Background normalization algorithms attempt to suppress the ambient and self-noise during the measurements of sonar, which enhance the detection performance and the display effect of weak signals. Conventional background normalization methods are usually sensitive to the accuracy of prior set filtering interval and threshold, while significant noise is still detected in low frequency. In this paper, an improved background normalization algorithm is proposed by thresholding the processing interval between several local peak values and local valley values. Compared to the existing scenarios, the proposed approach automatically calculates the filtering interval and threshold, with substantial resilience to the noise level in low frequency. Experimental results illustrate the effectiveness of our algorithm.

中文翻译：

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一种改进的低频噪声弹性背景归一化算法

背景归一化算法试图抑制声纳测量过程中的环境噪声和自身噪声，从而提高检测性能和弱信号的显示效果。传统的背景归一化方法通常对先验设置的滤波间隔和阈值的准确性很敏感，而在低频下仍然检测到显着的噪声。本文通过对几个局部峰值和局部谷值之间的处理间隔进行阈值化，提出了一种改进的背景归一化算法。与现有场景相比，所提出的方法自动计算过滤间隔和阈值，对低频噪声水平具有显着的弹性。实验结果说明了我们算法的有效性。