A data-driven control strategy in synergy with Continuous Active Sonar for littoral underwater surveillance

In this work, we describe a data-driven Mission Management Layer (MML) running on-board AUVs which manages the phases of a littoral surveillance mission and exploits the characteristics of Continuous Active Sonar (CAS) signal processing. The MML selects for further investigation the tracks which are likely originated by a target. In this case, the MML launches a receding horizon, non-myopic control algorithm which controls the AUV's heading to improve the tracking performance to ease the target classification. The algorithm minimises the expected target position estimation error over a prediction time window by achieving a trade-off amongst different objectives: keeping the target at broadside, reducing the distance to the target, avoiding areas of high reverberation and searching for geometric configurations with low bistatic target localisation error. We report at-sea experiments obtained during the LCAS15 sea trial, which demonstrated, for the first time, that the proposed autonomy architecture can be executed together with real-time Continuous Active Sonar (CAS) processing on-board the AUVs. CAS has recently gained interest for littoral Anti-Submarine Warfare, since it offers the promise of multiple detections per waveform cycle. This can potentially improve the quality/length of tracks, thus increasing the adaptive behaviour's performance, which, in turn, can increase the detection and tracking capabilities of the processing chain.