Online Mean Estimation for Multi-frame Optical Fiber Signals On Highways
arxiv(2024)
摘要
In the era of Big Data, prompt analysis and processing of data sets is
critical. Meanwhile, statistical methods provide key tools and techniques to
extract valuable insights and knowledge from complex data sets. This paper
creatively applies statistical methods to the field of traffic, particularly
focusing on the preprocessing of multi-frame signals obtained by optical
fiber-based Distributed Acoustic Sensing (DAS) system. An online non-parametric
regression model based on Local Polynomial Regression (LPR) and variable
bandwidth selection is employed to dynamically update the estimation of mean
function as signals flow in. This mean estimation method can derive average
information of multi-frame fiber signals, thus providing the basis for the
subsequent vehicle trajectory extraction algorithms. To further evaluate the
effectiveness of the proposed method, comparison experiments were conducted
under real highway scenarios, showing that our approach not only deals with
multi-frame signals more accurately than the classical filter-based Kalman and
Wavelet methods, but also meets the needs better under the condition of saving
memory and rapid responses. It provides a new reliable means for signal
processing which can be integrated with other existing methods.
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