Ultrasound time reversal imaging of extended targets using a broadband white noise constraint processor

Abstract

High resolution ultrasound time-reversal imaging typically requires adequate signal strength. The multiple signal classification (TR-MUSIC) algorithm can produce images of point scatterers with subwavelength resolution when a clear separation of the signal space and the noise space exists. When the size of the scatterers is on the order of the wavelength or larger, the TR-MUSIC algorithm suffers from poor image quality due to a tangled eigenstate spectrum. In this study, we use a coherent broadband white noise constraint (B-WNC) matched-field processor that requires zero knowledge of the eigenspace in the interrogated medium to obtain high quality ultrasound images. The WNC algorithm enhances the robustness to model mismatch of matched-field beamformers in poor signal conditions. The multi-tone broadband beamformer offers additional gain over a single-tone by augmenting the dimension of the physical array and exploiting the cross-frequency terms in the time reversal operator. The dynamic range bias obtained from a rank-deficient covariance matrix benefits the B-WNC images to retain better contrast than the TR-MUSIC images. This study also proposes improvements in modeling the replica vectors for the virtual time-reversal process. The transverse mode is combined with the longitudinal mode in the formulation using the free-field Green’s function. The resolution is further improved by exploiting the transverse mode information in the multistatic data. Adaptive spatial windows are applied to the replica vectors according to the displacement structure of the wave mode at medium-array interface. Numerical simulations and experimental testing demonstrate the potential for accurate sizing of extended targets that have a size comparable to the dominant wavelength using the proposed B-WNC algorithm.

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.…”