Abstract: Provided are a method and a device for reconstructing lost data of marine wireless sensor networks (MWSNs). The data reconstruction method includes following steps: establishing an initial topological structure of the MWSNs; using an improved hierarchical energy balance multipath (THEBM) routing protocol to cluster network nodes; using an improved radial basis function neural network (RBFNN) to predict lost data of nodes in the cluster based on a clustering of the nodes; and using a centralized principal component analysis (PCA) method to compress and reconstruct data of a cluster head node in a process of data transmission from a cluster head to a ship base station.

Abstract: Disclosed is a ship image track tracking and prediction method based on ship heading recognition, which includes the following steps: obtaining a ship image data set, preprocessing the data set to obtain a preprocessed data set; inputting the preprocessed data set into the rotating ship detection network for training, obtaining the trained rotating ship detection network, collecting the ship navigation video, and inputting the ship navigation video into the trained rotating ship detection network to obtain the ship detection result; inputting the ship detection result into the rotating ship tracking network and tracking the target ship to obtain the historical trajectory and the heading information of the target ship; inputting the historical trajectory and ship heading information of the target ship into the ship trajectory and ship heading prediction network, and predicting the navigation trajectory and ship heading at sea.

Abstract: A node positioning method for marine environmental monitoring wireless sensor networks is provided, which jointly estimates node positions and path loss factors by considering the node real-time movement and the path loss and absorption effect of underwater communication; transforms an original non-convex problem into a non-negative constrained least squares framework, and finds the optimal solution of marine node positions by two stages of interior point method and block coordinate update. In the first stage, the problem is re-expressed by using the penalty function according to the interior point method to obtain the approximate solution; and in the second stage, the original problem is transformed into a generalized trust region sub-problem, and the approximate solution obtained by the interior point method is used as the initial estimation, the accurate estimation values of the marine node position and path loss factor are obtained by iterative solutions combined with the block coordinate update.

Abstract: The application relates to an efficient particle filter node positioning method for ocean sensor networks, including the following steps: 1) obtaining a first state vector of mean value of posterior probability at k?1 moment; 2) establishing the state model of the nonlinear system; 3) obtaining the observation model of the nonlinear system; 4) propagating particles, and calculating the state vector ?+k at the next moment according to the particle vector at k?1 moment; 5) obtaining the observed value and calculating the weight; 6) judging whether it is degraded, if not, the weight is used to calculate the position expectation, otherwise, 7) is executed; 7) modifying small-weight particles; 8) resampling the improved residual and normalizing the weights; and 9) using the weight of 8) to calculate the position expectation and realize node positioning.

Abstract: The present invention provides a vessel traffic pattern identification method via data quality control and data compression, and includes the steps of assorting a collection of Automatic Identification System (AIS) data points according to Maritime Mobile Service Identity (MMSI) code; sorting each collection result by time ascending order; deleting duplicated vessel AIS data points considering time stamp, latitude, longitude and vessel speed over ground; segmenting vessel trajectories; obtaining high-quality AIS data with an AIS data anomaly detection; repairing and compressing each vessel trajectory with the Douglas-Peucker algorithm; clustering vessel trajectories with the Quick Bundles algorithm; and identifying a maritime traffic pattern. The invention can efficiently identify vessel traffic patterns and help maritime traffic management departments to accurately identify a traffic situation.

Abstract: Disclosed is a ship image track tracking and prediction method based on ship heading recognition, which includes the following steps: obtaining a ship image data set, preprocessing the data set to obtain a preprocessed data set; inputting the preprocessed data set into the rotating ship detection network for training, obtaining the trained rotating ship detection network, collecting the ship navigation video, and inputting the ship navigation video into the trained rotating ship detection network to obtain the ship detection result; inputting the ship detection result into the rotating ship tracking network and tracking the target ship to obtain the historical trajectory and the heading information of the target ship; inputting the historical trajectory and ship heading information of the target ship into the ship trajectory and ship heading prediction network, and predicting the navigation trajectory and ship heading at sea.

Abstract: The present invention proposes a vessel traffic pattern identification method via data quality control and data compression. Firstly, assort a collection of Automatic Identification system (AIS) data points according to Mobile Service Identify (MMSI) code and sort each collection result by time ascending order, and then delete duplicated vessel AIS data points considering time stamp, latitude, longitude and vessel speed over ground, then segment vessel trajectories. Secondly obtain high-quality AIS data with an AIS data anomaly detection and repair and compress each vessel trajectory with the Douglas-Peucker algorithm. Thirdly, cluster vessel trajectories with the Quick Bundles algorithm, and identify maritime traffic pattern. The invention can efficiently identify vessel traffic patterns, and help maritime traffic management departments to accurately identify a traffic situation.