can be implemented using high-level procedural and / or object-oriented programming languages, and / or assembly / machine languages. As used in the present invention, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, device, and / or device (e.g., disk, optical disk, memory, programmable logic device PLD) for providing machine instructions and / or data to a programmable processor, including a machine-readable medium that receives machine instructions as machine-readable signals. The term "machine-readable signal" refers to any signal for providing machine instructions and / or data to a programmable processor.

[0098] The above embodiments further illustrate the purpose, technical solutions and advantages of the present invention in detail. It should be understood that the above embodiments are only preferred implementation modes of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made to the present invention within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A giant constellation interference analysis method based on CUDA parallel computing, characterized in that: The implementation process of the method is: Step 1: CUDA parameter initialization: Import the required orbital parameters, constellation parameters, and earth station location parameters into the device-side GPU, and set the thread blocks and number of threads according to the number of satellites; Step 2: Orbital deduction: Use the J2 orbit model to write and run the kernel function kernel0 to deduce the satellite trajectory and calculate the satellite's position at each moment; Step 3: Visibility calculation: Using the known earth station position and the satellite position obtained in step 2, calculate whether the satellite and the earth station are visible. Write and run the kernel function kernel1 according to the following visibility calculation process: Calculate the distance D between the satellite and the earth station: In the above formula, x1, y1, z1 are the position vectors of the satellite, and x2, y2, z2 are the position vectors of the earth station; The distance R1 from the satellite to the center of the earth is: The distance R2 from the earth station to the center of the earth is: The distance D from the satellite and the earth station to the horizon i : In the above formula, R e is the radius of the Earth; When the distance D between the satellite and the earth station is less than D1+D2, the satellite and the earth station are visible; Step 4: Earth station selection: The earth station writes and runs the kernel function kernel2 according to the satellite selection strategy, and selects one satellite from the visible satellites obtained in step 3 to establish a link; Step 5: Calculate the visibility of the interfering link and the interfered link: After both the earth station of the interfering system and the earth station of the interfered system complete satellite selection and link establishment in step 4, the visibility calculation method in step 3 is used to analyze whether the transmitter of the interfering system and the receiver of the interfered system are visible; If it is not visible, the deduction time changes from T1 to T2, and returns to step 1 to start calculating the interference situation at time T2; if it is visible, jump to step 6 to calculate the interference index; Step 6: Interference index calculation: Write kernel function kernel3 to calculate interference index; Step 7: When all threads have completed the calculation, the calculation results of the giant constellation interference analysis are transferred from the GPU memory to the CPU memory, and the calculation is completed.

2. The method for analyzing interference of a giant constellation based on CUDA parallel computing according to claim 1, characterized in that: In step 4, the satellite selection strategy includes the longest tracking time, the maximum communication angle, the shortest communication distance and the maximum separation angle.

3. The method for analyzing interference of a giant constellation based on CUDA parallel computing according to claim 2, characterized in that: In step 6, interference indicators calculated by interference indicator include C / N, C / I, I / N, C / (I+N), ΔT / T, PFD, and EPFD.

4. A snow tourism product recommendation system based on neural collaborative filtering and linear confidence bound, characterized by: The system has a program module corresponding to the steps of any one of claims 1 to 3 above, and executes the steps in the giant constellation interference analysis method based on CUDA parallel computing when running.

5. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and the computer program is configured to implement the steps of the giant constellation interference analysis method based on CUDA parallel computing according to any one of claims 1 to 3 when called by a processor.

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