The USA and Europe are developing plans – NextGen and SESAR – to transform the processes of Air Traffic Management (ATM). These will improve safety and efficiency, and match predicted increases in air transportation demand. They use advanced networking technology updated with information from satellite navigation and digital non-voice communication. The strategic goal, envisaged for 15–20 years hence, is a new ATM paradigm. Aircraft would fly on Four-Dimensional (4D) trajectories, incorporating altitude, position, time, and other aircraft positions and vectors. This vision would involve extremely large investments from the airline industry and ATM service providers. Thus, development priorities need to be based on sound business cases. But will these necessarily lead to the strategic vision of a 4D-trajectory system? Will the changes in practice be limited to a series of short and medium term operational improvements rather than strategic improvements? So, are there ‘Killer Apps’ for 4D-trajectory ATM? ‘Killer App(lication)s’ is jargon for innovations so valuable that they prove the core value of some larger technology. Killer Apps generate high degrees of stakeholder technical and financial cooperation. Ironically, most past ATM Killer Apps have improved safety, e.g., modern radar data processing led to collision avoidance systems. The analysis here attempts to identify and then size potential 4D-trajectory ATM Killer Apps. The evidence for Killer Apps has to pass key tests. Killer Apps obviously have to offer enormous benefits to stakeholders in the context of the potential costs. The bulk of these benefits must not be obtainable through technologically ‘cut down’ non−4D-trajectory versions. Part 1 of this paper (Brooker, 2012a) sets out the framework for investigating these questions. Part 2 examines potential Killer Apps derived from improvements in Fuel Efficiency, Capacity and Cost. An abbreviated version of this paper was first presented at the European Navigation Conference (ENC 2011), London in November 2011.

The aim of this research was to clarify and quantify the demands of the working environment and watch-keeping regime for large commercial ships in relation to dark adaptation. The night lookout task requires the identification of the relatively bright navigational lights of other ships against the dark background of the sky and sea. The probability of detection is determined by the ambient lighting conditions on the bridge and the dark adapted state of vision. Light levels were such that threshold sensitivity (after 15 minutes) was reduced by around 2 log units in comparison to complete darkness. This has implications for the effective range of navigational lights at sea as defined in regulations. The intensity and position of navigation lights on larger vessels is such that the sensitivity of the eye under typical bridge conditions is likely to be sufficient for their visibility to be acceptable. This may not be the case for less well lit small craft.

Terrain Referenced Navigation (TRN) is based on the comparison of terrain altitude measurements with a reference map. Similar to code acquisition and tracking in Global Positioning System (GPS), the TRN system needs to find and track a particular sequence of measurements in a larger dataset. In our earlier work, these correspondences have been exploited to design a GPS inspired algorithm for TRN. The tracking loop is implemented as an early-late correlator, based on the DLL functional principle. Differences between GPS and terrain data in terms of signal properties are often based on the analysis of an ‘ideal’ GPS signal. In reality a GPS signal suffers from disturbances such as multipath and interference. This paper focuses on the identification and evaluation of potential GPS multipath mitigation strategies to be used to partly mitigate the issues caused by the non-ideal terrain signal. Results for the TRN implementations are described in a comparative manner. The content of this paper was presented during the European Navigation Conference 2011 in London.

Today's mobile technology features several sensors that when integrated can provide ubiquitous navigation assistance to pedestrians including wheelchair users. Common sensors found in most smartphones are Global Positioning System (GPS), accelerometer, and compass. In this paper, a user's movement pattern recognition algorithm to improve map matching efficiency and accuracy in pedestrian/wheelchair navigation systems/services is discussed. The algorithm integrates GPS positions, orientation data from compass, and movement states recognized from accelerometer data in a client/server architecture. The algorithm is tested in an Android mobile phone, and the results show that the proposed map matching algorithm is efficient and provides good accuracy.

The basic idea of a dead reckoning personal navigator is to integrate incremental motion information in the forms of step length and step direction over time. Considering that the displacement components are estimated for each step-cycle, it is essential to monitor the integrity of these parameters; otherwise, the error accumulation may render the system unstable. In this paper, a two-stage Kalman Filter (KF) augmented by a neural network is developed to facilitate integrity monitoring. The preliminary results, obtained from several tests performed on simulated and real-word data, indicate an 80% success rate in integrity monitoring.

Besides offset biases (such as range, the gain of range, azimuth, and elevation biases), for mobile radars, platform attitude biases (such as yaw, pitch, and roll biases) induced by the accumulated errors of the Inertial Measurement Units (IMU) of the Inertial Navigation System (INS) can also influence radar measurements. Both kinds of biases are coupled. Based on the analyses of the coupling influences and the observability of 3-D radars’ error registration model, in the article, an Attitude Bias Conversion Model (ABCM) based on Square Root Unscented Kalman Filter (SRUKF) is proposed. ABCM can estimate 3-D radars’ absolute offset biases under the influences of platform attitude biases. It converts platform attitude biases into radar measurement errors, by which the target East-North-Up (ENU) coordinates can be obtained from radar measurements directly without using the rotation transformation, which was usually used in the transition from platform frame to ENU considering attitude biases. In addition, SRUKF can avoid the inaccurate estimations caused by linearization, and it can weaken the adverse influences of the poor attitude bias estimation results in the application of ABCM. Theoretical derivations and simulation results show that 1) ABCM-SRUKF can improve elevation bias estimate accuracy to about 0·8 degree in the mean square error sense; 2) linearization is not the main reason for poor estimation of attitude biases; and 3) unobservability is the main reason.

