An Integrated Intelligent Framework for Low-Cost Visible Light Fingerprinting via Adaptive Sampling and Expansion in Obstructed Environments

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Abstract
To address the challenges of low sampling efficiency in obstructed environments, high fingerprint database construction costs, and insufficient positioning accuracy in indoor visible light positioning (VLP) systems, this article proposes an integrated intelligent framework termed the obstructed environment-based intelligent fingerprint positioning (OEIFP). This framework combines adaptive sampling, sparse expansion, and optimized positioning algorithms. The framework first employs the obstructed environment adaptive sampling (OEAS) algorithm to achieve highly representative sparse sampling by incorporating obstacle information, thereby significantly reducing the cost of fingerprint data collection. Subsequently, the variational autoencoder with convolutional encoding (VACE) model accomplishes high-quality reconstruction from sparse to dense fingerprint databases. On this basis, the firefly algorithm-based cross-variation optimized extreme learning machine (FA-CVO-ELM) positioning model is designed, integrating the firefly algorithm (FA) with cross-variation operation (CVO) to perform dual optimization of the input weights and biases of the extreme learning machine (ELM), which enhances positioning accuracy and generalization capability. Simulation and experimental results demonstrate superior performance across diverse obstacle scenarios. The simulation achieves a 99.75% reduction in database construction cost compared to the initial sampled fingerprint database, while maintaining an average positioning error (APE) as low as 0.0423 m. Experimental results show a 97.6% reduction in construction cost with a minimum APE of 0.0503 m. The framework realizes synergistic optimization between adaptive sampling for low-cost dense database construction and high-precision positioning, offering a viable solution for deploying visible light fingerprint positioning systems in obstructed environments.
Funding Information
  • Project 333 of Jiangsu Province
  • National Natural Science Foundation of China (62401232)
  • Senior Talent Foundation of Jiangsu University (JDKQ20240402)

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