In underwater acoustic communications, the received signal is strongly affected by Doppler shift due to the relative motion between the transmitter and the receiver. The signal is received compressed or dilated making it hard to detect and synchronize. Therefore, the doppler shift needs to be estimated and compensated. In this article, a new multisensor method to jointly estimate the Doppler shift, detect and synchronize the signal is proposed.

In this article, a filter bank is coupled with a CFAR detector to guarantee an efficient frame detection in presence of Doppler shift. This work is realized in scope of the NEMOSENS project, which aims to produce autonomous underwater vehicles (AUVs) able to communicate and move in a network thanks to UA modems. Many adverse phenomenons occur in the context of underwater acoustic. Most harmful effects for underwater acoustic communication (UAC) are the multi-path nature of the environment, the Doppler spread and the noise variability. The proposed method reduces the number of lost frames and gives a rough estimate of delay and Doppler shift. The followed approach is supported by simulations with simplified hypotheses, but the interest of this approach is also shown in real sea experiments.

We introduce an extension of the Random Distortion Testing (RDT) framework which allows its use when the noise variance is estimated. This asymptotic extension, named AsympRDT, shows that we asymptotically retain the level of the RDT test as the estimate of the noise variance converges to its real value. The validity of this approach is justified through both theoretical and simulation results. We make use of AsympRDT to develop a change-in-mean detection method for time series. It features three parameters: the size of the processed blocks, the maximum desired false alarm rate and a tolerance. We then show a use-case for this method in cybersecurity for Industrial Control Systems (ICS) as part of an anomaly and cyberattack detection system, where it can be used for segmenting signals and learning normal behaviors.Index Terms-Random Distortion Testing, Change point detection, anomaly detection, time series analysis, cybersecurityThis work was done at the chair Cyber CNI with support of the FEDER development fund of the Brittany region.