This paper looks at the navigational challenges faced by William Dampier when, as Captain of the Royal Navy ship Roebuck, in 1699, he approached, and then found his way along the coast of Western Australia (at that time known as New Holland). A discussion of the methods and instruments available to Dampier is followed by consideration of how, and with what success, he went about his task. Dampier's own account of the voyage was included in his book A Voyage to New Holland (Dampier, 1703) although this is likely to have been heavily edited, and perhaps even rewritten by a “man of letters” to make it more attractive to the general public. For this reason and, bearing in mind that autobiographical work may contain omissions or otherwise be slanted to show the writer in his best light, the navigational information in his book has been cross-checked with the Roebuck's original Master's Log, held by the UK National Archives at Kew (Documents ADM 52/94). Jacob Hughes was the ship's Master, but the Log entries were written in at least two hands.

Outliers in terrain data are an obstacle to achieving accurate and robust solutions of Underwater Terrain Relative Navigation (UTRN). If not handled properly, navigation may be degraded or even divergent. To address the problem, this paper proposes a terrain-matching algorithm based on the robust estimation theory. In contrast to the conventional approach, the proposed algorithm can significantly reduce the interference of the outliers. Experimental results confirm the good performance of the proposed method.

Multipath propagation can cause significant impairments to the performance of Global Navigation Satellite System (GNSS) receivers and is often the dominant source of accuracy degradation for high precision GNSS applications. Commonly used time-of-arrival estimation techniques cannot provide the required estimation accuracy in severely dense multipath environments such as urban canyons. Multipath components are highly correlated and this results in a rank deficiency of the signal autocorrelation matrix. In this paper the Doppler spectrum broadening of the fast fading channel resulting from the motion of the receiver or surrounding objects is employed to decorrelate signal reflections for the purpose of high-resolution estimation of multipath delays through the subspace-based Multiple Signal Classification (MUSIC) technique. Specifically, delay-domain correlator outputs at different Doppler frequencies are combined to enhance the rank of the signal autocorrelation matrix. Simulation and results of real data collected in an urban environment (downtown Calgary) are presented to compare the performance of the proposed method with the spatial-temporal-diversity-based MUSIC technique and a widely available algorithm in commercial GNSS receivers, namely the double-delta correlator technique. The performance metrics are based upon pseudorange and positioning errors, which are derived using an accurate reference trajectory established using a high precision GNSS-INS integrated system.

Gaussian process regression (GPR) is used in a Spare-grid Quadrature Kalman filter (SGQKF) for Strap-down Inertial Navigation System (SINS)/odometer integrated navigation to bridge uncertain observation outages and maintain an estimate of the evolving SINS biases. The SGQKF uses nonlinearized dynamic models with complex stochastic nonlinearities so the performance degrades significantly during observation outages owing to the uncertainties and noise. The GPR calculates the residual output after factoring in the contributions of the parametric model that is used as a nonlinear SINS error predictor integrated into the SGQKF. The sensor measurements and SINS output deviations from the odometer are collected in a data set during observation availability. The GPR is then applied to predict SINS deviations from the odometer and then the predicted SINS deviations are fed to the SGQKF as an actual update to estimate all SINS biases during observation outages. We demonstrate our method's effectiveness in bridging uncertain observation outages in simulations and in real road tests. The results agree with the theoretical analysis, which demonstrate that SGQKF using GPR can maintain an estimate of the evolving SINS biases during signal outages.

NextGen and SESAR have now been under development for several years, but have increasingly complex engineering and operational specifications. A variant Air Traffic Management (ATM) concept is sketched for generating fuel-efficient, very accurate and air-ground synchronized 4D-trajectories by using flight segment groundspeed profiles and linking Global Navigation Satellite Systems (GNSS) data to the aircraft Flight Management Systems (FMS) with feedback control. Is this a flawed concept or a feasible and operationally practical proposition?

Celestial navigation is an important type of autonomous navigation technology which could be used as an alternative to Global Navigation Satellite Systems (GNSS) when a vessel is at sea. After several centuries of development, a variety of astronomical vessel position (AVP) determination methods have been invented, but the basic concepts of these methods are all based on angular observations with a device such as a sextant, which has disadvantages including low accuracy, manual operation, and a limited period of observation. This paper proposes a new method that utilises a fisheye camera to image the celestial bodies and horizon simultaneously. Then, we calculate the obliquity of the fisheye camera's principal optical axis according to the image coordinates of the horizon. Next, we calculate the altitude of the celestial bodies according to the image coordinates of the celestial bodies and the obliquity. Finally, the AVP is determined by the altitudes according to the robust estimation method. Experimental results indicate that this method not only could realize automation and miniaturization of the AVP determination system, but could also greatly improve the efficiency of celestial navigation.

