In this paper, the author analyses piracy at sea in Somalia, to provide a general up-to-date vision of the problem which threatens the security of ships sailing in the Gulf of Aden. The pirates' modus operandi, the existing factors behind these punishable acts and the prevailing international response are analysed. Finally the paper reviews how the international community could act in order to tackle the problem with a view to its possible eradication.

James Cook's expeditious crossings of the north Atlantic each year, 1764–7, are discussed, in the light of modern advice that direct westerly passages are impractical for a sailing vessel.

The concept of Relative Receiver Autonomous Integrity Monitoring (RRAIM) using time differential carrier phase measurements is investigated in this paper. The precision of carrier phase measurements allows for mitigation of integrity hazards by implementing RRAIM monitors with tight thresholds without significantly affecting continuity. In order to avoid the need for cycle ambiguity resolution, time differences in carrier phase measurements are used as the basis for detection. In this work, we examine RRAIM within the context of the GNSS Evolutionary Architecture Study (GEAS), which explores potential architectures for aircraft navigation utilizing the satellite signals available in the mid-term future with GPS III. The objectives of the GEAS are focused on system implementations providing worldwide coverage to satisfy LPV-200 operations, and potentially beyond. In this work, we study two different GEAS implementations of RRAIM. General formulas are derived for positioning, fault detection, and protection level generation to meet a given set of integrity and continuity requirements.

Previous research by the authors has investigated the application of nature inspired search strategies and particle swarm optimisation to the guidance of autonomous vehicles in search roles. Findings suggest that nature inspired search strategies are beneficial under a wide range of search conditions. However, that research did not consider the potential for reacquisition of lost targets, which is especially of interest where the targets are travelling faster than the searching agents or in environments that allow for some means of visual or other sensory occlusion. This work investigates whether behaviours observed in nature can be applied to scenarios where target detection is lost. Through employing a variety of reacquisition strategies against a range of target movement types it is demonstrated that the overall effectiveness of the system can be improved in a variety of scenarios by incorporating nature inspired reacquisition strategies.

The precise geolocation of buried unexploded ordnance (UXO) is a significant component of the detection, characterization, and remediation process. Traditional geolocation methods associated with these procedures are inefficient in helping to distinguish buried UXO from relatively harmless geologic magnetic sources or anthropic clutter items such as exploded ordnance fragments and agricultural or industrial artefacts. The integrated INS/GPS geolocation system can satisfy both high spatial resolution and robust, uninterrupted positioning requirements for successful UXO detection and characterization. To maximize the benefits from this integration, non-linear filtering strategies (such as the unscented Kalman filter) have been developed and tested using laboratory data. In addition, adaptive filters and smoothers have been designed to address variable or inaccurate a priori knowledge of the process noise of the system during periods of GPS unavailability. In this paper, we study and compare the improvement in the geolocation accuracy when the neural network approach is applied to aid the adaptive versions of the extended Kalman filter (EKF) and the unscented Kalman filter (UKF). The test results show that the neural network based filters can improve overall position accuracy and can homogenize the performance of the integrated system over a range of relatively quiet to dynamic environments. Navigation-grade and medium-grade IMUs were compared and, with standard smoothing applied to the new filters, geolocation accuracy of 5 cm (13 cm) was achieved with the navigation- (medium-) grade unit within 8-second intervals that lack external control, which is at or close to the area-mapping accuracy requirement for UXO detection.

These days, Global Navigation Satellite System (GNSS) technology plays a critical role in positioning and navigation applications. Use of GNSS is becoming more of a need to the public. Therefore, much effort is needed to make the civilian part of the system more accurate, reliable and available, especially for the safety-of-life purposes. With the recent revitalization of Russian Global Navigation Satellite System (GLONASS), with a constellation of 20 satellites in August 2009 and the promise of 24 satellites by 2010, it is worthwhile concentrating on the GLONASS system as a method of GPS augmentation to achieve more reliable and accurate navigation solutions.

Integration of Global Positioning System (GPS) and Inertial Navigation System (INS) technologies can overcome the drawbacks of the individual systems. One of the advantages is that the integrated solution can provide continuous navigation capability even during GPS outages. However, bridging the GPS outages is still a challenge when Micro-Electro-Mechanical System (MEMS) inertial sensors are used. Methods being currently explored by the research community include applying vehicle motion constraints, optimal smoother, and artificial intelligence (AI) techniques. In the research area of AI, the neural network (NN) approach has been extensively utilised up to the present. In an NN-based integrated system, a Kalman filter (KF) estimates position, velocity and attitude errors, as well as the inertial sensor errors, to output navigation solutions while GPS signals are available. At the same time, an NN is trained to map the vehicle dynamics with corresponding KF states, and to correct INS measurements when GPS measurements are unavailable. To achieve good performance it is critical to select suitable quality and an optimal number of samples for the NN. This is sometimes too rigorous a requirement which limits real world application of NN-based methods.

The support vector machine (SVM) approach is based on the structural risk minimisation principle, instead of the minimised empirical error principle that is commonly implemented in an NN. The SVM can avoid local minimisation and over-fitting problems in an NN, and therefore potentially can achieve a higher level of global performance. This paper focuses on the least squares support vector machine (LS-SVM), which can solve highly nonlinear and noisy black-box modelling problems. This paper explores the application of the LS-SVM to aid the GPS/INS integrated system, especially during GPS outages. The paper describes the principles of the LS-SVM and of the KF hybrid method, and introduces the LS-SVM regression algorithm. Field test data is processed to evaluate the performance of the proposed approach.

