Abstract Many practical applications built around adaptive algorithms require solutions that work at low signal-to-noise ratio (SNR) values. In this situation, a variable step-size algorithm is a must to accommodate this scenario. A plethora of variable step algorithms exist in the literature for high SNR values. This paper presents a different variable step-size incremental least mean square strategy that provides excellent performance at low SNR values. A detailed theoretical analysis has been carried out for the proposed algorithm. Simulations have been performed for different scenarios to demonstrate the validity of the proposed strategy.

中文翻译：

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适用于低 SNR 应用的可变步长增量 LMS 解决方案

摘要 许多围绕自适应算法构建的实际应用需要在低信噪比 (SNR) 值下工作的解决方案。在这种情况下，必须使用可变步长算法来适应这种情况。文献中存在大量用于高 SNR 值的可变步长算法。本文提出了一种不同的可变步长增量最小均方策略，可在低 SNR 值下提供出色的性能。对所提出的算法进行了详细的理论分析。对不同场景进行了模拟，以证明所提出策略的有效性。