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- W2519960178 abstract "A gain matrix is used in order to study the optimal step-size required for the least-mean squares (LMS) algorithm. The case of additive measurement noise is studied and it is proven that the optimal gain matrix based LMS algorithm developed is identical to a Kalman filter. The key importance of this proof of equivalence is that it is shown that for correlated driving noise, the LMS algorithm is always sub-optimal. For white driving noise the optimal step-size is scalar and time-varying." @default.
- W2519960178 created "2016-09-23" @default.
- W2519960178 creator A5044116630 @default.
- W2519960178 date "2016-02-01" @default.
- W2519960178 modified "2023-09-26" @default.
- W2519960178 title "A new look at the LMS algorithm with correlated driving noise" @default.
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- W2519960178 doi "https://doi.org/10.1109/spin.2016.7566656" @default.
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