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Subspace leaky LMS
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DOI：10.1109/LSP.2003.821760.png)
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The least mean squared (LMS) adaptive filtering algorithm may experience uncontrolled parameter drift when its input signal is not persistently exciting,, leading to serious consequences when implemented with finite word-length. Though so-called tap-leakage modifications of LMS have been proposed to mitigate this drift, they inevitably introduce parameter bias which degrades mean-squared error performance. In this letter, we propose a novel algorithm which leaks only in the unexcited modes, thus introducing insignificant bias, while still retaining the low computational complexity of LMS.
Keyword:
adaptive filtering
leakage
leaky least mean squares
least mean squares (LMS)
subspace tracking
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9.6
论文数:
1.1W
被引数:
1.7W
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