With the rapid development of mobile communication technology in modern society, the demand for location services in complex indoor environments, such as large factories, shopping malls, and office buildings, has been growing rapidly. The current visible light positioning technology uses various sensors and hybrid complex algorithms to achieve positioning, which is difficult to operate and vulnerable to interference, resulting in unstable positioning accuracy of the system. The advantages of visible light communication include both lighting and communication, as well as stability and reliability. On this basis, to improve the accuracy and stability of visible light indoor positioning, a bio-inspired network integrating migration feature learning is proposed to achieve stable and high-precision indoor positioning in visible light imaging.

In this study, a visible-light indoor location method based on an image is proposed. The acquired image is first denoised to eliminate noise interference which has a significant impact on the extraction of the image depth features. Inaccurate feature extraction leads to poor positioning accuracy. An improved threshold denoising method is used to address the issue of signal loss caused by the oscillation of the threshold function. The adjustment function ensures good continuity of the signal and retains the original features of the image to the maximum extent based on image denoising. Second, the ResNet network is used to extract image depth features and establish a fingerprint database. The image depth features exhibit translation and rotation invariance. However, the ResNet network has deeper network layers than the traditional neural networks. Thus, residual learning is added to avoid a decrease in accuracy resulting from the increase in network layers. Finally, the BAS algorithm is used to optimize the connection weight matrix between the layers of the RBF neural network, improve the training speed and stability of the network, and determine the optimal weight between the layers of the network through back propagation for enhanced positioning accuracy.

In this study, we first build a positioning experimental platform (Fig. 4) consisting of a light environment that can simulate real indoor scenes to verify the applicability and effectiveness of the algorithm. The coordinate plate at the bottom of the experimental box is divided into several areas of equal size at an interval of 5 cm, and four LED light sources with the same size and power are installed on top of the experimental box to collect visible light images and extract depth features. Pictures are collected at three different heights by lifting and lowering the coordinate plate to establish a depth feature database for the collected pictures. The measured data are input into the neural network for training. The RBF neural network achieves 26983 target error iterations, the BAS optimized RBF neural network achieves 47352 iterations (Fig. 5), and the training speed is increased by approximately 40%. We randomly select 30 different coordinate points in the experimental box and collect the corresponding images without denoising for the positioning test. The average positioning error without denoising is 5.02 cm (Fig. 6), whereas, with denoising applied to the images collected at the same 30 points, the average positioning error is 4.26 cm (Fig. 7). The experiment shows that image denoising can effectively improve positioning accuracy. When compared to the RBF and back-propagation (BP)network algorithms, the BAS-RBF neural network algorithm provides significant improvements (Fig. 8) . Compared with the BP network algorithm, the confidence probability of a fixed error of less than 2 cm, 4 cm, and 6 cm increase by 9%, 11%, and 10%, respectively. The experimental results show that the performance of the RBF neural network optimized by BAS is better than those of the RBF and BP neural networks (Table 3). The average positioning error of the algorithm is 4.26 cm, which is 10.5% higher than that of the RBF neural network and 16.9% higher than that of the BP neural network.

This study proposes a visible light positioning technology for visual imaging that requires only images from indoor locations. Subsequently, the migration feature learning is used to extract the depth features of the denoised images to establish a database, which is brought into a neural network fused with biological algorithms for learning and training, with the goal of building a neural network training and testing model. Compared with the RBF and BP networks, this model can improve the positioning accuracy and training speed. In the actual measurement and positioning stage, 0.8 m×0.8 m×0.8 m physical model, the average positioning error of the prediction result is 4.26 cm; the probability of the prediction point error of less than 4 cm is 63.4% and the probability of the prediction point error of less than 6 cm is 78%. The positioning result is stable and reliable, providing a new feasible scheme for visible-light indoor positioning technology.