This paper reports the results of the first three in a series of experiments on tactile perception which form part of a larger project on tactile perceptions and spatial representations and the design of tactile interfaces for mobility devices for blind, partially sighted and deafblind people. The results indicate the potential of tactile interfaces, including to support environmental exploration and mobility. The participants showed reasonably good ability to determine the direction of motion of an arrow, with best recognition rates in the up and right directions. They showed reasonably good ability to use a tactile interface to detect and avoid obstacles after a very short learning period and more limited ability to learn and remember an environmental representation using information from a tactile interface and walking through the environment without specific instructions.

In order to achieve high accuracy of autonomous navigation for Mars probes, an integrated navigation method using X-ray pulsar measurement and optical data of viewing Martian moons is proposed. For single X-ray pulsar measurement on board a Mars probe, navigation accuracy is low due to its poor observability. On the other hand, Phobos and Deimos, two natural moons of Mars, are important optical navigation information sources available for Mars missions. However, the Martian moons ephemeris bias and the differences between barycentre and centre of brightness of Martian moons will result in low navigation accuracy. The method of integrated navigation using X-ray pulsar measurement and optical data of viewing Martian moons can overcome the defect and achieve accurate navigation. Two sequential orbit determination algorithms, Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF), are compared. The simulation results show this method can obtain high autonomous navigation accuracy during the phase of a probe orbiting Mars.

Navigational error accounts for half of the accidents and serious incidents in close to shore maritime transport in Norway predominantly due to the rapidly changing weather conditions and the dangerous nature of the narrow inshore waters found along the Norwegian coast. This creates a dependence on Differential Global Positioning System (DGPS) use and any disruption to this service can lead to an increased accident rate. The aim of this paper is to research the jamming vulnerability of existing maritime receivers and to understand if an upgrade to a multi-constellation or multi-frequency receiver would improve system resilience. The novelty of this work is a comparison of jamming resilience between different combinations of multiple constellations (GPS and Globalnaya Navigatsionnaya Sputnikovaya Sistema (GLONASS)) and multi-frequency Global Navigation Satellite System (GNSS) signals. This paper presents results from GNSS jamming trials conducted in the northern part of Norway, confirming previous research and indicating that typical maritime GPS receivers are easy to jam and may produce erroneous positional information. Results demonstrate that the single frequency multi-constellation receivers offer better jamming resilience than multi-frequency (L1 + L2) GPS receivers. Further, the GLONASS constellation demonstrated a better resilience than GPS. Results demonstrate a known correlation between GPS L1 and L2 frequencies, as well as a probable over-dependence on GPS for signal acquisition, meaning that no signal can be received without GPS L1 present. With these limitations in mind, the authors suggest that the most economic update to the single frequency GPS receivers, currently used for maritime applications, should be multi-constellation GPS + GLONASS receivers. This solution is cheaper and it also offer better jamming resistance for close to shore navigation than dual frequency receivers.

Automatic Dependent Surveillance Broadcast (ADS-B) Out supports various ground applications including Air Traffic Control (ATC) surveillance in radar airspace, non-radar airspace and on the airport surface. In addition, the capability of aircraft to receive ADS-B Out messages from other aircraft within their coverage (ADS-B In) enables enhanced airborne surveillance applications. The requirements of the application vary depending on its safety-criticality. More stringent applications will require higher levels of performance. It is therefore critical that the ADS-B system performance is measured against the most stringent application it is designed for. This paper reviews the various enhanced airborne surveillance applications and the required ADS-B information to support them. It identifies the ADS-B based applications required for Air Traffic Management (ATM) modernisation under the SESAR/NextGen programs. It discusses existing ADS-B Out versions and their capabilities. A mapping exercise is undertaken to assess the credibility of the ADS-B system performance to support the functionalities and requirements of the various enhanced airborne surveillance applications and establish those that require further research and development, highlighting some of the key challenges.

