Real-time implementation of the optimal two-dimensional filter for highly nonstationary frequency-modulated signal estimation

Abstract

Real-time implementation of the optimal filter for highly nonstationary two-dimensional (2D) signal estimation is designed. It is based on the real-time results of 2D nonstationary signal analysis, on the correspondence of the filter's region of support (FRS) to the signal's local frequency (LF) and on the real-time LF estimation, implemented by the sliding matrix operation. Multiple FRS detection in the observed 2D signal point is provided by the proposed system, enabling very efficient real-time estimation of nonstationary monocomponent and multicomponent frequency-modulated (FM) noisy signals. The design is proven through the verification of the sliding matrix operation, that represents central part of the designed filter. As well, it is tested by estimation of the nonstationary monocomponent and multicomponent FM signals exposed to the high white noise.