In conventional ultrasound systems, the compromise between frequency and temporal resolution limits the quality of the spectrograms and the ability to track fast blood flows. The main objective of this study was to identify a method that could reduce spectral broadening over time by reducing the observations and improving the spectral resolution and contrast. This problem is more pronounced in the process of imaging at higher blood velocities when using a short Doppler signal observation window (OW) in adaptive methods. The proposed adaptive technique, which is based on the covariance matrix Eigen space and the amplitude spectrum Capon (ASC) algorithm, managed to improve the spectral resolution and contrast compared with other adaptive algorithms within a shorter observation time, and it offered a narrower power spectrum and a more accurate spectrogram over time in combination with a coherence-based post filter. All methods were tested through various simulations. First, an analysis was carried out by simulating the femoral artery flow and the time-independent parabolic flow using the Field II simulator. Then, the performance of the proposed method was evaluated under more realistic conditions using a computational fluid dynamics simulation of complex flow fields in a carotid bifurcation model. Afterward, in vivo clinical data on the hepatic vein were used to validate the proposed method. Finally, the accuracy of the velocity estimated by different methods was evaluated through a mean-square-error assessment. Not only could the proposed method show significant improvements using extreme small OWs, N=[2, 4] , in the simulated data in terms of frequency resolution and contrast, but it also managed to offer an improvement of 74%, 73.3%, 22.2%, and 50% in frequency resolution, and an increase of 96.5, 90.2, 49, and 31.5 dB in contrast using in vivo clinical data compared with the Capon, amplitude and phase estimation (APES), projection-based Capon, and projection-based APES, respectively, for the OW of N=4 .