Passive Location Model of UAV Based on Geometric Derivation and Genetic Algorithm

In this paper, a location method of bearing-only UAV based on passive location principle is proposed. First of all, on the basis of establishing the relative plane Cartesian coordinate system, a three-point positioning model is established by geometric derivation, so that the UAV can locate passively when only the angle information is received. The reliability of the model is verified by Monte Carlo simulation. Secondly, by analyzing the relationship between the polar diameter variation range of the signal receiver and the circumferential angle, the minimum number of UAV is determined. In addition, the genetic algorithm with uniform mutation factor is used to optimize the solution, and the root mean square error between the actual position and the standard position of the UAV is minimized as the fitness function. Finally, through the iterative optimization method, the specific position adjustment scheme of UAV is obtained, and the effective positioning of UAV is realized. This study provides a theoretical reference for the actual UAV formation.

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Published
2024-09-27
DOI
https://doi.org/10.1145/3702879.3702922
Citations
1
Primary Topic
Robotic Path Planning Algorithms
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article
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0.25
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Passive Location Model of UAV Based on Geometric Derivation and Genetic Algorithm

Shengkun Liao, Pinlin Zhou
1 citations
Robotic Path Planning Algorithms
0.25
article

Passive Location Model of UAV Based on Geometric Derivation and Genetic Algorithm

Shengkun Liao, Pinlin Zhou
article en
1 citations

Abstract

In this paper, a location method of bearing-only UAV based on passive location principle is proposed. First of all, on the basis of establishing the relative plane Cartesian coordinate system, a three-point positioning model is established by geometric derivation, so that the UAV can locate passively when only the angle information is received. The reliability of the model is verified by Monte Carlo simulation. Secondly, by analyzing the relationship between the polar diameter variation range of the signal receiver and the circumferential angle, the minimum number of UAV is determined. In addition, the genetic algorithm with uniform mutation factor is used to optimize the solution, and the root mean square error between the actual position and the standard position of the UAV is minimized as the fitness function. Finally, through the iterative optimization method, the specific position adjustment scheme of UAV is obtained, and the effective positioning of UAV is realized. This study provides a theoretical reference for the actual UAV formation.

Dalian Maritime University (CN)
Openalex Percentile: Top 44%
Robotic Path Planning Algorithms
0.25
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