Baselines are used to establish the maritime boundaries of a coastal state which include the territorial sea, contiguous zone, exclusive economic zone and continental shelf; thus, they are instrumental in implementing state maritime policy. For Poland, as well as in other coastal states, baseline determination can be considered from both a legal and measurement-related point of view. This paper discusses an effective and optimal method of performing bathymetric measurements to enable territorial sea baseline determination in selected waterbodies of Poland. It presents a method for planning a hydrographic survey using both manned and unmanned vessels and presents oceanographic parameters that should be determined before and during hydrographic measurements, as well as a method of choosing the measuring equipment used in bathymetric measurements in ultra-shallow waters. The results of our analyses showed that using an unmanned vessel, on which a multi-GNSS receiver and a miniature MBES or SBES can be installed, is currently the optimum and the most effective method for determining the territorial sea baseline.

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The geometric distribution of navigational aids is one of the most important elements to be taken into account in the planning of maritime terrestrial navigation systems. It determines to a large extent the capability of vessels to obtain high-precision position coordinates. Therefore, the optimisation of their location is a key element at the planning stage, in particular on port approach fairways. This article attempts to use computer simulation methods to assess the positioning accuracy of a vessel that follows a constant course and speed on a port approach fairway. The analysis uses a technique based on the Extended Kalman Filter (EKF) Two-Dimensional (2D) Range-Bearing Simultaneous Location and Mapping (SLAM) method. In the simulation experiment conducted, the research object determined its position based on bearing and distance to fixed position beacons, which changed their locations in subsequent passages of the vessel. A geometrically optimal configuration of the terrestrial navigation marking system guaranteeing the highest positioning accuracy was identified as a result of the deliberations. The study analysed more than 20,000 cases of different configurations (locations) of the fixed position beacons, demonstrating that the adopted algorithm can be used successfully in the planning of their deployment in the context of ensuring minimum accuracy requirements for the positioning of navigational signs on port approach fairways and under restricted conditions by navigational marking services, as set out in International Maritime Organization (IMO) Resolutions A915 (21) and A953(22).

Video image processing and object classification using a Deep Learning Neural Network (DLNN) can significantly increase the autonomy of underwater vehicles. This paper describes the results of a project focused on using DLNN for Object Classification in Underwater Video (OCUV) implemented in a Biomimetic Underwater Vehicle (BUV). The BUV is intended to be used to detect underwater mines, explore shipwrecks or observe the process of corrosion of munitions abandoned on the seabed after World War II. Here, the pretrained DLNNs were used for classification of the following type of objects: fishes, underwater vehicles, divers and obstacles. The results of our research enabled us to estimate the effectiveness of using pretrained DLNNs for classification of different objects under the complex Baltic Sea environment. The Genetic Algorithm (GA) was used to establish tuning parameters of the DLNNs. Three different training methods were compared for AlexNet, then one training method was chosen for fifteen networks and the tests were provided with the description of the final results. The DLNNs were trained on servers with six medium class Graphics Processing Units (GPUs). Finally, the trained DLNN was implemented in the Nvidia JetsonTX2 platform installed on board of the BUV, and one of the network was verified in a real environment.

The purpose of this article is to present a study aimed at developing a method for the precise determination of unmanned surface vehicle (USV) movement parameters (heading (HDG), speed over ground (SOG) and rate of turn (ROT)) through appropriate processing. The technique employs a modified weighted ICP (Iterative Closest Point) algorithm and a 2D points layer arranged in the horizon plane obtained from measurements. This is performed with the help of Light Detection and Ranging (LIDAR). A new method of weighting is presented. It is based on a mean error in a given direction and the results of modified weighted ICP tests carried out on the basis of field measurement data. The first part of the paper characterizes LIDAR measuring errors and indicates the possibilities for their use in matching point clouds. The second part of the article deals with a method for determining the SOG and course over ground (COG), based on a modified weighted ICP algorithm. The main part of the paper reviews a test method aimed at evaluating the accuracy of determining the SOG and COG by the scan-matching method using a modified weighted ICP algorithm. The final part presents an analysis comparing the obtained SOG and COG results with reference results of GNSS RTK measurements and the resulting generalised conclusions.