This note commences with the adoption by the newly invented steam ships, of the sailing ship's custom of referring helm orders to the direction in which the tiller was to be shifted. It records something of the early resiling of the non-English speaking nations from this counter intuitive custom and draws attention to the confusion which the lack of an international approach created, especially for a ship under pilotage. The note concludes with the international uniformity achieved for helm orders by Convention in 1929.

For mobile radars installed on a gyro-stabilised platform (GSP) that can steadily follow an East-North-Up (ENU) frame, attitude biases (ABs) of the platform and offset biases (OBs) of the radar are linear dependent variables. Therefore ABs and OBs are unobservable in the linearized registration equations; however, when combining them as new variables, the system becomes observable, and this model has been called the unified registration model (URM). Unlike GSP mobile radars, un-stabilised GSP (or UGSP) mobile radars are installed on the platform directly and rotate with the platform simultaneously. For UGSP, it is testified that both types of biases are independent and observable because the time-varying attitude angles (AAs)1 of the platform are included in the registration equations, which destroy the dependencies of both kinds of biases and lead us to propose a completely different linearized registration model– the All Augmented Model (AAM). AAM employs all OBs and ABs in the state vector and a Kalman filter (KF) to produce their estimates. Numerical simulation results show that the estimated performance of AAM is close to the Cramér-Rao lower bound (CRLB) and that the Root Mean Square Errors (RMSEs) of the rectified measurements by using AAM are more than 500 m smaller than by URM in all directions.

Unmanned aerial vehicles (UAVs) have recently been widely applied in a comprehensive realm. By enhancing computer photography and artificial intelligence, UAVs can automatically discriminate against environmental objectives and detect events that occur in the real scene. The application of collaborative UAVs will offer diverse interpretations which support a multiperspective view of the scene. Due to the diverse interpretations of UAVs usually deviating, UAVs require a consensus interpretation for the scenario. This study presents an original consensus-based method to pilot multi-UAV systems for achieving consensus on their observation as well as constructing a group situation-based depiction of the scenario. Taylor series are used to describe the fuzzy nonlinear plant and derive the stability analysis using polynomial functions, which have the representations $V(x )={m_{\textrm{1} \le l \le N}}({{V_\textrm{l}}(x )} )$ and ${V_l}(x )={x^T}{P_l}(x )x$. Due to the fact that the ${\dot{P}_l}(x )$ in ${\dot{V}_l}(x )={\dot{x}^T}{P_l}(x )x + {x^T}{\dot{P}_l}(x )x + {x^T}{P_l}(x )\dot{x}$ will yield intricate terms to ensure a stability criterion, we aim to avoid these kinds of issues by proposing a polynomial homogeneous framework and using Euler's functions for homogeneous systems. First, this method permits each UAV to establish high-level conditions from the probed events via a fuzzy-based aggregation event. The evaluated consensus indicates how suitable is the scenario collective interpretation for every UAV perspective.

GNSS relative positioning technique is an important field of study, in which the standard ‘GNSS Baseline Model’ is often used. Differencing between observation equations is used to construct the mathematical model, since this method can eliminate some common errors in the GNSS signal measurements. The ‘Orthogonal Transformation’ method can also construct the GNSS Baseline Model. However, as is described by some scholars, this model may avoid some drawbacks of Double Differencing (DD) while maintaining all the advantages. For comparison purposes, this model is evaluated and the theoretical equivalence of both approaches is proved for the short baseline from two aspects: the Integer Ambiguity Resolution and the conditional least-squares baseline vector.

Adverse effects of intoxicated driving have been well documented over the years, with clear conclusions. In addition, it is evident that the cognitive and neurological functions and reaction times deteriorate with the consumption of alcohol. Addressing the lack of literature on the subject, this paper focuses on studying the effects of alcohol on manoeuvring, navigational and decision-making ability in ship navigation. Ten participants – five cadets and five experienced navigation officers – volunteered and carried out a standard manoeuvre using a computer-controlled vessel simulator, under three different blood alcohol concentrations (0⋅00%, 0⋅05% and 0⋅08% BAC). Results from the simulations were used to assess the performance and the decision-making ability of participants under the influence of alcohol. In addition, the responses and behaviour of the simulated vessel when the navigators were intoxicated were analysed. Workload experienced by participants during the simulations were also assessed using the NASA Task Load Index. Findings of this preliminary study proved that the ability to make the correct decisions at the right time was drastically deteriorated when the blood alcohol concentration was increased.

Whether Henry Ford or someone else gave us this famous quote, ‘If I had asked people what they wanted, they would have said faster horses’, we may agree that it implies there is a limit to what we can expect from the performance of an existing solution. Science and technology always try to push the boundaries and ‘improve’; improving the quality of our lives or improving the quality of technologies. We, as researchers in the area of navigation, are no exception; we want to improve the quality of navigation services. And there are many ways to do so, and challenges and limitations to those attempts. Some researchers look to improve the accuracy, the reliability, the integrity through different approaches. Some try to reduce or model noise, some try to minimise human error, and some use novel techniques and algorithms for better prediction. Of course, when ‘our horses cannot go any faster’ and there is not much space for improvement for a certain technology or service, researchers may come up with a completely new solution, such as an automobile. Almost all new technologies go through the same exploration period; at the beginning, we want to see how and if it works so we try simple tasks, but then we become more ambitious (or greedier!) and so we introduce it to more difficult challenges until it hits the breaking point. At this point, curious researchers and inventors try to push the boundaries and make the technology better, and if improvement is not possible, they build (invent) a new solution. But what is the ‘quality’ that many of us want to improve? How the quality of a technology or service can be measured in the first place?