Event identification based on sample feature correction algorithm for Φ-OTDR

Abstract

To address the problem of decreased recognition accuracy of event samples in practical phase-sensitive optical time-domain reflectometer (Φ-OTDR) monitoring scenarios due to external environmental interference, this paper proposes a feature correction algorithm based on sample feature weighting method. By establishing a correlation evaluation method and a weight allocation scheme based on sample feature correlation, combined with the back propagation (BP) algorithm, an average recognition rate of 99.50% for four types of events (climbing, strong wind, knocking and background, 6000 samples) in strong wind environments was achieved, which is 3% higher than the algorithm using BP classifier. The results demonstrate that the proposed algorithm can effectively enhance the performance of Φ-OTDR in complex environments.