Frequency Dispersion Compensation Through Variable Window Utilization in Time-Reversal Techniques for Electromagnetic Waves

Abstract

Abstract-In microwave imaging applications, propagating signals undergo additional attenuation in dispersive and lossy media when compared to non-dispersive and lossless media. In this paper, we introduce a threshold approach and Short-Time Fourier transform (STFT) based inverse filters to compensate for such additional attenuation in time reversal (TR) based imaging algorithms. The method introduced here is utilized to reduce the unwanted noise amplification in the received signals during the compensation stage. Additionally, optimum settings for window type and length in the STFT method are obtained through a scanning operation in the propagation medium. Different window types and lengths are studied to achieve best focusing resolution in TR applications. While utilizing large number of windows with short spatial lengths provides improved TR focusing performance, it also increases the overall cost and complexity of the imaging system. The threshold method introduced here achieves improved TR focusing performance without increasing the cost by utilizing a lower number of inverse filters.

Continuous Wavelet Transform-Based Frequency Dispersion Compensation Method for Electromagnetic Time-Reversal Imaging

“…The way [12] used to produce the inverse filter took the major part of the simulation time because the creation of the inverse filters in [12] needs two FDTD calculations while our approach with CWT does not require any FDTD calculations. Applying different FDTD sizes with different simulation times shows that the proposed method is 1.8 times faster than the STFT method in [12] and [17] in average.…”

“…The process of creating the inverse filters is empiric and time consuming. Later, the method in [12] is extended by adding a threshold approach to overcome the amplification of the noise in the inverse filter in [17]. Different window types and lengths are applied in [17] to obtain the optimum settings for the STFT technique.…”

“…Later, the method in [12] is extended by adding a threshold approach to overcome the amplification of the noise in the inverse filter in [17]. Different window types and lengths are applied in [17] to obtain the optimum settings for the STFT technique. However, the work only applied in dispersive and homogeneous media which do not accurately represent the real-life applications.…”

“…However, the work only applied in dispersive and homogeneous media which do not accurately represent the real-life applications. The inverse filters in [12] and [17] need the non-dispersive version of the propagation media to create the inverse filter which might be unknown in real-life scenarios. The STFT method uses a fixed time window length which provides a lower resolution in determining attenuation when compared with other size-adjustable window methods [18], [19].…”

“…Our method uses adjustable-length windows by applying long time-windows at low frequencies and short time-windows at high frequencies [20]. Therefore we do not need optimization of the time-window length as was carried out in [17]. The proposed inverse filters need only the complex permittivity of one dominant medium at the center frequency to create the inverse filter.…”