Channel processing-based time-reversal method for multi-target tunable focusing

Achieving tunable focus of electromagnetic field energy at multiple target points is a critical challenge in the wireless power transfer (WPT) domain. Although techniques such as optimal constrained power focusing (OCPF) and time reversal (TR) have been proposed. The former presents limited practical applicability while the latter is noteworthy for its adaptive spatio-temporal synchronous focusing characteristics. However, the time reversal mirror (TRM) method necessitates intricate pretesting and has highly complex systems. In this study, we introduce a novel channel processing method, named channel extraction, selection, weighting, and reconstruction (CESWR), to attain balanced power distribution for multiple users, characterized by low complexity, high computability, and rapid convergence. Diverging from the traditional TR approach, our proposed method, grounded in channel correlation considerations, filters the channel impulse response (CIR) for multiple targets, segregating them into distinct characteristic and similar components for each target. This method ensures focused generation at both receiving ends while facilitating high-precision regulation of the peak voltage of the received signal. Furthermore, this study embarks on a rigorous examination of the linearity intrinsic to the proposed methodology, explicating a singular correspondence between the tuning of theoretical weights and the resultant outcomes. In order to authenticate the efficacy of this methodology, we construct a single-input multiple-output time-reversal cavity (SIMO-TRC) system for the experimental section of this manuscript. Subsequent experimentation, conducted for both loosely and tightly correlated models, furnishes invaluable insights. Evidently, in the loosely correlated model, the CESWR method exhibits proficiency in attaining a peak voltage ratio (PVR) of nearly 1.00 at the two receivers, with a minuscule numerical discrepancy of merely 8×10-6 mV. In stark contrast, under the tightly correlated model, the CESWR method demonstrates an enhanced ability to differentiate between two targets, thus offering a noticeable improvement over the classic single-target TR method.