Low-cost, single-frequency GPS systems provide economical positioning solutions to many geomatics applications, including GIS and low-accuracy surveying applications. Unfortunately however, the positioning accuracy obtained with those systems is not sufficient for many surveying applications. This is mainly due to the presence of ionospheric delay and multipath. In this research ionospheric delay is accounted for using regional high-resolution ionospheric maps produced by the US National Oceanic and Atmospheric Administration (NOAA). The major remaining constraint and challenging problem is multipath. This is because multipath is environmentally-dependent, difficult to model mathematically and cannot be reduced through differential positioning. This research proposes a new approach to identify multipath-contaminated L1 measurements through wavelet analysis. First, the difference between the code and carrier-phase measurements is estimated, leaving essentially twice the ionospheric delay, multipath and system noise. The ionospheric delay is largely removed by using high-resolution ionospheric delay maps produced by NOAA. The remaining residuals contain mainly low-frequency multipath, if it exists, and high-frequency system noise, which are decomposed using Daubechies family wavelets (db8). A satellite signal is identified as contaminated by multipath based on the standard deviation of the low-frequency part of the residual component. The L1 measurements obtained from the satellites with the lowest multipath are used to compute the final positions using two software packages, namely Trimble Total Control (TTC) and Bernese scientific processing software. The Magellan AC12 low-cost single-frequency GPS receiver was extensively tested in static mode. It is shown that accuracies within 5 cm are routinely obtained for baselines up to 65 km under various multipath environments.

The Indian Regional Navigation Satellite System (IRNSS) is an autonomous and independent navigational system being developed by India. IRNSS will provide position, navigation and timing services for national applications. To improve accuracy, it can be augmented using GPS and pseudolites (pseudo-satellites). In this paper, the effect on DOP (Dilution of Precision) due to augmentation of the proposed constellation of IRNSS with pseudolites is investigated. GDOP is reduced to 1·75 (max) from 3·63 (max) due to augmentation of IRNSS with two airport pseudolites (APLs). Due to augmentation of IRNSS with GPS, GDOP is reduced to 2·4 (max). When IRNSS is augmented with an APL as well as with GPS, GDOP is further decreased to 1·65 (max). The regional effect on DOP due to IRNSS is also investigated at different locations in the Indian region.

Suitable route planning is related to the safety and economy of navigation. However, route planning has become increasingly complex over the years and the planning process requires a large amount of oceanic environmental information. In order to use the oceanic environmental information effectively and improve the efficiency of route planning, this research employed a Geographic Information System (GIS) as the platform for enabling two-phase automatic route generation design. Firstly, through GIS's spatial data management, spatial analysis and geometric computation capability, the presence of the obstacle is detected and candidate routes are automatically generated. These are provided to the evolutionary algorithm as the basis for preliminary population calculation. Then, a specially designed evolutionary algorithm is used for route elimination to obtain the optimal route, resulting in the most-recommended routes that encompass safety and economy. This technique is more efficient than evolutionary computation techniques that use traditional random searches. At the same time, this targets safety and economy, providing a reference for developing a route planning strategy.

This paper proposes a method that combines compact real-time kinematic (RTK) and reference station (RS) networking techniques, and shows that this approach can reduce both the temporal and spatial decorrelation error. The compact RTK method compatibility with all the conventional network RTK systems, i.e., Master-Auxiliary Concept (MAC), Virtual Reference Stations (VRS), and Flächen-Korrektur Parameter (FKP), is examined theoretically in this paper. To prove that the compact RTK approach is not only valid, but also helpful to the network RTK system, a field test was held using one hour of Receiver Independent Exchange Format (RINEX) data logged every second from Continuously Operating Reference Stations (CORS). No matter which network RTK method is applied, the Compact Network RTK approach resolves the ambiguity of the carrier phase in 10–40 s and determines position with 6–7 cm horizontal and 7–8 cm vertical error (95%) in a 100 by 100 km region. Moreover, the Compact Network RTK approach enables network RTK service providers to reduce the data-link bandwidth for correction messages to 5–700 bps (bit/s) down from several thousand bps, currently 9600 bps of GPRS/GSM, without a severe degradation of accuracy.

In order to analyse quantitative radar observations of bird movements, one needs to know the dimensions of the air space surveyed by the radar. The present study shows how to calibrate the operational beam width and the maximum range of any radar for cross sections for different body sizes of songbirds. The trial used an X-band fan beam ship radar of 25 KW (Furuno Fr-2125) and a captive balloon. Metal spheres of different diameters, fastened to the bottom of the balloon at defined distances, corresponding to the radar cross sections of song birds, were moved in all three dimensions of the air space. Simultaneously to the location by radar, the position of the suspended sphere was measured by theodolites.

This paper reports on innovative mathematical and analytical approaches to celestial navigation. Beyond the common-sense recommendation for automation of the calculations, it is necessary to consider the specific algorithms used. The advantages of these new algorithms become evident when navigational practice is extended beyond the usual twilight round of sights or the noon Sun line. But exploiting the full advantages of the algorithms would probably require new software. This leads us to consider the prospects of applying new information technologies to the task of taking celestial observations.