Visual navigation is comparatively advanced without a Global Positioning System (GPS). It obtains environmental information via real-time processing of the data gained through visual sensors. Compared with other methods, visual navigation is a passive method that does not launch light or other radiation applications, thus making it easier to hide. The novel navigation system described in this paper uses stereo-matching combined with Inertial Measurement Units (IMU). This system applies photogrammetric theory and a matching algorithm to identify the matching points of two images of the same scene taken from different views and obtains their 3D coordinates. Integrated with the orientation information output by the IMU, the system reduces model-accumulated errors and improves the point accuracy.

In the paper Piracy at Sea: Somalia an Area of Great Concern (Baniela, 2010), a general up-to-date vision of piracy at sea in Somalia was analysed. As piracy at sea has political, socio-economic, security and humanitarian dimensions, the international community requires a thorough approach that embraces a comprehensive and multi-faceted response of effective counter-measures, both onshore and offshore. So as a follow-up to that paper, the objective of this one is to analyse the impact of the current strategy in the struggle against piracy in Somalia carried out by the international community focused solely at sea and to examine the proposals to find a solution to the problem on land.

One of the issues regarding integrated GPS/pseudolite measurements is how to deliver a pseudolite's position to a receiver or to post-processing software and how to manage it. This paper presents a proposed solution to this problem. The standard navigation message is modified in such way that without changing receivers (or post-processing software), the calculated position of a transmitter is fixed at a pseudolite's known position. The formulae for modification of standard Ephemeris Data are also derived. This algorithm can be implemented in a transmitter's firmware or a navigation data file can be modified for post-processing.

This paper presents a sensor fusion method for the Ultra-Tightly Coupled (UTC) Global Positioning System (GPS)/Inertial Navigation System (INS) integrated navigation. The UTC structure, also known as the deep integration, exhibits many advantages, e.g., disturbance and multipath rejection capability, improved tracking capability for dynamic scenarios and weak signals, and reduction of acquisition time. This architecture involves the integration of I (in-phase) and Q (quadrature) components from the correlator of a GPS receiver with the INS data. The Particle Filter (PF) exhibits superior performance as compared to an Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) in state estimation for the nonlinear, non-Gaussian system. To handle the problem of heavy-tailed probability distribution, one of the strategies is to incorporate the UKF into the PF as the proposal distribution, leading to the Unscented Particle Filter (UPF). The combination of an adaptive UPF and Fuzzy Logic Adaptive System (FLAS) is adopted for reducing the number of particles with sufficiently good results. The GPS tracking loops may lose lock due to the signals being weak, subjected to excessive dynamics or completely blocked. One of the principal advantages of the UTC structure is that a Doppler frequency derived from the INS is integrated with the tracking loops to improve the receiver tracking capability. The Doppler frequency shift is calculated and fed to the GPS tracking loops for elimination of the effect of stochastic errors caused by the Doppler frequency. In this paper, several nonlinear filtering approaches, including EKF, UKF, UPF and ‘FLAS assisted UPF’ (FUPF), are adopted for performance comparison for ultra-tight integration of GPS and INS. It is assumed that no outage occurs such that the inertial sensor errors can be properly corrected and accordingly the aiding information is working well. Two examples are provided for performance assessment for the various data fusion methods. The FUPF algorithm with Doppler velocity aiding demonstrates remarkable improvement, especially in the high dynamic environments, in navigation estimation accuracy with reduction of number of particles.

Unmanned Marine Vehicles (UMVs), like their aerial cousins Unmanned Aerial Vehicles (UAVs), are not easily classified under existing legal regimes. Even though unmanned, should these seagoing drones be treated as ‘vessels’ under the Law of the Sea Convention articles on navigation rights and duties? Are they ‘vessels’ under the International Regulations for Preventing Collisions at Sea (COLREGs, 1972)? If so, should they be accorded a manoeuvring priority vis-à-vis other vessels? Are the differences between autonomous UMVs and the increasingly automated manned vessels all that great, such that classification should turn on whether the vessel is manned rather than on how navigation and collision avoidance decisions are made and executed?

Vessels are found frequently operating under the Automatic Identification System (AIS) ‘Not Under Command’ (NUC) option near ports and close to shores in order to report their manoeuvre status to nearby traffic. When asked if they are under any kind of restriction regarding their engines or steering, they often answer that they are just drifting or waiting for the pilot or shipowner's orders. The question that arises is this: “Are these vessels really under an NUC situation or can the AIS Navigational Status menu adapt to the different circumstances of navigation?”.