Multifrequency TR-MUSIC Processing to Locate Soft Faults in Cables Subject to Noise

Abstract

Time-Reversal multiple signal classification (TR-MUSIC) has recently been shown to be an effective technique to locate multiple soft faults in wire networks, thanks to its submillimeter location accuracy. TR-MUSIC processes transmission and reflection data measured at a single frequency into a function of space, the pseudo spectrum, expected to present singularities only at a fault position. At frequencies high enough, the spatial periodicity that comes with the propagation of harmonic signals leads to multiples such singularities, of which only one represents the fault position, while the remaining are ghosts faults. TR-MUSIC was therefore introduced using a single continuous-wave excitation at frequencies low enough to avoid ghosts, an approach suitable only to noiseless configurations. This paper explores the effects of noise on TR-MUSIC fault location by first highlighting its high sensitivity to noise at low frequency. A potentially lower sensitivity is shown to exist at high frequencies, where ghosts positions are found. A multi-frequency processing is introduced, allowing at the same time to solve the ambiguity in the fault position and to effectively control the impact of noise on its location accuracy. The proposed processing is shown to reinstate precise super-resolved estimates of fault locations even for signalto-noise ratios as low as 5 dB, without requiring to the use of wide-band signals.

Resolution-Enhanced and Accurate Cascade Time-Reversal Operator Decomposition (C-DORT) Approach for Positioning Radiated Passive Intermodulation Sources

“…Hence, the central frequency decomposition of the time-reversal operator (CF-DORT), the central frequency time-reversal multiple signals classification (CF-TR-MUSIC), the time domain decomposition of the time-reversal operator (TD-DORT), and UWB-TR-MUSIC methods are developed [25,26]. Though CF-TR-MUSIC and UWB-TR-MUSIC can distinguish the multiple closely spaced targets, they are sensitive to the noise, while CF-DORT and TD-DORT have a better anti-noise performance [27,28]. Differing from the SS-MDM, the row vector of the SF-MDM denotes the frequency information for each antenna element, and the column vector of SF-MDM represents the spatial information for each sampled frequency.…”