Sentence examples for robust estimation signal from inspiring English sources

Robust estimation signal processing schemes as well as cooperative localization algorithms are useful to improve localization accuracy.

Later works already segregated the diffuse and direct part by using beamforming or directional microphones, and obtained a more robust estimation of the signal-to-diffuse ratio [9, 13].

Although this method is computationally intensive, it provides a robust estimation of unknown true peak signal from noised data.

Many CASA systems and many source separation methods use prior knowledge about source signals for robust estimation to improve the performance.

We present a solution to this problem for accurate robust estimation of the respiration rate using signal quality indices (SQIs) and a modified Kalman Filter (KF) fusion framework which uses the SQIs to adaptively update the KF noise covariance estimate.

By default, we choose the top 5% (~500 regions) with the strongest tag signals to achieve a reliable and robust estimation.

Robust estimation of the information content require hundreds of splice signals, so it is impossible for the extremely intron-poor species.

In addition, since sparse LMS-based channel estimation methods have a common drawback of sensitivity to scaling of random training signal, it is very hard to choose a proper learning rate to achieve a robust estimation performance [21].

Robust estimation without source knowledge, and   5.

Thus, a more robust estimation may significantly improve the performance.

See Section 2.4 for possible robust estimation methods.