It is important for maritime authorities to effectively classify and identify unknown types of ships in historical trajectory data. This paper uses a logistic regression model to construct a ship classifier by utilising the features extracted from ship trajectories. First of all, three basic movement patterns are proposed according to ship sailing characteristics, with related sub-trajectory partitioning algorithms. Subsequently, three categories of trajectory features with their extraction methods are presented. Finally, a case study on building a model for classifying fishing boats and cargo ships based on real Automatic Identification System (AIS) data is given. Experimental results indicate that the proposed classification method can meet the needs of recognising uncertain types of targets in historical trajectory data, laying a foundation for further research on camouflaged ship identification, behaviour pattern mining, outlier behaviour detection and other applications.

An atomic interference gravimeter (AIG) is of great value in underwater aided navigation, but one of the constraints on its accuracy is vibration noise. For this reason, technology must be developed for its vibration isolation. Up to now, three methods have mainly been employed to suppress the vibration noise of an AIG, including passive vibration isolation, active vibration isolation and vibration compensation. This paper presents a study on how vibration noise affects the measurement of an AIG, a review of the research findings regarding the reduction of its vibration, and the prospective development of vibration isolation technology for an AIG. Along with the development of small and movable AIGs, vibration isolation technology will be better adapted to the challenging environment and be strongly resistant to disturbance in the future.

Abstract-In the life cycle of Tactical Data Link (TDL) system, the simulation is an indispensable key. This paper places emphasis upon a new object-oriented modular simulation framework-OMNeT++, and polling protocol of MAC layer is explored under its environment. Besides, TDL simulation model and Link11 network model based on the polling protocol are provided. The overall system performance such as network cycle, MAC port-to-port delay, packet delivery ratio and their analysis with network load were obtained. As is shown in simulation results, the network performance is fine, which could keep real time and reliability. It would be of a good value to the application and further research on TDL.

In various applications of wireless sensor network, sensor nodes must know their location information to fulfill further assignments such as target real-time monitoring and target tracking. In this paper, a new node self-localization algorithm based on the received signal strength indicator (RSSI) is proposed. The new algorithm takes both triangle centroid localization algorithm (ACLA) and approximate point-in-triangulation test (APIT) algorithm into consideration. In this algorithm, the triangle centroid localization model is improved by optimizing the coefficient of the weighted centroid. And in order to solve non-idealized triangle problem, we make use of APIT to select idealized triangle for localization. The simulation results show that the improved algorithm has less localization error compared to the existing RSSI algorithm. Keywords-wireless sensor networks(WSN); node selflocalization; received signal strength indicator (RSSI); approximate point-in-triangulation test (APIT)

To address the data storage, management, analysis, and mining of ship targets, the object-oriented method was employed to design the overall structure and functional modules of a ship trajectory data management and analysis system (STDMAS). This paper elaborates the detailed design and technical information of the system’s logical structure, module composition, physical deployment, and main functional modules such as database management, trajectory analysis, trajectory mining, and situation analysis. A ship identification method based on the motion features was put forward. With the method, ship trajectory was first partitioned into sub-trajectories in various behavioral patterns, and effective motion features were then extracted. Machine learning algorithms were utilized for training and testing to identify many types of ships. STDMAS implements such functions as database management, trajectory analysis, historical situation review, and ship identification and outlier detection based on trajectory classification. STDMAS can satisfy the practical needs for the data management, analysis, and mining of maritime targets because it is easy to apply, maintain, and expand.

The existing off-board active decoys face such common technical problems as short action time, limited jamming power, and difficult control over deployment situation and effective directive jamming beam direction. For this reason, this paper introduces a new mode and process of anti-missile combat, which places the radar active decoy on an unmanned surface vehicle (USV) to collaborate with the surface warship during the off-board active anti-missile combat. Following the principles of radar terminal guidance centroid jamming, a real-time calculation method for the effective area of off-board active jamming is developed, and the jamming position maneuver strategy under the collaboration of the USV and the surface warship is proposed to implement the off-board active anti-missile combat. The proposed strategy satisfies the needs of long-time, high-power, stable, and effective off-board jamming against incoming anti-ship missiles. This paper further verifies the effectiveness of the proposed strategy in the simulated and live firing confrontations. INDEX TERMS electronic countermeasures unmanned surface vehicle (ECM USV); off-board active decoy; centroid jamming; jamming-position maneuver.

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An adaptive channel estimation algorithm for the channel length is proposed to construct a channel estimation model suitable for orthogonal frequency division multiplexing (OFDM) underwater acoustic communication signals for the dependence of traditional channel estimation algorithms on channel length information. This algorithm can be adopted to evaluate channel estimation quality in real time and to adaptively adjust the channel length of the channel estimation algorithm according to the evaluation result, which satisfies the need of accurate estimation of unknown underwater acoustic channels and communication application; based on the study on the relationship between the OFDM communication bit error rate and the subcarrier signal to noise ratio, a self-adjusting optimization scheme for OFDM subcarrier transmitting power is proposed, which realizes underwater communication with the low bit error rate through higher energy efficiency. The validity of the research content is verified through simulation and field experiments.