UAV countermeasure technology encompasses three main strategies: destruction, interference blocking, and deception. To address the need for effective drone countermeasures in urban environments with minimal collateral damage and signal interference, this paper focuses on studying key technologies for estimating UAS uplink signal parameters. The primary objectives of this research are as follows: Addressing the challenge of estimating UAV uplink signal parameters, we propose an estimation algorithm specifically designed for frequency-hopping signals. We introduce an enhanced two-hop model to describe frequency-hopping(FH) signals that involve frequency switching. Building upon this model, we present a rapid estimation algorithm for frequency-hopping time based on Maximum Likelihood (ML) theory, significantly reducing the time required for estimation. Additionally, we tackle the issue of low accuracy in traditional frequency estimation algorithms by proposing a novel approach based on Compressed Sensing (CS) and kurtosis threshold. This method enables fast estimation of hopping frequencies without requiring prior information and enhances the accuracy of frequency estimation.