To consider increased difficulties of the GNSS signal acquisition due to a wider range of the frequency bandwidth in high dynamic environment, the transmission characteristics of the numeric intermediate frequency signal in the GNSS receiver was analyzed, as well as the correlation peak detection of the complex baseband signal processed by the FFT module, a fast detection method for high dynamic GNSS signal acquisition based on ML estimation is proposed. Firstly, by the construction of Binary hypothesis testing conditions based on the statistical theory of random signals, an acquisition threshold model was presented using the Neyman Pearson Criterion; Secondly, the variance of equivalent White Gaussian Noise based on ML is estimated by judgment statistical characteristics and an acquisition threshold is calculated from variance estimated value，in the meantime the estimation error caused by increased judgment samples was resolved by means of false alarm rate quantized amplification. Finally, the acquisition detection simulation experiment of Beidou B3I signal was conducted in different high dynamic conditions. The result showed the proposed method have a wider scope of high dynamic adaptive capacity, and the precision accuracy of the acquired doppler frequency shift was on a par with the SINS information aiding method, also increased by more than 28% compared to the sequential detection method, as well as a faster detection speed in the same acquisition condition.