  Chirp signals have established diverse applications caused by the capable of
producing time-dependent linear frequencies. Most feature extraction
transformation methods for chirp signals focus on enhancing the performance of
transform methods but neglecting the information derived from the
transformation process. Consequently, they may fail to fully exploit the
information from observations, resulting in decreased performance under
conditions of low signal-to-noise ratio and limited observations. In this work,
we develop a novel post-processing method called mapping information model to
addressing this challenge. The model establishes a link between the observation
space and feature space in feature extraction transform, enabling interference
suppression and obtain more accurate information by iteratively resampling and
assigning weights in both spaces. Analysis of the iteration process reveals a
continual increase in weight of signal samples and a gradual stability in
weight of noise samples. The demonstration of the noise suppression in the
iteration process and feature enhancement supports the effectiveness of the
mapping information model. Furthermore, numerical simulations also affirm the
high efficiency of the proposed model by showcasing enhanced signal detection
and estimation performances without requiring additional observations. This
superior model allows amplifying performance within feature extraction
transformation for chirp signal processing under low SNR and limited
observation conditions, opens up new opportunities for areas such as
communication, biomedicine, and remote sensing.
