Underground adaptive positioning algorithm based on PSO-BP neural network

Abstract

A kind of underground adaptive positioning algorithm based on PSO-BP neural network was proposed. In view of the problem that traditional positioning algorithm based on ranging model is sensitive to coal mine environment disturbance and ranging error is large, a fingerprint matching positioning model is selected for positioning. In view of the problem that strong time-varying nature of coal mine environment is easy to increase matching error between the fingerprint information collected in real time and the static fingerprint database information established in offline phase, the beacon node is used as calibration node to better reflect the condition of reference point changes with environment, and avoid adding additional calibration nodes. The dynamic compensation method is used to correct the fingerprint data of the target node in real time without increasing the hardware cost, which solves the problem of poor adaptation of the fingerprint matching positioning model. At the matching positioning stage, PSO is used to optimize weight of BP neural network to accelerate convergence of BP neural network and improve learning speed. The experimental results show that the algorithm is more adapted to the coal mine environment varies with time, and meets the requirement of adaptive underground positioning.

Keywords

• time-varying of underground environment
• underground personnel positioning
• adaptive positioning
• fingerprint matching
• particle swarm optimization algorithm
• bp neural network