Underground RSSI positioning method based on multi-stage denoising and dual-branch temporal network
FENG Ziyang
GONG Peilin
ZHAO Tong
YI Kang
LI Peng
SU Chao
BAI Libin
Abstract：The underground Received Signal Strength Indicator(RSSI)signal exhibits non-stationary characteristics such as sharp spikes,high-frequency jitter,and trend drift under the influence of multipath propagation,occlusion,and electromagnetic interference,resulting in large positioning errors.Existing positioning methods lack collaborative suppression of multi-source interference,and their feature extraction and multi-scale feature fusion are insufficient.To address these problems,an underground RSSI positioning method based on multi-stage denoising and a dual-branch temporal network was proposed.Multi-stage denoising suppressed spike interference,high-frequency jitter,and trend drift through outlier elimination and interpolation repair,adaptive Kalman filtering,and wavelet-domain adaptive gating,respectively,thereby producing a more stable RSSI sequence with preserved details.The dual-branch temporal network introduced the first-order difference as an auxiliary disturbance prior,extracted features in parallel through a trend branch and a disturbance branch,and adaptively fused them via a channel attention mechanism.A Bidirectional Long Short-Term Memory(Bi-LSTM)network was then used to capture contextual temporal dependencies,ensuring trajectory smoothness and continuity in complex dynamic environments.Test results showed that the RSSI signal became more stable after multi-stage denoising while preserving local dynamic features without excessive smoothing.The dual-branch temporal network achieved high accuracy,F1-score,precision,and recall with fast convergence;in tests under different scenarios,both accuracy and F1-score exceeded 85％,demonstrating good generalization.In continuous positioning tasks under dynamic environments,the average positioning error of the proposed method was only 0.12 m.
Keywords：underground coal-mine positioningReceived Signal Strength IndicatorRSSImulti-stage denoisingdual-branch temporal networkBi-LSTM
Publication Date：2025-10-30
Online Publishing Date：2025-12-02(First online date of this platform, not the publication date of the document)
Pages：7( 34-40 )