The study of the icebergs and their movements is one of many applications of scatterometer data in the study of the ecosystems of polar regions. SCATSAT-1 is the Indian Space Research Organisation’s (ISRO’s) Ku-band (13.515625 GHz) scatterometer. Using enhanced resolution Gamma0H (horizontally polarised incidence angle normalised backscattering coefficient) data of SCATSAT-1, we observed the movement of iceberg D28 and its interaction with wind, ocean currents and sea ice for one and a half years of its journey (JD 269, 2019 to JD 051, 2021). The data sets used are as follows: (1) SCATSAT-1 level-4 Gamma0H; (2) OSCAR (Ocean Surface Current Analysis Real-time) third-degree resolution ocean surface currents; (3) hourly wind speed data of ERA5; 4) NSIDC (National Snow and Ice Data Center) sea ice concentration data; and (5) NSIDC Polar Path-finder Daily EASE-Grid Sea Ice Motion Vectors, Version-3. For this study, we divide the continent into five different regions/sectors. It is found that the trajectory of the iceberg is influenced by the resultant of the wind and ocean current, at different scales in these regions. Moreover, sea ice motion can also change the course of iceberg. From the on-screen digitisation of the iceberg, the average area of the iceberg is found to be approximately 1509.82 km2 with approximate dimensions of 27 km × 55.5 km. We conclude that spatial and temporal behaviours of the iceberg can be ascertained from the scatterometer data.

For the path planning of autonomous underwater vehicles (AUVs) in the ocean environment, in addition to the planned path length and safe obstacle avoidance, it is also necessary to pay attention to the impact of ocean currents on the planned path. Therefore, this paper improves the original D* algorithm, and adds the obstacle cost item and the steering angle cost item as constraints on the basis of the original cost function, thus ensuring the navigation safety of the AUV. Considering that ocean currents have a greater impact on the energy consumption of AUVs, this paper establishes a cost model for the impact of ocean currents on AUV energy consumption and applies it to the D* path planning algorithm, so that AUVs can use ocean currents to reduce energy consumption, which can be seen through simulation experiments. The simulation results show that the improvement of the algorithm can plan an optimal energy consumption path.

This paper analyzes the speed of lateral movement of the Cross-stream Active Mooring (CSAM) system in an ocean current and its control methods in order to study the system's capability of tracking short term meandering of ocean currents for maximizing power generation capacity. The CSAM concept for marine current power features a hydro sail system that can deploy generator turbines transversely across streams in a marine current. The hydro sail system can further adjust the horizontal position of the turbines, by changing the angle of attack of the hydro sail, to actively track fast streams in the marine current to increase power generation capacity. A two-step motion model with analytical formulas was developed and analysis based on mechanics indicated that the simplified model is adequate to estimate the system speed. Using an example system, it was estimated that the proposed active mooring system is capable of matching typical lateral speed of short term meandering and tracking fast streams in an ocean current without any external propulsion other than controlling the angle of attack of the hydro sails. Methods of controlling the system speed of moving from one location to another were also developed.

Ocean currents impose a negative effect on Autonomous Underwater Vehicle (AUV) underwater target searches, which lengthens the search paths and consumes more energy and team effort. To solve this problem, an integrated algorithm is proposed to realise multi-AUV cooperative search in dynamic underwater environments with ocean currents. The proposed integrated algorithm combines the Biological Inspired Neurodynamics Model (BINM) and Velocity Synthesis (VS) method. Firstly, the BINM guides a team of AUVs to achieve target search in underwater environments; BINM search requires no specimen learning information and is thus easier to apply to practice, but the search path is longer because of the influence of ocean current. Next the VS algorithm offsets the effect of ocean current, and it is applied to optimise the search path for each AUV. Lastly, to demonstrate the effectiveness of the proposed integrated approach, simulation results are given in this paper. It is proved that this integrated algorithm can plan shorter search paths and thus the energy consumption is lower compared with BINM.