A post-processing technique to estimate the signal-to-noise ratio and remove echosounder background noise

TLDR

The study addresses the need to estimate signal‑to‑noise ratio and remove background noise in echosounder data. The authors develop a simple, effective post‑processing technique to estimate echosounder background‑noise levels and SNRs during active pinging. The method assumes that a portion of the sampled acoustic signal is dominated by background noise with negligible backscatter, enabling robust estimation. When the assumption holds, the technique yields accurate, repeatable background‑noise estimates equivalent to receiver measurements with the transmitter disabled, allows first‑order noise corrections and SNR calculations, and can improve acoustic inferences in low‑SNR scenarios such as noisy vessels, multifrequency surveys, long ranges, and weak targets. Published in ICES Journal of Marine Science, 2007, vol.

Abstract

Abstract De Robertis, A., and Higginbottom, I. 2007. A post-processing technique to estimate the signal-to-noise ratio and remove echosounder background noise. – ICES Journal of Marine Science, 64: 1282–1291. A simple and effective post-processing technique to estimate echosounder background-noise levels and signal-to-noise ratios (SNRs) during active pinging is developed. Similar to other methods of noise estimation during active pinging, this method assumes that some portion of the sampled acoustic signal is dominated by background noise, with a negligible contribution from the backscattered transmit signal. If this assumption is met, the method will provide robust and accurate estimates of background noise equivalent to that measured by the receiver if the transmitter were disabled. It provides repeated noise estimates over short intervals of time without user intervention, which is beneficial in cases where background noise changes over time. In situations where background noise is dominant in a portion of the recorded signal, it is straightforward to make first-order corrections for the effects of noise and to estimate the SNR to evaluate the effects of background noise on acoustic measurements. Noise correction and signal-to-noise-based thresholds have the potential to improve inferences from acoustic measurements in lower signal-to-noise situations, such as when surveying from noisy vessels, using multifrequency techniques, surveying at longer ranges, and when working with weak acoustic targets such as invertebrates and fish lacking swimbladders.