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Satellite selection method based on simulated annealing and gradient descent

A simulated annealing algorithm and gradient descent technology, applied in the field of visible navigation satellite selection, which can solve the problems of limited performance and short time consumption of embedded processors.

Active Publication Date: 2020-04-10
TAIYUAN UNIV OF TECH
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AI Technical Summary

Problems solved by technology

The traditional satellite selection algorithm determines the best subset by directly calculating the position precision factors of all possible satellite subset combinations. Most of the improved star selection algorithms are based on the elevation angle threshold or the contribution value of the position precision factor. However, these algorithms still need to be done A lot of matrix operations
However, the performance of embedded processors in general applications is limited. In order to solve the contradiction between positioning accuracy and fast positioning, it is necessary to propose a reasonable algorithm that consumes less time.

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  • Satellite selection method based on simulated annealing and gradient descent
  • Satellite selection method based on simulated annealing and gradient descent
  • Satellite selection method based on simulated annealing and gradient descent

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Embodiment Construction

[0040] The present invention will be further described below in conjunction with accompanying drawing.

[0041] A star selection method based on simulated annealing and gradient descent, comprising the following steps:

[0042] Step 1. Build a simulated annealing algorithm model:

[0043] 1) Initialization: Take a random number for the precision factor and set it as the initial temperature T (between 3-10, too many or too small will affect the algorithm effect); randomly select k visible satellites for the first time, and these k satellites are used as the initial solution State S, as the starting point of algorithm iteration; each precision factor iterates L times;

[0044] 2) Repeat steps 3 to 6 for each iteration;

[0045] 3) Randomly select k visible satellites again, thus generating a new solution S′ (newly extracted k visible satellites);

[0046] 4) Calculate increment ΔT=C(S')-C(S), wherein C(S) is the volume of a cube formed by k satellites;

[0047] 5) When ΔT<0,...

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Abstract

The invention discloses a satellite selection method based on simulated annealing and gradient descent. The method comprises: firstly, using a simulated annealing algorithm for global search; findingout the range of the optimal solution; finding a minimum value in an optimal solution range by utilizing a gradient descent method. The method specifically comprises the following steps: receiving allvisible satellite signals, selecting visible satellites with the maximum elevation angle and the minimum elevation angle, generating a new solution, selecting a method for generating the new solutionby simply converting the current new solution, and then calculating a precision factor corresponding to the new solution; subtracting the evaluation function of the old solution from the evaluation function of the new solution to obtain a temperature increment, and judging whether to accept the new solution or not through an acceptance criterion; taking the optimal local space searched by the simulated annealing algorithm as an input sample of gradient descent, and performing local accurate search by the gradient descent so as to output an optimal solution. The method is short in consumed time, and the contradiction between positioning precision and rapid positioning is solved.

Description

technical field [0001] The invention discloses a satellite selection method based on simulated annealing and gradient descent, and belongs to the technical field of visible navigation satellite selection methods. Background technique [0002] With the application of location-based services more and more widely, single-point precise positioning is gradually becoming a research hotspot worldwide. At the same time, the GNSS of various countries is constantly developing, and the number of visible satellites is also increasing rapidly. More visible satellites means higher redundancy and higher positioning reliability, but it also means more satellite data to process. This makes the algorithm run more time consuming. [0003] The main problem to be solved by the satellite selection algorithm is how to efficiently select a subset of visible satellites. The quality of this subset is usually judged by the value of the position precision factor. It can be seen that star selection i...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01S19/28
CPCG01S19/28
Inventor 李灯熬赵菊敏杨晨博
Owner TAIYUAN UNIV OF TECH
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