Bio-Inspired Swarm Intelligence Optimization Algorithm-Aided Hybrid TDOA/AOA-Based Localization

A TDOA/AOA hybrid location algorithm based on the crow search algorithm optimized by particle swarm optimization is proposed to address the challenge of solving the nonlinear equation of time of arrival (TDOA/AOA) location in the non-line-of-sight (NLoS) environment. This algorithm keeps its optimiz...

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Bibliographic Details
Published in:Biomimetics (Basel, Switzerland) Vol. 8; no. 2; p. 186
Main Authors: Cao, Li, Chen, Haishao, Chen, Yaodan, Yue, Yinggao, Zhang, Xin
Format: Journal Article
Language:English
Published: Switzerland MDPI AG 29.04.2023
MDPI
Subjects:
Accuracy
Algorithms
Communication
Convergence
crow search algorithm
Fitness
Global positioning systems
GPS
hybrid localization
Localization
maximum likelihood estimation
Maximum likelihood method
mobile location estimation
Navigation systems
Optimization
particle swarm optimization
Satellites
China
crow search algorithm
mobile location estimation
particle swarm optimization
maximum likelihood estimation
hybrid localization
ISSN:2313-7673, 2313-7673
Online Access:Get full text
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Summary:A TDOA/AOA hybrid location algorithm based on the crow search algorithm optimized by particle swarm optimization is proposed to address the challenge of solving the nonlinear equation of time of arrival (TDOA/AOA) location in the non-line-of-sight (NLoS) environment. This algorithm keeps its optimization mechanism on the basis of enhancing the performance of the original algorithm. To obtain a better fitness value throughout the optimization process and increase the algorithm’s optimization accuracy, the fitness function based on maximum likelihood estimation is modified. In order to speed up algorithm convergence and decrease needless global search without compromising population diversity, an initial solution is simultaneously added to the starting population location. Simulation findings demonstrate that the suggested method outperforms the TDOA/AOA algorithm and other comparable algorithms, including Taylor, Chan, PSO, CPSO, and basic CSA algorithms. The approach performs well in terms of robustness, convergence speed, and node positioning accuracy.
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ISSN:2313-7673
2313-7673
DOI:10.3390/biomimetics8020186