Indoor Visible-Light 3D Positioning System Based on GRU Neural Network
نویسندگان
چکیده
With the continuous development of artificial intelligence technology, visible-light positioning (VLP) based on machine learning and deep algorithms has become a research hotspot for indoor technology. To improve accuracy robot positioning, we established three-dimensional (3D) system consisting two LED lights three photodetectors. In this system, photodetectors are located robot’s head. We considered impact line-of-sight (LOS) non-line-of-sight (NLOS) links received signals used gated recurrent unit (GRU) neural networks to deal with nonlinearity in system. address problem poor stability during GRU network training, rate attenuation strategy performance network. The simulation results showed that average error was 2.69 cm space 4 m × 3 when only LOS were 2.66 both NLOS 95% errors within 7.88 cm. For two-dimensional (2D) fixed height, 80% 9.87 This had high anti-interference ability, could achieve centimeter-level accuracy, met requirements positioning.
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ژورنال
عنوان ژورنال: Photonics
سال: 2023
ISSN: ['2304-6732']
DOI: https://doi.org/10.3390/photonics10060633