Analog wavelet-like transform based on stimulated Brillouin scattering

Abstract

A photonics-enabled analog wavelet-like transform system, characterized by multiscale time-frequency analysis (TFA), is proposed based on a typical stimulated Brillouin scattering (SBS) pump–probe setup using an optical nonlinear frequency-sweep signal. The periodic SBS-based frequency-to-time mapping (FTTM) is implemented by using a periodic nonlinear frequency-sweep optical signal with a time-varying chirp rate. The frequency-domain information of the signal under test (SUT) in different periods is mapped to the time domain via the FTTM in the form of low-speed electrical pulses, which is then spliced to analyze the time-frequency relationship of the SUT in real-time. The time-varying chirp rate in each sweep period makes the signals with different frequencies have different frequency resolutions in the FTTM process, which is very similar to the characteristics of the wavelet transform, so we call it a wavelet-like transform. An experiment is carried out. Multiscale TFA of a variety of RF signals is carried out in a 4-GHz bandwidth limited only by the equipment.

Time-frequency transform based on frequency-to-time mapping and filtering

“…In comparison, the optical sideband of the SUT gradually moves away from the frequency-sweep optical carrier in different sweep periods because the SUT is assumed to be a linearly frequency-modulated (LFM) signal in Figure 2. Then, the system is configured to implement wavelet-like transform using a frequency-sweep optical signal with a nonlinearly varying time-frequency characteristic [8]. Here, the 1-μs frequency-sweep optical signal is divided equally into four 0.25-μs sections, and the four sections have a sweep chirp rate of 1 GHz/μs, 2 GHz/μs, 4 GHz/μs, and 8 GHz/μs, respectively.…”

“…In this work, time-frequency transform systems reported recently by us, including STFT and wavelet-like transform, are introduced [7][8][9]. These systems avoid the use of dispersions and are implemented by adding a time dimension to the filter-and frequency-to-time mapping (FTTM)-based frequency measurement system.…”

Scattering Suppression in Underwater LIDAR Signal Processing Based on Wavelet-ICA Method

“…It introduces the concept of scaling in the transform, which can realize multi-resolution analysis in the timefrequency domain [2,3]. In recent years, people have introduced different types of wavelet and processing methods [4][5][6][7][8], which were applied to optical filtering [9][10][11][12], and interference pattern analysis [13][14][15]. For processing underwater lidar echo signals, the wavelet transform method utilizes the spectral differences between the target echo and scattered clutters to separate the target and scattering.…”