Method for optimal layout of Indoor positioning network elements based on genetic algorithm and simulated annealing

Abstract

The invention discloses a method for optimal layout of Indoor positioning network elements based on a genetic algorithm and simulated annealing, and belongs to the field of indoor positioning. The method comprises the following steps of step (1), carrying out network element layout; step (2), determining control parameters required by a self-adaptive genetic algorithm; step (3), initializing the network element layout; step (4), calculating fitness; step (5), judging whether a genetic convergence condition is met or not; step (6), selecting the network element layout with relatively high fitness; step (7), carrying out cross operation on binary codes to obtain a filial generation; step (8), carrying out reverse operation on the binary codes to obtain a variation; step (9), generating a newnetwork element layout space; step (10), carrying out simulated annealing operation on a group; step (11), generating an optimal network element layout result; and step (12), outputting an optimal network element layout result, and ending. The method has stronger global searching capability and local searching capability, the positioning accuracy is improved, and the searching efficiency is improved.

Description

technical field [0001] The invention belongs to the field of indoor positioning, in particular to an indoor positioning network element optimization layout method based on genetic algorithm and simulated annealing. Background technique [0002] With the development of network technology and communication technology, location services have become increasingly important. Since most of people's activities are carried out indoors, indoor positioning has attracted more and more attention. Among them, UWB-based indoor positioning The technology can achieve centimeter-level positioning accuracy in specific scenarios, and has been widely used in various indoor positioning products. However, the deployment cost of UWB positioning base stations is very high. When the number of network elements is limited, how to layout can make positioning It is a very meaningful research topic to have the largest signal coverage and the highest positioning accuracy. [0003] The optimal layout of ne...

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Problems solved by technology

Among them, the genetic algorithm can find the optimal solution in a random manner in the sense of probability. The literature "Agapie A, Wright A H. Theoretical analysis of steady state genetic algorithms [J]. Applications of Mathematics, 2014, 59 (5): 509 -525.Theoretical analysis ofsteady state genetic algorithms" uses genetic algorithms to solve wireless network planning problems, but genetic algorithms do not have a feasible feedback mechanism. In some cases, a large number of redundant iterations will be generated, resulting in low efficiency. At the same time, in practical application, the genetic algorithm will appear prone to premature phenomenon, poor local optimization ability and other problems.

The simulated annealing algorithm has a strong local search ability, and can avoid the search process from falling into a local optimal solution, it is not suitable for the entire search space, and it is difficult to make the search process enter the most promising search area

The document "Han R, Feng C, Xia H, et al.Coverage optimization for dense deployment small cell based on ant colony algorithm[C], 2014IEEE80th.IEEE, 2014:1-5" uses the ant colony algorithm to layout the network elements mainly to The cost and coverage of network element deployment are used as layout goals, but in the early stages of ant colony algorithm search, there are cases where little or no information is available, so the ant colony algorithm converges slowly

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