Superimposing Electronic Navigational Chart (ENC) data on marine radar images can enrich information for navigation. However, direct image superposition is affected by the performance of various instruments such as Global Navigation Satellite Systems (GNSS) and compasses and may undermine the effectiveness of the resulting information. We propose a data fusion algorithm based on deep learning to extract robust features from radar images. By deep learning in this context we mean employing a class of machine learning algorithms, including artificial neural networks, that use multiple layers to progressively extract higher level features from raw input. We first exploit the ability of deep learning to perform target detection for the identification of marine radar targets. Then, image processing is performed on the identified targets to determine reference points for consistent data fusion of ENC and marine radar information. Finally, a more intelligent fusion algorithm is built to merge the marine radar and electronic chart data according to the determined reference points. The proposed fusion is verified through simulations using ENC data and marine radar images from real ships in narrow waters over a continuous period. The results suggest a suitable performance for edge matching of the shoreline and real-time applicability. The fused image can provide comprehensive information to support navigation, thus enhancing important aspects such as safety.
Summary
The increasing dependence on marine resources has encouraged the rapid development of dynamic positioning (DP) technology in ships and other marine vessels. This study proposes a novel DP scheme for ships subjected to comprehensive disturbances (unknown environmental disturbances and thruster faults). An integral nonsingular fast terminal sliding mode control (INFTSMC) scheme is initially designed without accounting for environmental disturbances. This scheme has a higher convergence rate and robustness against unknown environmental disturbances than the NFTSMC scheme. Furthermore, a new finite‐time disturbance observer is developed to adapt to the changes in the comprehensive disturbances and ensure that the observed errors converge within a small region around the origin in finite time. The INFTSMC scheme is then combined with the finite‐time observer to create a finite‐time observer‐based thruster fault‐tolerant control (FTOAFTC) scheme. Detailed simulation studies and quantitative analyses are carried out on the traditional sliding mode control (SMC), NFTSMC, and FTOAFTC schemes. The FTOAFTC scheme's transient and steady‐state performances, robustness against environmental disturbances, and fault‐tolerance ability are found to be superior to those of the other schemes.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.