In urban areas, Global Positioning System (GPS) accuracy deteriorates due to signal degradation and multipath effects. To provide accurate and robust navigation in such GPS-denied environments, multi-sensor integrated navigation systems are developed. This paper introduces a 3D multi-sensor navigation system that integrates inertial sensors, odometry and GPS for land-vehicle navigation. A new error model is developed and an efficient loosely coupled closed-loop Kalman Filter (Extended KF or EKF) integration scheme is proposed. In this EKF-based integration scheme, the inertial/odometry navigation output is continuously corrected by EKF-estimated errors, which keeps the errors within acceptable linearization ranges which improves the prediction accuracy of the linearized dynamic error model. Consequently, the overall performance of the integrated system is improved. Real road experiments and comparison with earlier works have demonstrated a more reliable performance during GPS signal degradation and accurate estimation of inertial sensor errors (biases) have led to a more sustainable performance reliability during long GPS complete outages.

In recent years, navigation by stellar refraction has received considerable attention, having advantages of high accuracy, simple construction, and low cost. Nevertheless, there are many limitations to the precision and application of this method using a traditional measurement model. This article studies the changing pattern of atmospheric density, the disturbed atmospheric density model and measurement model of stellar refraction ranging from 20 km to 50 km. Furthermore, a control algorithm of multiple mode switching and an adaptive measurement model are proposed. With this method, any refracted starlight from the scope of between 20 km and 50 km can be captured and the measurement model at the appropriate height can be automatically established. Due to this, the reliability and practicality of navigation have been raised considerably. Accuracy of navigation using the adaptive measurement method is observed to improve by about 14%, using computer simulation based on an Unscented Kalman Filter (UKF).

This article proposes a novel method to autonomously determine the relative orbits of a small spacecraft formation flying with a slowly rotating near-Earth asteroid for the asteroid mitigation mission. A combined model of spacecraft-to-asteroid measurements with inter-spacecraft communication is built up. The dynamic model accounting for the perturbations is derived to describe the state variables of the Unscented Kalman Filter (UKF). The Lyapunov function approach is used to design a control law to maintain the orbits of the spacecraft formation. Four navigation scenarios are presented based on the different combinations of multiple measurements: two of them are non-collaborative scenarios which only include the spacecraft-to-asteroid measurements in the measurement models, while the other two are collaborative scenarios with the inter-spacecraft communication added into the measurement models. Simulation results are analysed and compared with each other to show the improvements of the filter performance in the aspects of the convergence speed and the accuracy of the estimator.

Four methods to calculate the Vertical Protection Level (VPL) can be used in Advanced Receiver Autonomous Integrity Monitoring (A-RAIM), among which the ideal method is the strictest one. To obtain the ideal VPL satisfying the exact required integrity risk, the worst case bias with the maximum integrity risk is searched for. This investigation has found that the correct worst case highly depends on the choice of the input VPL. To gain the correct result, the computation becomes complex and the accuracy of the result is compromised. Therefore, a new procedure is designed with a new search: the maximum VPL is searched to encompass all possible bias sizes. Since VPL is calculated with a given integrity risk for each bias size, the uncertainty of the arbitrary VPL input in the ideal method is avoided. Also, an optimisation algorithm is adopted to improve computational efficiency. It is shown that the new method is more reliable and efficient than the current best method. Simulation results worldwide also show that the new approach has improved A-RAIM availability from 32%–38% to 74% with GPS and from 44%–43% to 85% with Galileo.

A type of fixed integral interval sliding method for optimization-based alignment is proposed in this paper. The method is more reasonable and suitable than those in the references due to the appropriate treatment of the noise in the outputs of the Inertial Measurement Unit (IMU). The undesired sensor noise of sensors includes both random noise and bias. Our optimal alignment is based on an integrated form where the bias will be integrated during the process of alignment. In this respect, the length of the integrated data is a key factor in determining current attitude due to the ambivalent effect of integrating the sensor noise. If the length is too great the precision deteriorates due to the integration of bias. If the length is too small the precision also deteriorates because of the randomness of the noise. The validity of the method is verified by simulation and measured data.