Abstract: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 (SNR) and limited observations. In this… Show more
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