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CUDA-based (compute unified device architecture-based) giant constellation coverage performance parallel computing method

A parallel computing and constellation technology, applied in the direction of reducing energy consumption, complex mathematical operations, climate sustainability, etc., can solve problems such as low computing efficiency, which is not conducive to the rapid design and optimization of giant constellations, real-time monitoring and operation and maintenance of constellation status, etc. achieve the effect of improving efficiency

Active Publication Date: 2022-06-03
NANJING UNIV
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  • Abstract
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  • Application Information

AI Technical Summary

Problems solved by technology

According to the traditional serial method, the calculation efficiency is very low, which is not conducive to the rapid design and optimization of giant constellations and the real-time monitoring and operation and maintenance of the constellation status in the later stage

Method used

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  • CUDA-based (compute unified device architecture-based) giant constellation coverage performance parallel computing method
  • CUDA-based (compute unified device architecture-based) giant constellation coverage performance parallel computing method
  • CUDA-based (compute unified device architecture-based) giant constellation coverage performance parallel computing method

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0037]Step 1. Set the initial parameters of the constellation, including the initial epoch of the constellation, the total number of satellites in the constellation N_sat, the initial orbital number of each satellite in the constellation (orbit height h, eccentricity e, orbit inclination angle i, ascending node point Right ascension Ω, perigee angular distance ω, true perigee angle f). Then convert the number of orbits into position speed, and finally according to the set simulation duration and simulation time interval, there are N_t moments in total;

[0038] Step 2. Set the coverage performance calculation conditions, that is, the time for calculating coverage (which must be consistent with the ephemeris data) and the spatial resolution requirements: such as figure 2 shown, the beam angle 2β of the ground directional antenna 0 , and the cut-off elevation angle h for ground observations 0 ; Condition 1 for the coverage of the satellite to the ground station is: The visi...

Embodiment 2

[0054] The difference between the present embodiment 2 and the embodiment 1 is that in the step 1, according to the Walker-δ constellation T / P / F parameter, the initial orbital number of satellites is generated in batches; the specific process is:

[0055] 1) Set the number of reference star orbits (orbital height h, eccentricity e, orbital inclination i, ascending node right ascension Ω, perigee angular distance ω, true perigee angle f);

[0056] 2) Generate the initial orbital number of satellites in batches according to the set T / P / F parameters; if the ascending node right ascension of the reference star is recorded as Ω 0 , the corresponding phase is u 0 =ω 0 +f 0 , then the ascending node right ascension Ω and phase u of the jth satellite on the ith orbital plane in the constellation are:

[0057]

[0058]

[0059] Other steps and parameters are the same as in Example 1.

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Abstract

The invention discloses a CUDA (Compute Unified Device Architecture)-based giant constellation coverage performance parallel computing method. The method comprises the following steps: (1) setting initial parameters of a constellation; (2) setting coverage performance calculation conditions; (3) pre-allocating a memory for ephemeris data in the GPU according to the initial parameters of the constellation; (4) pre-allocating a memory for a coverage calculation intermediate variable and a final calculation result in the GPU according to an initial parameter of the constellation and a coverage performance calculation condition; (5) performing orbit forecasting on the constellation; (6) coordinate conversion: converting a satellite ephemeris from a geocentric celestial coordinate system to an earth-fixed system; (7) copying ephemeris data from the CPU to the GPU; (8) performing coverage performance calculation in the GPU according to a given coverage performance calculation condition; and (9) copying a calculation result from the GPU to the CPU for output. According to the method, rapid calculation of the coverage performance of the giant constellation is realized, the simulation efficiency is greatly improved, and the method is operated on a Jetson series embedded development board and is applied to a satellite platform carrying a satellite-borne embedded GPU system.

Description

technical field [0001] The invention relates to the technical field of constellation design, in particular to a CUDA-based parallel computing method for the coverage performance of a giant constellation. Background technique [0002] Multiple satellites form a satellite constellation through networking, which is often used for earth observation and providing services such as communication and navigation. In the design of constellation such as earth observation, communication and navigation, its global coverage performance is an important design index. In addition, after the constellation is deployed, in the process of its operation, it is necessary to adjust and supplement the constellation in real time according to the actual engineering needs and coverage requirements, which requires monitoring the overall status of the constellation, especially its real-time coverage performance. [0003] The Compute Unified Device Architecture (CUDA) launched by NVIDIA in 2007 makes ful...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F30/20G06F17/10
CPCG06F30/20G06F17/10Y02D30/70
Inventor 高朝阳侯锡云胡松华谷晓松谭盼
Owner NANJING UNIV
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