Fast time-frequency domain reflectometry based on the AR coefficient estimation of a chirp signal

In this paper, a novel reflectometry, which is characterized by a simple autoregressive (AR) modeling of a chirp signal and an weighted robust least squares (WRLS) AR coefficient estimator, is proposed. In spite of its superior fault detection performance over the conventional reflectometries, the recently developed time-frequency domain reflectometry (TFDR) might not be suitable for real-time implementation because it requires heavy computational burden. In order to solve this critical limitation, in our method, the time-frequency analysis is performed based on the estimated time-varying AR coefficient of a chirp signal. To do this, a new chirp signal model which contains a single time-varying AR coefficient is suggested. In addition, to ensure the noise insensitivity, the WRLS estimator is used to estimate the time-varying AR coefficient. As a result, the proposed reflectometry method can drastically reduce the computational complexity and provide the satisfactory fault detection performance even in noisy environments. To evaluate the fault detection performance of the proposed method, simulations and experiments are carried out. The results demonstrate that the proposed algorithm could be an excellent choice for the real-time reflectometry.