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In our simulations, these statistics are estimated as follows. 102) where αy ∈ (0, 1) and αv ∈ (0, 1) are the forgetting factors that control the inﬂuence of the previous data samples on the current correlation matrix estimate (the initial estimates of these two matrices are obtained from the ﬁrst 100 signal frames with a short-time average). After the estimates of the Φy (k, n) and Φv (k, n) matrices are available at time frame n, the estimate of Φx (k, n) is computed as Φy (k, n) − Φv (k, n).
0 αy (= αv ) Fig. 2 Fullband performance of the MVDR ﬁlter for diﬀerent values of the ﬁlter length, L, as a function of the forgetting factor αy (= αv ) in the white Gaussian noise. The window size is K = 64 (8 ms) with a 75% overlap and the fullband input SNR is 10 dB. optimized separately in an iterative way, which will be left to the reader’s investigation. 3 plots the performance of the MVDR ﬁlter in the NYSE noise. The conditions of this simulation are the same as those in the previous one except that, this time, we take the NYSE noise.
Comparing Figs. 2, we can see that varying the forgetting factor αv can help optimize the performance even when the noise is stationary. 0 αy (= αv ) Fig. 2 Fullband performance of the MVDR ﬁlter for diﬀerent values of the ﬁlter length, L, as a function of the forgetting factor αy (= αv ) in the white Gaussian noise. The window size is K = 64 (8 ms) with a 75% overlap and the fullband input SNR is 10 dB. optimized separately in an iterative way, which will be left to the reader’s investigation.