Mapping global shipping density, including vessel density and traffic density, is important to reveal the distribution of ships and traffic. The Automatic Identification System (AIS) is an automatic reporting system widely installed on ships initially for collision avoidance by reporting their kinematic and identity information continuously. An algorithm was created to account for errors in the data when ship tracks seem to ‘jump’ large distances, an artefact resulting from the use of duplicate identities. The shipping density maps, including the vessel and traffic density maps, as well as AIS receiving frequency maps, were derived based on around 20 billion distinct records during the period from August 2012 to April 2015. Map outputs were created in three different spatial resolutions: 1° latitude by 1° longitude, 10 minutes latitude by 10 minutes longitude, and 1 minute latitude by 1 minute longitude. The results show that it takes only 56 hours to process these records to derive the density maps, 1·7 hours per month on average, including data retrieval, computation and updating of the database.

Measurement bias and lack of terrain features often cause false peaks during underwater terrain matching positioning, that is, there is more than one peak near the real position. Previous methods to address this problem have increased the number of measurement beams, but this also increases the data processing time and energy consumption. At the same time, the ratio of measured information that is used does not increase. In other words, we should increase the ratio of measured information that is used, not simply increase the amount of information that is measured. Conventional matching algorithms only use the height of nodes without considering surface information, which is composed of height and the position of multiple nodes in three-dimensional space. Multi-beam sonar can obtain the three-dimensional distribution of terrain nodes. This node information is not just a height sequence, as it is used in previous methods. If we consider the nodes as a three-dimensional distribution of points with height and position information, this increases the matching position information and more of the terrain features can be extracted from the same measured data. Hence, in this paper, a terrain positioning method called the Node Multi-information Fusion (NMIF) is presented. This method focuses on improving the stability and accuracy degraded by bias in the Digital Elevation Map (DEM), terrain repeatability, and other factors. First, the concept of a Single Node Data Packet (SNDP) is introduced. The SNDP includes elevation and surface information surrounding the node, such as roughness, gradient, and slope. This additional topographic feature information improves the robustness and accuracy of the system. A computer simulation using actual ocean bottom topography verifies the advantages of the proposed NMIF algorithm.

The acquisition and tracking strategies of the BeiDou navigation satellite signals are affected by the modulation of Neumann-Hoffman code (NH code), which increases the complexity of receiver baseband signal processing. Based on the analysis of probability statistics of the NH code, a special sequence of incoming signals is proposed to evade the bit transitions caused by the NH code, and an NH Code Evasion and Stripping method (NCES) based on the NH-pre-modulated code is proposed. The NCES can be applied in both 20-bit NH code and 10-bit NH code. The fine acquisition eliminates the impact of NH code on the traditional tracking loop. These methods were verified with a BeiDou PC-based software-defined receiver using the actual sampled signals. Compared with other acquisition schemes which try to determine or ignore the NH code phase, the NCES needs fewer incoming signals and the actual runtime is greatly reduced without sacrificing much time to search in the secondary code dimension, and the success rate of acquisition is effectively improved. An extension of Fast Fourier Transform (FFT)-based parallel code-phase search acquisition gives the NCES an advantage in engineering applications.

Relative position awareness is a vital premise for the implementation of emerging intelligent transportation systems. However, commercial Global Satellite Navigation Systems (GNSS) receivers do not satisfy the requirements of these applications. Fortunately, Cooperative Positioning (CP) systems, based on inter-vehicle communications, have improved performance of relative positioning in a Vehicular Ad Hoc Network (VANET). CP techniques rely primarily on measurements from the Global Positioning System (GPS) to deliver measurements or positions that describe the location of individual vehicles. In urban environments, the reduced quality or complete unavailability of GPS measurements challenges the effectiveness of any CP algorithm. In this paper, a new enhanced tightly–coupled CP technique is presented by adding the measurements from low-cost inertial sensors and the Doppler shift of the carrier of Dedicated Short-Range Communications (DSRC) signals. In the enhanced CP method proposed here, vehicles communicate their Inertial Measurement Unit (IMU) data and GPS measurements. Each vehicle fuses the GPS measurements and IMU data and the inter-node range-rates based on the Doppler shift of the carrier of DSRC signals. Based on analytical and experimental results, in a full GPS coverage environment, the new tight integration CP outperforms tight CP with Inertial Navigation System (INS), tight CP and differential GPS by at least by 6%, 15%, and 28%, respectively. In a GPS outage, the performance improvement can be up to 60%, 55%, and 66% respectively.

