Abstract
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.
Highlights
Background normalization is a kind of constant false-alarm rate (CFAR) processing that estimates the magnitude of background ambient and self-noise as a threshold, and thresholding the weak signals of interest by providing dynamic magnitude compression for the data visualization displays [1]
The approach we propose is an inverse beam characteristics scan (IBCS) algorithm that normalizes the data in an interval between two peaks instead of two valleys, followed by a pointwise nonlinear combination with a beam characteristics scan method
The paper proposes an improved background normalization scheme including an inverse beam characteristics scan algorithm based on the conventional beam characteristics scan method and a pointwise minimization operator that combines the above two approaches
Summary
Background normalization is a kind of constant false-alarm rate (CFAR) processing that estimates the magnitude of background ambient and self-noise as a threshold, and thresholding the weak signals of interest by providing dynamic magnitude compression for the data visualization displays [1]. This technique has been widely used in searching low level signals and detecting line spectrums, especially those in very low frequency (usually lower than 100 Hz) but submerged in background environmental noise and receiver-self-noise in most situations, since they possibly contain the power and character frequencies of targets like autonomous underwater vehicles (AUVs) and submersibles, etc. The background normalizers had been practically applied in shallow water with
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