Compressed sampling time difference of arrival estimation method based on amplitude and phase clustering

In the realm of signal processing, modulation signals like PSK (Phase Shift Keying) and QAM (Quadrature Amplitude Modulation) can achieve a substantial signal-to-noise ratio enhancement through aliasing transmission subsequent to clustering and sorting. This paper introduces a novel approach to compressed Time Difference of Arrival (TDOA) estimation, leveraging amplitude-phase clustering signals. The compression matrix is meticulously crafted based on the distinctive amplitude and phase characteristics. Subsequently, the study delves into an analysis of the Cramer-Rao Lower Bound (CRLB) under full sampling conditions. Ultimately, TDOA estimation is carried out employing the Approximate Maximum Likelihood (AML) method. Simulation outcomes underscore the commendable performance of compressed sampling TDOA estimation predicated on amplitude and phase clustering, falling within an order of magnitude of that achieved through full sampling.