Dataset Information

UWB indoor positioning optimization algorithm based on genetic annealing and clustering analysis.

ABSTRACT: Indoor location information is an indispensable parameter for modern intelligent warehouse management and robot navigation. Indoor wireless positioning exhibits large errors due to factors such as indoor non-line-of-sight (NLOS) obstructions. In the present study, the error value under the time of arrival (TOA) algorithm was evaluated, and the trilateral positioning method was optimized to minimize the errors. An optimization algorithm for indoor ultra-wideband (UWB) positioning was designed, which was referred as annealing evolution and clustering fusion optimization algorithm. The algorithm exploited the good local search capability of the simulated annealing algorithm and the good global search capability of the genetic algorithm to optimize cluster analysis. The optimal result from sampled data was quickly determined to achieve effective and accurate positioning. These features reduced the non-direct aiming error in the indoor UWB environment. The final experimental results showed that the optimized algorithm significantly reduced noise interference as well as improved positioning accuracy in an NLOS indoor environment with less than 10 cm positioning error.

SUBMITTER: Guo H

PROVIDER: S-EPMC9360920 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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UWB indoor positioning optimization algorithm based on genetic annealing and clustering analysis.

Guo Hua H Li Mengqi M Zhang Xuejing X Gao Xiaotian X Liu Qian Q

Frontiers in neurorobotics 20220726

Indoor location information is an indispensable parameter for modern intelligent warehouse management and robot navigation. Indoor wireless positioning exhibits large errors due to factors such as indoor non-line-of-sight (NLOS) obstructions. In the present study, the error value under the time of arrival (TOA) algorithm was evaluated, and the trilateral positioning method was optimized to minimize the errors. An optimization algorithm for indoor ultra-wideband (UWB) positioning was designed, wh ...[more]

PMID: 35958683

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Project description:Radio signal-based positioning in environments with complex propagation paths is a challenging task for classical positioning methods. For example, in a typical industrial environment, objects such as machines and workpieces cause reflections, diffractions, and absorptions, which are not taken into account by classical lateration methods and may lead to erroneous positions. Only a few data-driven methods developed in recent years can deal with these irregularities in the propagation paths or use them as additional information for positioning. These methods exploit the channel impulse responses (CIR) that are detected by ultra-wideband radio systems for positioning. These CIRs embed the signal properties of the underlying propagation paths that represent the environment. This article describes a feature-based localization approach that exploits machine-learning to derive characteristic information of the CIR signal for positioning. The approach is complete without highly time-synchronized receiver or arrival times. Various features were investigated based on signal propagation models for complex environments. These features were then assessed qualitatively based on their spatial relationship to objects and their contribution to a more accurate position estimation. Three datasets collected in environments of varying degrees of complexity were analyzed. The evaluation of the experiments showed that a clear relationship between the features and the environment indicates that features in complex propagation environments improve positional accuracy. A quantitative assessment of the features was made based on a hierarchical classification of stratified regions within the environment. Classification accuracies of over 90% could be achieved for region sizes of about 0.1 m 2 . An application-driven evaluation was made to distinguish between different screwing processes on a car door based on CIR measures. While in a static environment, even with a single infrastructure tag, nearly error-free classification could be achieved, the accuracy of changes in the environment decreases rapidly. To adapt to changes in the environment, the models were retrained with a small amount of CIR data. This increased performance considerably. The proposed approach results in highly accurate classification, even with a reduced infrastructure of one or two tags, and is easily adaptable to new environments. In addition, the approach does not require calibration or synchronization of the positioning system or the installation of a reference system.

Dataset Information

UWB indoor positioning optimization algorithm based on genetic annealing and clustering analysis.

Publications

UWB indoor positioning optimization algorithm based on genetic annealing and clustering analysis.

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