In this paper, we propose an algorithm for tuning both the kinematic and measurement noise Variance–Covariance (VCV) matrices to produce a more robust and adaptive Kalman filter. The proposed algorithm simultaneously considers both observation outliers and abrupt changes. This algorithm may be divided into two basic parts: 1. Robust estimation, from which the position components of the filtering estimates and the equivalent weight factor matrix can be obtained; and 2. Adaptive estimation, from which the adaptive kinematic noise VCV tuning matrix is calculated. To demonstrate the efficiency of our algorithm, we process a set of kinematic Global Positioning System (GPS) data received from a rover mounted on an aeroplane. The processing results are found to be very satisfactory, with observation outliers and abrupt changes detected and dealt with accordingly. The detailed calculation procedure for the adaptive VCV tuning matrix is also described.

This paper focuses on depth trajectory tracking control for a Remotely Operated Vehicle (ROV) with dead-zone nonlinearity and saturation nonlinearity of thruster; an adaptive sliding mode control method based on neural network is proposed. Through the analysis of dead-zone nonlinearity and saturation nonlinearity of thruster, the depth trajectory tracking control system model of a ROV which uses thruster control signals as system input has been established. According to the principle of sliding mode control, an adaptive sliding mode depth trajectory tracking controller is built by using three-layer feed-forward neural network for online identification of unknown items. The selection method and update laws of the control parameters are also given. The uniform ultimate boundedness of trajectory tracking error is analysed by Lyapunov theorem. Finally, the effectiveness of the proposed method is illustrated by simulations.

This paper presents a framework for a strapdown Inertial Navigation System (INS) algorithm design by using Lie group and Lie algebra. The general way to solve Lie group differential equations is introduced. Investigations reveal that this general Lie group method provides a simpler unified way to solve differential equations involving direction cosine matrix, quaternion and dual quaternion, which are widely used in INS algorithm design. Furthermore, we also present a new INS algorithm based on the Special Euclidean group se(3) under the guidelines of Lie group method. Analyses show that se(3) algorithm has the same accuracy as a dual quaternion algorithm, this is also justified by numerical simulations. Though the se(3) algorithm has no improvements in accuracy, the general Lie group method used in the design process shows its brevity and uniformity.

A region tracking fault tolerant control approach based on backstepping technique is proposed for Autonomous Underwater Vehicles (AUV). The proposed approach aims at driving tracking error to reach into the desired region in presence of ocean current disturbance, modelling uncertainty, unknown thruster faults and thruster amplitude and rate saturation constraints. At first, a type of piecewise and differential Lyapunov function is constructed to achieve region tracking control in the frame of backstepping technique. Then, the paper analyses and acquires the bound structures of the lumped uncertainty (including ocean current disturbance and model uncertainty) and the variation of thruster distribution matrix caused by unknown thruster faults, respectively. An adaptive technique is used to estimate the unknown coefficients in the above bound structures. In addition, an adaptive adjustment scheme for the desired trajectory is developed to achieve region tracking control with thruster amplitude and rate saturation constraints. The stability of the closed-loop system is analysed based on Barbalat's lemma. Finally, simulations and pool-experiments are presented to illustrate the effectiveness of the proposed method.

In this paper, a novel Predictive Variable Structure Filter (PVSF) and its adaptive deformation (APVSF) are presented for attitude synchronisation during Satellite Formation Flying (SFF). The PVSF is proposed based on the variable structure control concept and applied to any nonlinear system with model errors. The model errors in the PVSF need not satisfy the assumption of Gaussian white noise; therefore, it has advantages in dealing with various kinds of uncertainties, parameter variations or noises. Then, the APVSF is also presented to adjust the smoothing boundary layer of PVSF by minimising the Mean-Square Error (MSE). Simulations are performed to demonstrate the accuracy, robustness, and stability of the proposed methodologies for the attitude synchronisation estimation of the SFF system.